Hepatocellular Adenoma: Evaluation with Contrast-Enhanced Ultrasound and MRI and Correlation with Pathologic and Phenotypic Classification in 26 Lesions

PubMed Central

Manichon, Anne-Frédérique; Bancel, Brigitte; Durieux-Millon, Marion; Ducerf, Christian; Mabrut, Jean-Yves; Lepogam, Marie-Annick; Rode, Agnès

2012-01-01

Purpose. To review the contrast-enhanced ultrasonographic (CEUS) and magnetic resonance (MR) imaging findings in 25 patients with 26 hepatocellular adenomas (HCAs) and to compare imaging features with histopathologic results from resected specimen considering the new immunophenotypical classification. Material and Methods. Two abdominal radiologists reviewed retrospectively CEUS cineloops and MR images in 26 HCA. All pathological specimens were reviewed and classified into four subgroups (steatotic or HNF 1α mutated, inflammatory, atypical or β-catenin mutated, and unspecified). Inflammatory infiltrates were scored, steatosis, and telangiectasia semiquantitatively evaluated. Results. CEUS and MRI features are well correlated: among the 16 inflammatory HCA, 7/16 presented typical imaging features: hypersignal T2, strong arterial enhancement with a centripetal filling, persistent on delayed phase. 6 HCA were classified as steatotic with typical imaging features: a drop out signal, slight arterial enhancement, vanishing on late phase. Four HCA were classified as atypical with an HCC developed in one. Five lesions displayed important steatosis (>50%) without belonging to the HNF1α group. Conclusion. In half cases, inflammatory HCA have specific imaging features well correlated with the amount of telangiectasia and inflammatory infiltrates. An HCA with important amount of steatosis noticed on chemical shift images does not always belong to the HNF1α group. PMID:22811588

A feature-based approach to combine functional MRI, structural MRI and EEG brain imaging data.

PubMed

Calhoun, V; Adali, T; Liu, J

2006-01-01

The acquisition of multiple brain imaging types for a given study is a very common practice. However these data are typically examined in separate analyses, rather than in a combined model. We propose a novel methodology to perform joint independent component analysis across image modalities, including structural MRI data, functional MRI activation data and EEG data, and to visualize the results via a joint histogram visualization technique. Evaluation of which combination of fused data is most useful is determined by using the Kullback-Leibler divergence. We demonstrate our method on a data set composed of functional MRI data from two tasks, structural MRI data, and EEG data collected on patients with schizophrenia and healthy controls. We show that combining data types can improve our ability to distinguish differences between groups.

Computed tomography synthesis from magnetic resonance images in the pelvis using multiple random forests and auto-context features

NASA Astrophysics Data System (ADS)

Andreasen, Daniel; Edmund, Jens M.; Zografos, Vasileios; Menze, Bjoern H.; Van Leemput, Koen

2016-03-01

In radiotherapy treatment planning that is only based on magnetic resonance imaging (MRI), the electron density information usually obtained from computed tomography (CT) must be derived from the MRI by synthesizing a so-called pseudo CT (pCT). This is a non-trivial task since MRI intensities are neither uniquely nor quantitatively related to electron density. Typical approaches involve either a classification or regression model requiring specialized MRI sequences to solve intensity ambiguities, or an atlas-based model necessitating multiple registrations between atlases and subject scans. In this work, we explore a machine learning approach for creating a pCT of the pelvic region from conventional MRI sequences without using atlases. We use a random forest provided with information about local texture, edges and spatial features derived from the MRI. This helps to solve intensity ambiguities. Furthermore, we use the concept of auto-context by sequentially training a number of classification forests to create and improve context features, which are finally used to train a regression forest for pCT prediction. We evaluate the pCT quality in terms of the voxel-wise error and the radiologic accuracy as measured by water-equivalent path lengths. We compare the performance of our method against two baseline pCT strategies, which either set all MRI voxels in the subject equal to the CT value of water, or in addition transfer the bone volume from the real CT. We show an improved performance compared to both baseline pCTs suggesting that our method may be useful for MRI-only radiotherapy.

The imaging features of the meniscal roots on isotropic 3D MRI in young asymptomatic volunteers.

PubMed

Wang, Ping; Zhang, Cheng-Zhou; Zhang, Di; Liu, Quan-Yuan; Zhong, Xiao-Fei; Yin, Zhi-Jie; Wang, Bin

2018-05-01

This study aimed to describe clearly the normal imaging features of the meniscal roots on the magnetic resonance imaging (MRI) with a 3-dimensional (3D) proton density-weighted (PDW) sequence at 3T. A total of 60 knees of 31 young asymptomatic volunteers were examined using a 3D MRI. The insertion patterns, constitution patterns, and MR signals of the meniscal roots were recorded. The anterior root of the medial meniscus (ARMM), the anterior root of the lateral meniscus (ARLM), and the posterior root of the medial meniscus (PRMM) had 1 insertion site, whereas the posterior root of the lateral meniscus (PRLM) can be divided into major and minor insertion sites. The ARLM and the PRMM usually consisted of multiple fiber bundles (≥3), whereas the ARMM and the PRLM often consisted of a single fiber bundle. The ARMM and the PRLM usually appeared as hypointense, whereas the ARLM and the PRMM typically exhibited mixed signals. The meniscal roots can be complex and diverse, and certain characteristics of them were observed on 3D MRI. Understanding the normal imaging features of the meniscal roots is extremely beneficial for further diagnosis of root tears.

Topologic analysis and comparison of brain activation in children with epilepsy versus controls: an fMRI study

NASA Astrophysics Data System (ADS)

Oweis, Khalid J.; Berl, Madison M.; Gaillard, William D.; Duke, Elizabeth S.; Blackstone, Kaitlin; Loew, Murray H.; Zara, Jason M.

2010-03-01

This paper describes the development of novel computer-aided analysis algorithms to identify the language activation patterns at a certain Region of Interest (ROI) in Functional Magnetic Resonance Imaging (fMRI). Previous analysis techniques have been used to compare typical and pathologic activation patterns in fMRI images resulting from identical tasks but none of them analyzed activation topographically in a quantitative manner. This paper presents new analysis techniques and algorithms capable of identifying a pattern of language activation associated with localization related epilepsy. fMRI images of 64 healthy individuals and 31 patients with localization related epilepsy have been studied and analyzed on an ROI basis. All subjects are right handed with normal MRI scans and have been classified into three age groups (4-6, 7-9, 10-12 years). Our initial efforts have focused on investigating activation in the Left Inferior Frontal Gyrus (LIFG). A number of volumetric features have been extracted from the data. The LIFG has been cut into slices and the activation has been investigated topographically on a slice by slice basis. Overall, a total of 809 features have been extracted, and correlation analysis was applied to eliminate highly correlated features. Principal Component analysis was then applied to account only for major components in the data and One-Way Analysis of Variance (ANOVA) has been applied to test for significantly different features between normal and patient groups. Twenty Nine features have were found to be significantly different (p<0.05) between patient and control groups

The almost-invisible perineurioma.

PubMed

Restrepo, Carlos E; Amrami, Kimberly K; Howe, Benjamin M; Dyck, P James B; Mauermann, Michelle L; Spinner, Robert J

2015-09-01

Intraneural perineurioma is a rare, benign slow-growing lesion arising from the perineurial cells that surrounds the peripheral nerve fibers. Typically it presents during childhood and young adulthood as a motor mononeuropathy. MRI plays an essential role in the diagnosis and localization of the lesion, which appears as a fusiform enlargement of the nerve fascicles that enhances intensely with gadolinium. Despite the typical clinical and radiological features, intraneural perineurioma remains largely underdiagnosed because of the lack of familiarity with this entity, but also as a result of technical limitations with conventional MRI that is typically performed as a screening test over a large field of view and without contrast sequences. The purpose of this article is to present the pitfalls and pearls learned from years of experience in the diagnosis and management of this relatively rare condition. Clinical suspicion and detailed neurological examination followed by high-quality electrophysiological studies (EPS) must lead to an adequate preimaging localization of the lesion and narrowing of the imaging area. The use of high-resolution (3-T) MRI combined with gadolinium administration will allow adequate visualization of the internal anatomy of the nerve and help in differentiating other causes of neuropathy. In cases where the lesion is not recognized but clinical suspicion is high, possible errors must be assessed, including the EPS localization, area of imaging, MRI resolution, and slice thickness.

Reproducibility of EEG-fMRI results in a patient with fixation-off sensitivity.

PubMed

Formaggio, Emanuela; Storti, Silvia Francesca; Galazzo, Ilaria Boscolo; Bongiovanni, Luigi Giuseppe; Cerini, Roberto; Fiaschi, Antonio; Manganotti, Paolo

2014-07-01

Blood oxygenation level-dependent (BOLD) activation associated with interictal epileptiform discharges in a patient with fixation-off sensitivity (FOS) was studied using a combined electroencephalography-functional magnetic resonance imaging (EEG-fMRI) technique. An automatic approach for combined EEG-fMRI analysis and a subject-specific hemodynamic response function was used to improve general linear model analysis of the fMRI data. The EEG showed the typical features of FOS, with continuous epileptiform discharges during elimination of central vision by eye opening and closing and fixation; modification of this pattern was clearly visible and recognizable. During all 3 recording sessions EEG-fMRI activations indicated a BOLD signal decrease related to epileptiform activity in the parietal areas. This study can further our understanding of this EEG phenomenon and can provide some insight into the reliability of the EEG-fMRI technique in localizing the irritative zone.

Pathological findings of uterine tumors preoperatively diagnosed as red degeneration of leiomyoma by MRI.

PubMed

Nakai, Go; Yamada, Takashi; Hamada, Takamitsu; Atsukawa, Natsuko; Tanaka, Yoshikazu; Yamamoto, Kiyohito; Higashiyama, Akira; Juri, Hiroshi; Nakamoto, Atsushi; Yamamoto, Kazuhiro; Hirose, Yoshinobu; Ohmichi, Masahide; Narumi, Yoshifumi

2017-07-01

Venous infarction of a leiomyoma is known as red degeneration of leiomyoma (RDL) and can be a cause of acute abdomen. Although magnetic resonance imaging (MRI) is the only modality that can depict the inner condition of a leiomyoma, the typical MR findings of RDL are sometimes identified incidentally even in asymptomatic patients. The purpose of this study is to clarify common pathological findings of uterine tumors preoperatively diagnosed as RDL by MRI. We diagnosed 28 cases of RDL by MRI from March 2007 to April 2015. The ten lesions subjected to pathological analysis after resection were included in the study and reviewed by a gynecological pathologist. The average time from MRI to operation was 4.7 months. The typical beefy-red color was not observed on the cut surface of the tumor except in one tumor resected during the acute phase. All lesions diagnosed as RDL by MRI had common pathological findings consistent with red degeneration of leiomyoma, including coagulative necrosis. Other common pathological features of RDL besides extensive coagulative necrosis appear to be a lack of inflammatory cell infiltrate or hemorrhage in the entire lesion. Although RDL is known to cause acute abdomen, its typical MR findings can be observed even in asymptomatic patients in a condition that manifests long after red degeneration. The characteristic pathological findings in both the acute phase and the chronic phase that we found in this study, along with radiology reports, will be helpful references for gynecologists and pathologists in suspecting a history of red degeneration and confirming the diagnosis.

New method for predicting estrogen receptor status utilizing breast MRI texture kinetic analysis

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Hall, Lawrence O.; Goldgof, Dmitry B.; Gatenby, Robert A.; Gillies, Robert; Drukteinis, Jennifer S.

2014-03-01

Magnetic Resonance Imaging (MRI) of breast cancer typically shows that tumors are heterogeneous with spatial variations in blood flow and cell density. Here, we examine the potential link between clinical tumor imaging and the underlying evolutionary dynamics behind heterogeneity in the cellular expression of estrogen receptors (ER) in breast cancer. We assume, in an evolutionary environment, that ER expression will only occur in the presence of significant concentrations of estrogen, which is delivered via the blood stream. Thus, we hypothesize, the expression of ER in breast cancer cells will correlate with blood flow on gadolinium enhanced breast MRI. To test this hypothesis, we performed quantitative analysis of blood flow on dynamic contrast enhanced MRI (DCE-MRI) and correlated it with the ER status of the tumor. Here we present our analytic methods, which utilize a novel algorithm to analyze 20 volumetric DCE-MRI breast cancer tumors. The algorithm generates post initial enhancement (PIE) maps from DCE-MRI and then performs texture features extraction from the PIE map, feature selection, and finally classification of tumors into ER positive and ER negative status. The combined gray level co-occurrence matrices, gray level run length matrices and local binary pattern histogram features allow quantification of breast tumor heterogeneity. The algorithm predicted ER expression with an accuracy of 85% using a Naive Bayes classifier in leave-one-out cross-validation. Hence, we conclude that our data supports the hypothesis that imaging characteristics can, through application of evolutionary principles, provide insights into the cellular and molecular properties of cancer cells.

Epithelioid hemangioma of the spine: Two cases.

PubMed

O'Shea, Bendan M; Kim, Jinsuh

2014-01-01

We report two cases of epithelioid hemangioma (EH) manifested in the thoracic spine with associated clinical, radiographic, and pathological findings. Epithelioid hemangioma is a benign vascular tumor that can involve any bone (including the spine in a subset of patients). Although recognized as a benign tumor by the WHO, it can display locally aggressive features. Within the spine, these features may lead to pain, instability, and/or neurologic dysfunction. The radiographic appearance is most typically that of a lytic, well-defined lesion on plain film or CT. The MRI appearance is typically hypointense on T1WI, hyperintense on T2WI, and avidly enhancing, often with an extraosseous soft-tissue component.

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report

PubMed Central

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-01-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor. PMID:29552133

CT and MR imaging features in phosphaturic mesenchymal tumor-mixed connective tissue: A case report.

PubMed

Shi, Zhenshan; Deng, Yiqiong; Li, Xiumei; Li, Yueming; Cao, Dairong; Coossa, Vikash Sahadeo

2018-04-01

Phosphaturic mesenchymal tumor-mixed connective tissue (PMT-MCT) is rare and usually benign and slow-growing. The majority of these tumors is associated with sporadic tumor-induced osteomalacia (TIO) or rickets, affect middle-aged individuals and are located in the extremities. Previous imaging studies often focused on seeking the causative tumors of TIO, not on the radiological features of these tumors, especially magnetic resonance imaging (MRI) features. PMT-MCT remains a largely misdiagnosed, ignored or unknown entity by most radiologists and clinicians. In the present case report, a review of the known literature of PMT-MCT was conducted and the CT and MRI findings from three patient cases were described for diagnosing the small subcutaneous tumor. Typical MRI appearances of PMT-MCT were isointense relative to the muscles on T1-weighted imaging, and markedly hyperintense on T2-weighted imaging containing variably flow voids, with markedly heterogeneous/homogenous enhancement on post contrast T1-weighted fat-suppression imaging. Short time inversion recovery was demonstrated to be the optimal sequence in localizing the tumor.

Neuroimaging features in subacute encephalopathy with seizures in alcoholics (SESA syndrome)

PubMed Central

Drake-Pérez, Marta; de Lucas, Enrique Marco; Lyo, John; Fernández-Torre, José L.

2017-01-01

Purpose To describe the neuroimaging findings in subacute encephalopathy with seizures in alcoholics (SESA syndrome). Methods We reviewed all cases reported previously, as well as 4 patients diagnosed in our center. We included a total of 8 patients. All subjects had clinical and EEG findings compatible with SESA syndrome and at least one MRI study that did not show other underlying condition that could be responsible for the clinical presentation. Results Initial MRI studies revealed the following features: cortical-subcortical areas of increased T2/FLAIR signal and restricted diffusion (6 patients), hyperperfusion (3 patients), atrophy (5 patients), chronic microvascular ischemic changes (4 patients). Follow-up MRI was performed in half of the patients, all showing a resolution of the hyperintense lesions, but developing focal atrophic changes in 75%. Conclusions SESA syndrome should be included among the alcohol-related encephalopathies. Its radiological features include transient cortical-subcortical T2-hyperintense areas with restricted diffusion (overlapping the typical findings in status epilepticus) observed in a patient with atrophy and chronic multifocal vascular lesions. PMID:27391464

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

Segmentation of human brain using structural MRI is a key step of processing in imaging neuroscience. The methods have undergone a rapid development in the past two decades and are now widely available. This non-technical review aims at providing an overview and basic understanding of the most common software. Starting with the basis of structural MRI contrast in brain and imaging protocols, the concepts of voxel-based and surface-based segmentation are discussed. Special emphasis is given to the typical contrast features and morphological constraints of cortical and sub-cortical grey matter. In addition to the use for voxel-based morphometry, basic applications in quantitative MRI, cortical thickness estimations, and atrophy measurements as well as assignment of cortical regions and deep brain nuclei are briefly discussed. Finally, some fields for clinical applications are given.

Canonical Correlation Analysis for Feature-Based Fusion of Biomedical Imaging Modalities and Its Application to Detection of Associative Networks in Schizophrenia.

PubMed

Correa, Nicolle M; Li, Yi-Ou; Adalı, Tülay; Calhoun, Vince D

2008-12-01

Typically data acquired through imaging techniques such as functional magnetic resonance imaging (fMRI), structural MRI (sMRI), and electroencephalography (EEG) are analyzed separately. However, fusing information from such complementary modalities promises to provide additional insight into connectivity across brain networks and changes due to disease. We propose a data fusion scheme at the feature level using canonical correlation analysis (CCA) to determine inter-subject covariations across modalities. As we show both with simulation results and application to real data, multimodal CCA (mCCA) proves to be a flexible and powerful method for discovering associations among various data types. We demonstrate the versatility of the method with application to two datasets, an fMRI and EEG, and an fMRI and sMRI dataset, both collected from patients diagnosed with schizophrenia and healthy controls. CCA results for fMRI and EEG data collected for an auditory oddball task reveal associations of the temporal and motor areas with the N2 and P3 peaks. For the application to fMRI and sMRI data collected for an auditory sensorimotor task, CCA results show an interesting joint relationship between fMRI and gray matter, with patients with schizophrenia showing more functional activity in motor areas and less activity in temporal areas associated with less gray matter as compared to healthy controls. Additionally, we compare our scheme with an independent component analysis based fusion method, joint-ICA that has proven useful for such a study and note that the two methods provide complementary perspectives on data fusion.

CNS cavernous haemangioma: "popcorn" in the brain and spinal cord.

PubMed

Hegde, A N; Mohan, S; Lim, C C T

2012-04-01

Cavernous haemangiomas (CH) are relatively uncommon non-shunting vascular malformations of the central nervous system and can present with seizures or with neurological deficits due to haemorrhage. Radiologists can often suggest the diagnosis of CH based on characteristic magnetic resonance imaging (MRI) features, thus avoiding further invasive procedures such as digital subtraction angiography or surgical biopsy. Although typical MRI appearance combined with the presence of multiple focal low signal lesions on T2*-weighted images or the presence of one or more developmental venous anomaly within the brain can improve the diagnostic confidence, serial imaging studies are often required if a solitary CH presents at a time when the imaging appearances had not yet matured to the typical "popcorn" appearance. Copyright © 2011 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Objects and categories: feature statistics and object processing in the ventral stream.

PubMed

Tyler, Lorraine K; Chiu, Shannon; Zhuang, Jie; Randall, Billi; Devereux, Barry J; Wright, Paul; Clarke, Alex; Taylor, Kirsten I

2013-10-01

Recognizing an object involves more than just visual analyses; its meaning must also be decoded. Extensive research has shown that processing the visual properties of objects relies on a hierarchically organized stream in ventral occipitotemporal cortex, with increasingly more complex visual features being coded from posterior to anterior sites culminating in the perirhinal cortex (PRC) in the anteromedial temporal lobe (aMTL). The neurobiological principles of the conceptual analysis of objects remain more controversial. Much research has focused on two neural regions-the fusiform gyrus and aMTL, both of which show semantic category differences, but of different types. fMRI studies show category differentiation in the fusiform gyrus, based on clusters of semantically similar objects, whereas category-specific deficits, specifically for living things, are associated with damage to the aMTL. These category-specific deficits for living things have been attributed to problems in differentiating between highly similar objects, a process that involves the PRC. To determine whether the PRC and the fusiform gyri contribute to different aspects of an object's meaning, with differentiation between confusable objects in the PRC and categorization based on object similarity in the fusiform, we carried out an fMRI study of object processing based on a feature-based model that characterizes the degree of semantic similarity and difference between objects and object categories. Participants saw 388 objects for which feature statistic information was available and named the objects at the basic level while undergoing fMRI scanning. After controlling for the effects of visual information, we found that feature statistics that capture similarity between objects formed category clusters in fusiform gyri, such that objects with many shared features (typical of living things) were associated with activity in the lateral fusiform gyri whereas objects with fewer shared features (typical of nonliving things) were associated with activity in the medial fusiform gyri. Significantly, a feature statistic reflecting differentiation between highly similar objects, enabling object-specific representations, was associated with bilateral PRC activity. These results confirm that the statistical characteristics of conceptual object features are coded in the ventral stream, supporting a conceptual feature-based hierarchy, and integrating disparate findings of category responses in fusiform gyri and category deficits in aMTL into a unifying neurocognitive framework.

Language Laterality in Autism Spectrum Disorder and Typical Controls: A Functional, Volumetric, and Diffusion Tensor MRI Study

ERIC Educational Resources Information Center

Knaus, Tracey A.; Silver, Andrew M.; Kennedy, Meaghan; Lindgren, Kristen A.; Dominick, Kelli C.; Siegel, Jeremy; Tager-Flusberg, Helen

2010-01-01

Language and communication deficits are among the core features of autism spectrum disorder (ASD). Reduced or reversed asymmetry of language has been found in a number of disorders, including ASD. Studies of healthy adults have found an association between language laterality and anatomical measures but this has not been systematically…

Extracting intrinsic functional networks with feature-based group independent component analysis.

PubMed

Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro-connectome, particularly in the context of data fusion.

Role of MRI in the early diagnosis of tubal ectopic pregnancy.

PubMed

Si, Ming-Jue; Gui, Shuang; Fan, Qin; Han, Hong-Xiu; Zhao, Qian-Qian; Li, Zhi-Xin; Zhao, Jiang-Min

2016-07-01

To determine the role of MRI in the early diagnosis of tubal ectopic pregnancy (EP). Clinical and MRI features of 27 cases of tubal pregnancy were reviewed. A thick-walled gestational sac (GS)-like structure was demonstrated lateral to the uterus in all cases. On T2-weighted images, the thick wall typically exhibited 3 discrete rings in 22 cases (81 %), among which 17 cases (63 %) displayed small vessels and 6 cases (33 %) exhibited small areas of fresh haemorrhage inside the thick wall. The contents demonstrated non-specific liquid in 26 %, papillary solid components in 56 %, and fresh blood or fluid-fluid level in 19 % of the cases. Dilatation of the affected fallopian tube associated with hematosalpinx was demonstrated in 18 cases (67 %) and marked enhancement of the tubal wall was observed in 22 cases (81 %). No correlation was found between the size of the GS and the estimated gestational age (r = 0.056). MRI plays an important role in the early diagnosis and management of tubal pregnancy. The characteristic MRI features include a GS-like structure with a "three rings" appearance on T2-weighted images, presence of solid components in the sac, dilatation of the affected fallopian tube with hematosalpinx, and tubal wall enhancement. • MR imaging has served as a problem-solving procedure in ectopic pregnancy. • MR imaging features can be criteria for early diagnosis of tubal pregnancy. • Detailed assessment of ectopic implantation is necessary for management decision-making.

Diagnostic algorithm for relapsing acquired demyelinating syndromes in children.

PubMed

Hacohen, Yael; Mankad, Kshitij; Chong, W K; Barkhof, Frederik; Vincent, Angela; Lim, Ming; Wassmer, Evangeline; Ciccarelli, Olga; Hemingway, Cheryl

2017-07-18

To establish whether children with relapsing acquired demyelinating syndromes (RDS) and myelin oligodendrocyte glycoprotein antibodies (MOG-Ab) show distinctive clinical and radiologic features and to generate a diagnostic algorithm for the main RDS for clinical use. A panel reviewed the clinical characteristics, MOG-Ab and aquaporin-4 (AQP4) Ab, intrathecal oligoclonal bands, and Epstein-Barr virus serology results of 110 children with RDS. A neuroradiologist blinded to the diagnosis scored the MRI scans. Clinical, radiologic, and serologic tests results were compared. The findings showed that 56.4% of children were diagnosed with multiple sclerosis (MS), 25.4% with neuromyelitis optica spectrum disorder (NMOSD), 12.7% with multiphasic disseminated encephalomyelitis (MDEM), and 5.5% with relapsing optic neuritis (RON). Blinded analysis defined baseline MRI as typical of MS in 93.5% of children with MS. Acute disseminated encephalomyelitis presentation was seen only in the non-MS group. Of NMOSD cases, 30.7% were AQP4-Ab positive. MOG-Ab were found in 83.3% of AQP4-Ab-negative NMOSD, 100% of MDEM, and 33.3% of RON. Children with MOG-Ab were younger, were less likely to present with area postrema syndrome, and had lower disability, longer time to relapse, and more cerebellar peduncle lesions than children with AQP4-Ab NMOSD. A diagnostic algorithm applicable to any episode of CNS demyelination leads to 4 main phenotypes: MS, AQP4-Ab NMOSD, MOG-Ab-associated disease, and antibody-negative RDS. Children with MS and AQP4-Ab NMOSD showed features typical of adult cases. Because MOG-Ab-positive children showed notable and distinctive clinical and MRI features, they were grouped into a unified phenotype (MOG-Ab-associated disease), included in a new diagnostic algorithm. © 2017 American Academy of Neurology.

Functional MRI registration with tissue-specific patch-based functional correlation tensors.

PubMed

Zhou, Yujia; Zhang, Han; Zhang, Lichi; Cao, Xiaohuan; Yang, Ru; Feng, Qianjin; Yap, Pew-Thian; Shen, Dinggang

2018-06-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate intersubject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has suggested that such strategy cannot align functional regions well because functional areas are not necessarily consistent with anatomical structures. To alleviate this problem, a number of registration algorithms based directly on rs-fMRI data have been developed, most of which utilize functional connectivity (FC) features for registration. However, most of these methods usually extract functional features only from the thin and highly curved cortical grey matter (GM), posing great challenges to accurate estimation of whole-brain deformation fields. In this article, we demonstrate that additional useful functional features can also be extracted from the whole brain, not restricted to the GM, particularly the white-matter (WM), for improving the overall functional registration. Specifically, we quantify local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals using tissue-specific patch-based functional correlation tensors (ts-PFCTs) in both GM and WM. Functional registration is then performed by integrating the features from different tissues using the multi-channel large deformation diffeomorphic metric mapping (mLDDMM) algorithm. Experimental results show that our method achieves superior functional registration performance, compared with conventional registration methods. © 2018 Wiley Periodicals, Inc.

Gadoxetic acid enhanced MRI for differentiation of FNH and HCA: a single centre experience.

PubMed

Grieser, Christian; Steffen, Ingo G; Kramme, Incken-Birthe; Bläker, Hendrik; Kilic, Ergin; Perez Fernandez, Carmen Maria; Seehofer, Daniel; Schott, Eckart; Hamm, Bernd; Denecke, Timm

2014-06-01

Evaluation of enhancement characteristics of histopathologically confirmed focal nodular hyperplasias (FNHs) and hepatocellular adenomas (HCAs) with gadoxetic acid-enhanced MRI. Sixty-eight patients with 115 histopathologically proven lesions (FNHs, n=44; HCAs, n=71) examined with gadoxetic acid-enhanced MRI were retrospectively enrolled (standard of reference: surgical resection, n=53 patients (lesions: FNHs, n=37; HCAs, n=53); biopsy, n=15 (lesions: FNHs, n=7; HCAs, n=18)). Two radiologists evaluated all MR images regarding morphological features as well as the vascular and hepatocyte-specific enhancement in consensus. For the hepatobiliary phase, relative enhancement of the lesions and lesion to liver enhancement were significantly lower for HCAs (mean, 48.7 (±48.4)%and 49.4 (±33.9) %) compared to FNHs (159.3 (±92.5) %; and 151.7 (±79) %; accuracy of 89%and 90 %, respectively; P<0.001). Visual strong uptake of FNHs vs. hypointensity of HCAs in the hepatobiliary phase resulted in an accuracy of 92 %. This parameter was superior to all other morphological and dynamic vascular criteria alone and in combination (accuracy, 54–85 %). For differentiation of FNHs and HCAs by means of MRI, gadoxetic acid uptake in the hepatobiliary phase was found to be superior to all other criteria alone and in combination. EOB-MRI is well suited to differentiate FNHs and hepatocellular adenomas. For this purpose hepatobiliary phase is superior to unenhanced and dynamic imaging. Hepatobiliary phase (peripheral) hyper- or isointensity is typical for FNH. Hepatobiliary phase hypointensity is typical for hepatocellular adenomas. EOB-MRI helps to avoid misinterpretations of benign hepatocellular lesions.

Improving Functional MRI Registration Using Whole-Brain Functional Correlation Tensors.

PubMed

Zhou, Yujia; Yap, Pew-Thian; Zhang, Han; Zhang, Lichi; Feng, Qianjin; Shen, Dinggang

2017-09-01

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) largely rely on the accurate inter-subject registration of functional areas. This is typically achieved through registration of the corresponding T1-weighted MR images with more structural details. However, accumulating evidence has suggested that such strategy cannot well-align functional regions which are not necessarily confined by the anatomical boundaries defined by the T1-weighted MR images. To mitigate this problem, various registration algorithms based directly on rs-fMRI data have been developed, most of which have utilized functional connectivity (FC) as features for registration. However, most of the FC-based registration methods usually extract the functional features only from the thin and highly curved cortical grey matter (GM), posing a great challenge in accurately estimating the whole-brain deformation field. In this paper, we demonstrate that the additional useful functional features can be extracted from brain regions beyond the GM, particularly, white-matter (WM) based on rs-fMRI, for improving the overall functional registration. Specifically, we quantify the local anisotropic correlation patterns of the blood oxygenation level-dependent (BOLD) signals, modeled by functional correlation tensors (FCTs), in both GM and WM. Functional registration is then performed based on multiple components of the whole-brain FCTs using a multichannel Large Deformation Diffeomorphic Metric Mapping (mLDDMM) algorithm. Experimental results show that our proposed method achieves superior functional registration performance, compared with other conventional registration methods.

Imaging of Groin Pain: Magnetic Resonance and Ultrasound Imaging Features

PubMed Central

Lee, Susan C.; Endo, Yoshimi; Potter, Hollis G.

2017-01-01

Context: Evaluation of groin pain in athletes may be challenging as pain is typically poorly localized and the pubic symphyseal region comprises closely approximated tendons and muscles. As such, magnetic resonance imaging (MRI) and ultrasound (US) may help determine the etiology of groin pain. Evidence Acquisition: A PubMed search was performed using the following search terms: ultrasound, magnetic resonance imaging, sports hernia, athletic pubalgia, and groin pain. Date restrictions were not placed on the literature search. Study Design: Clinical review. Level of Evidence: Level 4. Results: MRI is sensitive in diagnosing pathology in groin pain. Not only can MRI be used to image rectus abdominis/adductor longus aponeurosis and pubic bone pathology, but it can also evaluate other pathology within the hip and pelvis. MRI is especially helpful when groin pain is poorly localized. Real-time capability makes ultrasound useful in evaluating the pubic symphyseal region, as it can be used for evaluation and treatment. Conclusion: MRI and US are valuable in diagnosing pathology in athletes with groin pain, with the added utility of treatment using US-guided intervention. Strength-of Recommendation Taxonomy: C PMID:28850315

Imaging of Groin Pain: Magnetic Resonance and Ultrasound Imaging Features.

PubMed

Lee, Susan C; Endo, Yoshimi; Potter, Hollis G

Evaluation of groin pain in athletes may be challenging as pain is typically poorly localized and the pubic symphyseal region comprises closely approximated tendons and muscles. As such, magnetic resonance imaging (MRI) and ultrasound (US) may help determine the etiology of groin pain. A PubMed search was performed using the following search terms: ultrasound, magnetic resonance imaging, sports hernia, athletic pubalgia, and groin pain. Date restrictions were not placed on the literature search. Clinical review. Level 4. MRI is sensitive in diagnosing pathology in groin pain. Not only can MRI be used to image rectus abdominis/adductor longus aponeurosis and pubic bone pathology, but it can also evaluate other pathology within the hip and pelvis. MRI is especially helpful when groin pain is poorly localized. Real-time capability makes ultrasound useful in evaluating the pubic symphyseal region, as it can be used for evaluation and treatment. MRI and US are valuable in diagnosing pathology in athletes with groin pain, with the added utility of treatment using US-guided intervention. Strength-of Recommendation Taxonomy: C.

Epidemiological findings and clinical and magnetic resonance presentations in subacute sclerosing panencephalitis.

PubMed

Cece, H; Tokay, L; Yildiz, S; Karakas, O; Karakas, E; Iscan, A

2011-01-01

Subacute sclerosing panencephalitis (SSPE) is a rare, progressive, inflammatory neurodegenerative disease. This study investigated the relationships of clinical stage with epidemiological and magnetic resonance imaging (MRI) findings in SSPE by retrospective review of 76 cases (57 male) diagnosed by typical periodic electroencephalographic features, clinical symptoms and elevated measles antibody titre in cerebrospinal fluid. Clinical stage at diagnosis was I or II in 48 patients, III in 25 and IV in three. Prominent findings at presentation were atonic/myoclonic seizures (57.9%) and mental deterioration with behaviour alteration (30.3%). Frequent MRI findings (13 - 32 patients) were subcortical, periventricular and cortical involvement and brain atrophy; the corpus callosum, basal ganglia, cerebellum and brainstem were less frequently involved. Five patients had pseudotumour cerebri. Cranial MRI at initial diagnosis was normal in 21 patients (19 stage I/II, two stage III/IV). Abnormal MRI findings were significantly more frequent in the later stages, thus a normal initial cranial MRI does not exclude SSPE, which should, therefore, be kept in mind in childhood demyelinating diseases even when the presentation is unusual.

A Method for Whole Brain Ex Vivo Magnetic Resonance Imaging with Minimal Susceptibility Artifacts

PubMed Central

Shatil, Anwar S.; Matsuda, Kant M.; Figley, Chase R.

2016-01-01

Magnetic resonance imaging (MRI) is a non-destructive technique that is capable of localizing pathologies and assessing other anatomical features (e.g., tissue volume, microstructure, and white matter connectivity) in postmortem, ex vivo human brains. However, when brains are removed from the skull and cerebrospinal fluid (i.e., their normal in vivo magnetic environment), air bubbles and air–tissue interfaces typically cause magnetic susceptibility artifacts that severely degrade the quality of ex vivo MRI data. In this report, we describe a relatively simple and cost-effective experimental setup for acquiring artifact-free ex vivo brain images using a clinical MRI system with standard hardware. In particular, we outline the necessary steps, from collecting an ex vivo human brain to the MRI scanner setup, and have also described changing the formalin (as might be necessary in longitudinal postmortem studies). Finally, we share some representative ex vivo MRI images that have been acquired using the proposed setup in order to demonstrate the efficacy of this approach. We hope that this protocol will provide both clinicians and researchers with a straight-forward and cost-effective solution for acquiring ex vivo MRI data from whole postmortem human brains. PMID:27965620

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

PubMed

Akhavan Aghdam, Maryam; Sharifi, Arash; Pedram, Mir Mohsen

2018-05-07

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Automatic classification of patients with idiopathic Parkinson's disease and progressive supranuclear palsy using diffusion MRI datasets

NASA Astrophysics Data System (ADS)

Talai, Sahand; Boelmans, Kai; Sedlacik, Jan; Forkert, Nils D.

2017-03-01

Parkinsonian syndromes encompass a spectrum of neurodegenerative diseases, which can be classified into various subtypes. The differentiation of these subtypes is typically conducted based on clinical criteria. Due to the overlap of intra-syndrome symptoms, the accurate differential diagnosis based on clinical guidelines remains a challenge with failure rates up to 25%. The aim of this study is to present an image-based classification method of patients with Parkinson's disease (PD) and patients with progressive supranuclear palsy (PSP), an atypical variant of PD. Therefore, apparent diffusion coefficient (ADC) parameter maps were calculated based on diffusion-tensor magnetic resonance imaging (MRI) datasets. Mean ADC values were determined in 82 brain regions using an atlas-based approach. The extracted mean ADC values for each patient were then used as features for classification using a linear kernel support vector machine classifier. To increase the classification accuracy, a feature selection was performed, which resulted in the top 17 attributes to be used as the final input features. A leave-one-out cross validation based on 56 PD and 21 PSP subjects revealed that the proposed method is capable of differentiating PD and PSP patients with an accuracy of 94.8%. In conclusion, the classification of PD and PSP patients based on ADC features obtained from diffusion MRI datasets is a promising new approach for the differentiation of Parkinsonian syndromes in the broader context of decision support systems.

Cross Sectional Imaging of Solitary Lesions of the Neurocranium.

PubMed

Schäfer, Max-Ludwig; Koch, Arend; Streitparth, Florian; Wiener, Edzard

2017-12-01

Background  Although a wide range of processes along the neurocranium are of a benign nature, there are often difficulties in the differential diagnosis. Method  In the review CT/MRI scans of the head were evaluated retrospectively regarding solitary lesions along the neurocranium. The majority of the lesions were histologically proven. Results  The purpose of the review is to present typical pathologies of the neurocranium and provide a systematic overview based on 12 entities, their locations, prevalence and radiological characteristics. Conclusion  Processes, which primarily originate from the neurocranium have to be differentiated from secondary processes infiltrating the neurocranium. For this important diagnostic feature, MRI is typically essential, while the definitive diagnosis is often made on the basis of the medical history and the typical appearance on computer tomography. Key Points   · There are often difficulties in the precise differential diagnosis of solitary lesions along the neurocranium. Typical solitary pathologies of the neurocranium based on 12 entities were presented. Both magnetic resonance imaging and computed tomography are often essential for an exact differential diagnosis.. Citation Format · Schäfer M, Koch A, Streitparth F et al. Cross Sectional Diagnosis of Solitary Lesions of the Neurocranium. Fortschr Röntgenstr 2017; 189: 1135 - 1144. © Georg Thieme Verlag KG Stuttgart · New York.

Utility of fat-suppressed sequences in differentiation of aggressive vs typical asymptomatic haemangioma of the spine.

PubMed

Nabavizadeh, Seyed Ali; Mamourian, Alexander; Schmitt, James E; Cloran, Francis; Vossough, Arastoo; Pukenas, Bryan; Loevner, Laurie A; Mohan, Suyash

2016-01-01

While haemangiomas are common benign vascular lesions involving the spine, some behave in an aggressive fashion. We investigated the utility of fat-suppressed sequences to differentiate between benign and aggressive vertebral haemangiomas. Patients with the diagnosis of aggressive vertebral haemangioma and available short tau inversion-recovery or T2 fat saturation sequence were included in the study. 11 patients with typical asymptomatic vertebral body haemangiomas were selected as the control group. Region of interest signal intensity (SI) analysis of the entire haemangioma as well as the portion of each haemangioma with highest signal on fat-saturation sequences was performed and normalized to a reference normal vertebral body. A total of 8 patients with aggressive vertebral haemangioma and 11 patients with asymptomatic typical vertebral haemangioma were included. There was a significant difference between total normalized mean SI ratio (3.14 vs 1.48, p = 0.0002), total normalized maximum SI ratio (5.72 vs 2.55, p = 0.0003), brightest normalized mean SI ratio (4.28 vs 1.72, p < 0.0001) and brightest normalized maximum SI ratio (5.25 vs 2.45, p = 0.0003). Multiple measures were able to discriminate between groups with high sensitivity (>88%) and specificity (>82%). In addition to the conventional imaging features such as vertebral expansion and presence of extravertebral component, quantitative evaluation of fat-suppression sequences is also another imaging feature that can differentiate aggressive haemangioma and typical asymptomatic haemangioma. The use of quantitative fat-suppressed MRI in vertebral haemangiomas is demonstrated. Quantitative fat-suppressed MRI can have a role in confirming the diagnosis of aggressive haemangiomas. In addition, this application can be further investigated in future studies to predict aggressiveness of vertebral haemangiomas in early stages.

The MRI appearances of cancellous allograft bone chips after the excision of bone tumours.

PubMed

Kang, S; Han, I; Hong, S H; Cho, H S; Kim, W; Kim, H-S

2015-01-01

Cancellous allograft bone chips are commonly used in the reconstruction of defects in bone after removal of benign tumours. We investigated the MRI features of grafted bone chips and their change over time, and compared them with those with recurrent tumour. We retrospectively reviewed 66 post-operative MRIs from 34 patients who had undergone curettage and grafting with cancellous bone chips to fill the defect after excision of a tumour. All grafts showed consistent features at least six months after grafting: homogeneous intermediate or low signal intensities with or without scattered hyperintense foci (speckled hyperintensities) on T1 images; high signal intensities with scattered hypointense foci (speckled hypointensities) on T2 images, and peripheral rim enhancement with or without central heterogeneous enhancements on enhanced images. Incorporation of the graft occurred from the periphery to the centre, and was completed within three years. Recurrent lesions consistently showed the same signal intensities as those of pre-operative MRIs of the primary lesions. There were four misdiagnoses, three of which were chondroid tumours. We identified typical MRI features and clarified the incorporation process of grafted cancellous allograft bone chips. The most important characteristics of recurrent tumours were that they showed the same signal intensities as the primary tumours. It might sometimes be difficult to differentiate grafted cancellous allograft bone chips from a recurrent chondroid tumour. ©2015 The British Editorial Society of Bone & Joint Surgery.

Perception and Processing of Faces in the Human Brain Is Tuned to Typical Feature Locations

PubMed Central

Schwarzkopf, D. Samuel; Alvarez, Ivan; Lawson, Rebecca P.; Henriksson, Linda; Kriegeskorte, Nikolaus; Rees, Geraint

2016-01-01

Faces are salient social stimuli whose features attract a stereotypical pattern of fixations. The implications of this gaze behavior for perception and brain activity are largely unknown. Here, we characterize and quantify a retinotopic bias implied by typical gaze behavior toward faces, which leads to eyes and mouth appearing most often in the upper and lower visual field, respectively. We found that the adult human visual system is tuned to these contingencies. In two recognition experiments, recognition performance for isolated face parts was better when they were presented at typical, rather than reversed, visual field locations. The recognition cost of reversed locations was equal to ∼60% of that for whole face inversion in the same sample. Similarly, an fMRI experiment showed that patterns of activity evoked by eye and mouth stimuli in the right inferior occipital gyrus could be separated with significantly higher accuracy when these features were presented at typical, rather than reversed, visual field locations. Our findings demonstrate that human face perception is determined not only by the local position of features within a face context, but by whether features appear at the typical retinotopic location given normal gaze behavior. Such location sensitivity may reflect fine-tuning of category-specific visual processing to retinal input statistics. Our findings further suggest that retinotopic heterogeneity might play a role for face inversion effects and for the understanding of conditions affecting gaze behavior toward faces, such as autism spectrum disorders and congenital prosopagnosia. SIGNIFICANCE STATEMENT Faces attract our attention and trigger stereotypical patterns of visual fixations, concentrating on inner features, like eyes and mouth. Here we show that the visual system represents face features better when they are shown at retinal positions where they typically fall during natural vision. When facial features were shown at typical (rather than reversed) visual field locations, they were discriminated better by humans and could be decoded with higher accuracy from brain activity patterns in the right occipital face area. This suggests that brain representations of face features do not cover the visual field uniformly. It may help us understand the well-known face-inversion effect and conditions affecting gaze behavior toward faces, such as prosopagnosia and autism spectrum disorders. PMID:27605606

Reversible brain atrophy in glutaric aciduria type 1.

PubMed

Numata-Uematsu, Yurika; Sakamoto, Osamu; Kakisaka, Yosuke; Okubo, Yukimune; Oikawa, Yoshitsugu; Arai-Ichinoi, Natsuko; Kure, Shigeo; Uematsu, Mitsugu

2017-06-01

Glutaric aciduria type 1 (GA1) is a rare metabolic disorder caused by a deficiency of glutaryl-CoA dehydrogenase. The typical clinical onset features an acute encephalopathic crisis developed in early childhood, causing irreversible striatal injury. Recently, tandem mass spectrometry of spots of dried blood has allowed pre-symptomatic detection of GA1 in newborns. Early treatment can prevent irreversible neurological injury. We report the case of a girl with GA1 who exhibited a characteristic reversible change upon brain magnetic resonance imaging (MRI). She was diagnosed with GA1 as a newborn. She commenced dietary carnitine and her intake of lysine and tryptophan were reduced at the age of 4weeks. After treatment commenced, her mean glutarylcarnitine level was lower than that in the previous reports. The plasma lysine and tryptophan levels were maintained below the normal ranges. At 4months, brain MRI revealed a widened operculum with dilatation of the subarachnoid spaces surrounding the atrophic bilateral frontotemporal lobes; this is typical of GA1 patients. However, at 17months, MRI revealed that the atrophic lesion had disappeared and she subsequently underwent normal maturation. She has never suffered a metabolic decompensation episode. At 26months, her development and brain MRI were normal. The present reversible brain atrophy in a patient with GA1 indicates that early dietary modifications with a lower level of glutarylcarnitine and administration of carnitine can lead to normal development. Copyright © 2017 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Invasive placenta previa: Placental bulge with distorted uterine outline and uterine serosal hypervascularity at 1.5T MRI - useful features for differentiating placenta percreta from placenta accreta.

PubMed

Chen, Xin; Shan, Ruiqin; Zhao, Lianxin; Song, Qingxu; Zuo, Changting; Zhang, Xinjuan; Wang, Shanshan; Shi, Honglu; Gao, Fei; Qian, Tianyi; Wang, Guangbin; Limperopoulos, Catherine

2018-02-01

To characterise MRI features of invasive placenta previa and to identify specific features for differentiating placenta percreta (PP) from placenta accreta (PA). Forty-five women with PP and 93 women with PA who underwent 1.5T placental MRI were included. Two radiologists independently evaluated the MRI features of invasive placenta previa, including our novel type of placental bulge (i.e. placental bulge type-II, characterized by placental bulge with distorted uterine outline). Pearson's chi-squared or Fisher's two-sided exact test was performed to compare the MRI features between PP and PA. Logistic stepwise regression analysis and the area under the receiver operating characteristic curve (AUC) were performed to select the optimal features for differentiating PP from PA. Significant differences were found in nine MRI features between women with PP and those with PA (P <0.05). Placental bulge type-II and uterine serosal hypervascularity were independently associated with PP (odds ratio = 48.618, P < 0.001; odds ratio = 4.165, P = 0.018 respectively), and the combination of the two MRI features to distinguish PP from PA yielded an AUC of 0.92 for its predictive performance. Placental bulge type-II and uterine serosal hypervascularity are useful MRI features for differentiating PP from PA. • Placental bulge type-II demonstrated the strongest independent association with PP. • Uterine serosal hypervascularity is a useful feature for differentiating PP from PA. • MRI features associated with abnormal vessels increase the risk of massive haemorrhage.

Liver biopsy for diagnosis of presumed benign hepatocellular lesions lacking magnetic resonance imaging diagnostic features of focal nodular hyperplasia.

PubMed

Sannier, Aurélie; Cazejust, Julien; Lequoy, Marie; Cervera, Pascale; Scatton, Olivier; Rosmorduc, Olivier; Wendum, Dominique

2016-11-01

The contribution of liver biopsy for the diagnosis of presumed benign hepatocellular lesions lacking the diagnostic features of focal nodular hyperplasia (FNH) on magnetic resonance imaging (MRI) is unknown. We evaluated liver biopsy and MRI performances in this setting. Magnetic resonance imaging and slides of liver biopsies performed for a presumed benign hepatocellular lesion (2006-2013) without the typical features of FNH on MRI were blindly reviewed (n = 45). Eighteen lesions were surgically removed and also analyzed. The final diagnosis was the diagnosis established after surgery or on the biopsy in the absence of surgery. The final diagnosis was FNH (n = 19), hepatocellular adenoma (HCA, n = 15), hepatocellular carcinoma (n = 3) and indefinite (n = 4). Four lesions corresponded to non hepatocellular lesions. FNH, HNF1A mutated and inflammatory HCA were diagnosed accurately on the biopsy in 95%, 67% and 100% of the cases respectively. Diagnostic performance of liver biopsy for HNF1A mutated HCA was lower because of the lack of non-tumoral tissue. Diagnosis based on morphological analysis was certain and correct in 27 cases. Immunostaining allowed a definite diagnosis in 12 additionnal cases. Radiological diagnosis was in agreement with the histological diagnosis in 75.6% of the cases, with a very high sensitivity (97%) and specificity (100%) for the diagnosis of HNF1A mutated HCA. Liver biopsy has a good diagnostic performance particularly for FNH and inflammatory HCA, and sampling of non-lesional tissue is highly recommended. A biopsy does not seem necessary if H-HCA is diagnosed on MRI. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The Repaired Rotator Cuff: MRI and Ultrasound Evaluation.

PubMed

Lee, Susan C; Williams, Danielle; Endo, Yoshimi

2018-03-01

The purposes of this review were to provide an overview of the current practice of evaluating the postoperative rotator cuff on imaging and to review the salient imaging findings of the normal and abnormal postoperative rotator cuff, as well as of postoperative complications. The repaired rotator cuff frequently appears abnormal on magnetic resonance imaging (MRI) and ultrasound (US). Recent studies have shown that while the tendons typically normalize, they can demonstrate clinically insignificant abnormal imaging appearances for longer than 6 months. Features of capsular thickening or subacromial-subdeltoid bursal thickening and fluid distension were found to decrease substantially in the first 6-month postoperative period. MRI and US were found to be highly comparable in the postoperative assessment of the rotator cuff, although they had a lower sensitivity for partial thickness tears. Imaging evaluation of newer techniques such as patch augmentation and superior capsular reconstruction needs to be further investigated. MRI and US are useful in the postoperative assessment of the rotator cuff, not only for evaluation of the integrity of the rotator cuff, but also for detecting hardware complications and other etiologies of shoulder pain.

Can fMRI safely replace the Wada test for preoperative assessment of language lateralisation? A meta-analysis and systematic review.

PubMed

Bauer, Prisca R; Reitsma, Johannes B; Houweling, Bernard M; Ferrier, Cyrille H; Ramsey, Nick F

2014-05-01

Recent studies have shown that fMRI (functional magnetic resonance imaging) may be of value for pre-surgical assessment of language lateralisation. The aim of this study was to systematically review and analyse the available literature. A systematic electronic search for studies comparing fMRI with Wada testing was conducted in the PubMed database between March 2009 and November 2011. Studies involving unilateral Wada testing, study population consisting exclusively of children younger than 12 years of age or involving five patients or fewer were excluded. 22 studies (504 patients) were included. A random effects meta-analysis was conducted to obtain pooled estimates of the positive and negative predictive values of the fMRI using the Wada test as the reference standard. The impact of several study features on the performance of fMRI was assessed. The results showed that 81% of patients were correctly classified as having left or right language dominance or mixed language representation. Techniques were discordant in 19% of patients. fMRI and Wada test agreed in 94% for typical language lateralisation and in 51% for atypical language lateralisation. Language production or language comprehension tasks and different regions of interest did not yield statistically significant different results. It can be concluded that fMRI is reliable when there is strong left-lateralised language. The Wada test is warranted when fMRI fails to show clear left-lateralisation.

Diffeomorphic functional brain surface alignment: Functional demons.

PubMed

Nenning, Karl-Heinz; Liu, Hesheng; Ghosh, Satrajit S; Sabuncu, Mert R; Schwartz, Ernst; Langs, Georg

2017-08-01

Aligning brain structures across individuals is a central prerequisite for comparative neuroimaging studies. Typically, registration approaches assume a strong association between the features used for alignment, such as macro-anatomy, and the variable observed, such as functional activation or connectivity. Here, we propose to use the structure of intrinsic resting state fMRI signal correlation patterns as a basis for alignment of the cortex in functional studies. Rather than assuming the spatial correspondence of functional structures between subjects, we have identified locations with similar connectivity profiles across subjects. We mapped functional connectivity relationships within the brain into an embedding space, and aligned the resulting maps of multiple subjects. We then performed a diffeomorphic alignment of the cortical surfaces, driven by the corresponding features in the joint embedding space. Results show that functional alignment based on resting state fMRI identifies functionally homologous regions across individuals with higher accuracy than alignment based on the spatial correspondence of anatomy. Further, functional alignment enables measurement of the strength of the anatomo-functional link across the cortex, and reveals the uneven distribution of this link. Stronger anatomo-functional dissociation was found in higher association areas compared to primary sensory- and motor areas. Functional alignment based on resting state features improves group analysis of task based functional MRI data, increasing statistical power and improving the delineation of task-specific core regions. Finally, a comparison of the anatomo-functional dissociation between cohorts is demonstrated with a group of left and right handed subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Hand-Schüller-Christian Disease and Erdheim-Chester Disease: Coexistence and Discrepancy

PubMed Central

Yin, Jun; Zhang, Feng; Zhang, Huizhen; Shen, Li; Li, Qing; Hu, Shundong; Tian, Qinghua; Bao, Yuqian

2013-01-01

Langerhans cell histiocytosis (LCH) and Erdheim-Chester disease (ECD) share similar clinical features and mechanisms. In very rare circumstances, the two diseases coexist in the same patient. Here we report such a patient, who was first diagnosed with Hand-Schüller-Christian disease (HSC), a type of LCH. Several years later, the patient presented with severe exophthalmos and osteosclerosis on radiograph. New biopsy revealed ECD. We also analyze 54 cases of LCH and 6 cases of ECD diagnosed in our hospital, as well as their progression during a follow-up period of 8 years. In five cases of HSC (9.3% of LCH), a triad of central diabetes insipidus, hyperprolactinemia, and pituitary stalk thickening on magnetic resonance imaging (MRI) preceded the typical bone lesions by 4–9 years. In addition, LCH was featured as elevated plasma alkaline phosphatase (ALP), which was normal in ECD. Combined with a literature review, several features are summarized to differentiate ECD from HSC. In patients with diabetes insipidus, concomitant hyperprolactinemia and pituitary stalk thickening on MRI indicate a possible HSC. Additionally, if osteosclerosis is observed in a patient with LCH, the coexistence of ECD should be considered. PMID:23299772

Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2017-03-01

We propose using multi-scale image textures to investigate links between neuroanatomical regions and clinical variables in MRI. Texture features are derived at multiple scales of resolution based on the Laplacian-of-Gaussian (LoG) filter. Three quantifier functions (Average, Standard Deviation and Entropy) are used to summarize texture statistics within standard, automatically segmented neuroanatomical regions. Significance tests are performed to identify regional texture differences between ASD vs. TDC and male vs. female groups, as well as correlations with age (corrected p < 0.05). The open-access brain imaging data exchange (ABIDE) brain MRI dataset is used to evaluate texture features derived from 31 brain regions from 1112 subjects including 573 typically developing control (TDC, 99 females, 474 males) and 539 Autism spectrum disorder (ASD, 65 female and 474 male) subjects. Statistically significant texture differences between ASD vs. TDC groups are identified asymmetrically in the right hippocampus, left choroid-plexus and corpus callosum (CC), and symmetrically in the cerebellar white matter. Sex-related texture differences in TDC subjects are found in primarily in the left amygdala, left cerebellar white matter, and brain stem. Correlations between age and texture in TDC subjects are found in the thalamus-proper, caudate and pallidum, most exhibiting bilateral symmetry.

Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI.

PubMed

Ahmed, Shaheen; Iftekharuddin, Khan M; Vossough, Arastoo

2011-03-01

Our previous works suggest that fractal texture feature is useful to detect pediatric brain tumor in multimodal MRI. In this study, we systematically investigate efficacy of using several different image features such as intensity, fractal texture, and level-set shape in segmentation of posterior-fossa (PF) tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques, respectively, to discriminate tumor regions from normal tissue in multimodal brain MRI. We further study the selective fusion of these features for improved PF tumor segmentation. Our result suggests that Kullback-Leibler divergence measure for feature ranking and selection and the expectation maximization algorithm for feature fusion and tumor segmentation offer the best results for the patient data in this study. We show that for T1 and fluid attenuation inversion recovery (FLAIR) MRI modalities, the best PF tumor segmentation is obtained using the texture feature such as multifractional Brownian motion (mBm) while that for T2 MRI is obtained by fusing level-set shape with intensity features. In multimodality fused MRI (T1, T2, and FLAIR), mBm feature offers the best PF tumor segmentation performance. We use different similarity metrics to evaluate quality and robustness of these selected features for PF tumor segmentation in MRI for ten pediatric patients.

T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.

PubMed

Nketiah, Gabriel; Elschot, Mattijs; Kim, Eugene; Teruel, Jose R; Scheenen, Tom W; Bathen, Tone F; Selnæs, Kirsten M

2017-07-01

To evaluate the diagnostic relevance of T2-weighted (T2W) MRI-derived textural features relative to quantitative physiological parameters derived from diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI in Gleason score (GS) 3+4 and 4+3 prostate cancers. 3T multiparametric-MRI was performed on 23 prostate cancer patients prior to prostatectomy. Textural features [angular second moment (ASM), contrast, correlation, entropy], apparent diffusion coefficient (ADC), and DCE pharmacokinetic parameters (K trans and V e ) were calculated from index tumours delineated on the T2W, DW, and DCE images, respectively. The association between the textural features and prostatectomy GS and the MRI-derived parameters, and the utility of the parameters in differentiating between GS 3+4 and 4+3 prostate cancers were assessed statistically. ASM and entropy correlated significantly (p < 0.05) with both GS and median ADC. Contrast correlated moderately with median ADC. The textural features correlated insignificantly with K trans and V e . GS 4+3 cancers had significantly lower ASM and higher entropy than 3+4 cancers, but insignificant differences in median ADC, K trans , and V e . The combined texture-MRI parameters yielded higher classification accuracy (91%) than the individual parameter sets. T2W MRI-derived textural features could serve as potential diagnostic markers, sensitive to the pathological differences in prostate cancers. • T2W MRI-derived textural features correlate significantly with Gleason score and ADC. • T2W MRI-derived textural features differentiate Gleason score 3+4 from 4+3 cancers. • T2W image textural features could augment tumour characterization.

Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI

NASA Astrophysics Data System (ADS)

Ahmed, S.; Iftekharuddin, K. M.; Ogg, R. J.; Laningham, F. H.

2009-02-01

Our previous works suggest that fractal-based texture features are very useful for detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. In this work, we investigate and compare efficacy of our texture features such as fractal and multifractional Brownian motion (mBm), and intensity along with another useful level-set based shape feature in PF tumor segmentation. We study feature selection and ranking using Kullback -Leibler Divergence (KLD) and subsequent tumor segmentation; all in an integrated Expectation Maximization (EM) framework. We study the efficacy of all four features in both multimodality as well as disparate MRI modalities such as T1, T2 and FLAIR. Both KLD feature plots and information theoretic entropy measure suggest that mBm feature offers the maximum separation between tumor and non-tumor tissues in T1 and FLAIR MRI modalities. The same metrics show that intensity feature offers the maximum separation between tumor and non-tumor tissue in T2 MRI modality. The efficacies of these features are further validated in segmenting PF tumor using both single modality and multimodality MRI for six pediatric patients with over 520 real MR images.

Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques.

PubMed

Bigler, Erin D

2015-09-01

Magnetic resonance imaging (MRI) of the brain provides exceptional image quality for visualization and neuroanatomical classification of brain structure. A variety of image analysis techniques provide both qualitative as well as quantitative methods to relate brain structure with neuropsychological outcome and are reviewed herein. Of particular importance are more automated methods that permit analysis of a broad spectrum of anatomical measures including volume, thickness and shape. The challenge for neuropsychology is which metric to use, for which disorder and the timing of when image analysis methods are applied to assess brain structure and pathology. A basic overview is provided as to the anatomical and pathoanatomical relations of different MRI sequences in assessing normal and abnormal findings. Some interpretive guidelines are offered including factors related to similarity and symmetry of typical brain development along with size-normalcy features of brain anatomy related to function. The review concludes with a detailed example of various quantitative techniques applied to analyzing brain structure for neuropsychological outcome studies in traumatic brain injury.

Symmetric corticobasal degeneration (S-CBD).

PubMed

Hassan, Anhar; Whitwell, Jennifer L; Boeve, Bradley F; Jack, Clifford R; Parisi, Joseph E; Dickson, Dennis W; Josephs, Keith A

2010-03-01

Corticobasal degeneration (CBD) is a neurodegenerative disease characterized pathologically by neuronal loss, gliosis and tau deposition in neocortex, basal ganglia and brainstem. Typical clinical presentation is known as corticobasal syndrome (CBS) and involves the core features of progressive asymmetric rigidity and apraxia, accompanied by other signs of cortical and extrapyramidal dysfunction. Asymmetry is also emphasized on neuroimaging. To describe a series of cases of CBD with symmetric clinical features and to compare clinical and imaging features of these symmetric CBD cases (S-CBD) to typical cases of CBS with CBD pathology. All cases of pathologically confirmed CBD from the Mayo Clinic Rochester database were identified. Clinical records were reviewed and quantitative volumetric analysis of symmetric atrophy on head MRI using atlas based parcellation was performed. Subjects were classified as S-CBD if no differences had been observed between right- and left-sided cortical or extrapyramidal signs or symptoms. S-CBD cases were compared to 10 randomly selected typical CBS cases. Five cases (2 female) met criteria for S-CBD. None had limb dystonia, myoclonus, apraxia or alien limb phenomena. S-CBD cases had significantly less asymmetric atrophy when compared with CBS cases (p=0.009); they were also younger at onset (median 61 versus 66 years, p<0.05) and death (67 versus 73 years, p<0.05). Family history was present in 40% of S-CBD cases. CBD can have a symmetric presentation, clinically and radiologically, in which typical features of CBS, such as limb apraxia, myoclonus, dystonia and alien limb phenomenon, may be absent. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Clinical and mutational spectrum in Korean patients with Rubinstein-Taybi syndrome: the spectrum of brain MRI abnormalities.

PubMed

Lee, Jin Sook; Byun, Christine K; Kim, Hunmin; Lim, Byung Chan; Hwang, Hee; Choi, Ji Eun; Hwang, Yong Seung; Seong, Moon-Woo; Park, Sung Sup; Kim, Ki Joong; Chae, Jong-Hee

2015-04-01

Rubinstein-Taybi syndrome (RSTS) is one of the neurodevelopmental disorders caused by mutations of epigenetic genes. The CREBBP gene is the most common causative gene, encoding the CREB-binding protein with histone acetyltransferase (HAT) activity, an epigenetic modulator. To date, there have been few reports on the structural abnormalities of the brain in RSTS patients. In addition, there are no reports on the analysis of CREBBP mutations in Korean RSTS patients. We performed mutational analyses on 16 unrelated patients with RSTS, with diagnosis based on the typical clinical features. Their medical records and brain MRI images were reviewed retrospectively. Ten of 16 patients (62.5%) had mutations in the CREBBP gene. The mutations included five frameshift mutations (31.2%), two nonsense mutations (12.5%), and three multiexon deletions (18.8%). There were no remarkable significant differences in the clinical features between those with and without a CREBBP mutation, although brain MRI abnormalities were more frequently observed in those with a CREBBP mutation. Seven of 10 patients in whom brain imaging was performed had structural abnormalities, including Chiari malformation type 1, thinning of the corpus callosum, and delayed myelination. There were no differences in delayed development or cognitive impairment between those with and without abnormal brain images, while epilepsy was involved in two patients who had abnormalities on brain MRI images. We investigated the spectrum of CREBBP mutations in Korean patients with RSTS for the first time. Eight novel mutations extended the genetic spectrum of CREBBP mutations in RSTS patients. This is also the first study showing the prevalence and spectrum of abnormalities on brain MRI in RSTS patients. Copyright © 2014 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Visual perceptual training reconfigures post-task resting-state functional connectivity with a feature-representation region.

PubMed

Sarabi, Mitra Taghizadeh; Aoki, Ryuta; Tsumura, Kaho; Keerativittayayut, Ruedeerat; Jimura, Koji; Nakahara, Kiyoshi

2018-01-01

The neural mechanisms underlying visual perceptual learning (VPL) have typically been studied by examining changes in task-related brain activation after training. However, the relationship between post-task "offline" processes and VPL remains unclear. The present study examined this question by obtaining resting-state functional magnetic resonance imaging (fMRI) scans of human brains before and after a task-fMRI session involving visual perceptual training. During the task-fMRI session, participants performed a motion coherence discrimination task in which they judged the direction of moving dots with a coherence level that varied between trials (20, 40, and 80%). We found that stimulus-induced activation increased with motion coherence in the middle temporal cortex (MT+), a feature-specific region representing visual motion. On the other hand, stimulus-induced activation decreased with motion coherence in the dorsal anterior cingulate cortex (dACC) and bilateral insula, regions involved in decision making under perceptual ambiguity. Moreover, by comparing pre-task and post-task rest periods, we revealed that resting-state functional connectivity (rs-FC) with the MT+ was significantly increased after training in widespread cortical regions including the bilateral sensorimotor and temporal cortices. In contrast, rs-FC with the MT+ was significantly decreased in subcortical regions including the thalamus and putamen. Importantly, the training-induced change in rs-FC was observed only with the MT+, but not with the dACC or insula. Thus, our findings suggest that perceptual training induces plastic changes in offline functional connectivity specifically in brain regions representing the trained visual feature, emphasising the distinct roles of feature-representation regions and decision-related regions in VPL.

Utility of fat-suppressed sequences in differentiation of aggressive vs typical asymptomatic haemangioma of the spine

PubMed Central

Nabavizadeh, Seyed Ali; Mamourian, Alexander; Schmitt, James E; Cloran, Francis; Vossough, Arastoo; Pukenas, Bryan; Loevner, Laurie A

2016-01-01

Objective: While haemangiomas are common benign vascular lesions involving the spine, some behave in an aggressive fashion. We investigated the utility of fat-suppressed sequences to differentiate between benign and aggressive vertebral haemangiomas. Methods: Patients with the diagnosis of aggressive vertebral haemangioma and available short tau inversion-recovery or T2 fat saturation sequence were included in the study. 11 patients with typical asymptomatic vertebral body haemangiomas were selected as the control group. Region of interest signal intensity (SI) analysis of the entire haemangioma as well as the portion of each haemangioma with highest signal on fat-saturation sequences was performed and normalized to a reference normal vertebral body. Results: A total of 8 patients with aggressive vertebral haemangioma and 11 patients with asymptomatic typical vertebral haemangioma were included. There was a significant difference between total normalized mean SI ratio (3.14 vs 1.48, p = 0.0002), total normalized maximum SI ratio (5.72 vs 2.55, p = 0.0003), brightest normalized mean SI ratio (4.28 vs 1.72, p < 0.0001) and brightest normalized maximum SI ratio (5.25 vs 2.45, p = 0.0003). Multiple measures were able to discriminate between groups with high sensitivity (>88%) and specificity (>82%). Conclusion: In addition to the conventional imaging features such as vertebral expansion and presence of extravertebral component, quantitative evaluation of fat-suppression sequences is also another imaging feature that can differentiate aggressive haemangioma and typical asymptomatic haemangioma. Advances in knowledge: The use of quantitative fat-suppressed MRI in vertebral haemangiomas is demonstrated. Quantitative fat-suppressed MRI can have a role in confirming the diagnosis of aggressive haemangiomas. In addition, this application can be further investigated in future studies to predict aggressiveness of vertebral haemangiomas in early stages. PMID:26511277

[Tuberculous otomastoiditis: advantage of MRI in the treatment survey].

PubMed

Moya, Plana A; Malinvaud, D; Mimoun, M; Huart, J; Bonfils, P

2008-01-01

Mycobacterium tuberculosis is a rare cause of otomastoiditis, accounting for less than a percent of chronic otitis media. The diagnosis is difficult and typically delayed because most physicians are unfamiliar with its presenting features and special laboratory requirements. Such delayed diagnosis leads to delayed treatment onset, and thus, increases complications frequency as irreversible hearing loss, facial palsy or meningo-encephalitis complications. Moreover non specific CT findings do not allow any accurate evaluation of inner ear lesions initially and under treatment. We described the first case of MRI of tuberculous mastoiditis and the evolution over a 2-years follow-up period. A patient with a clinical history of chronic otorrhea, resistant to conventional therapy, was referred to our department. CT and MRI permitted to describe the initial lesions and to appreciate the medical treatment efficiency (in order to perform surgery in case of failure or complications). Under medical treatment, MRI showed abscess volume decrease at three months while CT was still unchanged. Remineralization only was observed on CT at 12 months. The patient's healing was obtained after 15 months of antituberculous medication. MRI has the advantage over CT to demonstrate directly abscess collections that superimposed to areas of bone destructions within the temporal bone. Initially, MRI allows an accurate evaluation of abscess collections and possible meningo-encephalitis complications. Moreover, MRI precises earlier than CT the improvement of lesions and the efficacy of medical treatment, and thus, permitting us to postpone surgery where it is unnecessary.

Evaluating Kurtosis-based Diffusion MRI Tissue Models for White Matter with Fiber Ball Imaging

PubMed Central

Jensen, Jens H.; McKinnon, Emilie T.; Glenn, G. Russell; Helpern, Joseph A.

2018-01-01

In order to quantify well-defined microstructural properties of brain tissue from diffusion MRI (dMRI) data, tissue models are typically employed that relate biological features, such as cell morphology and cell membrane permeability, to the diffusion dynamics. A variety of such models have been proposed for white matter, and their validation is a topic of active interest. In this paper, three different tissue models are tested by comparing their predictions for a specific microstructural parameter to the value measured independently with a recently proposed dMRI method known as fiber ball imaging (FBI). The three tissue models are all constructed with the diffusion and kurtosis tensors, and they are hence compatible with diffusional kurtosis imaging (DKI). Nevertheless, the models differ significantly in their details and predictions. For voxels with fractional anisotropies (FA) exceeding 0.5, all three are reasonably consistent with FBI. However, for lower FA values, one of these, called the white matter tract integrity (WMTI) model, is found to be in much better accord with FBI than the other two, suggesting that the WMTI model has a broader range of applicability. PMID:28085211

Improved sparse decomposition based on a smoothed L0 norm using a Laplacian kernel to select features from fMRI data.

PubMed

Zhang, Chuncheng; Song, Sutao; Wen, Xiaotong; Yao, Li; Long, Zhiying

2015-04-30

Feature selection plays an important role in improving the classification accuracy of multivariate classification techniques in the context of fMRI-based decoding due to the "few samples and large features" nature of functional magnetic resonance imaging (fMRI) data. Recently, several sparse representation methods have been applied to the voxel selection of fMRI data. Despite the low computational efficiency of the sparse representation methods, they still displayed promise for applications that select features from fMRI data. In this study, we proposed the Laplacian smoothed L0 norm (LSL0) approach for feature selection of fMRI data. Based on the fast sparse decomposition using smoothed L0 norm (SL0) (Mohimani, 2007), the LSL0 method used the Laplacian function to approximate the L0 norm of sources. Results of the simulated and real fMRI data demonstrated the feasibility and robustness of LSL0 for the sparse source estimation and feature selection. Simulated results indicated that LSL0 produced more accurate source estimation than SL0 at high noise levels. The classification accuracy using voxels that were selected by LSL0 was higher than that by SL0 in both simulated and real fMRI experiment. Moreover, both LSL0 and SL0 showed higher classification accuracy and required less time than ICA and t-test for the fMRI decoding. LSL0 outperformed SL0 in sparse source estimation at high noise level and in feature selection. Moreover, LSL0 and SL0 showed better performance than ICA and t-test for feature selection. Copyright © 2015 Elsevier B.V. All rights reserved.

MRI Features of Hepatocellular Carcinoma Related to Biologic Behavior

PubMed Central

Cho, Eun-Suk

2015-01-01

Imaging studies including magnetic resonance imaging (MRI) play a crucial role in the diagnosis and staging of hepatocellular carcinoma (HCC). Several recent studies reveal a large number of MRI features related to the prognosis of HCC. In this review, we discuss various MRI features of HCC and their implications for the diagnosis and prognosis as imaging biomarkers. As a whole, the favorable MRI findings of HCC are small size, encapsulation, intralesional fat, high apparent diffusion coefficient (ADC) value, and smooth margins or hyperintensity on the hepatobiliary phase of gadoxetic acid-enhanced MRI. Unfavorable findings include large size, multifocality, low ADC value, non-smooth margins or hypointensity on hepatobiliary phase images. MRI findings are potential imaging biomarkers in patients with HCC. PMID:25995679

Radiomics for ultrafast dynamic contrast-enhanced breast MRI in the diagnosis of breast cancer: a pilot study

NASA Astrophysics Data System (ADS)

Drukker, Karen; Anderson, Rachel; Edwards, Alexandra; Papaioannou, John; Pineda, Fred; Abe, Hiroyuke; Karzcmar, Gregory; Giger, Maryellen L.

2018-02-01

Radiomics for dynamic contrast-enhanced (DCE) breast MRI have shown promise in the diagnosis of breast cancer as applied to conventional DCE-MRI protocols. Here, we investigate the potential of using such radiomic features in the diagnosis of breast cancer applied on ultrafast breast MRI in which images are acquired every few seconds. The dataset consisted of 64 lesions (33 malignant and 31 benign) imaged with both `conventional' and ultrafast DCE-MRI. After automated lesion segmentation in each image sequence, we calculated 38 radiomic features categorized as describing size, shape, margin, enhancement-texture, kinetics, and enhancement variance kinetics. For each feature, we calculated the 95% confidence interval of the area under the ROC curve (AUC) to determine whether the performance of each feature in the task of distinguishing between malignant and benign lesions was better than random guessing. Subsequently, we assessed performance of radiomic signatures in 10-fold cross-validation repeated 10 times using a support vector machine with as input all the features as well as features by category. We found that many of the features remained useful (AUC>0.5) for the ultrafast protocol, with the exception of some features, e.g., those designed for latephase kinetics such as the washout rate. For ultrafast MRI, the radiomics enhancement-texture signature achieved the best performance, which was comparable to that of the kinetics signature for `conventional' DCE-MRI, both achieving AUC values of 0.71. Radiomic developed for `conventional' DCE-MRI shows promise for translation to the ultrafast protocol, where enhancement texture appears to play a dominant role.

Classification of small lesions in dynamic breast MRI: Eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement over time.

PubMed

Nagarajan, Mahesh B; Huber, Markus B; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2013-10-01

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they don't exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of sixty annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were also used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter , thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( p < 0.05). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.

[MRI semiotics features of experimental acute intracerebral hematomas].

PubMed

Burenchev, D V; Skvortsova, V I; Tvorogova, T V; Guseva, O I; Gubskiĭ, L V; Kupriianov, D A; Pirogov, Iu A

2009-01-01

The aim of this study was to assess the possibility of revealing intracerebral hematomas (ICH), using MRI, within the first hours after onset and to determine their MRI semiotics features. Thirty animals with experimental ICH were studied. A method of two-stage introduction of autologous blood was used to develop ICH as human spontaneous intracranial hematomas. Within 3-5h after blood introduction to the rat brain. The control MRI was performed in the 3rd and 7th days after blood injections. ICH were definitely identified in the first MRI scans. The MRI semiotics features of acute ICH and their transformations were assessed. The high sensitivity of MRI to ICH as well as the uniform manifestations in all animals were shown. In conclusion, the method has high specificity for acute ICH detection.

Mapping axonal density and average diameter using non-monotonic time-dependent gradient-echo MRI

NASA Astrophysics Data System (ADS)

Nunes, Daniel; Cruz, Tomás L.; Jespersen, Sune N.; Shemesh, Noam

2017-04-01

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo.

Mapping axonal density and average diameter using non-monotonic time-dependent gradient-echo MRI.

PubMed

Nunes, Daniel; Cruz, Tomás L; Jespersen, Sune N; Shemesh, Noam

2017-04-01

White Matter (WM) microstructures, such as axonal density and average diameter, are crucial to the normal function of the Central Nervous System (CNS) as they are closely related with axonal conduction velocities. Conversely, disruptions of these microstructural features may result in severe neurological deficits, suggesting that their noninvasive mapping could be an important step towards diagnosing and following pathophysiology. Whereas diffusion based MRI methods have been proposed to map these features, they typically entail the application of powerful gradients, which are rarely available in the clinic, or extremely long acquisition schemes to extract information from parameter-intensive models. In this study, we suggest that simple and time-efficient multi-gradient-echo (MGE) MRI can be used to extract the axon density from susceptibility-driven non-monotonic decay in the time-dependent signal. We show, both theoretically and with simulations, that a non-monotonic signal decay will occur for multi-compartmental microstructures - such as axons and extra-axonal spaces, which were here used as a simple model for the microstructure - and that, for axons parallel to the main magnetic field, the axonal density can be extracted. We then experimentally demonstrate in ex-vivo rat spinal cords that its different tracts - characterized by different microstructures - can be clearly contrasted using the MGE-derived maps. When the quantitative results are compared against ground-truth histology, they reflect the axonal fraction (though with a bias, as evident from Bland-Altman analysis). As well, the extra-axonal fraction can be estimated. The results suggest that our model is oversimplified, yet at the same time evidencing a potential and usefulness of the approach to map underlying microstructures using a simple and time-efficient MRI sequence. We further show that a simple general-linear-model can predict the average axonal diameters from the four model parameters, and map these average axonal diameters in the spinal cords. While clearly further modelling and theoretical developments are necessary, we conclude that salient WM microstructural features can be extracted from simple, SNR-efficient multi-gradient echo MRI, and that this paves the way towards easier estimation of WM microstructure in vivo. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI.

PubMed

Adler, Sophie; Lorio, Sara; Jacques, Thomas S; Benova, Barbora; Gunny, Roxana; Cross, J Helen; Baldeweg, Torsten; Carmichael, David W

2017-01-01

Focal cortical dysplasias (FCDs) are a range of malformations of cortical development each with specific histopathological features. Conventional radiological assessment of standard structural MRI is useful for the localization of lesions but is unable to accurately predict the histopathological features. Quantitative MRI offers the possibility to probe tissue biophysical properties in vivo and may bridge the gap between radiological assessment and ex-vivo histology. This review will cover histological, genetic and radiological features of FCD following the ILAE classification and will explain how quantitative voxel- and surface-based techniques can characterise these features. We will provide an overview of the quantitative MRI measures available, their link with biophysical properties and finally the potential application of quantitative MRI to the problem of FCD subtyping. Future research linking quantitative MRI to FCD histological properties should improve clinical protocols, allow better characterisation of lesions in vivo and tailored surgical planning to the individual.

Learning Computational Models of Video Memorability from fMRI Brain Imaging.

PubMed

Han, Junwei; Chen, Changyuan; Shao, Ling; Hu, Xintao; Han, Jungong; Liu, Tianming

2015-08-01

Generally, various visual media are unequally memorable by the human brain. This paper looks into a new direction of modeling the memorability of video clips and automatically predicting how memorable they are by learning from brain functional magnetic resonance imaging (fMRI). We propose a novel computational framework by integrating the power of low-level audiovisual features and brain activity decoding via fMRI. Initially, a user study experiment is performed to create a ground truth database for measuring video memorability and a set of effective low-level audiovisual features is examined in this database. Then, human subjects' brain fMRI data are obtained when they are watching the video clips. The fMRI-derived features that convey the brain activity of memorizing videos are extracted using a universal brain reference system. Finally, due to the fact that fMRI scanning is expensive and time-consuming, a computational model is learned on our benchmark dataset with the objective of maximizing the correlation between the low-level audiovisual features and the fMRI-derived features using joint subspace learning. The learned model can then automatically predict the memorability of videos without fMRI scans. Evaluations on publically available image and video databases demonstrate the effectiveness of the proposed framework.

Radiological and clinical features of adult non-puerperal mastitis.

PubMed

Tan, H; Li, R; Peng, W; Liu, H; Gu, Y; Shen, X

2013-04-01

To describe the radiological and clinical features of adult non-puerperal mastitis and to determine the most accurate method of preventing unnecessary surgical procedures. Clinical and imaging findings were retrospectively reviewed in 51 females with non-puerperal mastitis, which was confirmed by biopsy/surgical pathology. All 51 patients had pre-operative MRI; 45 patients also had sonograms and 25 also had mammograms, pre-operatively. Of the 51 cases with non-puerperal mastitis, 94.1% (48/51) were confirmed as having acute or chronic inflammation, and the other 3 had plasma cell mastitis; areola papillaris inflammation was found in 39.2% (20/51) of the cases. Overall, 6 of the 25 cases that were examined with mammography and 2 of the 45 cases that were examined with sonography appeared normal, but all 51 lesions were positively identified on MRI. Asymmetrical density (12/25) on mammograms and solitary or separated/contiguous, clustered, hypoechoic mass-like lesions (31/45) on ultrasound were the most common signs of non-puerperal mastitis. On enhanced MRI, 90.2% (46/51) of patients showed non-mass-like enhanced lesions. Multiple regional enhancements in the pattern of distribution (32/46) and separated or contiguous, clustered, rim-like enhancements in the pattern of internal enhancement (29/46) were the most common manifestations in non-mass-like enhanced lesions. Of the 51 patients, mastitis Type 1 and Type 2 in the time-signal intensity curve were detected in 47.1% and 51.0% of the patients, respectively. The breast imaging reporting and data system categories with the highest number of patients were Category 0 (9/25) on mammography, Category 4a on sonography (18/45) and Category 4a on MRI (29/51). The findings from mammography and ultrasound are non-specific; therefore, using MR can be helpful in the diagnosis, especially in the presence of non-mass-like enhancements that are multiple, regional, separated, or contiguous, clustered and rim-like. Mastitis is often neglected because of the lack of typical clinical signs and symptoms. This study has assessed and described the clinical features and imaging findings of adult non-puerperal mastitis on mammograms, sonograms and MRI and found that MRI is more specific in the diagnosis of disease.

Radiological and clinical features of adult non-puerperal mastitis

PubMed Central

Tan, H; Li, R; Liu, H; Gu, Y; Shen, X

2013-01-01

Objective: To describe the radiological and clinical features of adult non-puerperal mastitis and to determine the most accurate method of preventing unnecessary surgical procedures. Methods: Clinical and imaging findings were retrospectively reviewed in 51 females with non-puerperal mastitis, which was confirmed by biopsy/surgical pathology. All 51 patients had pre-operative MRI; 45 patients also had sonograms and 25 also had mammograms, pre-operatively. Results: Of the 51 cases with non-puerperal mastitis, 94.1% (48/51) were confirmed as having acute or chronic inflammation, and the other 3 had plasma cell mastitis; areola papillaris inflammation was found in 39.2% (20/51) of the cases. Overall, 6 of the 25 cases that were examined with mammography and 2 of the 45 cases that were examined with sonography appeared normal, but all 51 lesions were positively identified on MRI. Asymmetrical density (12/25) on mammograms and solitary or separated/contiguous, clustered, hypoechoic mass-like lesions (31/45) on ultrasound were the most common signs of non-puerperal mastitis. On enhanced MRI, 90.2% (46/51) of patients showed non-mass-like enhanced lesions. Multiple regional enhancements in the pattern of distribution (32/46) and separated or contiguous, clustered, rim-like enhancements in the pattern of internal enhancement (29/46) were the most common manifestations in non-mass-like enhanced lesions. Of the 51 patients, mastitis Type 1 and Type 2 in the time–signal intensity curve were detected in 47.1% and 51.0% of the patients, respectively. The breast imaging reporting and data system categories with the highest number of patients were Category 0 (9/25) on mammography, Category 4a on sonography (18/45) and Category 4a on MRI (29/51). Conclusion: The findings from mammography and ultrasound are non-specific; therefore, using MR can be helpful in the diagnosis, especially in the presence of non-mass-like enhancements that are multiple, regional, separated, or contiguous, clustered and rim-like. Advances in knowledge: Mastitis is often neglected because of the lack of typical clinical signs and symptoms. This study has assessed and described the clinical features and imaging findings of adult non-puerperal mastitis on mammograms, sonograms and MRI and found that MRI is more specific in the diagnosis of disease. PMID:23392197

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Association between background parenchymal enhancement of breast MRI and BIRADS rating change in the subsequent screening

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Mirniaharikandehei, Seyedehnafiseh; Hollingsworth, Alan B.; Stoug, Rebecca G.; Pearce, Melanie; Liu, Hong; Zheng, Bin

2018-03-01

Although breast magnetic resonance imaging (MRI) has been used as a breast cancer screening modality for high-risk women, its cancer detection yield remains low (i.e., <= 3%). Thus, increasing breast MRI screening efficacy and cancer detection yield is an important clinical issue in breast cancer screening. In this study, we investigated association between the background parenchymal enhancement (BPE) of breast MRI and the change of diagnostic (BIRADS) status in the next subsequent breast MRI screening. A dataset with 65 breast MRI screening cases was retrospectively assembled. All cases were rated BIRADS-2 (benign findings). In the subsequent screening, 4 cases were malignant (BIRADS-6), 48 remained BIRADS-2 and 13 were downgraded to negative (BIRADS-1). A computer-aided detection scheme was applied to process images of the first set of breast MRI screening. Total of 33 features were computed including texture feature and global BPE features. Texture features were computed from either a gray-level co-occurrence matrix or a gray level run length matrix. Ten global BPE features were also initially computed from two breast regions and bilateral difference between the left and right breasts. Box-plot based analysis shows positive association between texture features and BIRADS rating levels in the second screening. Furthermore, a logistic regression model was built using optimal features selected by a CFS based feature selection method. Using a leave-one-case-out based cross-validation method, classification yielded an overall 75% accuracy in predicting the improvement (or downgrade) of diagnostic status (to BIRAD-1) in the subsequent breast MRI screening. This study demonstrated potential of developing a new quantitative imaging marker to predict diagnostic status change in the short-term, which may help eliminate a high fraction of unnecessary repeated breast MRI screenings and increase the cancer detection yield.

Rheumatoid arthritis: what do MRI and ultrasound show

PubMed Central

Jans, Lennart; Teh, James

2017-01-01

Rheumatoid arthritis is the most common inflammatory arthritis, affecting approximately 1% of the world’s population. Its pathogenesis has not been completely understood. However, there is evidence that the disease may involve synovial joints, subchondral bone marrow as well as intra- and extraarticular fat tissue, and may lead to progressive joint destruction and disability. Over the last two decades, significant improvement in its prognosis has been achieved owing to new strategies for disease management, the emergence of new biologic therapies and better utilization of conventional disease-modifying antirheumatic drugs. Prompt diagnosis and appropriate therapy have been recognized as essential for improving clinical outcomes in patients with early rheumatoid arthritis. Despite the potential of ultrasonography and magnetic resonance imaging to visualize all tissues typically involved in the pathogenesis of rheumatoid arthritis, the diagnosis of early disease remains difficult due to limited specificity of findings. This paper summarizes the pathogenesis phenomena of rheumatoid arthritis and describes rheumatoid arthritis-related features of the disease within the synovium, subchondral bone marrow and articular fat tissue on MRI and ultrasound. Moreover, the paper aims to illustrate the significance of MRI and ultrasound findings in rheumatoid arthritis in the diagnosis of subclinical and early inflammation, and the importance of MRI and US in the follow-up and establishing remission. Finally, we also discuss MRI of the spine in rheumatoid arthritis, which may help assess the presence of active inflammation and complications. PMID:28439423

Visual feature-tolerance in the reading network.

PubMed

Rauschecker, Andreas M; Bowen, Reno F; Perry, Lee M; Kevan, Alison M; Dougherty, Robert F; Wandell, Brian A

2011-09-08

A century of neurology and neuroscience shows that seeing words depends on ventral occipital-temporal (VOT) circuitry. Typically, reading is learned using high-contrast line-contour words. We explored whether a specific VOT region, the visual word form area (VWFA), learns to see only these words or recognizes words independent of the specific shape-defining visual features. Word forms were created using atypical features (motion-dots, luminance-dots) whose statistical properties control word-visibility. We measured fMRI responses as word form visibility varied, and we used TMS to interfere with neural processing in specific cortical circuits, while subjects performed a lexical decision task. For all features, VWFA responses increased with word-visibility and correlated with performance. TMS applied to motion-specialized area hMT+ disrupted reading performance for motion-dots, but not line-contours or luminance-dots. A quantitative model describes feature-convergence in the VWFA and relates VWFA responses to behavioral performance. These findings suggest how visual feature-tolerance in the reading network arises through signal convergence from feature-specialized cortical areas. Copyright © 2011 Elsevier Inc. All rights reserved.

Bayesian deconvolution of [corrected] fMRI data using bilinear dynamical systems.

PubMed

Makni, Salima; Beckmann, Christian; Smith, Steve; Woolrich, Mark

2008-10-01

In Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993], a particular case of the Linear Dynamical Systems (LDSs) was used to model the dynamic behavior of the BOLD response in functional MRI. This state-space model, called bilinear dynamical system (BDS), is used to deconvolve the fMRI time series in order to estimate the neuronal response induced by the different stimuli of the experimental paradigm. The BDS model parameters are estimated using an expectation-maximization (EM) algorithm proposed by Ghahramani and Hinton [Ghahramani, Z., Hinton, G.E. 1996. Parameter Estimation for Linear Dynamical Systems. Technical Report, Department of Computer Science, University of Toronto]. In this paper we introduce modifications to the BDS model in order to explicitly model the spatial variations of the haemodynamic response function (HRF) in the brain using a non-parametric approach. While in Penny et al. [Penny, W., Ghahramani, Z., Friston, K.J. 2005. Bilinear dynamical systems. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1457) 983-993] the relationship between neuronal activation and fMRI signals is formulated as a first-order convolution with a kernel expansion using basis functions (typically two or three), in this paper, we argue in favor of a spatially adaptive GLM in which a local non-parametric estimation of the HRF is performed. Furthermore, in order to overcome the overfitting problem typically associated with simple EM estimates, we propose a full Variational Bayes (VB) solution to infer the BDS model parameters. We demonstrate the usefulness of our model which is able to estimate both the neuronal activity and the haemodynamic response function in every voxel of the brain. We first examine the behavior of this approach when applied to simulated data with different temporal and noise features. As an example we will show how this method can be used to improve interpretability of estimates from an independent component analysis (ICA) analysis of fMRI data. We finally demonstrate its use on real fMRI data in one slice of the brain.

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.

PubMed

Qin, Jiang-Bo; Liu, Zhenyu; Zhang, Hui; Shen, Chen; Wang, Xiao-Chun; Tan, Yan; Wang, Shuo; Wu, Xiao-Feng; Tian, Jie

2017-05-07

BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in glioma grading, and to improve the power of glioma grading by combining features. MATERIAL AND METHODS Sixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated. RESULTS A total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity. CONCLUSIONS The combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.

Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

PubMed

Soltaninejad, Mohammadreza; Yang, Guang; Lambrou, Tryphon; Allinson, Nigel; Jones, Timothy L; Barrick, Thomas R; Howe, Franklyn A; Ye, Xujiong

2018-04-01

Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. Copyright © 2018 Elsevier B.V. All rights reserved.

Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.

PubMed

Singanamalli, Asha; Rusu, Mirabela; Sparks, Rachel E; Shih, Natalie N C; Ziober, Amy; Wang, Li-Ping; Tomaszewski, John; Rosen, Mark; Feldman, Michael; Madabhushi, Anant

2016-01-01

To identify computer extracted in vivo dynamic contrast enhanced (DCE) MRI markers associated with quantitative histomorphometric (QH) characteristics of microvessels and Gleason scores (GS) in prostate cancer. This study considered retrospective data from 23 biopsy confirmed prostate cancer patients who underwent 3 Tesla multiparametric MRI before radical prostatectomy (RP). Representative slices from RP specimens were stained with vascular marker CD31. Tumor extent was mapped from RP sections onto DCE MRI using nonlinear registration methods. Seventy-seven microvessel QH features and 18 DCE MRI kinetic features were extracted and evaluated for their ability to distinguish low from intermediate and high GS. The effect of temporal sampling on kinetic features was assessed and correlations between those robust to temporal resolution and microvessel features discriminative of GS were examined. A total of 12 microvessel architectural features were discriminative of low and intermediate/high grade tumors with area under the receiver operating characteristic curve (AUC) > 0.7. These features were most highly correlated with mean washout gradient (WG) (max rho = -0.62). Independent analysis revealed WG to be moderately robust to temporal resolution (intraclass correlation coefficient [ICC] = 0.63) and WG variance, which was poorly correlated with microvessel features, to be predictive of low grade tumors (AUC = 0.77). Enhancement ratio was the most robust (ICC = 0.96) and discriminative (AUC = 0.78) kinetic feature but was moderately correlated with microvessel features (max rho = -0.52). Computer extracted features of prostate DCE MRI appear to be correlated with microvessel architecture and may be discriminative of low versus intermediate and high GS. © 2015 Wiley Periodicals, Inc.

Peroneal tendon pathology: Pre- and post-operative high resolution US and MR imaging.

PubMed

Kumar, Yogesh; Alian, Ali; Ahlawat, Shivani; Wukich, Dane K; Chhabra, Avneesh

2017-07-01

Peroneal tendon pathology is an important cause of lateral ankle pain and instability. Typical peroneal tendon disorders include tendinitis, tenosynovitis, partial and full thickness tendon tears, peroneal retinacular injuries, and tendon subluxations and dislocations. Surgery is usually indicated when conservative treatment fails. Familiarity with the peroneal tendon surgeries and expected postoperative imaging findings is essential for accurate assessment and to avoid diagnostic pitfalls. Cross-sectional imaging, especially ultrasound and MRI provide accurate pre-operative and post-operative evaluation of the peroneal tendon pathology. In this review article, the normal anatomy, clinical presentation, imaging features, pitfalls and commonly performed surgical treatments for peroneal tendon abnormalities will be reviewed. The role of dynamic ultrasound and kinematic MRI for the evaluation of peroneal tendons will be discussed. Normal and abnormal postsurgical imaging appearances will be illustrated. Copyright © 2017 Elsevier B.V. All rights reserved.

Posterior impingement syndromes of the ankle.

PubMed

Lee, Justin C; Calder, James D F; Healy, Jeremiah C

2008-06-01

Acute, or repetitive, compression of the posterior structures of the ankle may lead to posterior ankle impingement (PAI) syndrome, posteromedial ankle impingement (PoMI) syndrome, or Haglund's syndrome. The etiology of each of these conditions is quite different. Variations in posterior ankle osseous and soft tissue anatomy contribute to the etiology of PAI and Haglund's syndromes. The presence of an os trigonum or Stieda process is classically associated with PAI syndrome, whereas a prominent posterosuperior tubercle of the os calcis or Haglund's deformity is the osseous predisposing factor in Haglund's syndrome. PoMI has no defined predisposing anatomical variants but typically follows an inversion-supination injury of the ankle joint. This article discusses the biomechanics, clinical features, imaging, and management of each of these conditions. Magnetic resonance imaging (MRI) provides the optimal tool in posterior ankle assessment, and this review focuses on the MRI findings of each of the conditions just listed.

Magnetic Labeling of Activated Microglia in Experimental Gliomas1

PubMed Central

Fleige, Gerrit; Nolte, Christiane; Synowitz, Michael; Seeberger, Florian; Kettenmann, Helmut; Zimmer, Claus

2001-01-01

Abstract Microglia, as intrinsic immunoeffector cells of the central nervous system (CNS), play a very sensitive, crucial role in the response to almost any brain pathology where they are activated to a phagocytic state. Based on the characteristic features of activated microglia, we investigated whether these cells can be visualized with magnetic resonance imaging (MRI) using ultrasmall superparamagnetic iron oxides (USPIOs). The hypothesis of this study was that MR microglia visualization could not only reveal the extent of the tumor, but also allow for assessing the status of immunologic defense. Using USPIOs in cell culture experiments and in a rat glioma model, we showed that microglia can be labeled magnetically. Labeled microglia are detected by confocal microscopy within and around tumors in a typical border-like pattern. Quantitative in vitro studies revealed that microglia internalize amounts of USPIOs that are significantly higher than those incorporated by tumor cells and astrocytes. Labeled microglia can be detected and quantified with MRI in cell phantoms, and the extent of the tumor can be seen in glioma-bearing rats in vivo. We conclude that magnetic labeling of microglia provides a potential tool for MRI of gliomas, which reflects tumor morphology precisely. Furthermore, the results suggest that MRI may yield functional data on the immunologic reaction of the CNS. PMID:11774031

Functional differentiation of posterior superior temporal sulcus in autism: A functional connectivity MRI study

PubMed Central

Shih, Patricia; Keehn, Brandon; Oram, Jessica K.; Leyden, Kelly M.; Keown, Christopher L.; Müller, Ralph-Axel

2012-01-01

Background Socio-communicative impairments are salient features of autism spectrum disorder (ASD). Abnormal development of posterior superior temporal sulcus (pSTS), a key processing area for language, biological motion, and social context, may play a role in these deficits. Methods Functional connectivity MRI (fcMRI) was used to examine the synchronization of low frequency BOLD fluctuations during continuous performance on a visual search task. Twenty-one children and adolescents with ASD and 26 typically developing (TD) individuals, matched on age, sex, and IQ, participated in the study. Three subregions of pSTS were delineated with a data-driven approach, and differentiation of pSTS was examined by comparing the connectivity of each subregion. Results In TD individuals, differentiation of networks was positively associated with age and anatomical maturation (cortical thinning in pSTS, greater white matter volume). In the ASD group, differentiation of pSTS connectivity was significantly reduced and correlations with anatomical measures were weak or absent. Moreover, pSTS differentiation was inversely correlated with autism symptom severity. Conclusions Atypical maturation of pSTS suggests altered trajectories for functional segregation and integration of networks in ASD, potentially related to impaired cognitive and sensorimotor development. Furthermore, our findings provide a novel explanation for atypically increased connectivity in ASD observed in some fcMRI studies. PMID:21601832

Multiresolution texture models for brain tumor segmentation in MRI.

PubMed

Iftekharuddin, Khan M; Ahmed, Shaheen; Hossen, Jakir

2011-01-01

In this study we discuss different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) for estimating random structures and varying appearance of brain tissues and tumors in magnetic resonance images (MRI). We use different selection techniques including KullBack - Leibler Divergence (KLD) for ranking different texture and intensity features. We then exploit graph cut, self organizing maps (SOM) and expectation maximization (EM) techniques to fuse selected features for brain tumors segmentation in multimodality T1, T2, and FLAIR MRI. We use different similarity metrics to evaluate quality and robustness of these selected features for tumor segmentation in MRI for real pediatric patients. We also demonstrate a non-patient-specific automated tumor prediction scheme by using improved AdaBoost classification based on these image features.

Parahippocampal epilepsy with subtle dysplasia: A cause of "imaging negative" partial epilepsy.

PubMed

Pillay, Neelan; Fabinyi, Gavin C A; Myles, Terry S; Fitt, Gregory J; Berkovic, Samuel F; Jackson, Graeme D

2009-12-01

Lesion-negative refractory partial epilepsy is a major challenge in the assessment of patients for potential surgery. Finding a potential epileptogenic lesion simplifies assessment and is associated with good outcome. Here we describe imaging features of subtle parahippocampal dysplasia in five cases that were initially assessed as having imaging-negative frontal or temporal lobe epilepsy. We analyzed the clinical and imaging features of five patients with seizures from the parahippocampal region. Five patients had subtle but distinctive magnetic resonance imaging (MRI) abnormalities in the parahippocampal gyrus. This was a unilateral signal abnormality in the parahippocampal white matter extending into gray matter on heavily T(1)- and T(2)-weighted images with relative preservation of the gray-white matter boundary on T(1)-weighted volume sequences. Only one of these patients had typical electroclinical unilateral temporal lobe epilepsy (TLE); one mimicked frontal lobe epilepsy, two showed bitemporal seizures, and one had unlocalized partial seizures. All have had surgery; four are seizure-free (one has occasional auras only, follow-up 6 months to 10 years), and one has a >50% seizure reduction. Histopathologic evaluation suggested dysplastic features in the surgical specimens in all. In patients with lesion-negative partial epilepsy with frontal or temporal semiology, or in cases with apparent bitemporal seizures, subtle parahippocampal abnormalities should be carefully excluded. Recognizing the MRI findings of an abnormal parahippocampal gyrus can lead to successful surgery without invasive monitoring, despite apparently incongruent electroclinical features.

Decision forests for learning prostate cancer probability maps from multiparametric MRI

NASA Astrophysics Data System (ADS)

Ehrenberg, Henry R.; Cornfeld, Daniel; Nawaf, Cayce B.; Sprenkle, Preston C.; Duncan, James S.

2016-03-01

Objectives: Advances in multiparametric magnetic resonance imaging (mpMRI) and ultrasound/MRI fusion imaging offer a powerful alternative to the typical undirected approach to diagnosing prostate cancer. However, these methods require the time and expertise needed to interpret mpMRI image scenes. In this paper, a machine learning framework for automatically detecting and localizing cancerous lesions within the prostate is developed and evaluated. Methods: Two studies were performed to gather MRI and pathology data. The 12 patients in the first study underwent an MRI session to obtain structural, diffusion-weighted, and dynamic contrast enhanced image vol- umes of the prostate, and regions suspected of being cancerous from the MRI data were manually contoured by radiologists. Whole-mount slices of the prostate were obtained for the patients in the second study, in addition to structural and diffusion-weighted MRI data, for pathology verification. A 3-D feature set for voxel-wise appear- ance description combining intensity data, textural operators, and zonal approximations was generated. Voxels in a test set were classified as normal or cancer using a decision forest-based model initialized using Gaussian discriminant analysis. A leave-one-patient-out cross-validation scheme was used to assess the predictions against the expert manual segmentations confirmed as cancer by biopsy. Results: We achieved an area under the average receiver-operator characteristic curve of 0.923 for the first study, and visual assessment of the probability maps showed 21 out of 22 tumors were identified while a high level of specificity was maintained. In addition to evaluating the model against related approaches, the effects of the individual MRI parameter types were explored, and pathological verification using whole-mount slices from the second study was performed. Conclusions: The results of this paper show that the combination of mpMRI and machine learning is a powerful tool for quantitatively diagnosing prostate cancer.

Typical and Atypical Neurodevelopment for Face Specialization: An fMRI Study

ERIC Educational Resources Information Center

Joseph, Jane E.; Zhu, Xun; Gundran, Andrew; Davies, Faraday; Clark, Jonathan D.; Ruble, Lisa; Glaser, Paul; Bhatt, Ramesh S.

2015-01-01

Individuals with autism spectrum disorder (ASD) and their relatives process faces differently from typically developed (TD) individuals. In an fMRI face-viewing task, TD and undiagnosed sibling (SIB) children (5-18 years) showed face specialization in the right amygdala and ventromedial prefrontal cortex, with left fusiform and right amygdala face…

High- and low-risk profiles for the development of multiple sclerosis within 10 years after optic neuritis: experience of the optic neuritis treatment trial.

PubMed

Beck, Roy W; Trobe, Jonathan D; Moke, Pamela S; Gal, Robin L; Xing, Dongyuan; Bhatti, M Tariq; Brodsky, Michael C; Buckley, Edward G; Chrousos, Georgia A; Corbett, James; Eggenberger, Eric; Goodwin, James A; Katz, Barrett; Kaufman, David I; Keltner, John L; Kupersmith, Mark J; Miller, Neil R; Nazarian, Sarkis; Orengo-Nania, Silvia; Savino, Peter J; Shults, William T; Smith, Craig H; Wall, Michael

2003-07-01

To identify factors associated with a high and low risk of developing multiple sclerosis after an initial episode of optic neuritis. Three hundred eighty-eight patients who experienced acute optic neuritis between July 1, 1988, and June 30, 1991, were followed up prospectively for the development of multiple sclerosis. Consenting patients were reassessed after 10 to 13 years. The 10-year risk of multiple sclerosis was 38% (95% confidence interval, 33%-43%). Patients (160) who had 1 or more typical lesions on the baseline magnetic resonance imaging (MRI) scan of the brain had a 56% risk; those with no lesions (191) had a 22% risk (P<.001, log rank test). Among the patients who had no lesions on MRI, male gender and optic disc swelling were associated with a lower risk of multiple sclerosis, as was the presence of the following atypical features for optic neuritis: no light perception vision; absence of pain; and ophthalmoscopic findings of severe optic disc edema, peripapillary hemorrhages, or retinal exudates. The 10-year risk of multiple sclerosis following an initial episode of acute optic neuritis is significantly higher if there is a single brain MRI lesion; higher numbers of lesions do not appreciably increase that risk. However, even when brain lesions are seen on MRI, more than 40% of the patients will not develop clinical multiple sclerosis after 10 years. In the absence of MRI lesions, certain demographic and clinical features seem to predict a very low likelihood of developing multiple sclerosis. This natural history information is a critical input for estimating a patient's 10-year multiple sclerosis risk and for weighing the benefit of initiating prophylactic treatment at the time of optic neuritis or other initial demyelinating events in the central nervous system.

Imaging of autoimmune encephalitis--Relevance for clinical practice and hippocampal function.

PubMed

Heine, J; Prüss, H; Bartsch, T; Ploner, C J; Paul, F; Finke, C

2015-11-19

The field of autoimmune encephalitides associated with antibodies targeting cell-surface antigens is rapidly expanding and new antibodies are discovered frequently. Typical clinical presentations include cognitive deficits, psychiatric symptoms, movement disorders and seizures and the majority of patients respond well to immunotherapy. Pathophysiological mechanisms and clinical features are increasingly recognized and indicate hippocampal dysfunction in most of these syndromes. Here, we review the neuroimaging characteristics of autoimmune encephalitides, including N-methyl-d-aspartate (NMDA) receptor, leucine-rich glioma inactivated 1 (LGI1), contactin-associated protein-like 2 (CASPR2) encephalitis as well as more recently discovered and less frequent forms such as dipeptidyl-peptidase-like protein 6 (DPPX) or glycine receptor encephalitis. We summarize findings of routine magnetic resonance imaging (MRI) investigations as well as (18)F-fluoro-2-deoxy-d-glucose (FDG)-positron emission tomography (PET) and single photon emission tomography (SPECT) imaging and relate these observations to clinical features and disease outcome. We furthermore review results of advanced imaging analyses such as diffusion tensor imaging, volumetric analyses and resting-state functional MRI. Finally, we discuss contributions of these neuroimaging observations to the understanding of the pathophysiology of autoimmune encephalitides. Copyright © 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

PubMed

Yoo, Youngjin; Tang, Lisa Y W; Brosch, Tom; Li, David K B; Kolind, Shannon; Vavasour, Irene; Rauscher, Alexander; MacKay, Alex L; Traboulsee, Anthony; Tam, Roger C

2018-01-01

Myelin imaging is a form of quantitative magnetic resonance imaging (MRI) that measures myelin content and can potentially allow demyelinating diseases such as multiple sclerosis (MS) to be detected earlier. Although focal lesions are the most visible signs of MS pathology on conventional MRI, it has been shown that even tissues that appear normal may exhibit decreased myelin content as revealed by myelin-specific images (i.e., myelin maps). Current methods for analyzing myelin maps typically use global or regional mean myelin measurements to detect abnormalities, but ignore finer spatial patterns that may be characteristic of MS. In this paper, we present a machine learning method to automatically learn, from multimodal MR images, latent spatial features that can potentially improve the detection of MS pathology at early stage. More specifically, 3D image patches are extracted from myelin maps and the corresponding T1-weighted (T1w) MRIs, and are used to learn a latent joint myelin-T1w feature representation via unsupervised deep learning. Using a data set of images from MS patients and healthy controls, a common set of patches are selected via a voxel-wise t -test performed between the two groups. In each MS image, any patches overlapping with focal lesions are excluded, and a feature imputation method is used to fill in the missing values. A feature selection process (LASSO) is then utilized to construct a sparse representation. The resulting normal-appearing features are used to train a random forest classifier. Using the myelin and T1w images of 55 relapse-remitting MS patients and 44 healthy controls in an 11-fold cross-validation experiment, the proposed method achieved an average classification accuracy of 87.9% (SD = 8.4%), which is higher and more consistent across folds than those attained by regional mean myelin (73.7%, SD = 13.7%) and T1w measurements (66.7%, SD = 10.6%), or deep-learned features in either the myelin (83.8%, SD = 11.0%) or T1w (70.1%, SD = 13.6%) images alone, suggesting that the proposed method has strong potential for identifying image features that are more sensitive and specific to MS pathology in normal-appearing brain tissues.

Combined 18F-Fluciclovine PET/MRI Shows Potential for Detection and Characterization of High-Risk Prostate Cancer.

PubMed

Elschot, Mattijs; Selnæs, Kirsten M; Sandsmark, Elise; Krüger-Stokke, Brage; Størkersen, Øystein; Giskeødegård, Guro F; Tessem, May-Britt; Moestue, Siver A; Bertilsson, Helena; Bathen, Tone F

2018-05-01

The objective of this study was to investigate whether quantitative imaging features derived from combined 18 F-fluciclovine PET/multiparametric MRI show potential for detection and characterization of primary prostate cancer. Methods: Twenty-eight patients diagnosed with high-risk prostate cancer underwent simultaneous 18 F-fluciclovine PET/MRI before radical prostatectomy. Volumes of interest (VOIs) for prostate tumors, benign prostatic hyperplasia (BPH) nodules, prostatitis, and healthy tissue were delineated on T2-weighted images, using histology as a reference. Tumor VOIs were marked as high-grade (≥Gleason grade group 3) or not. MRI and PET features were extracted on the voxel and VOI levels. Partial least-squared discriminant analysis (PLS-DA) with double leave-one-patient-out cross-validation was performed to distinguish tumors from benign tissue (BPH, prostatitis, or healthy tissue) and high-grade tumors from other tissue (low-grade tumors or benign tissue). The performance levels of PET, MRI, and combined PET/MRI features were compared using the area under the receiver-operating-characteristic curve (AUC). Results: Voxel and VOI features were extracted from 40 tumor VOIs (26 high-grade), 36 BPH VOIs, 6 prostatitis VOIs, and 37 healthy-tissue VOIs. PET/MRI performed better than MRI and PET alone for distinguishing tumors from benign tissue (AUCs of 87%, 81%, and 83%, respectively, at the voxel level and 96%, 93%, and 93%, respectively, at the VOI level) and high-grade tumors from other tissue (AUCs of 85%, 79%, and 81%, respectively, at the voxel level and 93%, 93%, and 91%, respectively, at the VOI level). T2-weighted MRI, diffusion-weighted MRI, and PET features were the most important for classification. Conclusion: Combined 18 F-fluciclovine PET/multiparametric MRI shows potential for improving detection and characterization of high-risk prostate cancer, in comparison to MRI and PET alone. © 2018 by the Society of Nuclear Medicine and Molecular Imaging.

Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.

PubMed

Xie, Tian; Chen, Xiao; Fang, Jingqin; Kang, Houyi; Xue, Wei; Tong, Haipeng; Cao, Peng; Wang, Sumei; Yang, Yizeng; Zhang, Weiguo

2018-04-01

Presurgical glioma grading by dynamic contrast-enhanced MRI (DCE-MRI) has unresolved issues. The aim of this study was to investigate the ability of textural features derived from pharmacokinetic model-based or model-free parameter maps of DCE-MRI in discriminating between different grades of gliomas, and their correlation with pathological index. Retrospective. Forty-two adults with brain gliomas. 3.0T, including conventional anatomic sequences and DCE-MRI sequences (variable flip angle T1-weighted imaging and three-dimensional gradient echo volumetric imaging). Regions of interest on the cross-sectional images with maximal tumor lesion. Five commonly used textural features, including Energy, Entropy, Inertia, Correlation, and Inverse Difference Moment (IDM), were generated. All textural features of model-free parameters (initial area under curve [IAUC], maximal signal intensity [Max SI], maximal up-slope [Max Slope]) could effectively differentiate between grade II (n = 15), grade III (n = 13), and grade IV (n = 14) gliomas (P < 0.05). Two textural features, Entropy and IDM, of four DCE-MRI parameters, including Max SI, Max Slope (model-free parameters), vp (Extended Tofts), and vp (Patlak) could differentiate grade III and IV gliomas (P < 0.01) in four measurements. Both Entropy and IDM of Patlak-based K trans and vp could differentiate grade II (n = 15) from III (n = 13) gliomas (P < 0.01) in four measurements. No textural features of any DCE-MRI parameter maps could discriminate between subtypes of grade II and III gliomas (P < 0.05). Both Entropy and IDM of Extended Tofts- and Patlak-based vp showed highest area under curve in discriminating between grade III and IV gliomas. However, intraclass correlation coefficient (ICC) of these features revealed relatively lower inter-observer agreement. No significant correlation was found between microvascular density and textural features, compared with a moderate correlation found between cellular proliferation index and those features. Textural features of DCE-MRI parameter maps displayed a good ability in glioma grading. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1099-1111. © 2017 International Society for Magnetic Resonance in Medicine.

Feature-space-based FMRI analysis using the optimal linear transformation.

PubMed

Sun, Fengrong; Morris, Drew; Lee, Wayne; Taylor, Margot J; Mills, Travis; Babyn, Paul S

2010-09-01

The optimal linear transformation (OLT), an image analysis technique of feature space, was first presented in the field of MRI. This paper proposes a method of extending OLT from MRI to functional MRI (fMRI) to improve the activation-detection performance over conventional approaches of fMRI analysis. In this method, first, ideal hemodynamic response time series for different stimuli were generated by convolving the theoretical hemodynamic response model with the stimulus timing. Second, constructing hypothetical signature vectors for different activity patterns of interest by virtue of the ideal hemodynamic responses, OLT was used to extract features of fMRI data. The resultant feature space had particular geometric clustering properties. It was then classified into different groups, each pertaining to an activity pattern of interest; the applied signature vector for each group was obtained by averaging. Third, using the applied signature vectors, OLT was applied again to generate fMRI composite images with high SNRs for the desired activity patterns. Simulations and a blocked fMRI experiment were employed for the method to be verified and compared with the general linear model (GLM)-based analysis. The simulation studies and the experimental results indicated the superiority of the proposed method over the GLM-based analysis in detecting brain activities.

Abnormal brain magnetic resonance imaging in two patients with Smith-Magenis syndrome.

PubMed

Maya, Idit; Vinkler, Chana; Konen, Osnat; Kornreich, Liora; Steinberg, Tamar; Yeshaya, Josepha; Latarowski, Victoria; Shohat, Mordechai; Lev, Dorit; Baris, Hagit N

2014-08-01

Smith-Magenis syndrome (SMS) is a clinically recognizable contiguous gene syndrome ascribed to an interstitial deletion in chromosome 17p11.2. Seventy percent of SMS patients have a common deletion interval spanning 3.5 megabases (Mb). Clinical features of SMS include characteristic mild dysmorphic features, ocular anomalies, short stature, brachydactyly, and hypotonia. SMS patients have a unique neurobehavioral phenotype that includes intellectual disability, self-injurious behavior and severe sleep disturbance. Little has been reported in the medical literature about anatomical brain anomalies in patients with SMS. Here we describe two patients with SMS caused by the common deletion in 17p11.2 diagnosed using chromosomal microarray (CMA). Both patients had a typical clinical presentation and abnormal brain magnetic resonance imaging (MRI) findings. One patient had subependymal periventricular gray matter heterotopia, and the second had a thin corpus callosum, a thin brain stem and hypoplasia of the cerebellar vermis. This report discusses the possible abnormal MRI images in SMS and reviews the literature on brain malformations in SMS. Finally, although structural brain malformations in SMS patients are not a common feature, we suggest baseline routine brain imaging in patients with SMS in particular, and in patients with chromosomal microdeletion/microduplication syndromes in general. Structural brain malformations in these patients may affect the decision-making process regarding their management. © 2014 Wiley Periodicals, Inc.

Reliability Correction for Functional Connectivity: Theory and Implementation

PubMed Central

Mueller, Sophia; Wang, Danhong; Fox, Michael D.; Pan, Ruiqi; Lu, Jie; Li, Kuncheng; Sun, Wei; Buckner, Randy L.; Liu, Hesheng

2016-01-01

Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe’s contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity. PMID:26493163

Morphological differences in the mirror neuron system in Williams syndrome.

PubMed

Ng, Rowena; Brown, Timothy T; Erhart, Matthew; Järvinen, Anna M; Korenberg, Julie R; Bellugi, Ursula; Halgren, Eric

2016-01-01

Williams syndrome (WS) is a genetic condition characterized by an overly gregarious personality, including high empathetic concern for others. Although seemingly disparate from the profile of autism spectrum disorder (ASD), both are associated with deficits in social communication/cognition. Notably, the mirror neuron system (MNS) has been implicated in social dysfunction for ASD; yet, the integrity of this network and its association with social functioning in WS remains unknown. Magnetic resonance imaging (MRI) methods were used to examine the structural integrity of the MNS of adults with WS versus typically developing (TD) individuals. The Social Responsiveness Scale (SRS), a tool typically used to screen for social features of ASD, was also employed to assess the relationships between social functioning with the MNS morphology in WS participants. WS individuals showed reduced cortical surface area of MNS substrates yet relatively preserved cortical thickness as compared to TD adults. Increased cortical thickness of the inferior parietal lobule (IPL) was associated with increased deficits in social communication, social awareness, social cognition, and autistic mannerisms. However, social motivation was not related to anatomical features of the MNS. Our findings indicate that social deficits typical to both ASD and WS may be attributed to an aberrant MNS, whereas the unusual social drive marked in WS is subserved by substrates distinct from this network.

Non-negative matrix factorization in texture feature for classification of dementia with MRI data

NASA Astrophysics Data System (ADS)

Sarwinda, D.; Bustamam, A.; Ardaneswari, G.

2017-07-01

This paper investigates applications of non-negative matrix factorization as feature selection method to select the features from gray level co-occurrence matrix. The proposed approach is used to classify dementia using MRI data. In this study, texture analysis using gray level co-occurrence matrix is done to feature extraction. In the feature extraction process of MRI data, we found seven features from gray level co-occurrence matrix. Non-negative matrix factorization selected three features that influence of all features produced by feature extractions. A Naïve Bayes classifier is adapted to classify dementia, i.e. Alzheimer's disease, Mild Cognitive Impairment (MCI) and normal control. The experimental results show that non-negative factorization as feature selection method able to achieve an accuracy of 96.4% for classification of Alzheimer's and normal control. The proposed method also compared with other features selection methods i.e. Principal Component Analysis (PCA).

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

PubMed

Sen, Bhaskar; Borle, Neil C; Greiner, Russell; Brown, Matthew R G

2018-01-01

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Fat Embolism Syndrome: A Case Report and Review Literature.

PubMed

Uransilp, Nattaphol; Muengtaweepongsa, Sombat; Chanalithichai, Nuttawut; Tammachote, Nattapol

2018-01-01

Fat embolism syndrome (FES) is a life-threatening complication in patients with orthopedic trauma, especially long bone fractures. The diagnosis of fat embolism is made by clinical features alone with no specific laboratory findings. FES has no specific treatment and requires supportive care, although it can be prevented by early fixation of bone fractures. Here, we report a case of FES in a patient with right femoral neck fracture, which was diagnosed initially by Gurd's criteria and subsequently confirmed by typical appearances on magnetic resonance imaging (MRI) of the brain. The patient received supportive management and a short course of intravenous methylprednisolone.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

Is hypertension predictive of clinical recurrence in posterior reversible encephalopathy syndrome?

PubMed

Li, Richard; Mitchell, Peter; Dowling, Richard; Yan, Bernard

2013-02-01

Posterior reversible encephalopathy syndrome (PRES) has a distinctive clinical presentation and typical neuroimaging findings. However, data on its clinical course and recurrence are scarce. This study aims to investigate its clinical profile and factors that predict recurrence. We included patients diagnosed with PRES between 2005 and 2010 and collected data on demographics, presenting symptoms, co-morbidities, risk factors, clinical parameters, MRI findings, complications and recurrence. Patients were categorized into two groups: PRES due to primary hypertension and PRES due to secondary causes. Correlation with presenting symptoms, radiological features, and recurrence were analyzed. PRES was identified in 28 patients. Fourteen (50%) had primary hypertension. Secondary causes included immunosuppression-related (39%), preeclampsia/eclampsia (7%), and marijuana-intake-related (4%) causes. Patients presented with altered mental status (79%), headache (75%), seizure (68%), visual disturbance (39%) and hemiparesis (21%). On MRI 93% had the typical parietal-occipital involvement. The frontal lobe was affected in 64%, cerebellum in 29%, brainstem in 21%, and basal ganglia in 11%. About 36% had cortical involvement; 21% had diffusion-restricted lesions. Non-aneurysmal subarachnoid haemorrhage was found in 18% of patients and intracerebral hemorrhage in 14% of patients. No significant difference existed in presenting symptoms and the MRI distribution of vasogenic edema between the primary hypertension group and the secondary causes group. Recurrence occurred in four patients (14.3%, 95% confidence interval 4.2-33.7) and was significantly associated (p=0.05) with primary hypertension as the etiology. Intensive monitoring and treatment of hypertension is recommended for reducing morbidity. Copyright © 2012 Elsevier Ltd. All rights reserved.

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer's disease progression.

PubMed

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M; Galván-Tejada, Jorge I; Treviño, Victor; Tamez-Peña, Jose

2014-10-01

Early diagnoses of Alzheimer's disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different ([Formula: see text]). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones.

TU-CD-BRB-12: Radiogenomics of MRI-Guided Prostate Cancer Biopsy Habitats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stoyanova, R; Lynne, C; Abraham, S

2015-06-15

Purpose: Diagnostic prostate biopsies are subject to sampling bias. We hypothesize that quantitative imaging with multiparametric (MP)-MRI can more accurately direct targeted biopsies to index lesions associated with highest risk clinical and genomic features. Methods: Regionally distinct prostate habitats were delineated on MP-MRI (T2-weighted, perfusion and diffusion imaging). Directed biopsies were performed on 17 habitats from 6 patients using MRI-ultrasound fusion. Biopsy location was characterized with 52 radiographic features. Transcriptome-wide analysis of 1.4 million RNA probes was performed on RNA from each habitat. Genomics features with insignificant expression values (<0.25) and interquartile range <0.5 were filtered, leaving total of 212more » genes. Correlation between imaging features, genes and a 22 feature genomic classifier (GC), developed as a prognostic assay for metastasis after radical prostatectomy was investigated. Results: High quality genomic data was derived from 17 (100%) biopsies. Using the 212 ‘unbiased’ genes, the samples clustered by patient origin in unsupervised analysis. When only prostate cancer related genomic features were used, hierarchical clustering revealed samples clustered by needle-biopsy Gleason score (GS). Similarly, principal component analysis of the imaging features, found the primary source of variance segregated the samples into high (≥7) and low (6) GS. Pearson’s correlation analysis of genes with significant expression showed two main patterns of gene expression clustering prostate peripheral and transitional zone MRI features. Two-way hierarchical clustering of GC with radiomics features resulted in the expected groupings of high and low expressed genes in this metastasis signature. Conclusions: MP-MRI-targeted diagnostic biopsies can potentially improve risk stratification by directing pathological and genomic analysis to clinically significant index lesions. As determinant lesions are more reliably identified, targeting with radiotherapy should improve outcome. This is the first demonstration of a link between quantitative imaging features (radiomics) with genomic features in MRI-directed prostate biopsies. The research was supported by NIH- NCI R01 CA 189295 and R01 CA 189295; E Davicioni is partial owner of GenomeDx Biosciences, Inc. M Takhar, N Erho, L Lam, C Buerki and E Davicioni are current employees at GenomeDx Biosciences, Inc.« less

Assessment of Diverse Biological Indicators in Gulf War Illness: Are They Replicable Are They Related

DTIC Science & Technology

2015-10-01

that includes physical and neuropsychological evaluations, neuroimaging (MRI, fMRI , DTI), adrenal function tests, and diverse immune, inflammatory...characterized by a profile of concurrent symptoms that typically includes persistent headaches, memory and cognitive difficulties, widespread pain, unexplained...includes physical examinations, neuroimaging (MRI volumetric assessments, fMRI , diffusion tensor imaging), neuropsychological evaluations, assessment

Altered Dynamics of the fMRI Response to Faces in Individuals with Autism

ERIC Educational Resources Information Center

Kleinhans, Natalia M.; Richards, Todd; Greenson, Jessica; Dawson, Geraldine; Aylward, Elizabeth

2016-01-01

Abnormal fMRI habituation in autism spectrum disorders (ASDs) has been proposed as a critical component in social impairment. This study investigated habituation to fearful faces and houses in ASD and whether fMRI measures of brain activity discriminate between ASD and typically developing (TD) controls. Two identical fMRI runs presenting masked…

Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.

PubMed

Gnep, Khémara; Fargeas, Auréline; Gutiérrez-Carvajal, Ricardo E; Commandeur, Frédéric; Mathieu, Romain; Ospina, Juan D; Rolland, Yan; Rohou, Tanguy; Vincendeau, Sébastien; Hatt, Mathieu; Acosta, Oscar; de Crevoisier, Renaud

2017-01-01

To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy. In all, 74 patients with peripheral zone localized prostate adenocarcinoma underwent pretreatment 3.0T MRI before external beam radiotherapy. Median follow-up of 47 months revealed 11 patients with biochemical recurrence. Prostate tumors were segmented on T 2 -weighted sequences (T 2 -w) and contours were propagated onto the coregistered apparent diffusion coefficient (ADC) images. We extracted 140 image features from normalized T 2 -w and ADC images corresponding to first-order (n = 6), gradient-based (n = 4), and second-order Haralick textural features (n = 130). Four geometrical features (tumor diameter, perimeter, area, and volume) were also computed. Correlations between Gleason score and MRI features were assessed. Cox regression analysis and random survival forests (RSF) were performed to assess the association between MRI features and biochemical recurrence. Three T 2 -w and one ADC Haralick textural features were significantly correlated with Gleason score (P < 0.05). Twenty-eight T 2 -w Haralick features and all four geometrical features were significantly associated with biochemical recurrence (P < 0.05). The most relevant features were Haralick features T 2 -w contrast, T 2 -w difference variance, ADC median, along with tumor volume and tumor area (C-index from 0.76 to 0.82; P < 0.05). By combining these most powerful features in an RSF model, the obtained C-index was 0.90. T 2 -w Haralick features appear to be strongly associated with biochemical recurrence following prostate cancer radiotherapy. 3 J. Magn. Reson. Imaging 2017;45:103-117. © 2016 International Society for Magnetic Resonance in Medicine.

Diagnosing pyogenic, brucella and tuberculous spondylitis using histopathology and MRI: A retrospective study.

PubMed

Li, Tao; Liu, Tao; Jiang, Zhensong; Cui, Xingang; Sun, Jianmin

2016-10-01

The present study examined the histopathological and magnetic resonance imaging (MRI) features of pyogenic, brucella and tuberculous spondylitis (PS, BS and TS, respectively). A total of 22 PS, 20 BS and 20 TS patients were included in the study. Histopathological examination was used to assess the lesion structure and composition, and the MRI observation identified the lesion location and signal features. The following histopathological and MRI features were identified significantly more in patients with PS than in patients with BS and TS: Predominant neutrophil infiltration, abnormal intervertebral disk signal, lesions on the ventral and lateral sides of the vertebral bodies, and thick and irregular abscess walls. The following histopathological and MRI features were identified significantly more in patients with BS than in patients with PS and TS: Predominant lymphocyte infiltration, new bone formation, epithelioid granuloma, lesions on the ventral sides of the vertebral bodies, no, or very mild, vertebral body deformation, no abnormal paraspinal soft tissue signal, no intraosseous or paraspinal abscesses, and thin and irregular abscess walls. The following histopathological and MRI features were identified significantly more in patients with TS than in patients with BS and PS: Sequestrum, Langerhans giant cells, caseous necrosis, lesions primarily in the thoracic region and on the lateral sides of the vertebral bodies, no obvious intervertebral disk damage, obvious vertebral body deformation, abnormal paraspinal soft tissue signal, intraosseous or paraspinal abscesses, and thin and smooth abscess walls. In conclusion, it can be suggested that these significant differences in histopathological and MRI features between the three different types of spondylitis may contribute towards the differential diagnosis of the diseases.

Longitudinal MRI assessment: the identification of relevant features in the development of Posterior Fossa Syndrome in children

NASA Astrophysics Data System (ADS)

Spiteri, M.; Lewis, E.; Windridge, D.; Avula, S.

2015-03-01

Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in lobes within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children's Hospital, Liver- pool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children's Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally.

Skeletal muscle metastases on magnetic resonance imaging: analysis of 31 cases.

PubMed

Li, Qi; Wang, Lei; Pan, Shinong; Shu, Hong; Ma, Ying; Lu, Zaiming; Fu, Xihu; Jiang, Bo; Guo, Qiyong

2016-01-01

To investigate the magnetic resonance imaging (MRI) features of skeletal muscle metastases (SMM). The records of 31 patients with proven SMM were retrospectively reviewed. Clinical history, type of primary malignancy, location of metastases, and MRI features of SMM were evaluated. Based on MRI findings, SMM were divided into three MRI types. The correlation between MRI types with ages and pathology category, between MRI types of SMM and ages, as well as MRI types of SMM and pathology category were analysed with Spearman's rho. The most common primary tumour was genital tumour (25.8%) and bronchial carcinoma (19.4%), and the most common cell type was adenocarcinoma (58.1%). SMM were located in the iliopsoas muscle (26.3%), paravertebral muscles (21.1%), and upper extremity muscles (18.4%). MRI features: (1) Type-I localised lesions (12.90%), round-like mass limited to local regions with heterogeneous iso-signal intensity in T1WI and heterogeneous hyper-intensity in T2WI; (2) Type-II diffuse lesions without bone destruction (35.48%), abnormal diffuse swelling of the muscle with irregular boundaries and slightly hypo- to iso-intensity in T1WI and hyper-intensity in T2WI; and (3) Type-III diffuse lesions with bone destruction (51.61%), distinct irregular lump with iso-intensity in T1WI and heterogeneous hyper-intensity in T2WI with adjacent bone invasion. There was positive correlation between MRI types and ages (r = 0.431, p < 0.05). There were no significant differences of MRI types with pathology category (p > 0.05). SMM features on MRI can be broadly used to classify lesions, which is beneficial for SMM diagnosis.

Skeletal muscle metastases on magnetic resonance imaging: analysis of 31 cases

PubMed Central

Li, Qi; Wang, Lei; Shu, Hong; Ma, Ying; Lu, Zaiming; Fu, Xihu; Jiang, Bo; Guo, Qiyong

2016-01-01

Aim of the study To investigate the magnetic resonance imaging (MRI) features of skeletal muscle metastases (SMM). Material and methods The records of 31 patients with proven SMM were retrospectively reviewed. Clinical history, type of primary malignancy, location of metastases, and MRI features of SMM were evaluated. Based on MRI findings, SMM were divided into three MRI types. The correlation between MRI types with ages and pathology category, between MRI types of SMM and ages, as well as MRI types of SMM and pathology category were analysed with Spearman's rho. Results The most common primary tumour was genital tumour (25.8%) and bronchial carcinoma (19.4%), and the most common cell type was adenocarcinoma (58.1%). SMM were located in the iliopsoas muscle (26.3%), paravertebral muscles (21.1%), and upper extremity muscles (18.4%). MRI features: (1) Type-I localised lesions (12.90%), round-like mass limited to local regions with heterogeneous iso-signal intensity in T1WI and heterogeneous hyper-intensity in T2WI; (2) Type-II diffuse lesions without bone destruction (35.48%), abnormal diffuse swelling of the muscle with irregular boundaries and slightly hypo- to iso-intensity in T1WI and hyper-intensity in T2WI; and (3) Type-III diffuse lesions with bone destruction (51.61%), distinct irregular lump with iso-intensity in T1WI and heterogeneous hyper-intensity in T2WI with adjacent bone invasion. There was positive correlation between MRI types and ages (r = 0.431, p < 0.05). There were no significant differences of MRI types with pathology category (p > 0.05). Conclusions SMM features on MRI can be broadly used to classify lesions, which is beneficial for SMM diagnosis. PMID:27647989

TU-F-CAMPUS-J-02: Evaluation of Textural Feature Extraction for Radiotherapy Response Assessment of Early Stage Breast Cancer Patients Using Diffusion Weighted MRI and Dynamic Contrast Enhanced MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Y; Wang, C; Horton, J

Purpose: To investigate the feasibility of using classic textural feature extraction in radiotherapy response assessment, we studied a unique cohort of early stage breast cancer patients with paired pre - and post-radiation Diffusion Weighted MRI (DWI-MRI) and Dynamic Contrast Enhanced MRI (DCE-MRI). Methods: 15 female patients from our prospective phase I trial evaluating preoperative radiotherapy were included in this retrospective study. Each patient received a single-fraction radiation treatment, and DWI and DCE scans were conducted before and after the radiotherapy. DWI scans were acquired using a spin-echo EPI sequence with diffusion weighting factors of b = 0 and b =more » 500 mm{sup 2} /s, and the apparent diffusion coefficient (ADC) maps were calculated. DCE-MRI scans were acquired using a T{sub 1}-weighted 3D SPGR sequence with a temporal resolution of about 1 minute. The contrast agent (CA) was intravenously injected with a 0.1 mmol/kg bodyweight dose at 2 ml/s. Two parameters, volume transfer constant (K{sup trans} ) and k{sub ep} were analyzed using the two-compartment Tofts kinetic model. For DCE parametric maps and ADC maps, 33 textural features were generated from the clinical target volume (CTV) in a 3D fashion using the classic gray level co-occurrence matrix (GLCOM) and gray level run length matrix (GLRLM). Wilcoxon signed-rank test was used to determine the significance of each texture feature’s change after the radiotherapy. The significance was set to 0.05 with Bonferroni correction. Results: For ADC maps calculated from DWI-MRI, 24 out of 33 CTV features changed significantly after the radiotherapy. For DCE-MRI pharmacokinetic parameters, all 33 CTV features of K{sup trans} and 33 features of k{sub ep} changed significantly. Conclusion: Initial results indicate that those significantly changed classic texture features are sensitive to radiation-induced changes and can be used for assessment of radiotherapy response in breast cancer.« less

Reversible splenial lesion syndrome associated with lobar pneumonia

PubMed Central

Li, Chunrong; Wu, Xiujuan; Qi, Hehe; Cheng, Yanwei; Zhang, Bing; Zhou, Hongwei; Lv, Xiaohong; Liu, Kangding; Zhang, Hong-Liang

2016-01-01

Abstract Background: Reversible splenial lesion syndrome (RESLES) is a rare clinico-radiological disorder with unclear pathophysiology. Clinically, RESLES is defined as reversible isolated splenial lesions in the corpus callosum, which can be readily identified by magnetic resonance imaging (MRI) and usually resolve completely over a period of time. RESLES could be typically triggered by infection, antiepileptic drugs (AEDs), poisoning, etc. More factors are increasingly recognized. Methods and results: We reported herein an 18-year-old female patient with lobar pneumonia who developed mental abnormalities during hospitalization. An isolated splenial lesion in the corpus callosum was found by head MRI and the lesion disappeared 15 days later. Based on her clinical manifestations and radiological findings, she was diagnosed with lobar pneumonia associated RESLES. We further summarize the up-to-date knowledge about the etiology, possible pathogenesis, clinical manifestations, radiological features, treatment, and prognosis of RESLES. Conclusion: This report contributes to the clinical understanding of RESLES which may present with mental abnormalities after infection. The characteristic imaging of reversible isolated splenial lesions in the corpus callosum was confirmed in this report. The clinical manifestations and lesions on MRI could disappear naturally after 1 month without special treatment. PMID:27684805

Diffusion MRI noise mapping using random matrix theory

PubMed Central

Veraart, Jelle; Fieremans, Els; Novikov, Dmitry S.

2016-01-01

Purpose To estimate the spatially varying noise map using a redundant magnitude MR series. Methods We exploit redundancy in non-Gaussian multi-directional diffusion MRI data by identifying its noise-only principal components, based on the theory of noisy covariance matrices. The bulk of PCA eigenvalues, arising due to noise, is described by the universal Marchenko-Pastur distribution, parameterized by the noise level. This allows us to estimate noise level in a local neighborhood based on the singular value decomposition of a matrix combining neighborhood voxels and diffusion directions. Results We present a model-independent local noise mapping method capable of estimating noise level down to about 1% error. In contrast to current state-of-the art techniques, the resultant noise maps do not show artifactual anatomical features that often reflect physiological noise, the presence of sharp edges, or a lack of adequate a priori knowledge of the expected form of MR signal. Conclusions Simulations and experiments show that typical diffusion MRI data exhibit sufficient redundancy that enables accurate, precise, and robust estimation of the local noise level by interpreting the PCA eigenspectrum in terms of the Marchenko-Pastur distribution. PMID:26599599

Predicting epidermal growth factor receptor gene amplification status in glioblastoma multiforme by quantitative enhancement and necrosis features deriving from conventional magnetic resonance imaging.

PubMed

Dong, Fei; Zeng, Qiang; Jiang, Biao; Yu, Xinfeng; Wang, Weiwei; Xu, Jingjing; Yu, Jinna; Li, Qian; Zhang, Minming

2018-05-01

To study whether some of the quantitative enhancement and necrosis features in preoperative conventional MRI (cMRI) had a predictive value for epidermal growth factor receptor (EGFR) gene amplification status in glioblastoma multiforme (GBM).Fifty-five patients with pathologically determined GBMs who underwent cMRI were retrospectively reviewed. The following cMRI features were quantitatively measured and recorded: long and short diameters of the enhanced portion (LDE and SDE), maximum and minimum thickness of the enhanced portion (MaxTE and MinTE), and long and short diameters of the necrotic portion (LDN and SDN). Univariate analysis of each feature and a decision tree model fed with all the features were performed. Area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the performance of features, and predictive accuracy was used to assess the performance of the model.For single feature, MinTE showed the best performance in differentiating EGFR gene amplification negative (wild-type) (nEGFR) GBM from EGFR gene amplification positive (pEGFR) GBM, and it got an AUC of 0.68 with a cut-off value of 2.6 mm. The decision tree model included 2 features MinTE and SDN, and got an accuracy of 0.83 in validation dataset.Our results suggest that quantitative measurement of the features MinTE and SDN in preoperative cMRI had a high accuracy for predicting EGFR gene amplification status in GBM.

Advanced flow MRI: emerging techniques and applications

PubMed Central

Markl, M.; Schnell, S.; Wu, C.; Bollache, E.; Jarvis, K.; Barker, A. J.; Robinson, J. D.; Rigsby, C. K.

2016-01-01

Magnetic resonance imaging (MRI) techniques provide non-invasive and non-ionising methods for the highly accurate anatomical depiction of the heart and vessels throughout the cardiac cycle. In addition, the intrinsic sensitivity of MRI to motion offers the unique ability to acquire spatially registered blood flow simultaneously with the morphological data, within a single measurement. In clinical routine, flow MRI is typically accomplished using methods that resolve two spatial dimensions in individual planes and encode the time-resolved velocity in one principal direction, typically oriented perpendicular to the two-dimensional (2D) section. This review describes recently developed advanced MRI flow techniques, which allow for more comprehensive evaluation of blood flow characteristics, such as real-time flow imaging, 2D multiple-venc phase contrast MRI, four-dimensional (4D) flow MRI, quantification of complex haemodynamic properties, and highly accelerated flow imaging. Emerging techniques and novel applications are explored. In addition, applications of these new techniques for the improved evaluation of cardiovascular (aorta, pulmonary arteries, congenital heart disease, atrial fibrillation, coronary arteries) as well as cerebrovascular disease (intra-cranial arteries and veins) are presented. PMID:26944696

The Attentional Field Revealed by Single-Voxel Modeling of fMRI Time Courses

PubMed Central

DeYoe, Edgar A.

2015-01-01

The spatial topography of visual attention is a distinguishing and critical feature of many theoretical models of visuospatial attention. Previous fMRI-based measurements of the topography of attention have typically been too crude to adequately test the predictions of different competing models. This study demonstrates a new technique to make detailed measurements of the topography of visuospatial attention from single-voxel, fMRI time courses. Briefly, this technique involves first estimating a voxel's population receptive field (pRF) and then “drifting” attention through the pRF such that the modulation of the voxel's fMRI time course reflects the spatial topography of attention. The topography of the attentional field (AF) is then estimated using a time-course modeling procedure. Notably, we are able to make these measurements in many visual areas including smaller, higher order areas, thus enabling a more comprehensive comparison of attentional mechanisms throughout the full hierarchy of human visual cortex. Using this technique, we show that the AF scales with eccentricity and varies across visual areas. We also show that voxels in multiple visual areas exhibit suppressive attentional effects that are well modeled by an AF having an enhancing Gaussian center with a suppressive surround. These findings provide extensive, quantitative neurophysiological data for use in modeling the psychological effects of visuospatial attention. PMID:25810532

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

PubMed

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive thresholding are applied to the corresponding cancer response maps for PCa foci localization. Evaluation based on 160 patient data with 12-core systematic TRUS-guided prostate biopsy as the reference standard demonstrates that our system achieves a sensitivity of 0.46, 0.92 and 0.97 at 0.1, 1 and 10 false positives per normal/benign patient which is significantly superior to two state-of-the-art CNN-based methods (Oquab et al., 2015; Zhou et al., 2015) and 6-core systematic prostate biopsies. Copyright © 2017 Elsevier B.V. All rights reserved.

Neuroradiologic characteristics of astroblastoma and systematic review of the literature: 2 new cases and 125 cases reported in 59 publications.

PubMed

Cunningham, Danielle A; Lowe, Lisa H; Shao, Lei; Acosta, Natasha R

2016-08-01

Astroblastoma is a rare tumor of uncertain origin most commonly presenting in the cerebrum of children and young adults. The literature contains only case reports and small series regarding its radiologic features. This systematic review is the largest study of imaging findings of astroblastoma to date and serves to identify features that might differentiate it from other neoplasms. This study describes the imaging features of astroblastoma based on a systematic review of the literature and two new cases. We conducted a PubMed and Google Scholar database search that identified 59 publications containing 125 cases of pathology-confirmed astroblastoma, and we also added two new cases from our own institution. Data collected include patient age, gender, tumor location, morphology, calcifications and calvarial changes. We recorded findings on CT, MRI, diffusion-weighted imaging (DWI), MR spectroscopy, positron emission tomography (PET) and catheter angiography. Age at diagnosis ranged 0-70 years (mean 18 years; median 14 years). Female-to-male ratio was 8:1. Of 127 cases, 66 reported CT, 78 reported MRI and 47 reported both findings. Not all authors reported all features, but the tumor features reported included supratentorial in 96% (122/127), superficial in 72% (48/67), well-demarcated in 96% (79/82), mixed cystic-solid in 93% (79/85), and enhancing in 99% (78/79). On CT, 84% (26/31) of astroblastomas were hyperattenuated, 73% (27/37) had calcifications and 7 cases reported adjacent calvarial erosion. Astroblastomas were hypointense on T1-W in 58% (26/45) and on T2-W in 50% (23/46) of MRI sequences. Peritumoral edema was present in 80% (40/50) of cases but was typically described as slight. Six cases included DWI findings, with 100% showing restricted diffusion. On MR spectroscopy, 100% (5/5) showed nonspecific tumor spectra with elevated choline and decreased N-acetylaspartate (NAA). PET revealed nonspecific reduced uptake of [F-18] 2-fluoro-2-deoxyglucose ((18)F-FDG) and increased uptake of [11C]-Methionine in 100% (3/3) of cases. Catheter angiography findings (n=12) were variable, including hypervascularity in 67%, arteriovenous shunting in 33% and avascular areas in 25%. Astroblastomas occur most often in adolescent girls. Imaging often shows a supratentorial, superficial, well-defined, cystic-solid enhancing mass. On CT, most are hyperattenuated, have calcifications, and may remodel adjacent bone if superficial. MRI characteristically reveals a hypointense mass on T1-W and T2-W sequences with restricted diffusion. MR spectroscopy, PET and catheter angiography findings are nonspecific.

Automatic and Reproducible Positioning of Phase-Contrast MRI for the Quantification of Global Cerebral Blood Flow

PubMed Central

Liu, Peiying; Lu, Hanzhang; Filbey, Francesca M.; Pinkham, Amy E.; McAdams, Carrie J.; Adinoff, Bryon; Daliparthi, Vamsi; Cao, Yan

2014-01-01

Phase-Contrast MRI (PC-MRI) is a noninvasive technique to measure blood flow. In particular, global but highly quantitative cerebral blood flow (CBF) measurement using PC-MRI complements several other CBF mapping methods such as arterial spin labeling and dynamic susceptibility contrast MRI by providing a calibration factor. The ability to estimate blood supply in physiological units also lays a foundation for assessment of brain metabolic rate. However, a major obstacle before wider applications of this method is that the slice positioning of the scan, ideally placed perpendicular to the feeding arteries, requires considerable expertise and can present a burden to the operator. In the present work, we proposed that the majority of PC-MRI scans can be positioned using an automatic algorithm, leaving only a small fraction of arteries requiring manual positioning. We implemented and evaluated an algorithm for this purpose based on feature extraction of a survey angiogram, which is of minimal operator dependence. In a comparative test-retest study with 7 subjects, the blood flow measurement using this algorithm showed an inter-session coefficient of variation (CoV) of . The Bland-Altman method showed that the automatic method differs from the manual method by between and , for of the CBF measurements. This is comparable to the variance in CBF measurement using manually-positioned PC MRI alone. In a further application of this algorithm to 157 consecutive subjects from typical clinical cohorts, the algorithm provided successful positioning in 89.7% of the arteries. In 79.6% of the subjects, all four arteries could be planned using the algorithm. Chi-square tests of independence showed that the success rate was not dependent on the age or gender, but the patients showed a trend of lower success rate (p = 0.14) compared to healthy controls. In conclusion, this automatic positioning algorithm could improve the application of PC-MRI in CBF quantification. PMID:24787742

Magnetization-prepared rapid acquisition with gradient echo magnetic resonance imaging signal and texture features for the prediction of mild cognitive impairment to Alzheimer’s disease progression

PubMed Central

Martinez-Torteya, Antonio; Rodriguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, Jose

2014-01-01

Abstract. Early diagnoses of Alzheimer’s disease (AD) would confer many benefits. Several biomarkers have been proposed to achieve such a task, where features extracted from magnetic resonance imaging (MRI) have played an important role. However, studies have focused exclusively on morphological characteristics. This study aims to determine whether features relating to the signal and texture of the image could predict mild cognitive impairment (MCI) to AD progression. Clinical, biological, and positron emission tomography information and MRI images of 62 subjects from the AD neuroimaging initiative were used in this study, extracting 4150 features from each MRI. Within this multimodal database, a feature selection algorithm was used to obtain an accurate and small logistic regression model, generated by a methodology that yielded a mean blind test accuracy of 0.79. This model included six features, five of them obtained from the MRI images, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index. The groups were statistically different (p-value=2.04e−11). These results demonstrated that MRI features related to both signal and texture add MCI to AD predictive power, and supported the ongoing notion that multimodal biomarkers outperform single-modality ones. PMID:26158047

Aneurysmal bone cyst.

PubMed

Rapp, Timothy B; Ward, James P; Alaia, Michael J

2012-04-01

Aneurysmal bone cysts are rare skeletal tumors that most commonly occur in the first two decades of life. They primarily develop about the knee but may arise in any portion of the axial or appendicular skeleton. Pathogenesis of these tumors remains controversial and may be vascular, traumatic, or genetic. Radiographic features include a dilated, radiolucent lesion typically located within the metaphyseal portion of the bone, with fluid-fluid levels visible on MRI. Histologic features include blood-filled lakes interposed between fibrous stromata. Differential diagnosis includes conditions such as telangiectatic osteosarcoma and giant cell tumor. The mainstay of treatment is curettage and bone graft, with or without adjuvant treatment. Other management options include cryotherapy, sclerotherapy, radionuclide ablation, and en bloc resection. The recurrence rate is low after appropriate treatment; however, more than one procedure may be required to completely eradicate the lesion.

MR appearance of the temporal evolution and resolution of spontaneous osteonecrosis of the knee: a case report.

PubMed

Geijer, Mats; Jureus, Jan; Hanni, Mari; Shalabi, Adel

2017-02-01

Spontaneous osteonecrosis of the knee (SONK) is a feared condition of unknown cause, in its classic form appearing in the medial femoral condyle in middle-aged or elderly subjects. Diagnosis with radiography is notoriously difficult with a long latency before typical changes appear. Magnetic resonance imaging (MRI) is regarded as a diagnostic tool with the possibility to give an earlier diagnosis with improved chances for treatment. However, also with MRI there may be an initial diagnostic blind spot before typical changes appear. Little is known about the temporal evolution of the MRI changes. In the current case report, a case of SONK is reported where serial imaging with MRI was performed, from initial symptoms to eventual resolution after almost three years.

MR appearance of the temporal evolution and resolution of spontaneous osteonecrosis of the knee: a case report

PubMed Central

Jureus, Jan; Hanni, Mari; Shalabi, Adel

2017-01-01

Spontaneous osteonecrosis of the knee (SONK) is a feared condition of unknown cause, in its classic form appearing in the medial femoral condyle in middle-aged or elderly subjects. Diagnosis with radiography is notoriously difficult with a long latency before typical changes appear. Magnetic resonance imaging (MRI) is regarded as a diagnostic tool with the possibility to give an earlier diagnosis with improved chances for treatment. However, also with MRI there may be an initial diagnostic blind spot before typical changes appear. Little is known about the temporal evolution of the MRI changes. In the current case report, a case of SONK is reported where serial imaging with MRI was performed, from initial symptoms to eventual resolution after almost three years. PMID:28203389

A Space Affine Matching Approach to fMRI Time Series Analysis.

PubMed

Chen, Liang; Zhang, Weishi; Liu, Hongbo; Feng, Shigang; Chen, C L Philip; Wang, Huili

2016-07-01

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Ovarian torsion: diagnostic features on CT and MRI with pathologic correlation.

PubMed

Duigenan, Shauna; Oliva, Esther; Lee, Susanna I

2012-02-01

The CT and MRI features of ovarian torsion are illustrated with gross pathologic correlation. Ovarian enlargement with or without an underlying mass is the finding most frequently associated with torsion, but it is nonspecific. A twisted pedicle, although not often detected on imaging, is pathognomonic when seen. Subacute ovarian hemorrhage and abnormal enhancement is usually seen, and both features show characteristic patterns on CT and MRI. Ipsilateral uterine deviation can also be seen. Diagnostic pitfalls that may mimic ovarian torsion and observations for discriminating them are discussed.

Relationship Between Clinical and Immunological Features with Magnetic Resonance Imaging Abnormalities in Female Patients with Neuropsychiatric Systemic Lupus Erythematosus

PubMed Central

Wang, Hai-Peng; Wang, Cui-Yan; Pan, Zheng-Lun; Zhao, Jun-Yu; Zhao, Bin

2016-01-01

Background: Conventional magnetic resonance imaging (MRI) is the preferred neuroimaging method in the evaluation of neuropsychiatric systemic lupus erythematosus (NPSLE). The purpose of this study was to investigate the association between clinical and immunological features with MRI abnormalities in female patients with NPSLE, to screen for the value of conventional MRI in NPSLE. Methods: A total of 59 female NPSLE patients with conventional MRI examinations were enrolled in this retrospective study. All patients were classified into different groups according to MRI abnormalities. Both clinical and immunological features were compared between MRI abnormal and normal groups. One-way analysis of variance was used to compare the systemic lupus erythematosus disease activity index (SLEDAI) score for MRI abnormalities. Multivariate logistic regression analysis investigated the correlation between immunological features, neuropsychiatric manifestations, and MRI abnormalities. Results: Thirty-six NPSLE patients (61%) showed a variety of MRI abnormalities. There were statistically significant differences in SLEDAI scores (P < 0.001), incidence of neurologic disorders (P = 0.001), levels of 24-h proteinuria (P = 0.001) and immunoglobulin M (P = 0.004), and incidence of acute confusional state (P = 0.002), cerebrovascular disease (P = 0.004), and seizure disorder (P = 0.028) between MRI abnormal and normal groups. In the MRI abnormal group, SLEDAI scores for cerebral atrophy (CA), cortex involvement, and restricted diffusion (RD) were much higher than in the MRI normal group (P < 0.001, P = 0.002, P = 0.038, respectively). Statistically significant positive correlations between seizure disorder and cortex involvement (odds ratio [OR] = 14.90; 95% confidence interval [CI], 1.50–151.70; P = 0.023) and cerebrovascular disease and infratentorial involvement (OR = 10.00; 95% CI, 1.70–60.00; P = 0.012) were found. Conclusions: MRI abnormalities in NPSLE, especially CA, cortex involvement, and RD might be markers of high systemic lupus erythematosus activity. Some MRI abnormalities might correspond to neuropsychiatric manifestations and might be helpful in understanding the pathophysiology of NPSLE. PMID:26904988

How Does Brain Activation Differ in Children with Unilateral Cerebral Palsy Compared to Typically Developing Children, during Active and Passive Movements, and Tactile Stimulation? An fMRI Study

ERIC Educational Resources Information Center

Van de Winckel, Ann; Klingels, Katrijn; Bruyninckx, Frans; Wenderoth, Nici; Peeters, Ron; Sunaert, Stefan; Van Hecke, Wim; De Cock, Paul; Eyssen, Maria; De Weerdt, Willy; Feys, Hilde

2013-01-01

The aim of the functional magnetic resonance imaging (fMRI) study was to investigate brain activation associated with active and passive movements, and tactile stimulation in 17 children with right-sided unilateral cerebral palsy (CP), compared to 19 typically developing children (TD). The active movements consisted of repetitive opening and…

Dextroposition of the Heart

DTIC Science & Technology

2007-10-01

The atrial chamber that is connected to the inferior vena cava is typically the right atrium . The pulmonary veins typically empty into the left ...only “a left chest wall 6 cm scar consistent with surgical history.” The screening chest x-ray is presented below (Fig 1A). Technical limitations...Cardiac MRI images further define the internal cardiac anatomy. On a coronal bright blood MRI image (Fig. 1B; LA = left atrium ; LPA = left

Cell internalizable and intracellularly degradable cationic polyurethane micelles as a potential platform for efficient imaging and drug delivery.

PubMed

Ding, Mingming; Zeng, Xin; He, Xueling; Li, Jiehua; Tan, Hong; Fu, Qiang

2014-08-11

A cell internalizable and intracellularly degradable micellar system, assembled from multiblock polyurethanes bearing cell-penetrating gemini quaternary ammonium pendent groups in the side chain and redox-responsive disulfide linkages throughout the backbone, was developed for potential magnetic resonance imaging (MRI) and drug delivery. The nanocarrier is featured as a typical "cleavable core-internalizable shell-protective corona" architecture, which exhibits small size, positive surface charge, high loading capacity, and reduction-triggered destabilization. Furthermore, it can rapidly enter tumor cells and release its cargo in response to an intracellular level of glutathione, resulting in enhanced drug efficacy in vitro. The magnetic micelles loaded with superparamagnetic iron oxide (SPIO) nanoparticles demonstrate excellent MRI contrast enhancement, with T2 relaxivity found to be affected by the morphology of SPIO-clustering inside the micelle core. The multifunctional carrier with good cytocompatibility and nontoxic degradation products can serve as a promising theranostic candidate for efficient intracellular delivery of anticancer drugs and real-time monitoring of therapeutic effect.

Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer

NASA Astrophysics Data System (ADS)

Ginsburg, Shoshana B.; Rusu, Mirabela; Kurhanewicz, John; Madabhushi, Anant

2014-03-01

In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these T2w MRI texture features are potential independent prognostic markers of PSA failure, we implement a partial least squares (PLS) method to embed the data in a low{dimensional space and then use the variable importance in projections (VIP) method to quantify the contributions of individual features to classification on the PLS embedding. In spite of the poor resolution of the 1.5 T MRI data, we are able to identify three Gabor wavelet features that, in conjunction with a logistic regression classifier, yield an area under the receiver operating characteristic curve of 0.83 for predicting the probability of biochemical recurrence following radiation therapy. In comparison to both the Kattan nomogram and semantic MRI attributes, the ability of these three computer-extracted features to predict biochemical recurrence risk is demonstrated.

Brain correlates of subjective freedom of choice.

PubMed

Filevich, Elisa; Vanneste, Patricia; Brass, Marcel; Fias, Wim; Haggard, Patrick; Kühn, Simone

2013-12-01

The subjective feeling of free choice is an important feature of human experience. Experimental tasks have typically studied free choice by contrasting free and instructed selection of response alternatives. These tasks have been criticised, and it remains unclear how they relate to the subjective feeling of freely choosing. We replicated previous findings of the fMRI correlates of free choice, defined objectively. We introduced a novel task in which participants could experience and report a graded sense of free choice. BOLD responses for conditions subjectively experienced as free identified a postcentral area distinct from the areas typically considered to be involved in free action. Thus, the brain correlates of subjective feeling of free action were not directly related to any established brain correlates of objectively-defined free action. Our results call into question traditional assumptions about the relation between subjective experience of choosing and activity in the brain's so-called voluntary motor areas. Copyright © 2013. Published by Elsevier Inc.

Brain correlates of subjective freedom of choice

PubMed Central

Filevich, Elisa; Vanneste, Patricia; Brass, Marcel; Fias, Wim; Haggard, Patrick; Kühn, Simone

2013-01-01

The subjective feeling of free choice is an important feature of human experience. Experimental tasks have typically studied free choice by contrasting free and instructed selection of response alternatives. These tasks have been criticised, and it remains unclear how they relate to the subjective feeling of freely choosing. We replicated previous findings of the fMRI correlates of free choice, defined objectively. We introduced a novel task in which participants could experience and report a graded sense of free choice. BOLD responses for conditions subjectively experienced as free identified a postcentral area distinct from the areas typically considered to be involved in free action. Thus, the brain correlates of subjective feeling of free action were not directly related to any established brain correlates of objectively-defined free action. Our results call into question traditional assumptions about the relation between subjective experience of choosing and activity in the brain’s so-called voluntary motor areas. PMID:24021855

Individual MRI and radiographic features of knee OA in subjects with unilateral knee pain: Health ABC study

PubMed Central

Javaid, MK; Kiran, A; Guermazi, A; Kwoh, K; Zaim, S; Carbone, L; Harris, T.; McCulloch, C.E.; Arden, NK; Lane, NE; Felson, D; Nevitt, M

2012-01-01

Strong associations between radiographic features of knee OA and pain have been demonstrated in persons with unilateral knee symptoms. Our objectives were to compare radiographic with MRI features of knee OA and assess the discrimination between painful and non-painful knees in persons with unilateral symptoms. 283 individuals with unilateral knee pain aged 71 to 80 years from Health ABC, a study of weight-related diseases and mobility, had bilateral knee radiographs, read for KL grade and individual radiographic features, and 1.5T MRIs, read using WORMS. The association of structural features with pain was assessed using a within-person case/control design and conditional logistic regression. Receiver operator characteristics (ROC) were then used to test the discriminatory performance of structural features. In conditional logistic analyses, knee pain was significantly associated with both radiographic (any JSN grade >=1: OR 3.20 (1.79 – 5.71) and MRI (any cartilage defect:>=2: OR 3.67 (1.49 – 9.04)) features. However, most subjects had MR detected osteophytes, cartilage and bone marrow lesions in both knees and no individual structural feature discriminated well between painful and non-painful knees using ROC. The best performing MRI feature (synovitis/effusion) was not significantly more informative than KL grade >=2 (p=0.42). In persons with unilateral knee pain, MR and radiographic features were associated with knee pain confirming an important role in the etiology of pain. However, no single MRI or radiographic finding performed well in discriminating painful from non-painful knees. Further work is needed to examine how structural and non-structural factors influence knee pain. PMID:22736267

Glucocorticoids for Management of Polymyalgia Rheumatica and Giant Cell Arteritis.

PubMed

Matteson, Eric L; Buttgereit, Frank; Dejaco, Christian; Dasgupta, Bhaskar

2016-02-01

Diagnosis of polymyalgia rheumatica (PMR) and giant cell arteritis (GCA) is based on typical clinical, histologic, and laboratory features. Ultrasonographic imaging in PMR with assessment especially of subdeltoid bursitis can aid in diagnosis and in following response to treatment. In GCA, diagnosis and disease activity are supported with ultrasonographic, MRI, or [(18)F]fluorodeoxyglucose PET evaluation of large vessels. Glucocorticoids are the primary therapy for PMR and GCA. Methotrexate may be used in patients at high risk for glucocorticoid adverse effects and patients with frequent relapse or needing protracted therapy. Other therapeutic approaches including interleukin 6 antagonists are under evaluation. Copyright © 2016 Elsevier Inc. All rights reserved.

Fat Embolism Syndrome: A Case Report and Review Literature

PubMed Central

Uransilp, Nattaphol

2018-01-01

Fat embolism syndrome (FES) is a life-threatening complication in patients with orthopedic trauma, especially long bone fractures. The diagnosis of fat embolism is made by clinical features alone with no specific laboratory findings. FES has no specific treatment and requires supportive care, although it can be prevented by early fixation of bone fractures. Here, we report a case of FES in a patient with right femoral neck fracture, which was diagnosed initially by Gurd's criteria and subsequently confirmed by typical appearances on magnetic resonance imaging (MRI) of the brain. The patient received supportive management and a short course of intravenous methylprednisolone. PMID:29853905

SU-E-J-264: Using Magnetic Resonance Imaging-Derived Features to Quantify Radiotherapy-Induced Normal Tissue Morbidity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thor, M; Tyagi, N; Deasy, J

2015-06-15

Purpose: The aim of this study was to explore the use of Magnetic Resonance Imaging (MRI)-derived features as indicators of Radiotherapy (RT)-induced normal tissue morbidity. We also investigate the relationship between these features and RT dose in four critical structures. Methods: We demonstrate our approach for four patients treated with RT for base of tongue cancer in 2005–2007. For each patient, two MRI scans (T1-weighted pre (T1pre) and post (T1post) gadolinium contrast-enhancement) were acquired within the first six months after RT. The assessed morbidity endpoint observed in 2/4 patients was Grade 2+ CTCAEv.3 trismus. Four ipsilateral masticatory-related structures (masseter, lateralmore » and medial pterygoid, and the temporal muscles) were delineated on both T1pre and T1post and these scans were co-registered to the treatment planning CT using a deformable demons algorithm. For each structure, the maximum and mean RT dose, and six MRI-derived features (the second order texture features entropy and homogeneity, and the first order mean, median, kurtosis, and skewness) were extracted and compared structure-wise between patients with and without trismus. All MRI-derived features were calculated as the difference between T1pre and T1post, ΔS. Results: For 5/6 features and all structures, ΔS diverged between trismus and non-trismus patients particularly for the masseter, lateral pterygoid, and temporal muscles using the kurtosis feature (−0.2 vs. 6.4 for lateral pterygoid). Both the maximum and mean RT dose in all four muscles were higher amongst the trismus patients (with the maximum dose being up to 25 Gy higher). Conclusion: Using MRI-derived features to quantify RT-induced normal tissue complications is feasible. We showed that several features are different between patients with and without morbidity and that the RT dose in all investigated structures are higher amongst patients with morbidity. MRI-derived features, therefore, has the potential to improve predictions of normal tissue morbidity.« less

CT versus MR Techniques in the Detection of Cervical Artery Dissection.

PubMed

Hanning, Uta; Sporns, Peter B; Schmiedel, Meilin; Ringelstein, Erich B; Heindel, Walter; Wiendl, Heinz; Niederstadt, Thomas; Dittrich, Ralf

2017-11-01

Spontaneous cervical artery dissection (sCAD) is an important etiology of juvenile stroke. The gold standard for the diagnosis of sCAD is convential angiography. However, magnetic resonance imaging (MRI)/MR angiography (MRA) and computed tomography (CT)/CT angiography (CTA) are frequently used alternatives. New developments such as multislice CT/CTA have enabled routine acquisition of thinner sections with rapid imaging times. The goal of this study was to compare the capability of recent developed 128-slice CT/CTA to MRI/MRA to detect radiologic features of sCAD. Retrospective review of patients with suspected sCAD (n = 188) in a database of our Stroke center (2008-2014), who underwent CT/CTA and MRI/MRA on initial clinical work-up. A control group of 26 patients was added. All Images were evaluated concerning specific and sensitive radiological features for dissection by two experienced neuroradiologists. Imaging features were compared between the two modalities. Forty patients with 43 dissected arteries received both modalities (29 internal carotid arteries [ICAs] and 14 vertebral arteries [VAs]). All CADs were identified in CT/CTA and MRI/MRA. The features intimal flap, stenosis, and lumen irregularity appeared in both modalities. One high-grade stenosis was identified by CT/CTA that was expected occluded on MRI/MRA. Two MRI/MRA-confirmed pseudoaneurysms were missed by CT/CTA. None of the controls evidenced specific imaging signs for dissection. CT/CTA is a reliable and better available alternative to MRI/MRA for diagnosis of sCAD. CT/CTA should be used to complement MRI/MRA in cases where MRI/MRA suggests occlusion. Copyright © 2017 by the American Society of Neuroimaging.

Association between mammogram density and background parenchymal enhancement of breast MRI

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Danala, Gopichandh; Wang, Yunzhi; Zarafshani, Ali; Qian, Wei; Liu, Hong; Zheng, Bin

2018-02-01

Breast density has been widely considered as an important risk factor for breast cancer. The purpose of this study is to examine the association between mammogram density results and background parenchymal enhancement (BPE) of breast MRI. A dataset involving breast MR images was acquired from 65 high-risk women. Based on mammography density (BIRADS) results, the dataset was divided into two groups of low and high breast density cases. The Low-Density group has 15 cases with mammographic density (BIRADS 1 and 2), while the High-density group includes 50 cases, which were rated by radiologists as mammographic density BIRADS 3 and 4. A computer-aided detection (CAD) scheme was applied to segment and register breast regions depicted on sequential images of breast MRI scans. CAD scheme computed 20 global BPE features from the entire two breast regions, separately from the left and right breast region, as well as from the bilateral difference between left and right breast regions. An image feature selection method namely, CFS method, was applied to remove the most redundant features and select optimal features from the initial feature pool. Then, a logistic regression classifier was built using the optimal features to predict the mammogram density from the BPE features. Using a leave-one-case-out validation method, the classifier yields the accuracy of 82% and area under ROC curve, AUC=0.81+/-0.09. Also, the box-plot based analysis shows a negative association between mammogram density results and BPE features in the MRI images. This study demonstrated a negative association between mammogram density and BPE of breast MRI images.

Custom fit 3D-printed brain holders for comparison of histology with MRI in marmosets.

PubMed

Guy, Joseph R; Sati, Pascal; Leibovitch, Emily; Jacobson, Steven; Silva, Afonso C; Reich, Daniel S

2016-01-15

MRI has the advantage of sampling large areas of tissue and locating areas of interest in 3D space in both living and ex vivo systems, whereas histology has the ability to examine thin slices of ex vivo tissue with high detail and specificity. Although both are valuable tools, it is currently difficult to make high-precision comparisons between MRI and histology due to large differences inherent to the techniques. A method combining the advantages would be an asset to understanding the pathological correlates of MRI. 3D-printed brain holders were used to maintain marmoset brains in the same orientation during acquisition of ex vivo MRI and pathologic cutting of the tissue. The results of maintaining this same orientation show that sub-millimeter, discrete neuropathological features in marmoset brain consistently share size, shape, and location between histology and ex vivo MRI, which facilitates comparison with serial imaging acquired in vivo. Existing methods use computational approaches sensitive to data input in order to warp histologic images to match large-scale features on MRI, but the new method requires no warping of images, due to a preregistration accomplished in the technique, and is insensitive to data formatting and artifacts in both MRI and histology. The simple method of using 3D-printed brain holders to match brain orientation during pathologic sectioning and MRI acquisition enables rapid and precise comparison of small features seen on MRI to their underlying histology. Published by Elsevier B.V.

Unique Neural Characteristics of Atypical Lateralization of Language in Healthy Individuals

PubMed Central

Biduła, Szymon P.; Przybylski, Łukasz; Pawlak, Mikołaj A.; Króliczak, Gregory

2017-01-01

Using functional magnetic resonance imaging (fMRI) in 63 healthy participants, including left-handed and ambidextrous individuals, we tested how atypical lateralization of language—i. e., bilateral or right hemispheric language representation—differs from the typical left-hemisphere dominance. Although regardless of their handedness, all 11 participants from the atypical group engaged classical language centers, i.e., Broca's and Wernicke's areas, the right-hemisphere components of the default mode network (DMN), including the angular gyrus and middle temporal gyrus, were also critically involved during the verbal fluency task. Importantly, activity in these regions could not be explained in terms of mirroring the typical language pattern because left-hemisphere dominant individuals did not exhibit similar significant signal modulations. Moreover, when spatial extent of language-related activity across whole brain was considered, the bilateral language organization entailed more diffuse functional processing. Finally, we detected significant differences between the typical and atypical group in the resting-state connectivity at the global and local level. These findings suggest that the atypical lateralization of language has unique features, and is not a simple mirror image of the typical left hemispheric language representation. PMID:28983238

Renal carcinomas associated with Xp11.2 translocations/TFE3 gene fusions: findings on MRI and computed tomography imaging.

PubMed

Liu, Kefu; Xie, Ping; Peng, Weijun; Zhou, Zhengrong

2014-08-01

To retrospectively analyze MRI and computed tomographic (CT) findings from renal carcinomas associated with Xp11.2 translocations/TFE3 gene fusions (Xp11-RCC). Institutional review board permission was obtained to review patient medical records, and the requirement for informed consent was waved . The clinical and MRI/CT features of five cases with Xp11-RCC that were confirmed by pathology were analyzed retrospectively. The image characteristics included the lesion location and size, contribution of cystic and solid components, intratumoral necrosis or hemorrhage, invasion of perinephric tissue and renal sinus, lymphadenopathy, major venous or arterial vascular invasion, pattern of the tumor growth, intratumor calcification and lipids, homogeneity of SI on T2-weighted images, attenuation and SI of the mass with respect to the normal renal cortex on precontrast and contrasted CT/MRI images, tumor SIs, tumor attenuations and tumor-to-cortex indices, homogeneity of enhancement on the contrasted images. The mean age was 32 years (range, 15-47 years). Most patients (4/5) were women. All tumors showed a cortical location. The average tumor size was 9 cm (range, 4-18 cm). Four tumors comprised a predominantly solid lesion with focal necrosis, and one tumor comprised a solid lesion with significant necrosis. All tumors showed intertumor hemorrhage, infiltrative growth and invasion of the perirenal adipose/renal sinus. Four cases showed retroperitoneal lymphadenopathy, of which one case showed simultaneous mediastinal and supraclavicular lymphadenopathy. All tumors from four cases showed mild hyperintensity on T1-weighted MRI images, and three tumors showed hypointensity on T2-weighted MRI images relative to the renal cortex except for 1 tumor that showed significant hemorrhage and a relative hyperintensity. For 3 cases who were imaged with CT, two tumors imaged using nonenhanced CT images showed mild hyperdensity relative to the renal cortex. Calcification was noted in all three tumors. All tumors showed mild, persistent enhancement. Typical Xp11-RCC manifests as an advanced, solid renal mass with mild persistent enhancement, a prevalence of intertumor hemorrhage/calcification, and a cortical epicenter location. The predilection for children and young adults is a useful clinical feature when confirming a diagnosis of Xp11-RCC. © 2013 Wiley Periodicals, Inc.

Brief Report: Methods for Acquiring Structural MRI Data in Very Young Children with Autism Without the Use of Sedation

PubMed Central

Simon, Tony J.; Zierhut, Cynthia; Solomon, Marjorie; Rogers, Sally J.; Amaral, David G.

2016-01-01

We describe a protocol with which we achieved a 93% success rate in acquiring high quality MRI scans without the use of sedation in 2.5–4.5 year old children with autism, developmental delays, and typical development. Our main strategy was to conduct MRIs during natural nocturnal sleep in the evenings after the child's normal bedtime. Alternatively, with some older and higher functioning children, the MRI was conducted while the child was awake and watching a video. Both strategies relied heavily on the creation of a child and family friendly MRI environment and the involvement of parents as collaborators in the project. Scanning very young children with autism, typical development, and developmental delays without the use of sedation or anesthesia was possible in the majority of cases. PMID:18157624

Plasma MRI Experiments at UW-Madison

NASA Astrophysics Data System (ADS)

Flanagan, K.; Clark, M.; Desangles, V.; Siller, R.; Wallace, J.; Weisberg, D.; Forest, C. B.

2015-11-01

Experiments for driving Keplerian-like flow profiles on both the Plasma Couette Experiment Upgrade (PCX-U) and the Wisconsin Plasma Astrophysics Laboratory (WiPAL) user facility are described. Instead of driving flow at the boundaries, as is typical in many liquid metal Couette experiments, a global drive is implemented. A large radial current is drawn across a small axial field generating torque across the whole profile. This global electrically driven flow is capable of producing profiles similar to Keplerian flow. PCX-U has been purposely constructed for MRI experiments, while similar experiments on the WiPAL device show the versatility of the user facility and provide a larger plasma volume. Numerical calculations show the predicted parameter spaces for exciting the MRI in these plasmas and the equilibrium flow profiles expected. In both devices, relevant MRI parameters appear to be within reach of typical operating characteristics.

Juvenile Osteochondritis Dissecans: Correlation Between Histopathology and MRI.

PubMed

Zbojniewicz, Andrew M; Stringer, Keith F; Laor, Tal; Wall, Eric J

2015-07-01

The objective of our study was to correlate specimens of juvenile osteochondritis dissecans (OCD) lesions of the knee to MRI examinations to elucidate the histopathologic basis of characteristic imaging features. Five children (three boys and two girls; age range, 12-13 years old) who underwent transarticular biopsy of juvenile OCD lesions of the knee were retrospectively included in this study. Two radiologists reviewed the MRI examinations and a pathologist reviewed the histopathologic specimens and recorded characteristic features. Digital specimen photographs were calibrated to the size of the respective MR image with the use of a reference scale. Photographs were rendered semitransparent and over-laid onto the MR image with the location chosen on the basis of the site of the prior biopsy. A total of seven biopsy specimens were included. On MRI, all lesions showed cystlike foci in the subchondral bone, bone marrow edema pattern on proton density-or T2-weighted images, and relatively thick unossified epiphyseal cartilage. In four patients, a laminar signal intensity pattern was seen, and two patients had multiple breaks in the subchondral bone plate. Fibrovascular tissue was found at histopathology in all patients. Cleft spaces near the cartilage-bone interface and were seen in all patients while chondrocyte cloning was present in most cases. Focal bone necrosis and inflammation were infrequent MRI findings. Precise correlation of the MRI appearance to the histopathologic overlays consistently was found. A direct correlation exists between the histopathologic findings and the MRI features in patients with juvenile OCD. Additional studies are needed to correlate these MRI features with juvenile OCD healing success rates.

Folded concave penalized learning in identifying multimodal MRI marker for Parkinson’s disease

PubMed Central

Liu, Hongcheng; Du, Guangwei; Zhang, Lijun; Lewis, Mechelle M.; Wang, Xue; Yao, Tao; Li, Runze; Huang, Xuemei

2016-01-01

Background Brain MRI holds promise to gauge different aspects of Parkinson’s disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data. New method This study introduces folded concave penalized (FCP) sparse logistic regression to identify biomarkers for PD from a large number of potential factors. The proposed statistical procedures target the challenges of high-dimensionality with limited data samples acquired. The maximization problem associated with the sparse logistic regression model is solved by local linear approximation. The proposed procedures then are applied to the empirical analysis of multimodal MRI data. Results From 45 features, the proposed approach identified 15 MRI markers and the UPSIT, which are known to be clinically relevant to PD. By combining the MRI and clinical markers, we can enhance substantially the specificity and sensitivity of the model, as indicated by the ROC curves. Comparison to existing methods We compare the folded concave penalized learning scheme with both the Lasso penalized scheme and the principle component analysis-based feature selection (PCA) in the Parkinson’s biomarker identification problem that takes into account both the clinical features and MRI markers. The folded concave penalty method demonstrates a substantially better clinical potential than both the Lasso and PCA in terms of specificity and sensitivity. Conclusions For the first time, we applied the FCP learning method to MRI biomarker discovery in PD. The proposed approach successfully identified MRI markers that are clinically relevant. Combining these biomarkers with clinical features can substantially enhance performance. PMID:27102045

Development of PEGylated KMnF3 nanoparticles as a T1-weighted contrast agent: chemical synthesis, in vivo brain MR imaging, and accounting for high relaxivity

NASA Astrophysics Data System (ADS)

Liu, Zhi-Jun; Song, Xiao-Xia; Tang, Qun

2013-05-01

Magnetic nanoparticles consisting of manganese-based T1-weighted contrast agents have rapidly achieved clinical application, however low proton relaxivity impedes further development. In this report, by analyzing nanoparticles' surface oxidation states we propose the possible reason for the low r1 relaxivity of common MnO nanoparticles and develop PEGylated fluoroperovskite KMnF3 nanoparticles as new T1-weighted contrast agents, which exhibit the highest longitudinal relaxivity (r1 = 23.15 mM-1 s-1) among all the reported manganese-based T1-weighted contrast agents. We, for the first time, illustrate a typical example showing that the surface oxidation states of metal ions exposed on the nanoparticles' surfaces are able to influence not only the optical, magnetic, electronic or catalytic properties but also water proton longitudinal relaxivity when applied as an MRI contrast agent. Cytotoxicity tests demonstrate that the PEGylated KMnF3 nanoparticles are free from toxicity. Further in vivo MRI experiments distinctively depict fine anatomical features in brain imaging at a low dose of 5 mg of Mn per kg and possible removal from the kidneys due to their small size and biocompatibility.Magnetic nanoparticles consisting of manganese-based T1-weighted contrast agents have rapidly achieved clinical application, however low proton relaxivity impedes further development. In this report, by analyzing nanoparticles' surface oxidation states we propose the possible reason for the low r1 relaxivity of common MnO nanoparticles and develop PEGylated fluoroperovskite KMnF3 nanoparticles as new T1-weighted contrast agents, which exhibit the highest longitudinal relaxivity (r1 = 23.15 mM-1 s-1) among all the reported manganese-based T1-weighted contrast agents. We, for the first time, illustrate a typical example showing that the surface oxidation states of metal ions exposed on the nanoparticles' surfaces are able to influence not only the optical, magnetic, electronic or catalytic properties but also water proton longitudinal relaxivity when applied as an MRI contrast agent. Cytotoxicity tests demonstrate that the PEGylated KMnF3 nanoparticles are free from toxicity. Further in vivo MRI experiments distinctively depict fine anatomical features in brain imaging at a low dose of 5 mg of Mn per kg and possible removal from the kidneys due to their small size and biocompatibility. Electronic supplementary information (ESI) available: Experimental procedure for two types of MnO nanoparticles, T1-weighted mapping. See DOI: 10.1039/c3nr00721a

Identifying MRI markers to evaluate early treatment-related changes post-laser ablation for cancer pain management

NASA Astrophysics Data System (ADS)

Tiwari, Pallavi; Danish, Shabbar; Madabhushi, Anant

2014-03-01

Laser interstitial thermal therapy (LITT) has recently emerged as a new treatment modality for cancer pain management that targets the cingulum (pain center in the brain), and has shown promise over radio-frequency (RF) based ablation which is reported to provide temporary relief. One of the major advantages enjoyed by LITT is its compatibility with magnetic resonance imaging (MRI), allowing for high resolution in vivo imaging to be used in LITT procedures. Since laser ablation for pain management is currently exploratory and is only performed at a few centers worldwide, its short-, and long-term effects on the cingulum are currently unknown. Traditionally treatment effects are evaluated by monitoring changes in volume of the ablation zone post-treatment. However, this is sub-optimal since it involves evaluating a single global parameter (volume) to detect changes pre-, and post-MRI. Additionally, the qualitative observations of LITT-related changes on multi-parametric MRI (MPMRI) do not specifically address differentiation between the appearance of treatment related changes (edema, necrosis) from recurrence of the disease (pain recurrence). In this work, we explore the utility of computer extracted texture descriptors on MP-MRI to capture early treatment related changes on a per-voxel basis by extracting quantitative relationships that may allow for an in-depth understanding of tissue response to LITT on MRI, subtle changes that may not be appreciable on original MR intensities. The second objective of this work is to investigate the efficacy of different MRI protocols in accurately capturing treatment related changes within and outside the ablation zone post-LITT. A retrospective cohort of studies comprising pre- and 24-hour post-LITT 3 Tesla T1-weighted (T1w), T2w, T2-GRE, and T2-FLAIR acquisitions was considered. Our scheme involved (1) inter-protocol as well as inter-acquisition affine registration of pre- and post-LITT MRI, (2) quantitation of MRI parameters by correcting for intensity drift in order to examine tissue-specific response, and (3) quantification of MRI maps via texture and intensity features to evaluate changes in MR markers pre- and post-LITT. A total of 78 texture features comprising of non-steerable and steerable gradient and second order statistical features were extracted from pre- and post-LITT MP-MRI on a per-voxel basis. Quantitative, voxel-wise comparison of the changes in MRI texture features between pre-, and post-LITT MRI indicate that (a) steerable and non-steerable gradient texture features were highly sensitive as well as specific in predicting subtle micro-architectural changes within and around the ablation zone pre- and post-LITT, (b) FLAIR was identified as the most sensitive MRI protocol in identifying early treatment changes yielding a normalized percentage change of 360% within the ablation zone relative to its pre-LITT value, and (c) GRE was identified as the most sensitive MRI protocol in quantifying changes outside the ablation zone post-LITT. Our preliminary results thus indicate great potential for non-invasive computerized MRI features in determining localized micro-architectural focal treatment related changes post-LITT.

MRI differentiation of low-grade from high-grade appendicular chondrosarcoma.

PubMed

Douis, Hassan; Singh, Leanne; Saifuddin, Asif

2014-01-01

To identify magnetic resonance imaging (MRI) features which differentiate low-grade chondral lesions (atypical cartilaginous tumours/grade 1 chondrosarcoma) from high-grade chondrosarcomas (grade 2, grade 3 and dedifferentiated chondrosarcoma) of the major long bones. We identified all patients treated for central atypical cartilaginous tumours and central chondrosarcoma of major long bones (humerus, femur, tibia) over a 13-year period. The MRI studies were assessed for the following features: bone marrow oedema, soft tissue oedema, bone expansion, cortical thickening, cortical destruction, active periostitis, soft tissue mass and tumour length. The MRI-features were compared with the histopathological tumour grading using univariate, multivariate logistic regression and receiver operating characteristic curve (ROC) analyses. One hundred and seventy-nine tumours were included in this retrospective study. There were 28 atypical cartilaginous tumours, 79 grade 1 chondrosarcomas, 36 grade 2 chondrosarcomas, 13 grade 3 chondrosarcomas and 23 dedifferentiated chondrosarcomas. Multivariate analysis demonstrated that bone expansion (P = 0.001), active periostitis (P = 0.001), soft tissue mass (P < 0.001) and tumour length (P < 0.001) were statistically significant differentiating factors between low-grade and high-grade chondral lesions with an area under the ROC curve of 0.956. On MRI, bone expansion, active periostitis, soft tissue mass and tumour length can reliably differentiate high-grade chondrosarcomas from low-grade chondral lesions of the major long bones. • Accurate differentiation of low-grade from high-grade chondrosarcomas is essential before surgery • MRI can reliably differentiate high-grade from low-grade chondrosarcomas of long bone • Differentiating features are bone expansion, periostitis, soft tissue mass and tumour length • Presence of these four MRI features demonstrated a diagnostic accuracy (AUC) of 95.6 % • The findings may result in more accurate diagnosis before definitive surgery.

Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

NASA Astrophysics Data System (ADS)

Hamrouni, Sameh; Rougon, Nicolas; Pr"teux, Françoise

2011-03-01

In perfusion MRI (p-MRI) exams, short-axis (SA) image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent (Gd-DTPA) through the cardiac chambers and muscle. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion. In this paper, we introduce a novel unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on normalized mutual information (NMI) between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template. The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing NMI to be computed directly from feature samples. Specifically, a computationally efficient kth-nearest neighbor (kNN) estimation framework is retained, leading to closed-form expressions for the gradient flow of NMI over finite- and infinite-dimensional motion spaces. This approach is applied to the groupwise alignment of cardiac p-MRI exams using a free-form Deformation (FFD) model for cardio-thoracic motions. Experiments on simulated and natural datasets suggest its accuracy and robustness for registering p-MRI exams comprising more than 30 frames.

[Clinical features and magnetic resonance imaging evaluation of encephalopathy in high-risk late preterm infants].

PubMed

Zhu, Yan; Zhang, Ke; Hu, Lan; Xiao, Mi-Li; Li, Zhi-Hua; Chen, Chao

2017-05-01

To investigate the risk factors, clinical features, and magnetic resonance imaging (MRI) changes of encephalopathy in high-risk late preterm infants. Head MRI scan was performed for late preterm infants with high-risk factors for brain injury who were hospitalized between January 2009 and December 2014. The risk factors, clinical features, and head MRI features of encephalopathy in late preterm infants were analyzed. A total of 1 007 late preterm infants underwent MRI scan, among whom 313 (31.1%) had imaging features in accordance with the features of encephalopathy of prematurity. Of all infants, 76.7% had white matter damage. There was no association between the development of encephalopathy and gestational age in late preterm infants, but the detection rate of encephalopathy gradually increased with the increasing birth weight (P<0.05). The logistic regression analysis showed that a history of resuscitation was an independent risk factor for encephalopathy of prematurity (P<0.01). Encephalopathy of prematurity is commonly seen in high-risk late preterm infants, especially white matter damage. A history of resuscitation is an independent risk factor for encephalopathy in late preterm infants.

MRI in acute disseminated encephalomyelitis following Semple antirabies vaccine.

PubMed

Murthy, J M

1998-07-01

I reviewed MRI findings in five patients with acute disseminated encephalomyelitis following vaccination with Semple antirabies vaccine. MRI in two patients with encephalitis features showed multiple white matter lesions in the cerebrum, cerebellar peduncles and brain stem. Two patients who had features of cord involvement showed signal alterations in the cord extending over a few segments. Asymptomatic lesions in the cerebrum were seen in two patients. In a patient with encephalomyelitis MRI 50 days later showed resolution of the lesions. The white matter lesions described were indistinguishable from those seen in acute disseminated encephalomyelitis following other infections.

Learning in data-limited multimodal scenarios: Scandent decision forests and tree-based features.

PubMed

Hor, Soheil; Moradi, Mehdi

2016-12-01

Incomplete and inconsistent datasets often pose difficulties in multimodal studies. We introduce the concept of scandent decision trees to tackle these difficulties. Scandent trees are decision trees that optimally mimic the partitioning of the data determined by another decision tree, and crucially, use only a subset of the feature set. We show how scandent trees can be used to enhance the performance of decision forests trained on a small number of multimodal samples when we have access to larger datasets with vastly incomplete feature sets. Additionally, we introduce the concept of tree-based feature transforms in the decision forest paradigm. When combined with scandent trees, the tree-based feature transforms enable us to train a classifier on a rich multimodal dataset, and use it to classify samples with only a subset of features of the training data. Using this methodology, we build a model trained on MRI and PET images of the ADNI dataset, and then test it on cases with only MRI data. We show that this is significantly more effective in staging of cognitive impairments compared to a similar decision forest model trained and tested on MRI only, or one that uses other kinds of feature transform applied to the MRI data. Copyright © 2016. Published by Elsevier B.V.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Presurgical language fMRI: Clinical practices and patient outcomes in epilepsy surgical planning.

PubMed

Benjamin, Christopher F A; Li, Alexa X; Blumenfeld, Hal; Constable, R Todd; Alkawadri, Rafeed; Bickel, Stephan; Helmstaedter, Christoph; Meletti, Stefano; Bronen, Richard; Warfield, Simon K; Peters, Jurriaan M; Reutens, David; Połczyńska, Monika; Spencer, Dennis D; Hirsch, Lawrence J

2018-03-12

The goal of this study was to document current clinical practice and report patient outcomes in presurgical language functional MRI (fMRI) for epilepsy surgery. Epilepsy surgical programs worldwide were surveyed as to the utility, implementation, and efficacy of language fMRI in the clinic; 82 programs responded. Respondents were predominantly US (61%) academic programs (85%), and evaluated adults (44%), adults and children (40%), or children only (16%). Nearly all (96%) reported using language fMRI. Surprisingly, fMRI is used to guide surgical margins (44% of programs) as well as lateralize language (100%). Sites using fMRI for localization most often use a distance margin around activation of 10mm. While considered useful, 56% of programs reported at least one instance of disagreement with other measures. Direct brain stimulation typically confirmed fMRI findings (74%) when guiding margins, but instances of unpredicted decline were reported by 17% of programs and 54% reported unexpected preservation of function. Programs reporting unexpected decline did not clearly differ from those which did not. Clinicians using fMRI to guide surgical margins do not typically map known language-critical areas beyond Broca's and Wernicke's. This initial data shows many clinical teams are confident using fMRI not only for language lateralization but also to guide surgical margins. Reported cases of unexpected language preservation when fMRI activation is resected, and cases of language decline when it is not, emphasize a critical need for further validation. Comprehensive studies comparing commonly-used fMRI paradigms to predict stimulation mapping and post-surgical language decline remain of high importance. © 2018 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Common component classification: what can we learn from machine learning?

PubMed

Anderson, Ariana; Labus, Jennifer S; Vianna, Eduardo P; Mayer, Emeran A; Cohen, Mark S

2011-05-15

Machine learning methods have been applied to classifying fMRI scans by studying locations in the brain that exhibit temporal intensity variation between groups, frequently reporting classification accuracy of 90% or better. Although empirical results are quite favorable, one might doubt the ability of classification methods to withstand changes in task ordering and the reproducibility of activation patterns over runs, and question how much of the classification machines' power is due to artifactual noise versus genuine neurological signal. To examine the true strength and power of machine learning classifiers we create and then deconstruct a classifier to examine its sensitivity to physiological noise, task reordering, and across-scan classification ability. The models are trained and tested both within and across runs to assess stability and reproducibility across conditions. We demonstrate the use of independent components analysis for both feature extraction and artifact removal and show that removal of such artifacts can reduce predictive accuracy even when data has been cleaned in the preprocessing stages. We demonstrate how mistakes in the feature selection process can cause the cross-validation error seen in publication to be a biased estimate of the testing error seen in practice and measure this bias by purposefully making flawed models. We discuss other ways to introduce bias and the statistical assumptions lying behind the data and model themselves. Finally we discuss the complications in drawing inference from the smaller sample sizes typically seen in fMRI studies, the effects of small or unbalanced samples on the Type 1 and Type 2 error rates, and how publication bias can give a false confidence of the power of such methods. Collectively this work identifies challenges specific to fMRI classification and methods affecting the stability of models. Copyright © 2010 Elsevier Inc. All rights reserved.

Comprehensive Review on Magnetic Resonance Imaging in Alzheimer's Disease.

PubMed

Dona, Olga; Thompson, Jeff; Druchok, Cheryl

2016-01-01

Alzheimer's disease (AD) is the most common cause of dementia in the elderly. However, definitive diagnosis of AD is only achievable postmortem and currently relies on clinical neurological evaluation. Magnetic resonance imaging (MRI) can evaluate brain changes typical of AD, including brain atrophy, presence of amyloid β (Aβ) plaques, and functional and biochemical abnormalities. Structural MRI (sMRI) has historically been used to assess the inherent brain atrophy present in AD. However, new techniques have recently emerged that have refined sMRI into a more precise tool to quantify the thickness and volume of AD-sensitive cerebral structures. Aβ plaques, a defining pathology of AD, are widely believed to contribute to the progressive cognitive decline in AD, but accurate assessment is only possible on autopsy. In vivo MRI of plaques, although currently limited to mouse models of AD, is a very promising technique. Measuring changes in activation and connectivity in AD-specific regions of the brain can be performed with functional MRI (fMRI). To help distinguish AD from diseases with similar symptoms, magnetic resonance spectroscopy (MRS) can be used to look for differing metabolite concentrations in vivo. Together, these MR techniques, evaluating various brain changes typical of AD, may help to provide a more definitive diagnosis and ease the assessment of the disease over time, noninvasively.

Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group.

PubMed

Lambert, Robert G W; Bakker, Pauline A C; van der Heijde, Désirée; Weber, Ulrich; Rudwaleit, Martin; Hermann, K G; Sieper, Joachim; Baraliakos, Xenofon; Bennett, Alex; Braun, Jürgen; Burgos-Vargas, Rubén; Dougados, Maxime; Pedersen, Susanne Juhl; Jurik, Anne Grethe; Maksymowych, Walter P; Marzo-Ortega, Helena; Østergaard, Mikkel; Poddubnyy, Denis; Reijnierse, Monique; van den Bosch, Filip; van der Horst-Bruinsma, Irene; Landewé, Robert

2016-11-01

To review and update the existing definition of a positive MRI for classification of axial spondyloarthritis (SpA). The Assessment in SpondyloArthritis International Society (ASAS) MRI working group conducted a consensus exercise to review the definition of a positive MRI for inclusion in the ASAS classification criteria of axial SpA. Existing definitions and new data relevant to the MRI diagnosis and classification of sacroiliitis and spondylitis in axial SpA, published since the ASAS definition first appeared in print in 2009, were reviewed and discussed. The precise wording of the existing definition was examined in detail and the data and a draft proposal were presented to and voted on by the ASAS membership. The clear presence of bone marrow oedema on MRI in subchondral bone is still considered to be the defining observation that determines the presence of active sacroiliitis. Structural damage lesions seen on MRI may contribute to a decision by the observer that inflammatory lesions are genuinely due to SpA but are not required to meet the definition. The existing definition was clarified adding guidelines and images to assist in the application of the definition. The definition of a positive MRI for classification of axial SpA should continue to primarily depend on the imaging features of 'active sacroiliitis' until more data are available regarding MRI features of structural damage in the sacroiliac joint and MRI features in the spine and their utility when used for classification purposes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Imaging discrepancies between magnetic resonance imaging and brain perfusion single-photon emission computed tomography in the diagnosis of Alzheimer's disease, and verification with amyloid positron emission tomography.

PubMed

Yokoyama, Shunichi; Kajiya, Yoriko; Yoshinaga, Takuma; Tani, Atsushi; Hirano, Hirofumi

2014-06-01

In the diagnosis of Alzheimer's disease (AD), discrepancies are often observed between magnetic resonance imaging (MRI) and brain perfusion single-photon emission computed tomography (SPECT) findings. MRI, brain perfusion SPECT, and amyloid positron emission tomography (PET) findings were compared in patients with mild cognitive impairment or early AD to clarify the discrepancies between imaging modalities. Several imaging markers were investigated, including the cortical average standardized uptake value ratio on amyloid PET, the Z-score of a voxel-based specific regional analysis system for AD on MRI, periventricular hyperintensity grade, deep white matter hyperintense signal grade, number of microbleeds, and three indicators of the easy Z-score imaging system for a specific SPECT volume-of-interest analysis. Based on the results of the regional analysis and the three indicators, we classified patients into four groups and then compared the results of amyloid PET, periventricular hyperintensity grade, deep white matter hyperintense signal grade, and the numbers of microbleeds among the groups. The amyloid deposition was the highest in the group that presented typical AD findings on both the regional analysis and the three indicators. The two groups that showed an imaging discrepancy between the regional analysis and the three indicators demonstrated intermediate amyloid deposition findings compared with the typical and atypical groups. The patients who showed hippocampal atrophy on the regional analysis and atypical AD findings using the three indicators were approximately 60% amyloid-negative. The mean periventricular hyperintensity grade was highest in the typical group. Patients showing discrepancies between MRI and SPECT demonstrated intermediate amyloid deposition findings compared with patients who showed typical or atypical findings. Strong white matter signal abnormalities on MRI in patients who presented typical AD findings provided further evidence for the involvement of vascular factors in AD. © 2014 The Authors. Psychogeriatrics © 2014 Japanese Psychogeriatric Society.

MRI and 18F-fluorodeoxyglucose positron emission tomography in hemimegalencephaly.

PubMed

Hoffmann, K T; Amthauer, H; Liebig, T; Hosten, N; Etou, A; Lehmann, T N; Farahati, J; Felix, R

2000-10-01

We report hemimegalencephaly in a 44-year-old woman with mental retardation, epilepsy and a mild hemiparesis. In addition to typical findings on MRI, 2-deoxy-2[18F]fluorodeoxyglucose positron-emission tomography (PET) demonstrated glucose hypometabolism of the affected hemisphere. The results of PET have been coregistered with morphological information from the MRI studies by image fusion.

Language Lateralization in Children Aged 10 to 11 Years: A Combined fMRI and Dichotic Listening Study

PubMed Central

Norrelgen, Fritjof; Lilja, Anders; Ingvar, Martin; Gisselgård, Jens; Fransson, Peter

2012-01-01

Objective The aims of this study were to develop and assess a method to map language networks in children with two auditory fMRI protocols in combination with a dichotic listening task (DL). The method is intended for pediatric patients prior to epilepsy surgery. To evaluate the potential clinical usefulness of the method we first wanted to assess data from a group of healthy children. Methods In a first step language test materials were developed, intended for subsequent implementation in fMRI protocols. An evaluation of this material was done in 30 children with typical development, 10 from the 1st, 4th and the 7th grade, respectively. The language test material was then adapted and implemented in two fMRI protocols intended to target frontal and posterior language networks. In a second step language lateralization was assessed in 17 typical 10–11 year olds with fMRI and DL. To reach a conclusion about language lateralization, firstly, quantitative analyses of the index data from the two fMRI tasks and the index data from the DL task were done separately. In a second step a set of criteria were applied to these results to reach a conclusion about language lateralization. The steps of these analyses are described in detail. Results The behavioral assessment of the language test material showed that it was well suited for typical children. The results of the language lateralization assessments, based on fMRI data and DL data, showed that for 15 of the 17 subjects (88%) a conclusion could be reached about hemispheric language dominance. In 2 cases (12%) DL provided critical data. Conclusions The employment of DL combined with language mapping using fMRI for assessing hemispheric language dominance is novel and it was deemed valuable since it provided additional information compared to the results gained from each method individually. PMID:23284796

The value of specific MRI features in the evaluation of suspected placental invasion.

PubMed

Lax, Allison; Prince, Martin R; Mennitt, Kevin W; Schwebach, J Reid; Budorick, Nancy E

2007-01-01

The objective of this study was to determine imaging features that may help predict the presence of placenta accreta, placenta increta or placenta percreta on prenatal MRI scanning. A retrospective review of the prenatal MR scans of 10 patients with a diagnosis of placenta accreta, placenta increta or placenta percreta made by pathologic and clinical reports and of 10 patients without placental invasion was performed. Two expert MRI readers were blinded to the patients' true diagnosis and were asked to score a total of 17 MRI features of the placenta and adjacent structures. The interrater reliability was assessed using kappa statistics. The features with a moderate kappa statistic or better (kappa > .40) were then compared with the true diagnosis for each observer. Seven of the scored features had an interobserver reliability of kappa > .40: placenta previa (kappa = .83); abnormal uterine bulging (kappa = .48); intraplacental hemorrhage (kappa = .51); heterogeneity of signal intensity on T2-weighted (T2W) imaging (kappa = .61); the presence of dark intraplacental bands on T2W imaging (kappa = .53); increased placental thickness (kappa = .69); and visualization of the myometrium beneath the placenta on T2W imaging (kappa = .44). Using Fisher's two-sided exact test, there was a statistically significant difference between the proportion of patients with placental invasion and those without placental invasion for three of the features: abnormal uterine bulging (Rater 1, P = .005; Rater 2, P = .011); heterogeneity of T2W imaging signal intensity (Rater 1, P = .006; Rater 2, P = .010); and presence of dark intraplacental bands on T2W imaging (Rater 1, P = .003; Rater 2, P = .033). MRI can be a useful adjunct to ultrasound in diagnosing placenta accreta prenatally. Three features that are seen on MRI in patients with placental invasion appear to be useful for diagnosis: uterine bulging; heterogeneous signal intensity within the placenta; and the presence of dark intraplacental bands on T2W imaging.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

PubMed

Dimitriadis, S I; Liparas, Dimitris; Tsolaki, Magda N

2018-05-15

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI. Based on preprocessed MRI images from the organizers of a neuroimaging challenge, 3 we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme. In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition. The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature. Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD. Copyright © 2017 Elsevier B.V. All rights reserved.

CT AND MRI FEATURES OF CAROTID BODY PARAGANGLIOMAS IN 16 DOGS.

PubMed

Mai, Wilfried; Seiler, Gabriela S; Lindl-Bylicki, Britany J; Zwingenberger, Allison L

2015-01-01

Carotid body tumors (paragangliomas) arise from chemoreceptors located at the carotid bifurcation. In imaging studies, this neoplasm may be confused with other neck neoplasms such as thyroid carcinoma. The purpose of this retrospective, cross-sectional study was to describe computed tomographic (CT) and magnetic resonance imaging (MRI) characteristics of confirmed carotid body tumors in a multi-institutional sample of dogs. A total of 16 dogs met inclusion criteria (14 examined using CT and two with MRI). The most common reason for imaging was a palpable cervical mass or respiratory signs (i.e., dyspnea or increased respiratory noises). The most commonly affected breed was Boston terrier (n = 5). Dogs were predominantly male castrated (n = 10) and the median age was 9 years [range 3-14.5]. Most tumors appeared as a large mass centered at the carotid bifurcation, with poor margination in six dogs and discrete margins in ten dogs. Masses were iso- to hypoattenuating to adjacent muscles in CT images and hyperintense to muscles in T1- and T2-weighted MRI. For both CT and MRI, masses typically showed strong and heterogeneous contrast enhancement. There was invasion into the adjacent structures in 9/16 dogs. In six of these nine dogs, the basilar portion of the skull was affected. The external carotid artery was entrapped in seven dogs. There was invasion into the internal jugular vein in three dogs, and into the external jugular, maxillary, and linguo-facial veins in one dog. Imaging characteristics helped explain some clinical presentations such as breathing difficulties, Horner's syndrome, head tilt, or facial nerve paralysis. © 2015 American College of Veterinary Radiology.

Magnetic Resonance Imaging of a Case of Central Neurocytoma.

PubMed

Dedushi, Kreshnike; Kabashi, Serbeze; Ugurel, Mehmet Sahin; Ramadani, Naser; Mucaj, Sefedin; Zeqiraj, Kamber

2016-12-01

The purpose of this study is to investigate the MRI features of central neurocytoma. A 45 year old man with 3 months of worsening daily headaches. These headaches were diffuse, lasted for several hours, and mostly occurred in the morning. She was initially diagnosed and treated for migraines but later he had epileptic attack and diplopia and neurolog recomaded MRI. precontrast MRI; TSE/T2Wsequence in axial/coronal planes; 3D-Hi-resolution T1W sagittal; FLAIR/T2W axial; FLAIR/T2W and Flash/T2W oblique coronal plane (perpendicular to temporal lobes) GRE/T2W axial plane for detection of heme products. Post-contrast TSE/T1W sequence in axial, coronal and sagittal planes. Diffusion weighted and ADC mapping MRI images for EPI sequence in axial plane. A 23x12mm heterogeneous mass within aqueductus cerebri, with calcified and hemorrhagic foci and extending downwards till fourth ventricle. It's originating from the right paramedian posterior aqueductal wall (tectum), and also extending to and involving the tegmentum of mesencephalon at its right paramedian aspect. CSF flow obstruction secondary to described aqueductal mass, with resultant triventricular hydrocephalus). Marked transependymal CSF leak can be noted at periventricular white matter, secondary to severe hydrocephalus. After IV injection of contrast media, this mass shows mild-to-moderate heterogenous speckled enhancement. MRI is helpful in defining tumor extension, which is important in preoperative planning. Although IN is a relatively rare lesion, it should be considered in the differential diagnosis of intraventricular lesions in the presence of such typical MR findings. However, a definitive diagnosis requires immunochemical study and electron microscopy.

Magnetic Resonance Imaging of a Case of Central Neurocytoma

PubMed Central

Dedushi, Kreshnike; Kabashi, Serbeze; Ugurel, Mehmet Sahin; Ramadani, Naser; Mucaj, Sefedin; Zeqiraj, Kamber

2016-01-01

Background: The purpose of this study is to investigate the MRI features of central neurocytoma. Case report: A 45 year old man with 3 months of worsening daily headaches. These headaches were diffuse, lasted for several hours, and mostly occurred in the morning. She was initially diagnosed and treated for migraines but later he had epileptic attack and diplopia and neurolog recomaded MRI. Methods: precontrast MRI; TSE/T2Wsequence in axial/coronal planes; 3D–Hi-resolution T1W sagittal; FLAIR/T2W axial; FLAIR/T2W and Flash/T2W oblique coronal plane (perpendicular to temporal lobes) GRE/T2W axial plane for detection of heme products. Post-contrast TSE/T1W sequence in axial, coronal and sagittal planes. Diffusion weighted and ADC mapping MRI images for EPI sequence in axial plane. Results: A 23x12mm heterogeneous mass within aqueductus cerebri, with calcified and hemorrhagic foci and extending downwards till fourth ventricle. It’s originating from the right paramedian posterior aqueductal wall (tectum), and also extending to and involving the tegmentum of mesencephalon at its right paramedian aspect. CSF flow obstruction secondary to described aqueductal mass, with resultant triventricular hydrocephalus). Marked transependymal CSF leak can be noted at periventricular white matter, secondary to severe hydrocephalus. After IV injection of contrast media, this mass shows mild-to-moderate heterogenous speckled enhancement. Conclusion: MRI is helpful in defining tumor extension, which is important in preoperative planning. Although IN is a relatively rare lesion, it should be considered in the differential diagnosis of intraventricular lesions in the presence of such typical MR findings. However, a definitive diagnosis requires immunochemical study and electron microscopy. PMID:28077908

Imaging features of colovesical fistulae on MRI.

PubMed

Tang, Y Z; Booth, T C; Swallow, D; Shahabuddin, K; Thomas, M; Hanbury, D; Chang, S; King, C

2012-10-01

MRI is routinely used in the investigation of colovesical fistulae at our institute. Several papers have alluded to its usefulness in achieving the diagnosis; however, there is a paucity of literature on its imaging findings. Our objective was to quantify the MRI characteristics of these fistulae. We selected all cases over a 4-year period with a final clinical diagnosis of colovesical fistula which had been investigated with MRI. The MRI scans were reviewed in a consensus fashion by two consultant uroradiologists. Their MRI features were quantified. There were 40 cases of colovesical fistulae. On MRI, the fistula morphology consistently fell into three patterns. The most common pattern (71%) demonstrated an intervening abscess between the bowel wall and bladder wall. The second pattern (15%) had a visible track between the affected bowel and bladder. The third pattern (13%) was a complete loss of fat plane between the affected bladder and bowel wall. MRI correctly determined the underlying aetiology in 63% of cases. MRI is a useful imaging modality in the diagnosis of colovesical fistulae. The fistulae appear to have three characteristic morphological patterns that may aid future diagnoses of colovesical fistulae. To the authors' knowledge, this is the first publication of the MRI findings in colovesical fistulae.

Aphasic variant of Alzheimer disease: Clinical, anatomic, and genetic features.

PubMed

Rogalski, Emily; Sridhar, Jaiashre; Rader, Benjamin; Martersteck, Adam; Chen, Kewei; Cobia, Derin; Thompson, Cynthia K; Weintraub, Sandra; Bigio, Eileen H; Mesulam, M-Marsel

2016-09-27

To identify features of primary progressive aphasia (PPA) associated with Alzheimer disease (AD) neuropathology. A related objective was to determine whether logopenic PPA is a clinical marker for AD. A total of 139 prospectively enrolled participants with a root diagnosis of PPA constituted the reference set. Those with autopsy or biomarker evidence of AD, and who had been evaluated at mild disease stages (Aphasia Quotient ≥85), were included (n = 19). All had quantitative language testing and APOE genotyping. Fifteen had MRI morphometry. Impaired word-finding was the universal presenting complaint in the aphasic AD group. PPA clinical subtype was logopenic (n = 13) and agrammatic (n = 6). Fluency, repetition, naming, and grammaticality ranged from preserved to severely impaired. All had relative preservation of word comprehension. Eight of the 15 aphasic participants with AD showed no appreciable cortical atrophy at the individual level on MRI. As a group, atrophy was asymmetrically concentrated in the left perisylvian cortex. APOE ε4 frequency was not elevated. There is a close, but not obligatory, association between logopenic PPA and AD. No language measure, with the possible exception of word comprehension, can confirm or exclude AD in PPA. Biomarkers are therefore essential for diagnosis. Asymmetry of cortical atrophy and normal APOE ε4 prevalence constitute deviations from typical AD. These and additional neuropathologic features suggest that AD has biological subtypes, one of which causes PPA. Better appreciation of this fact should promote the inclusion of individuals with PPA and positive AD biomarkers into relevant clinical trials. © 2016 American Academy of Neurology.

Clinical spectrum of 4H leukodystrophy caused by POLR3A and POLR3B mutations

PubMed Central

Vanderver, Adeline; van Spaendonk, Rosalina M.L.; Schiffmann, Raphael; Brais, Bernard; Bugiani, Marianna; Sistermans, Erik; Catsman-Berrevoets, Coriene; Kros, Johan M.; Pinto, Pedro Soares; Pohl, Daniela; Tirupathi, Sandya; Strømme, Petter; de Grauw, Ton; Fribourg, Sébastien; Demos, Michelle; Pizzino, Amy; Naidu, Sakkubai; Guerrero, Kether; van der Knaap, Marjo S.; Bernard, Geneviève

2014-01-01

Objective: To study the clinical and radiologic spectrum and genotype–phenotype correlation of 4H (hypomyelination, hypodontia, hypogonadotropic hypogonadism) leukodystrophy caused by mutations in POLR3A or POLR3B. Methods: We performed a multinational cross-sectional observational study of the clinical, radiologic, and molecular characteristics of 105 mutation-proven cases. Results: The majority of patients presented before 6 years with gross motor delay or regression. Ten percent had an onset beyond 10 years. The disease course was milder in patients with POLR3B than in patients with POLR3A mutations. Other than the typical neurologic, dental, and endocrine features, myopia was seen in almost all and short stature in 50%. Dental and hormonal findings were not invariably present. Mutations in POLR3A and POLR3B were distributed throughout the genes. Except for French Canadian patients, patients from European backgrounds were more likely to have POLR3B mutations than other populations. Most patients carried the common c.1568T>A POLR3B mutation on one allele, homozygosity for which causes a mild phenotype. Systematic MRI review revealed that the combination of hypomyelination with relative T2 hypointensity of the ventrolateral thalamus, optic radiation, globus pallidus, and dentate nucleus, cerebellar atrophy, and thinning of the corpus callosum suggests the diagnosis. Conclusions: 4H is a well-recognizable clinical entity if all features are present. Mutations in POLR3A are associated with a more severe clinical course. MRI characteristics are helpful in addressing the diagnosis, especially if patients lack the cardinal non-neurologic features. PMID:25339210

Calvarial Plasmacytoma Mimicking Meningioma as the Initial Presentation of Multiple Myeloma

PubMed Central

Pisapia, David; Ramakrishna, Rohan

2017-01-01

Plasmacytoma of the calvarium is a well-described feature of multiple myeloma and in some cases has been reported as a solitary lesion. However, when associated with multiple myeloma these are typically identified after the initial diagnosis is made. This case is unusual in that the diagnosis of plasmacytoma was first suspected in a patient thought to have a meningioma on the day of surgery, when a magnetic resonance imaging (MRI) demonstrated spontaneous involution of the mass. Recognition of evolving changes in a calvarial or dural-based lesion should prompt the practitioner to consider alternative diagnoses other than meningioma prior to proceeding with surgical resection. PMID:28465873

Linear Discriminant Analysis Achieves High Classification Accuracy for the BOLD fMRI Response to Naturalistic Movie Stimuli

PubMed Central

Mandelkow, Hendrik; de Zwart, Jacco A.; Duyn, Jeff H.

2016-01-01

Naturalistic stimuli like movies evoke complex perceptual processes, which are of great interest in the study of human cognition by functional MRI (fMRI). However, conventional fMRI analysis based on statistical parametric mapping (SPM) and the general linear model (GLM) is hampered by a lack of accurate parametric models of the BOLD response to complex stimuli. In this situation, statistical machine-learning methods, a.k.a. multivariate pattern analysis (MVPA), have received growing attention for their ability to generate stimulus response models in a data-driven fashion. However, machine-learning methods typically require large amounts of training data as well as computational resources. In the past, this has largely limited their application to fMRI experiments involving small sets of stimulus categories and small regions of interest in the brain. By contrast, the present study compares several classification algorithms known as Nearest Neighbor (NN), Gaussian Naïve Bayes (GNB), and (regularized) Linear Discriminant Analysis (LDA) in terms of their classification accuracy in discriminating the global fMRI response patterns evoked by a large number of naturalistic visual stimuli presented as a movie. Results show that LDA regularized by principal component analysis (PCA) achieved high classification accuracies, above 90% on average for single fMRI volumes acquired 2 s apart during a 300 s movie (chance level 0.7% = 2 s/300 s). The largest source of classification errors were autocorrelations in the BOLD signal compounded by the similarity of consecutive stimuli. All classifiers performed best when given input features from a large region of interest comprising around 25% of the voxels that responded significantly to the visual stimulus. Consistent with this, the most informative principal components represented widespread distributions of co-activated brain regions that were similar between subjects and may represent functional networks. In light of these results, the combination of naturalistic movie stimuli and classification analysis in fMRI experiments may prove to be a sensitive tool for the assessment of changes in natural cognitive processes under experimental manipulation. PMID:27065832

Prenatal diagnosis of Joubert syndrome

PubMed Central

Zhu, Lingling; Xie, Limei

2017-01-01

Abstract Introduction: Joubert syndrome (JS) is a rare autosomal recessive inherited disease belonging to ciliopathy with the causative mutation of genes. Except for X-linked inheritance, the high recurrence rate of a family is about 25%. After birth, it may cause a series of neurological symptoms, even with retina, kidney, liver, and other organ abnormalities, which is defined as Joubert syndrome and related disorders (JSRD). Molecular genetics research contributes to disease prediction and genetic counseling. Prenatal diagnosis is rare. Magnetic resonance imaging (MRI) is usually the first-choice diagnostic modality with typical brain images characterized by the molar tooth sign. We describe a case of JS prenatally and Dandy-Walker malformation for the differential diagnosis based on ultrasonograms. We also review the etiology, imaging features, clinical symptoms, and diagnosis of JSRD. Case presentation: A 22-year-old woman was pregnant at 27 1/7 weeks’ gestation with fetal cerebellar vermis hypoplasia. Fetal ultrasonography and MRI confirmed a diagnosis of JS at our center. The couple finally opted to terminate the fetus, which had a normal appearance and growth parameters. The couple also had an AHI1 gene mutation on chromosome 6. Conclusions: Currently, a diagnosis of JS is commonly made after birth. Fewer cases of prenatal diagnosis by ultrasonography have been made, and they are more liable to be misdirected because of some nonspecial features that also manifest in Dandy-Walker malformation, cranio-cerebello-cardiac syndrome, and so on. PMID:29390414

MRI abnormalities of peripheral nerve and muscle are common in amyotrophic lateral sclerosis and share features with multifocal motor neuropathy

PubMed Central

Staff, Nathan P.; Amrami, Kimberly K.; Howe, Benjamin M.

2015-01-01

Introduction MRI of peripheral nerve and muscle in patients with ALS may be performed to investigate alternative diagnoses including multifocal motor neuropathy (MMN). MRI findings of peripheral nerve and muscle are not well described in these conditions, making interpretation of results difficult. Methods We examined systematically the peripheral nerve and muscle MRI findings in patients with ALS (n=60) and MMN (n=8). Results In patients with ALS and MMN, abnormal MRIs were common (85% and 75%, respectively) but did not correlate with disease severity. Peripheral nerve MRI abnormalities were similar in frequency (ALS: 58% vs. MMN: 63%) with most changes being of mild-to-moderate severity. Muscle MRI changes were more common in ALS (57% vs. 33%), and no muscle atrophy was seen in patients with MMN. Discussion MRI abnormalities of peripheral nerve and muscle in ALS and MMN are common and share some features. PMID:25736373

Single-trial EEG-informed fMRI analysis of emotional decision problems in hot executive function.

PubMed

Guo, Qian; Zhou, Tiantong; Li, Wenjie; Dong, Li; Wang, Suhong; Zou, Ling

2017-07-01

Executive function refers to conscious control in psychological process which relates to thinking and action. Emotional decision is a part of hot executive function and contains emotion and logic elements. As a kind of important social adaptation ability, more and more attention has been paid in recent years. Gambling task can be well performed in the study of emotional decision. As fMRI researches focused on gambling task show not completely consistent brain activation regions, this study adopted EEG-fMRI fusion technology to reveal brain neural activity related with feedback stimuli. In this study, an EEG-informed fMRI analysis was applied to process simultaneous EEG-fMRI data. First, relative power-spectrum analysis and K-means clustering method were performed separately to extract EEG-fMRI features. Then, Generalized linear models were structured using fMRI data and using different EEG features as regressors. The results showed that in the win versus loss stimuli, the activated regions almost covered the caudate, the ventral striatum (VS), the orbital frontal cortex (OFC), and the cingulate. Wide activation areas associated with reward and punishment were revealed by the EEG-fMRI integration analysis than the conventional fMRI results, such as the posterior cingulate and the OFC. The VS and the medial prefrontal cortex (mPFC) were found when EEG power features were performed as regressors of GLM compared with results entering the amplitudes of feedback-related negativity (FRN) as regressors. Furthermore, the brain region activation intensity was the strongest when theta-band power was used as a regressor compared with the other two fusion results. The EEG-based fMRI analysis can more accurately depict the whole-brain activation map and analyze emotional decision problems.

Magnetic resonance characteristics and susceptibility weighted imaging of the brain in gadolinium encephalopathy.

PubMed

Samardzic, Dejan; Thamburaj, Krishnamoorthy

2015-01-01

To report the brain imaging features on magnetic resonance imaging (MRI) in inadvertent intrathecal gadolinium administration. A 67-year-old female with gadolinium encephalopathy from inadvertent high dose intrathecal gadolinium administration during an epidural steroid injection was studied with multisequence 3T MRI. T1-weighted imaging shows pseudo-T2 appearance with diffusion of gadolinium into the brain parenchyma, olivary bodies, and membranous labyrinth. Nulling of cerebrospinal fluid (CSF) signal is absent on fluid attenuation recovery (FLAIR). Susceptibility-weighted imaging (SWI) demonstrates features similar to subarachnoid hemorrhage. CT may demonstrate a pseudo-cerebral edema pattern given the high attenuation characteristics of gadolinium. Intrathecal gadolinium demonstrates characteristic imaging features on MRI of the brain and may mimic subarachnoid hemorrhage on susceptibility-weighted imaging. Identifying high dose gadolinium within the CSF spaces on MRI is essential to avoid diagnostic and therapeutic errors. Copyright © 2013 by the American Society of Neuroimaging.

The role of magnetic resonance imaging and ultrasound in patients with adnexal masses.

PubMed

Sohaib, S A; Mills, T D; Sahdev, A; Webb, J A W; Vantrappen, P O; Jacobs, I J; Reznek, R H

2005-03-01

To evaluate the accuracy of ultrasonography (US) and magnetic resonance imaging (MRI) in characterizing adnexal masses, and to determine which patients may benefit from MRI. We prospectively studied 72 women (mean age 53 years, range 19 to 86 years) with clinically suspected adnexal masses. A single experienced sonographer performed transabdominal and transvaginal greyscale spectral and colour Doppler examinations. MRI was carried out on a 1.5T system using T1, T2 and fat-suppressed T1-weighted sequences before and after intravenous injection of gadolinium. The adnexal masses were categorized as benign or malignant without knowledge of clinical details, according to the imaging features which were compared with the surgical and pathological findings. For characterizing lesions as malignant, the sensitivity, specificity and accuracy of MRI were 96.6%, 83.7% and 88.9%, respectively, and of US were 100%, 39.5% and 63.9%, respectively. MRI was more specific (p<0.05) than US. Both MRI and US correctly diagnosed 17 (24%) cases with benign and 28 (39%) cases with malignant masses. MRI correctly diagnosed 19 (26%) cases with benign lesion(s), which on US were thought to be malignant. The age, menopausal status and CA-125 levels in these women made benign disease likely, but US features were suggestive of malignancy (large masses and solid-cystic lesions with nodules). MRI is more specific and accurate than US and Doppler assessment for characterizing adnexal masses. Women who clinically have a relatively low risk of malignancy but who have complex sonographic features may benefit from MRI.

Development of a Support Vector Machine - Based Image Analysis System for Focal Liver Lesions Classification in Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Gatos, I.; Tsantis, S.; Karamesini, M.; Skouroliakou, A.; Kagadis, G.

2015-09-01

Purpose: The design and implementation of a computer-based image analysis system employing the support vector machine (SVM) classifier system for the classification of Focal Liver Lesions (FLLs) on routine non-enhanced, T2-weighted Magnetic Resonance (MR) images. Materials and Methods: The study comprised 92 patients; each one of them has undergone MRI performed on a Magnetom Concerto (Siemens). Typical signs on dynamic contrast-enhanced MRI and biopsies were employed towards a three class categorization of the 92 cases: 40-benign FLLs, 25-Hepatocellular Carcinomas (HCC) within Cirrhotic liver parenchyma and 27-liver metastases from Non-Cirrhotic liver. Prior to FLLs classification an automated lesion segmentation algorithm based on Marcov Random Fields was employed in order to acquire each FLL Region of Interest. 42 texture features derived from the gray-level histogram, co-occurrence and run-length matrices and 12 morphological features were obtained from each lesion. Stepwise multi-linear regression analysis was utilized to avoid feature redundancy leading to a feature subset that fed the multiclass SVM classifier designed for lesion classification. SVM System evaluation was performed by means of leave-one-out method and ROC analysis. Results: Maximum accuracy for all three classes (90.0%) was obtained by means of the Radial Basis Kernel Function and three textural features (Inverse- Different-Moment, Sum-Variance and Long-Run-Emphasis) that describe lesion's contrast, variability and shape complexity. Sensitivity values for the three classes were 92.5%, 81.5% and 96.2% respectively, whereas specificity values were 94.2%, 95.3% and 95.5%. The AUC value achieved for the selected subset was 0.89 with 0.81 - 0.94 confidence interval. Conclusion: The proposed SVM system exhibit promising results that could be utilized as a second opinion tool to the radiologist in order to decrease the time/cost of diagnosis and the need for patients to undergo invasive examination.

Accuracy of ultrasonography and magnetic resonance imaging in the diagnosis of placenta accreta.

PubMed

Riteau, Anne-Sophie; Tassin, Mikael; Chambon, Guillemette; Le Vaillant, Claudine; de Laveaucoupet, Jocelyne; Quéré, Marie-Pierre; Joubert, Madeleine; Prevot, Sophie; Philippe, Henri-Jean; Benachi, Alexandra

2014-01-01

To evaluate the accuracy of ultrasonography and magnetic resonance imaging (MRI) in the diagnosis of placenta accreta and to define the most relevant specific ultrasound and MRI features that may predict placental invasion. This study was approved by the institutional review board of the French College of Obstetricians and Gynecologists. We retrospectively reviewed the medical records of all patients referred for suspected placenta accreta to two university hospitals from 01/2001 to 05/2012. Our study population included 42 pregnant women who had been investigated by both ultrasonography and MRI. Ultrasound images and MRI were blindly reassessed for each case by 2 raters in order to score features that predict abnormal placental invasion. Sensitivity in the diagnosis of placenta accreta was 100% with ultrasound and 76.9% for MRI (P = 0.03). Specificity was 37.5% with ultrasonography and 50% for MRI (P = 0.6). The features of greatest sensitivity on ultrasonography were intraplacental lacunae and loss of the normal retroplacental clear space. Increased vascularization in the uterine serosa-bladder wall interface and vascularization perpendicular to the uterine wall had the best positive predictive value (92%). At MRI, uterine bulging had the best positive predictive value (85%) and its combination with the presence of dark intraplacental bands on T2-weighted images improved the predictive value to 90%. Ultrasound imaging is the mainstay of screening for placenta accreta. MRI appears to be complementary to ultrasonography, especially when there are few ultrasound signs.

Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model.

PubMed

Rusu, Mirabela; Golden, Thea; Wang, Haibo; Gow, Andrew; Madabhushi, Anant

2015-08-01

Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on in vivo imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation in vivo. Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering ex vivo histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors' framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with chronic pulmonary inflammation. The authors' image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology-MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors' evaluation considered three mice, two with an inflammation phenotype and one control. The authors' iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. The authors' 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for inflammation on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.

Magnetic Resonance Imaging Features as Surrogate Markers of X-Linked Hypophosphatemic Rickets Activity.

PubMed

Lempicki, Marta; Rothenbuhler, Anya; Merzoug, Valérie; Franchi-Abella, Stéphanie; Chaussain, Catherine; Adamsbaum, Catherine; Linglart, Agnès

2017-01-01

X-linked hypophosphatemic rickets (XLH) is the most common form of inheritable rickets. Rickets treatment is monitored by assessing alkaline phosphatase (ALP) levels, clinical features, and radiographs. Our objectives were to describe the magnetic resonance imaging (MRI) features of XLH and to assess correlations with disease activity. Twenty-seven XLH patients (median age 9.2 years) were included in this prospective single-center observational study. XLH activity was assessed using height, leg bowing, dental abscess history, and serum ALP levels. We looked for correlations between MRI features and markers of disease activity. On MRI, the median maximum width of the physis was 5.6 mm (range 4.8-7.8; normal <1.5), being >1.5 mm in all of the patients. The appearance of the zone of provisional calcification was abnormal on 21 MRI images (78%), Harris lines were present on 24 (89%), and bone marrow signal abnormalities were present on 16 (59%). ALP levels correlated with the maximum physeal widening and with the transverse extent of the widening. MRI of the knee provides precise rickets patterns that are correlated with ALP, an established biochemical marker of the disease, avoiding X-ray exposure and providing surrogate quantitative markers of disease activity. © 2017 S. Karger AG, Basel.

A SVM-based quantitative fMRI method for resting-state functional network detection.

PubMed

Song, Xiaomu; Chen, Nan-kuei

2014-09-01

Resting-state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting-state fMRI data analysis. Specifically, the resting-state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting-state analysis were extracted and examined using an SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting-state quantitative fMRI studies. Copyright © 2014 Elsevier Inc. All rights reserved.

Clinical and Neuroradiological Spectrum of Metronidazole Induced Encephalopathy: Our Experience and the Review of Literature

PubMed Central

Panwar, Ajay; Pandit, Alak; Das, Susanta Kumar; Joshi, Bhushan

2016-01-01

Metronidazole is an antimicrobial agent mainly used in the treatment of several protozoal and anaerobic infections, additionally, is often used in hepatic encephalopathy and Crohn disease. Apart from peripheral neuropathy, metronidazole can also cause symptoms of central nervous system dysfunction like ataxic gait, dysarthria, seizures, and encephalopathy which may result from both short term and chronic use of this drug and is collectively termed as “metronidazole induced encephalopathy”(MIE). Neuroimaging forms the backbone in clinching the diagnosis of this uncommon entity, especially in cases where there is high index of suspicion of intoxication. Although typical sites of involvement include cerebellum, brain stem and corpus callosum, however, lesions of other sites have also been reported. Once diagnosed, resolution of findings on Magnetic Resonance Imaging (MRI) of the Brain along with clinical improvement remains the mainstay of monitoring. Here we review the key clinical features and MRI findings of MIE as reported in medical literature. We also analyze implication of use of this drug in special situations like hepatic encephalopathy and brain abscess and discuss our experience regarding this entity. PMID:27504340

Ganglioglioma in the Third Ventricle: A Case Report and Literature Review

PubMed Central

Higa, Nayuta; Yonezawa, Hajime; Oyoshi, Tatsuki; Hiraki, Tsubasa; Hirano, Hirofumi; Arita, Kazunori

2016-01-01

Gangliogliomas typically arise in the cerebral hemispheres, but may occur rarely in the ventricles. Herein, we report a 38-year-old woman who was treated for hydrocephalus caused by a ganglioglioma of the third ventricle. Magnetic resonance imaging (MRI) revealed a heterogeneously enhanced tumor occupying the anterior part of the third ventricle. A left trans-lateral ventricular trans-foramen of Monroi approach was effective in achieving subtotal resection of the tumor, which had arisen from the medial part of left thalamus to the hypothalamus. Follow-up MRI showed no recurrence of the tumor 5-years after surgery. On pathological examination, the tumor was composed of a glial component that presented features mimicking pilocytic astrocytoma with proliferations of large gangliocytic cells that stained positive for neuronal markers. A review of six similar cases in the literature, including our own, revealed hydrocephalus to be the main symptom of gangliogliomas, with pituitary insufficiencies and visual disturbances having also been reported. In conclusion, we highlight the importance of gangliogliomas in the differential diagnosis of third ventricular tumors presenting with hydrocephalus. PMID:28664007

Brain Morphometry Using Anatomical Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Bansal, Ravi; Gerber, Andrew J.; Peterson, Bradley S.

2008-01-01

The efficacy of anatomical magnetic resonance imaging (MRI) in studying the morphological features of various regions of the brain is described, also providing the steps used in the processing and studying of the images. The ability to correlate these features with several clinical and psychological measures can help in using anatomical MRI to…

WE-B-BRD-00: MRI for Radiation Oncology

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

The use of MRI in radiation therapy is rapidly increasing. Applications vary from the MRI simulator, to the MRI fused with CT, and to the integrated MRI+RT system. Compared with the standard MRI QA, a broader scope of QA features has to be defined in order to maximize the benefits of using MRI in radiation therapy. These QA features include geometric fidelity, image registration, motion management, cross-system alignment, and hardware interference. Advanced MRI techniques require a specific type of QA, as they are being widely used in radiation therapy planning, dose calculations, post-implant dosimetry, and prognoses. A vigorous and adaptivemore » QA program is crucial to defining the responsibility of the entire radiation therapy group and detecting deviations from the performance of high-quality treatment. As a drastic departure from CT simulation, MRI simulation requires changes in the work flow of treatment planning and image guidance. MRI guided radiotherapy platforms are being developed and commercialized to take the advantage of the advance in knowledge, technology and clinical experience. This symposium will from an educational perspective discuss the scope and specific issues related to MRI guided radiotherapy. Learning Objectives: Understand the difference between a standard and a radiotherapy-specific MRI QA program. Understand the effects of MRI artifacts (geometric distortion and motion) on radiotherapy. Understand advanced MRI techniques (ultrashort echo, fast MRI including dynamic MRI and 4DMRI, diffusion, perfusion, and MRS) and related QA. Understand the methods to prepare MRI for treatment planning (electron density assignment, multimodality image registration, segmentation and motion management). Current status of MRI guided treatment platforms. Dr. Jihong Wang has a research grant with Elekta-MRL project. Dr. Ke Sheng receives research grants from Varian Medical systems.« less

On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

PubMed

Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

2018-06-01

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder.

PubMed

Rashid, Barnaly; Blanken, Laura M E; Muetzel, Ryan L; Miller, Robyn; Damaraju, Eswar; Arbabshirani, Mohammad R; Erhardt, Erik B; Verhulst, Frank C; van der Lugt, Aad; Jaddoe, Vincent W V; Tiemeier, Henning; White, Tonya; Calhoun, Vince

2018-03-30

Recent advances in neuroimaging techniques have provided significant insights into developmental trajectories of human brain function. Characterizations of typical neurodevelopment provide a framework for understanding altered neurodevelopment, including differences in brain function related to developmental disorders and psychopathology. Historically, most functional connectivity studies of typical and atypical development operate under the assumption that connectivity remains static over time. We hypothesized that relaxing stationarity assumptions would reveal novel features of both typical brain development related to children on the autism spectrum. We employed a "chronnectomic" (recurring, time-varying patterns of connectivity) approach to evaluate transient states of connectivity using resting-state functional MRI in a population-based sample of 774 6- to 10-year-old children. Dynamic connectivity was evaluated using a sliding-window approach, and revealed four transient states. Internetwork connectivity increased with age in modularized dynamic states, illustrating an important pattern of connectivity in the developing brain. Furthermore, we demonstrated that higher levels of autistic traits and ASD diagnosis were associated with longer dwell times in a globally disconnected state. These results provide a roadmap to the chronnectomic organization of the developing brain and suggest that characteristics of functional brain connectivity are related to children on the autism spectrum. © 2018 Wiley Periodicals, Inc.

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

fMRI and MEG in the study of typical and atypical cognitive development.

PubMed

Taylor, M J; Donner, E J; Pang, E W

2012-01-01

The tremendous changes in brain structure over childhood are critical to the development of cognitive functions. Neuroimaging provides a means of linking these brain-behaviour relations, as task protocols can be adapted for use with young children to assess the development of cognitive functions in both typical and atypical populations. This paper reviews some of our research using magnetoencephalography (MEG) and functional MRI (fMRI) in the study of cognitive development, with a focus on frontal lobe functions. Working memory for complex abstract patterns showed clear development in terms of the recruitment of frontal regions, seen with fMRI, with indications of strategy differences across the age range, from 6 to 35 years of age. Right hippocampal involvement was also evident in these n-back tasks, demonstrating its involvement in recognition in simple working memory protocols. Children born very preterm (7 to 9 years of age) showed reduced fMRI activation particularly in the precuneus and right hippocampal regions relative to control children. In a large normative n-back study (n=90) with upright and inverted faces, MEG data also showed right hippocampal activation that was present across the age range; frontal sources were evident only from 10 years of age. Other studies have investigated the development of set shifting, an executive function that is often deficit in atypical populations. fMRI showed recruitment of frontal areas, including the insula, that have significantly different patterns in children (7 to 14 years of age) with autism spectrum disorder compared to typically developing children, indicating that successful performance implicated differing strategies in these two groups of children. These types of studies will help our understanding of both normal brain-behaviour development and cognitive dysfunction in atypically developing populations. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Pitfalls in soft tissue sarcoma imaging: chronic expanding hematomas.

PubMed

Jahed, Kiarash; Khazai, Behnaz; Umpierrez, Monica; Subhawong, Ty K; Singer, Adam D

2018-01-01

Solid or nodular enhancement is typical of soft tissue sarcomas although high grade soft tissue sarcomas and those with internal hemorrhage often appear heterogeneous with areas of nonenhancement and solid or nodular enhancement. These MRI findings often prompt an orthopedic oncology referral, a biopsy or surgery. However, not all masses with these imaging findings are malignant. We report the multimodality imaging findings of two surgically proven chronic expanding hematomas (CEH) with imaging features that mimicked sarcomas. A third case of nonenhancing CEH of the lower extremity is also presented as a comparison. It is important that in the correct clinical scenario with typical imaging findings, the differential diagnosis of a chronic expanding hematoma be included in the workup of these patients. An image-guided biopsy of nodular tissue within such masses that proves to be negative for malignancy should not necessarily be considered discordant. A correct diagnosis may prevent a morbid unnecessary surgery and may indicate the need for a conservative noninvasive follow-up with imaging.

Stress fractures: pathophysiology, clinical presentation, imaging features, and treatment options.

PubMed

Matcuk, George R; Mahanty, Scott R; Skalski, Matthew R; Patel, Dakshesh B; White, Eric A; Gottsegen, Christopher J

2016-08-01

Stress fracture, in its most inclusive description, includes both fatigue and insufficiency fracture. Fatigue fractures, sometimes equated with the term "stress fractures," are most common in runners and other athletes and typically occur in the lower extremities. These fractures are the result of abnormal, cyclical loading on normal bone leading to local cortical resorption and fracture. Insufficiency fractures are common in elderly populations, secondary to osteoporosis, and are typically located in and around the pelvis. They are a result of normal or traumatic loading on abnormal bone. Subchondral insufficiency fractures of the hip or knee may cause acute pain that may present in the emergency setting. Medial tibial stress syndrome is a type of stress injury of the tibia related to activity and is a clinical syndrome encompassing a range of injuries from stress edema to frank-displaced fracture. Atypical subtrochanteric femoral fracture associated with long-term bisphosphonate therapy is also a recently discovered entity that needs early recognition to prevent progression to a complete fracture. Imaging recommendations for evaluation of stress fractures include initial plain radiographs followed, if necessary, by magnetic resonance imaging (MRI), which is preferred over computed tomography (CT) and bone scintigraphy. Radiographs are the first-line modality and may reveal linear sclerosis and periosteal reaction prior to the development of a frank fracture. MRI is highly sensitive with findings ranging from periosteal edema to bone marrow and intracortical signal abnormality. Additionally, a brief description of relevant clinical management of stress fractures is included.

Feasibility of an intracranial EEG-fMRI protocol at 3T: risk assessment and image quality.

PubMed

Boucousis, Shannon M; Beers, Craig A; Cunningham, Cameron J B; Gaxiola-Valdez, Ismael; Pittman, Daniel J; Goodyear, Bradley G; Federico, Paolo

2012-11-15

Integrating intracranial EEG (iEEG) with functional MRI (iEEG-fMRI) may help elucidate mechanisms underlying the generation of seizures. However, the introduction of iEEG electrodes in the MR environment has inherent risk and data quality implications that require consideration prior to clinical use. Previous studies of subdural and depth electrodes have confirmed low risk under specific circumstances at 1.5T and 3T. However, no studies have assessed risk and image quality related to the feasibility of a full iEEG-fMRI protocol. To this end, commercially available platinum subdural grid/strip electrodes (4×5 grid or 1×8 strip) and 4 or 6-contact depth electrodes were secured to the surface of a custom-made phantom mimicking the conductivity of the human brain. Electrode displacement, temperature increase of electrodes and surrounding phantom material, and voltage fluctuations in electrode contacts were measured in a GE Discovery MR750 3T MR scanner during a variety of imaging sequences, typical of an iEEG-fMRI protocol. An electrode grid was also used to quantify the spatial extent of susceptibility artifact. The spatial extent of susceptibility artifact in the presence of an electrode was also assessed for typical imaging parameters that maximize BOLD sensitivity at 3T (TR=1500 ms; TE=30 ms; slice thickness=4mm; matrix=64×64; field-of-view=24 cm). Under standard conditions, all electrodes exhibited no measurable displacement and no clinically significant temperature increase (<1°C) during scans employed in a typical iEEG-fMRI experiment, including 60 min of continuous fMRI. However, high SAR sequences, such as fast spin-echo (FSE), produced significant heating in almost all scenarios (>2.0°C) that in some cases exceeded 10°C. Induced voltages in the frequency range that could elicit neuronal stimulation (<10 kHz) were well below the threshold of 100 mV. fMRI signal intensity was significantly reduced within 20mm of the electrodes for the imaging parameters used in this study. Thus, for the conditions tested, a full iEEG-fMRI protocol poses a low risk at 3T; however, fMRI sensitivity may be reduced immediately adjacent to the electrodes. In addition, high SAR sequences must be avoided. Copyright © 2012 Elsevier Inc. All rights reserved.

Unsupervised classification of cirrhotic livers using MRI data

NASA Astrophysics Data System (ADS)

Lee, Gobert; Kanematsu, Masayuki; Kato, Hiroki; Kondo, Hiroshi; Zhou, Xiangrong; Hara, Takeshi; Fujita, Hiroshi; Hoshi, Hiroaki

2008-03-01

Cirrhosis of the liver is a chronic disease. It is characterized by the presence of widespread nodules and fibrosis in the liver which results in characteristic texture patterns. Computerized analysis of hepatic texture patterns is usually based on regions-of-interest (ROIs). However, not all ROIs are typical representatives of the disease stage of the liver from which the ROIs originated. This leads to uncertainties in the ROI labels (diseased or non-diseased). On the other hand, supervised classifiers are commonly used in determining the assignment rule. This presents a problem as the training of a supervised classifier requires the correct labels of the ROIs. The main purpose of this paper is to investigate the use of an unsupervised classifier, the k-means clustering, in classifying ROI based data. In addition, a procedure for generating a receiver operating characteristic (ROC) curve depicting the classification performance of k-means clustering is also reported. Hepatic MRI images of 44 patients (16 cirrhotic; 28 non-cirrhotic) are used in this study. The MRI data are derived from gadolinium-enhanced equilibrium phase images. For each patient, 10 ROIs selected by an experienced radiologist and 7 texture features measured on each ROI are included in the MRI data. Results of the k-means classifier are depicted using an ROC curve. The area under the curve (AUC) has a value of 0.704. This is slightly lower than but comparable to that of LDA and ANN classifiers which have values 0.781 and 0.801, respectively. Methods in constructing ROC curve in relation to k-means clustering have not been previously reported in the literature.

Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

PubMed

Ortiz-Ramón, Rafael; Larroza, Andrés; Ruiz-España, Silvia; Arana, Estanislao; Moratal, David

2018-05-14

To examine the capability of MRI texture analysis to differentiate the primary site of origin of brain metastases following a radiomics approach. Sixty-seven untreated brain metastases (BM) were found in 3D T1-weighted MRI of 38 patients with cancer: 27 from lung cancer, 23 from melanoma and 17 from breast cancer. These lesions were segmented in 2D and 3D to compare the discriminative power of 2D and 3D texture features. The images were quantized using different number of gray-levels to test the influence of quantization. Forty-three rotation-invariant texture features were examined. Feature selection and random forest classification were implemented within a nested cross-validation structure. Classification was evaluated with the area under receiver operating characteristic curve (AUC) considering two strategies: multiclass and one-versus-one. In the multiclass approach, 3D texture features were more discriminative than 2D features. The best results were achieved for images quantized with 32 gray-levels (AUC = 0.873 ± 0.064) using the top four features provided by the feature selection method based on the p-value. In the one-versus-one approach, high accuracy was obtained when differentiating lung cancer BM from breast cancer BM (four features, AUC = 0.963 ± 0.054) and melanoma BM (eight features, AUC = 0.936 ± 0.070) using the optimal dataset (3D features, 32 gray-levels). Classification of breast cancer and melanoma BM was unsatisfactory (AUC = 0.607 ± 0.180). Volumetric MRI texture features can be useful to differentiate brain metastases from different primary cancers after quantizing the images with the proper number of gray-levels. • Texture analysis is a promising source of biomarkers for classifying brain neoplasms. • MRI texture features of brain metastases could help identifying the primary cancer. • Volumetric texture features are more discriminative than traditional 2D texture features.

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

E-wave generated intraventricular diastolic vortex to L-wave relation: model-based prediction with in vivo validation.

PubMed

Ghosh, Erina; Caruthers, Shelton D; Kovács, Sándor J

2014-08-01

The Doppler echocardiographic E-wave is generated when the left ventricle's suction pump attribute initiates transmitral flow. In some subjects E-waves are accompanied by L-waves, the occurrence of which has been correlated with diastolic dysfunction. The mechanisms for L-wave generation have not been fully elucidated. We propose that the recirculating diastolic intraventricular vortex ring generates L-waves and based on this mechanism, we predict the presence of L-waves in the right ventricle (RV). We imaged intraventricular flow using Doppler echocardiography and phase-contrast magnetic resonance imaging (PC-MRI) in 10 healthy volunteers. L-waves were recorded in all subjects, with highest velocities measured typically 2 cm below the annulus. Fifty-five percent of cardiac cycles (189 of 345) had L-waves. Color M-mode images eliminated mid-diastolic transmitral flow as the cause of the observed L-waves. Three-dimensional intraventricular flow patterns were imaged via PC-MRI and independently validated our hypothesis. Additionally as predicted, L-waves were observed in the RV, by both echocardiography and PC-MRI. The re-entry of the E-wave-generated vortex ring flow through a suitably located echo sample volume can be imaged as the L-wave. These waves are a general feature and a direct consequence of LV and RV diastolic fluid mechanics. Copyright © 2014 the American Physiological Society.

Fiber Orientation Estimation Guided by a Deep Network.

PubMed

Ye, Chuyang; Prince, Jerry L

2017-09-01

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted ℓ 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.

Nuclear Potential Clustering As a New Tool to Detect Patterns in High Dimensional Datasets

NASA Astrophysics Data System (ADS)

Tonkova, V.; Paulus, D.; Neeb, H.

2013-02-01

We present a new approach for the clustering of high dimensional data without prior assumptions about the structure of the underlying distribution. The proposed algorithm is based on a concept adapted from nuclear physics. To partition the data, we model the dynamic behaviour of nucleons interacting in an N-dimensional space. An adaptive nuclear potential, comprised of a short-range attractive (strong interaction) and a long-range repulsive term (Coulomb force) is assigned to each data point. By modelling the dynamics, nucleons that are densely distributed in space fuse to build nuclei (clusters) whereas single point clusters repel each other. The formation of clusters is completed when the system reaches the state of minimal potential energy. The data are then grouped according to the particles' final effective potential energy level. The performance of the algorithm is tested with several synthetic datasets showing that the proposed method can robustly identify clusters even when complex configurations are present. Furthermore, quantitative MRI data from 43 multiple sclerosis patients were analyzed, showing a reasonable splitting into subgroups according to the individual patients' disease grade. The good performance of the algorithm on such highly correlated non-spherical datasets, which are typical for MRI derived image features, shows that Nuclear Potential Clustering is a valuable tool for automated data analysis, not only in the MRI domain.

Accuracy of Ultrasonography and Magnetic Resonance Imaging in the Diagnosis of Placenta Accreta

PubMed Central

Riteau, Anne-Sophie; Tassin, Mikael; Chambon, Guillemette; Le Vaillant, Claudine; de Laveaucoupet, Jocelyne; Quéré, Marie-Pierre; Joubert, Madeleine; Prevot, Sophie; Philippe, Henri-Jean; Benachi, Alexandra

2014-01-01

Purpose To evaluate the accuracy of ultrasonography and magnetic resonance imaging (MRI) in the diagnosis of placenta accreta and to define the most relevant specific ultrasound and MRI features that may predict placental invasion. Material and Methods This study was approved by the institutional review board of the French College of Obstetricians and Gynecologists. We retrospectively reviewed the medical records of all patients referred for suspected placenta accreta to two university hospitals from 01/2001 to 05/2012. Our study population included 42 pregnant women who had been investigated by both ultrasonography and MRI. Ultrasound images and MRI were blindly reassessed for each case by 2 raters in order to score features that predict abnormal placental invasion. Results Sensitivity in the diagnosis of placenta accreta was 100% with ultrasound and 76.9% for MRI (P = 0.03). Specificity was 37.5% with ultrasonography and 50% for MRI (P = 0.6). The features of greatest sensitivity on ultrasonography were intraplacental lacunae and loss of the normal retroplacental clear space. Increased vascularization in the uterine serosa-bladder wall interface and vascularization perpendicular to the uterine wall had the best positive predictive value (92%). At MRI, uterine bulging had the best positive predictive value (85%) and its combination with the presence of dark intraplacental bands on T2-weighted images improved the predictive value to 90%. Conclusion Ultrasound imaging is the mainstay of screening for placenta accreta. MRI appears to be complementary to ultrasonography, especially when there are few ultrasound signs. PMID:24733409

The effects of orientation and attention during surround suppression of small image features: A 7 Tesla fMRI study.

PubMed

Schallmo, Michael-Paul; Grant, Andrea N; Burton, Philip C; Olman, Cheryl A

2016-08-01

Although V1 responses are driven primarily by elements within a neuron's receptive field, which subtends about 1° visual angle in parafoveal regions, previous work has shown that localized fMRI responses to visual elements reflect not only local feature encoding but also long-range pattern attributes. However, separating the response to an image feature from the response to the surrounding stimulus and studying the interactions between these two responses demands both spatial precision and signal independence, which may be challenging to attain with fMRI. The present study used 7 Tesla fMRI with 1.2-mm resolution to measure the interactions between small sinusoidal grating patches (targets) at 3° eccentricity and surrounds of various sizes and orientations to test the conditions under which localized, context-dependent fMRI responses could be predicted from either psychophysical or electrophysiological data. Targets were presented at 8%, 16%, and 32% contrast while manipulating (a) spatial extent of parallel (strongly suppressive) or orthogonal (weakly suppressive) surrounds, (b) locus of attention, (c) stimulus onset asynchrony between target and surround, and (d) blocked versus event-related design. In all experiments, the V1 fMRI signal was lower when target stimuli were flanked by parallel versus orthogonal context. Attention amplified fMRI responses to all stimuli but did not show a selective effect on central target responses or a measurable effect on orientation-dependent surround suppression. Suppression of the V1 fMRI response by parallel surrounds was stronger than predicted from psychophysics but showed a better match to previous electrophysiological reports.

Fine-tuning convolutional deep features for MRI based brain tumor classification

NASA Astrophysics Data System (ADS)

Ahmed, Kaoutar B.; Hall, Lawrence O.; Goldgof, Dmitry B.; Liu, Renhao; Gatenby, Robert A.

2017-03-01

Prediction of survival time from brain tumor magnetic resonance images (MRI) is not commonly performed and would ordinarily be a time consuming process. However, current cross-sectional imaging techniques, particularly MRI, can be used to generate many features that may provide information on the patient's prognosis, including survival. This information can potentially be used to identify individuals who would benefit from more aggressive therapy. Rather than using pre-defined and hand-engineered features as with current radiomics methods, we investigated the use of deep features extracted from pre-trained convolutional neural networks (CNNs) in predicting survival time. We also provide evidence for the power of domain specific fine-tuning in improving the performance of a pre-trained CNN's, even though our data set is small. We fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the survival time prediction task, obtaining over 81% accuracy in a leave one out cross validation.

Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rusu, Mirabela, E-mail: mirabela.rusu@gmail.com; Wang, Haibo; Madabhushi, Anant

Purpose: Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on in vivo imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation in vivo. Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering exmore » vivo histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors’ framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with chronic pulmonary inflammation. Methods: The authors’ image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. Results: The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology—MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors’ evaluation considered three mice, two with an inflammation phenotype and one control. The authors’ iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. Conclusions: The authors’ 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for inflammation on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.« less

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data

PubMed Central

Satterthwaite, Theodore D.; Elliott, Mark A.; Gerraty, Raphael T.; Ruparel, Kosha; Loughead, James; Calkins, Monica E.; Eickhoff, Simon B.; Hakonarson, Hakon; Gur, Ruben C.; Gur, Raquel E.; Wolf, Daniel H.

2013-01-01

Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. PMID:22926292

Dynamic timecourse of typical childhood absence seizures: EEG, behavior and fMRI

PubMed Central

Bai, X; Vestal, M; Berman, R; Negishi, M; Spann, M; Vega, C; Desalvo, M; Novotny, EJ; Constable, RT; Blumenfeld, H

2010-01-01

Absence seizures are 5–10 second episodes of impaired consciousness accompanied by 3–4Hz generalized spike-and-wave discharge on electroencephalography (EEG). The timecourse of functional magnetic resonance imaging (fMRI) changes in absence seizures in relation to EEG and behavior is not known. We acquired simultaneous EEG-fMRI in 88 typical childhood absence seizures from 9 pediatric patients. We investigated behavior concurrently using a continuous performance task (CPT) or simpler repetitive tapping task (RTT). EEG time-frequency analysis revealed abrupt onset and end of 3–4 Hz spike-wave discharges with a mean duration of 6.6 s. Behavioral analysis also showed rapid onset and end of deficits associated with electrographic seizure start and end. In contrast, we observed small early fMRI increases in the orbital/medial frontal and medial/lateral parietal cortex >5s before seizure onset, followed by profound fMRI decreases continuing >20s after seizure end. This timecourse differed markedly from the hemodynamic response function (HRF) model used in conventional fMRI analysis, consisting of large increases beginning after electrical event onset, followed by small fMRI decreases. Other regions, such as the lateral frontal cortex, showed more balanced fMRI increases followed by approximately equal decreases. The thalamus showed delayed increases after seizure onset followed by small decreases, most closely resembling the HRF model. These findings reveal a complex and long lasting sequence of fMRI changes in absence seizures, which are not detectible by conventional HRF modeling in many regions. These results may be important mechanistically for seizure initiation and termination and may also contribute to changes in EEG and behavior. PMID:20427649

White matter hyperintensities are a core feature of Alzheimer's disease: Evidence from the dominantly inherited Alzheimer network.

PubMed

Lee, Seonjoo; Viqar, Fawad; Zimmerman, Molly E; Narkhede, Atul; Tosto, Giuseppe; Benzinger, Tammie L S; Marcus, Daniel S; Fagan, Anne M; Goate, Alison; Fox, Nick C; Cairns, Nigel J; Holtzman, David M; Buckles, Virginia; Ghetti, Bernardino; McDade, Eric; Martins, Ralph N; Saykin, Andrew J; Masters, Colin L; Ringman, John M; Ryan, Natalie S; Förster, Stefan; Laske, Christoph; Schofield, Peter R; Sperling, Reisa A; Salloway, Stephen; Correia, Stephen; Jack, Clifford; Weiner, Michael; Bateman, Randall J; Morris, John C; Mayeux, Richard; Brickman, Adam M

2016-06-01

White matter hyperintensities (WMHs) are areas of increased signal on T2-weighted magnetic resonance imaging (MRI) scans that most commonly reflect small vessel cerebrovascular disease. Increased WMH volume is associated with risk and progression of Alzheimer's disease (AD). These observations are typically interpreted as evidence that vascular abnormalities play an additive, independent role contributing to symptom presentation, but not core features of AD. We examined the severity and distribution of WMH in presymptomatic PSEN1, PSEN2, and APP mutation carriers to determine the extent to which WMH manifest in individuals genetically determined to develop AD. The study comprised participants (n = 299; age = 39.03 ± 10.13) from the Dominantly Inherited Alzheimer Network, including 184 (61.5%) with a mutation that results in AD and 115 (38.5%) first-degree relatives who were noncarrier controls. We calculated the estimated years from expected symptom onset (EYO) by subtracting the affected parent's symptom onset age from the participant's age. Baseline MRI data were analyzed for total and regional WMH. Mixed-effects piece-wise linear regression was used to examine WMH differences between carriers and noncarriers with respect to EYO. Mutation carriers had greater total WMH volumes, which appeared to increase approximately 6 years before expected symptom onset. Effects were most prominent for the parietal and occipital lobe, which showed divergent effects as early as 22 years before estimated onset. Autosomal-dominant AD is associated with increased WMH well before expected symptom onset. The findings suggest the possibility that WMHs are a core feature of AD, a potential therapeutic target, and a factor that should be integrated into pathogenic models of the disease. Ann Neurol 2016;79:929-939. © 2016 American Neurological Association.

Hepatic capsular retraction: spectrum of diagnosis at MRI

PubMed Central

Mons, Antoine; Braidy, Chadi; Montoriol, Pierre François; Garcier, Jean-Marc; Vilgrain, Valérie

2014-01-01

Hepatic capsular retraction is an imaging feature that deserves the attention of the radiologist. Hepatic capsular retraction is associated with a number of hepatic lesions, benign or malignant, treated or untreated. The purpose of this pictorial review is to discuss the most common benign and malignant hepatic lesions associated with this feature with an emphasis on magnetic resonance imaging (MRI). PMID:25535571

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI.

PubMed

Qin, Yuan-Yuan; Hsu, Johnny T; Yoshida, Shoko; Faria, Andreia V; Oishi, Kumiko; Unschuld, Paul G; Redgrave, Graham W; Ying, Sarah H; Ross, Christopher A; van Zijl, Peter C M; Hillis, Argye E; Albert, Marilyn S; Lyketsos, Constantine G; Miller, Michael I; Mori, Susumu; Oishi, Kenichi

2013-01-01

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Magnetic resonance imaging criteria for thrombolysis in hyperacute cerebral infarction.

PubMed

Ahmetgjekaj, Ilir; Kabashi-Muçaj, Serbeze; Lascu, Luana Corina; Kabashi, Antigona; Bondari, A; Bondari, Simona; Dedushi-Hoti, Kreshnike; Biçaku, Ardian; Shatri, Jeton

2014-01-01

Selection of patients with cerebral infarction for MRI that is suitable for thrombolytic therapy as an emerging application. Although the efficiency of the therapy with i.v. tissue plasminogen activator (tPA) within 3 hours after onset of symptoms has been proven in selected patients with CT, now these criteria are determined by MRI, as the data we gather are fast and accurate in the first hours. MRI screening in patients with acute cerebral infarction before application of thrombolytic therapy was done in a UCC Mannheim in Germany. Unlike trials with CT, MRI studies demonstrated the benefits of therapy up to 6 hours after the onset of symptoms. We studied 21 patients hospitalized in Clinic of Neuroradiology at University Clinical Centre in Mannheim-Germany. They all undergo brain MRI evaluation for stroke. This article reviews literature that has followed application of thrombolysis in patients with cerebral infarction based on MRI. We have analyzed the MRI criteria for i.v. application of tPA at this University Centre. Alongside the personal viewpoints of clinicians, survey reveals a variety of clinical aspects and MRI features that are opened for further more exploration: therapeutic effects, the use of the MRI angiography, dynamics, and other. MRI is a tested imaging method for rapid evaluation of patients with hyperacute cerebral infarction, replacing the use of CT imaging and clinical features. MRI criteria for thrombolytic therapy are being applied in some cerebral vascular centres. In Kosovo, the application of thrombolytic therapy has not started yet.

Pathologic Findings of Breast Lesions Detected on Magnetic Resonance Imaging.

PubMed

Jabbar, Seema B; Lynch, Beverly; Seiler, Stephen; Hwang, Helena; Sahoo, Sunati

2017-11-01

- Breast magnetic resonance imaging (MRI) is now used routinely for high-risk screening and in the evaluation of the extent of disease in newly diagnosed breast cancer patients. Morphologic characteristics and the kinetic pattern largely determine how suspicious a breast lesion is on MRI. Because of its high sensitivity, MRI identifies a large number of suspicious lesions. However, the low to moderate specificity and the additional cost have raised questions regarding its frequent use. - To identify the pathologic entities that frequently present as suspicious enhancing lesions and to identify specific MRI characteristics that may be predictive of malignancy. - One hundred seventy-seven MRI-guided biopsies from 152 patients were included in the study. The indication for MRI, MRI features, pathologic findings, and patient demographics were recorded. The MRI findings and the pathology slides were reviewed by a dedicated breast radiologist and breast pathologists. - Seventy-one percent (126 of 177) of MRI-guided breast biopsies were benign, 11% (20 of 177) showed epithelial atypia, and 18% (31 of 177) showed malignancy. The vast majority (84%; 62 of 74) of MRI lesions with persistent kinetics were benign. However, 57% (17 of 30) of lesions with washout kinetics and 65% (62 of 95) of mass lesions were also benign. - Magnetic resonance imaging detects malignancies undetected by other imaging modalities but also detects a wide variety of benign lesions. Benign and malignant lesions identified by MRI share similar morphologic and kinetic features, necessitating biopsy for histologic confirmation.

Aphasic variant of Alzheimer disease

PubMed Central

Sridhar, Jaiashre; Rader, Benjamin; Martersteck, Adam; Chen, Kewei; Cobia, Derin; Thompson, Cynthia K.; Weintraub, Sandra; Bigio, Eileen H.; Mesulam, M.-Marsel

2016-01-01

Objective: To identify features of primary progressive aphasia (PPA) associated with Alzheimer disease (AD) neuropathology. A related objective was to determine whether logopenic PPA is a clinical marker for AD. Methods: A total of 139 prospectively enrolled participants with a root diagnosis of PPA constituted the reference set. Those with autopsy or biomarker evidence of AD, and who had been evaluated at mild disease stages (Aphasia Quotient ≥85), were included (n = 19). All had quantitative language testing and APOE genotyping. Fifteen had MRI morphometry. Results: Impaired word-finding was the universal presenting complaint in the aphasic AD group. PPA clinical subtype was logopenic (n = 13) and agrammatic (n = 6). Fluency, repetition, naming, and grammaticality ranged from preserved to severely impaired. All had relative preservation of word comprehension. Eight of the 15 aphasic participants with AD showed no appreciable cortical atrophy at the individual level on MRI. As a group, atrophy was asymmetrically concentrated in the left perisylvian cortex. APOE ε4 frequency was not elevated. Conclusions: There is a close, but not obligatory, association between logopenic PPA and AD. No language measure, with the possible exception of word comprehension, can confirm or exclude AD in PPA. Biomarkers are therefore essential for diagnosis. Asymmetry of cortical atrophy and normal APOE ε4 prevalence constitute deviations from typical AD. These and additional neuropathologic features suggest that AD has biological subtypes, one of which causes PPA. Better appreciation of this fact should promote the inclusion of individuals with PPA and positive AD biomarkers into relevant clinical trials. PMID:27566743

Prenatal diagnosis of Joubert syndrome: A case report and literature review.

PubMed

Zhu, Lingling; Xie, Limei

2017-12-01

Joubert syndrome (JS) is a rare autosomal recessive inherited disease belonging to ciliopathy with the causative mutation of genes. Except for X-linked inheritance, the high recurrence rate of a family is about 25%. After birth, it may cause a series of neurological symptoms, even with retina, kidney, liver, and other organ abnormalities, which is defined as Joubert syndrome and related disorders (JSRD). Molecular genetics research contributes to disease prediction and genetic counseling. Prenatal diagnosis is rare. Magnetic resonance imaging (MRI) is usually the first-choice diagnostic modality with typical brain images characterized by the molar tooth sign. We describe a case of JS prenatally and Dandy-Walker malformation for the differential diagnosis based on ultrasonograms. We also review the etiology, imaging features, clinical symptoms, and diagnosis of JSRD. A 22-year-old woman was pregnant at 27 1/7 weeks' gestation with fetal cerebellar vermis hypoplasia. Fetal ultrasonography and MRI confirmed a diagnosis of JS at our center. The couple finally opted to terminate the fetus, which had a normal appearance and growth parameters. The couple also had an AHI1 gene mutation on chromosome 6. Currently, a diagnosis of JS is commonly made after birth. Fewer cases of prenatal diagnosis by ultrasonography have been made, and they are more liable to be misdirected because of some nonspecial features that also manifest in Dandy-Walker malformation, cranio-cerebello-cardiac syndrome, and so on. Copyright © 2017 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.

Unsupervised spatiotemporal analysis of fMRI data using graph-based visualizations of self-organizing maps.

PubMed

Katwal, Santosh B; Gore, John C; Marois, Rene; Rogers, Baxter P

2013-09-01

We present novel graph-based visualizations of self-organizing maps for unsupervised functional magnetic resonance imaging (fMRI) analysis. A self-organizing map is an artificial neural network model that transforms high-dimensional data into a low-dimensional (often a 2-D) map using unsupervised learning. However, a postprocessing scheme is necessary to correctly interpret similarity between neighboring node prototypes (feature vectors) on the output map and delineate clusters and features of interest in the data. In this paper, we used graph-based visualizations to capture fMRI data features based upon 1) the distribution of data across the receptive fields of the prototypes (density-based connectivity); and 2) temporal similarities (correlations) between the prototypes (correlation-based connectivity). We applied this approach to identify task-related brain areas in an fMRI reaction time experiment involving a visuo-manual response task, and we correlated the time-to-peak of the fMRI responses in these areas with reaction time. Visualization of self-organizing maps outperformed independent component analysis and voxelwise univariate linear regression analysis in identifying and classifying relevant brain regions. We conclude that the graph-based visualizations of self-organizing maps help in advanced visualization of cluster boundaries in fMRI data enabling the separation of regions with small differences in the timings of their brain responses.

Lumbar spinal canal MRI diameter is smaller in herniated disc cauda equina syndrome patients

PubMed Central

Kruit, Mark C.; Peul, Wilco C.; Vleggeert-Lankamp, Carmen L. A.

2017-01-01

Introduction Correlation between magnetic resonance imaging (MRI) and clinical features in cauda equina syndrome (CES) is unknown; nor is known whether there are differences in MRI spinal canal size between lumbar herniated disc patients with CES versus lumbar herniated discs patients without CES, operated for sciatica. The aims of this study are 1) evaluating the association of MRI features with clinical presentation and outcome of CES and 2) comparing lumbar spinal canal diameters of lumbar herniated disc patients with CES versus lumbar herniated disc patients without CES, operated because of sciatica. Methods MRIs of CES patients were assessed for the following features: level of disc lesion, type (uni- or bilateral) and severity of caudal compression. Pre- and postoperative clinical features (micturition dysfunction, defecation dysfunction, altered sensation of the saddle area) were retrieved from the medical files. In addition, anteroposterior (AP) lumbar spinal canal diameters of CES patients were measured at MRI. AP diameters of lumbar herniated disc patients without CES, operated for sciatica, were measured for comparison. Results 48 CES patients were included. At MRI, bilateral compression was seen in 82%; complete caudal compression in 29%. MRI features were not associated with clinical presentation nor outcome. AP diameter was measured for 26 CES patients and for 31 lumbar herniated disc patients without CES, operated for sciatica. Comparison displayed a significant smaller AP diameter of the lumbar spinal canal in CES patients (largest p = 0.002). Compared to average diameters in literature, diameters of CES patients were significantly more often below average than that of the sciatica patients (largest p = 0.021). Conclusion This is the first study demonstrating differences in lumbar spinal canal size between lumbar herniated disc patients with CES and lumbar herniated disc patients without CES, operated for sciatica. This finding might imply that lumbar herniated disc patients with a relative small lumbar spinal canal might need to be approached differently in managing complaints of herniated disc. Since the number of studied patients is relatively small, further research should be conducted before clinical consequences are considered. PMID:29023556

Minimizing Noise in Pediatric Task-Based functional MRI; Adolescents with Developmental Disabilities and Typical Development

PubMed Central

Fassbender, Catherine; Muhkerjee, Prerona; Schweitzer, Julie B.

2017-01-01

Functional Magnetic Resonance Imaging (fMRI) represents a powerful tool with which to examine brain functioning and development in typically developing pediatric groups as well as children and adolescents with clinical disorders. However, fMRI data can be highly susceptible to misinterpretation due to the effects of excessive levels of noise, often related to head motion. Imaging children, especially with developmental disorders, requires extra considerations related to hyperactivity, anxiety and the ability to perform and maintain attention to the fMRI paradigm. We discuss a number of methods that can be employed to minimize noise, in particular movement-related noise. To this end we focus on strategies prior to, during and following the data acquisition phase employed primarily within our own laboratory. We discuss the impact of factors such as experimental design, screening of potential participants and pre-scan training on head motion in our adolescents with developmental disorders and typical development. We make some suggestions that may minimize noise during data acquisition itself and finally we briefly discuss some current processing techniques that may help to identify and remove noise in the data. Many advances have been made in the field of pediatric imaging, particularly with regard to research involving children with developmental disorders. Mindfulness of issues such as those discussed here will ensure continued progress and greater consistency across studies. PMID:28130195

Association between dynamic features of breast DCE-MR imaging and clinical response of neoadjuvant chemotherapy: a preliminary analysis

NASA Astrophysics Data System (ADS)

Huang, Lijuan; Fan, Ming; Li, Lihua; Zhang, Juan; Shao, Guoliang; Zheng, Bin

2016-03-01

Neoadjuvant chemotherapy (NACT) is being used increasingly in the management of patients with breast cancer for systemically reducing the size of primary tumor before surgery in order to improve survival. The clinical response of patients to NACT is correlated with reduced or abolished of their primary tumor, which is important for treatment in the next stage. Recently, the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used for evaluation of the response of patients to NACT. To measure this correlation, we extracted the dynamic features from the DCE- MRI and performed association analysis between these features and the clinical response to NACT. In this study, 59 patients are screened before NATC, of which 47 are complete or partial response, and 12 are no response. We segmented the breast areas depicted on each MR image by a computer-aided diagnosis (CAD) scheme, registered images acquired from the sequential MR image scan series, and calculated eighteen features extracted from DCE-MRI. We performed SVM with the 18 features for classification between patients of response and no response. Furthermore, 6 of the 18 features are selected to refine the classification by using Genetic Algorithm. The accuracy, sensitivity and specificity are 87%, 95.74% and 50%, respectively. The calculated area under a receiver operating characteristic (ROC) curve is 0.79+/-0.04. This study indicates that the features of DCE-MRI of breast cancer are associated with the response of NACT. Therefore, our method could be helpful for evaluation of NACT in treatment of breast cancer.

More than one kind of inference: re-examining what's learned in feature inference and classification.

PubMed

Sweller, Naomi; Hayes, Brett K

2010-08-01

Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

Magnetic Resonance Imaging–Guided versus Surrogate-Based Motion Tracking in Liver Radiation Therapy: A Prospective Comparative Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Paganelli, Chiara, E-mail: chiara.paganelli@polimi.it; Seregni, Matteo; Fattori, Giovanni

Purpose: This study applied automatic feature detection on cine–magnetic resonance imaging (MRI) liver images in order to provide a prospective comparison between MRI-guided and surrogate-based tracking methods for motion-compensated liver radiation therapy. Methods and Materials: In a population of 30 subjects (5 volunteers plus 25 patients), 2 oblique sagittal slices were acquired across the liver at high temporal resolution. An algorithm based on scale invariant feature transform (SIFT) was used to extract and track multiple features throughout the image sequence. The position of abdominal markers was also measured directly from the image series, and the internal motion of each featuremore » was quantified through multiparametric analysis. Surrogate-based tumor tracking with a state-of-the-art external/internal correlation model was simulated. The geometrical tracking error was measured, and its correlation with external motion parameters was also investigated. Finally, the potential gain in tracking accuracy relying on MRI guidance was quantified as a function of the maximum allowed tracking error. Results: An average of 45 features was extracted for each subject across the whole liver. The multi-parametric motion analysis reported relevant inter- and intrasubject variability, highlighting the value of patient-specific and spatially-distributed measurements. Surrogate-based tracking errors (relative to the motion amplitude) were were in the range 7% to 23% (1.02-3.57mm) and were significantly influenced by external motion parameters. The gain of MRI guidance compared to surrogate-based motion tracking was larger than 30% in 50% of the subjects when considering a 1.5-mm tracking error tolerance. Conclusions: Automatic feature detection applied to cine-MRI allows detailed liver motion description to be obtained. Such information was used to quantify the performance of surrogate-based tracking methods and to provide a prospective comparison with respect to MRI-guided radiation therapy, which could support the definition of patient-specific optimal treatment strategies.« less

Development and reliability of a preliminary Foot Osteoarthritis Magnetic Resonance Imaging Score

PubMed Central

Halstead, Jill; Martín-Hervás, Carmen; Hensor, Elizabeth MA; McGonagle, Dennis; Keenan, Anne-Maree

2017-01-01

Objective Foot osteoarthritis (OA) is very common but under-investigated musculoskeletal condition and there is little consensus as to common MRI imaging features. The aim of this study was to develop a preliminary foot OA MRI score (FOAMRIS) and evaluate its reliability. Methods This preliminary semi-quantitative score included the hindfoot, midfoot and metatarsophalangeal joints. Joints were scored for joint space narrowing (JSN, 0-3), osteophytes (0-3), joint effusion-synovitis and bone cysts (present/absent). Erosions and bone marrow lesions (BMLs) were scored (0-3) and BMLs were evaluated adjacent to entheses and at sub-tendon sites (present/absent). Additionally, tenosynovitis was scored (0-3) and midfoot ligament pathology was scored (present/absent). Reliability was evaluated in 15 people with foot pain and MRI-detected OA using 3.0T MRI multi-sequence protocols and assessed using intraclass correlation coefficients (ICC) as an overall score and per anatomical site (see supplementary data). Results Intra-reader agreement (ICC) was generally good to excellent across the foot in joint features (JSN 0.94, osteophytes 0.94, effusion-synovitis 0.62 and cysts 0.93), bone features (BML 0.89, erosion 0.78, BML-entheses 0.79, BML sub-tendon 0.75) and soft-tissue features (tenosynovitis 0.90, ligaments 0.87). Inter-reader agreement was lower for joint features (JSN 0.60, osteophytes 0.41, effusion-synovitis 0.03) and cysts 0.65, bone features (BML 0.80, erosion 0.00, BML-entheses 0.49, BML sub-tendon -0.24) and soft-tissue features (tenosynovitis 0.48, ligaments 0.50). Conclusion This preliminary FOAMRIS demonstrated good intra-reader reliability and fair inter-reader reliability when assessing the total feature scores. Further development is required in cohorts with a range of pathologies and to assess the psychometric measurement properties. PMID:28572462

Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI

NASA Astrophysics Data System (ADS)

Chirra, Prathyush; Leo, Patrick; Yim, Michael; Bloch, B. Nicolas; Rastinehad, Ardeshir R.; Purysko, Andrei; Rosen, Mark; Madabhushi, Anant; Viswanath, Satish

2018-02-01

The recent advent of radiomics has enabled the development of prognostic and predictive tools which use routine imaging, but a key question that still remains is how reproducible these features may be across multiple sites and scanners. This is especially relevant in the context of MRI data, where signal intensity values lack tissue specific, quantitative meaning, as well as being dependent on acquisition parameters (magnetic field strength, image resolution, type of receiver coil). In this paper we present the first empirical study of the reproducibility of 5 different radiomic feature families in a multi-site setting; specifically, for characterizing prostate MRI appearance. Our cohort comprised 147 patient T2w MRI datasets from 4 different sites, all of which were first pre-processed to correct acquisition-related for artifacts such as bias field, differing voxel resolutions, as well as intensity drift (non-standardness). 406 3D voxel wise radiomic features were extracted and evaluated in a cross-site setting to determine how reproducible they were within a relatively homogeneous non-tumor tissue region; using 2 different measures of reproducibility: Multivariate Coefficient of Variation and Instability Score. Our results demonstrated that Haralick features were most reproducible between all 4 sites. By comparison, Laws features were among the least reproducible between sites, as well as performing highly variably across their entire parameter space. Similarly, the Gabor feature family demonstrated good cross-site reproducibility, but for certain parameter combinations alone. These trends indicate that despite extensive pre-processing, only a subset of radiomic features and associated parameters may be reproducible enough for use within radiomics-based machine learning classifier schemes.

Optic Nerve Assessment Using 7-Tesla Magnetic Resonance Imaging.

PubMed

Singh, Arun D; Platt, Sean M; Lystad, Lisa; Lowe, Mark; Oh, Sehong; Jones, Stephen E; Alzahrani, Yahya; Plesec, Thomas

2016-04-01

The purpose of this study was to correlate high-resolution magnetic resonance imaging (MRI) and histologic findings in a case of juxtapapillary choroidal melanoma with clinical evidence of optic nerve invasion. With institutional review board approval, an enucleated globe with choroidal melanoma and optic nerve invasion was imaged using a 7-tesla MRI followed by histopathologic evaluation. Optical coherence tomography, B-scan ultrasonography, and 1.5-tesla MRI of the orbit (1-mm sections) could not detect optic disc invasion. Ex vivo, 7-tesla MRI detected optic nerve invasion, which correlated with histopathologic features. Our case demonstrates the potential to document the existence of optic nerve invasion in the presence of an intraocular tumor, a feature that has a major bearing on decision making, particularly for consideration of enucleation.

Tracking brain arousal fluctuations with fMRI

PubMed Central

Chang, Catie; Leopold, David A.; Schölvinck, Marieke Louise; Mandelkow, Hendrik; Picchioni, Dante; Liu, Xiao; Ye, Frank Q.; Turchi, Janita N.; Duyn, Jeff H.

2016-01-01

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker. PMID:27051064

Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM.

PubMed

Hojjati, Seyed Hani; Ebrahimzadeh, Ata; Khazaee, Ali; Babajani-Feremi, Abbas

2017-04-15

We investigated identifying patients with mild cognitive impairment (MCI) who progress to Alzheimer's disease (AD), MCI converter (MCI-C), from those with MCI who do not progress to AD, MCI non-converter (MCI-NC), based on resting-state fMRI (rs-fMRI). Graph theory and machine learning approach were utilized to predict progress of patients with MCI to AD using rs-fMRI. Eighteen MCI converts (average age 73.6 years; 11 male) and 62 age-matched MCI non-converters (average age 73.0 years, 28 male) were included in this study. We trained and tested a support vector machine (SVM) to classify MCI-C from MCI-NC using features constructed based on the local and global graph measures. A novel feature selection algorithm was developed and utilized to select an optimal subset of features. Using subset of optimal features in SVM, we classified MCI-C from MCI-NC with an accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve of 91.4%, 83.24%, 90.1%, and 0.95, respectively. Furthermore, results of our statistical analyses were used to identify the affected brain regions in AD. To the best of our knowledge, this is the first study that combines the graph measures (constructed based on rs-fMRI) with machine learning approach and accurately classify MCI-C from MCI-NC. Results of this study demonstrate potential of the proposed approach for early AD diagnosis and demonstrate capability of rs-fMRI to predict conversion from MCI to AD by identifying affected brain regions underlying this conversion. Copyright © 2017 Elsevier B.V. All rights reserved.

Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features.

PubMed

Horikawa, Tomoyasu; Kamitani, Yukiyasu

2017-01-01

Dreaming is generally thought to be generated by spontaneous brain activity during sleep with patterns common to waking experience. This view is supported by a recent study demonstrating that dreamed objects can be predicted from brain activity during sleep using statistical decoders trained with stimulus-induced brain activity. However, it remains unclear whether and how visual image features associated with dreamed objects are represented in the brain. In this study, we used a deep neural network (DNN) model for object recognition as a proxy for hierarchical visual feature representation, and DNN features for dreamed objects were analyzed with brain decoding of fMRI data collected during dreaming. The decoders were first trained with stimulus-induced brain activity labeled with the feature values of the stimulus image from multiple DNN layers. The decoders were then used to decode DNN features from the dream fMRI data, and the decoded features were compared with the averaged features of each object category calculated from a large-scale image database. We found that the feature values decoded from the dream fMRI data positively correlated with those associated with dreamed object categories at mid- to high-level DNN layers. Using the decoded features, the dreamed object category could be identified at above-chance levels by matching them to the averaged features for candidate categories. The results suggest that dreaming recruits hierarchical visual feature representations associated with objects, which may support phenomenal aspects of dream experience.

Magnetic resonance imaging-guided core needle breast biopsies resulting in high-risk histopathologic findings: upstage frequency and lesion characteristics.

PubMed

Weinfurtner, R Jared; Patel, Bhavika; Laronga, Christine; Lee, Marie C; Falcon, Shannon L; Mooney, Blaise P; Yue, Binglin; Drukteinis, Jennifer S

2015-06-01

Analysis of magnetic resonance imaging-guided breast biopsies yielding high-risk histopathologic features at a single institution found an overall upstage rate to malignancy of 14% at surgical excision. All upstaged lesions were associated with atypical ductal hyperplasia. Flat epithelial atypia and atypical lobular hyperplasia alone or with lobular carcinoma in situ were not associated with an upstage to malignancy. The purpose of the present study w as to determine the malignancy upstage rates and imaging features of high-risk histopathologic findings resulting from magnetic resonance imaging (MRI)-guided core needle breast biopsies. These features include atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH), flat epithelial atypia (FEA), and lobular carcinoma in situ (LCIS). A retrospective medical record review was performed on all MRI-guided core needle breast biopsies at a single institution from June 1, 2007 to December 1, 2013 to select biopsies yielding high-risk histopathologic findings. The patient demographics, MRI lesion characteristics, and histopathologic features at biopsy and surgical excision were analyzed. A total of 257 MRI-guided biopsies had been performed, and 50 yielded high-risk histopathologic features (19%). Biopsy site and surgical excision site correlation was confirmed in 29 of 50 cases. Four of 29 lesions (14%) were upstaged: 1 case to invasive ductal carcinoma and 3 cases to ductal carcinoma in situ. ADH alone had an overall upstage rate of 7% (1 of 14), mixed ADH/ALH a rate of 75% (3 of 4), ALH alone or with LCIS a rate of 0% (0 of 7), and FEA a rate of 0% (0 of 4). Only mixed ADH/ALH had a statistically significant upstage rate to malignancy compared with the other high-risk histopathologic subtypes combined. No specific imaging characteristics on MRI were associated with an upstage to malignancy on the statistical analysis. MRI-guided breast biopsies yielding high-risk histopathologic features were associated with an overall upstage to malignancy rate of 14% at surgical excision. All upstaged lesions were associated with ADH. FEA and ALH alone or with LCIS were not associated with an upstage to malignancy. Copyright © 2015 Elsevier Inc. All rights reserved.

Competitive Advantage of PET/MRI

PubMed Central

Jadvar, Hossein; Colletti, Patrick M.

2013-01-01

Multimodality imaging has made great strides in the imaging evaluation of patients with a variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now established as the imaging modality of choice in many clinical conditions, particularly in oncology. While the initial development of combined PET/magnetic resonance imaging (PET/MRI) was in the preclinical arena, hybrid PET/MR scanners are now available for clinical use. PET/MRI combines the unique features of MRI including excellent soft tissue contrast, diffusion-weighted imaging, dynamic contrast-enhanced imaging, fMRI and other specialized sequences as well as MR spectroscopy with the quantitative physiologic information that is provided by PET. Most evidence for the potential clinical utility of PET/MRI is based on studies performed with side-by-side comparison or software-fused MRI and PET images. Data on distinctive utility of hybrid PET/MRI are rapidly emerging. There are potential competitive advantages of PET/MRI over PET/CT. In general, PET/MRI may be preferred over PET/CT where the unique features of MRI provide more robust imaging evaluation in certain clinical settings. The exact role and potential utility of simultaneous data acquisition in specific research and clinical settings will need to be defined. It may be that simultaneous PET/MRI will be best suited for clinical situations that are disease-specific, organ-specific, related to diseases of the children or in those patients undergoing repeated imaging for whom cumulative radiation dose must be kept as low as reasonably achievable. PET/MRI also offers interesting opportunities for use of dual modality probes. Upon clear definition of clinical utility, other important and practical issues related to business operational model, clinical workflow and reimbursement will also be resolved. PMID:23791129

Competitive advantage of PET/MRI.

PubMed

Jadvar, Hossein; Colletti, Patrick M

2014-01-01

Multimodality imaging has made great strides in the imaging evaluation of patients with a variety of diseases. Positron emission tomography/computed tomography (PET/CT) is now established as the imaging modality of choice in many clinical conditions, particularly in oncology. While the initial development of combined PET/magnetic resonance imaging (PET/MRI) was in the preclinical arena, hybrid PET/MR scanners are now available for clinical use. PET/MRI combines the unique features of MRI including excellent soft tissue contrast, diffusion-weighted imaging, dynamic contrast-enhanced imaging, fMRI and other specialized sequences as well as MR spectroscopy with the quantitative physiologic information that is provided by PET. Most evidence for the potential clinical utility of PET/MRI is based on studies performed with side-by-side comparison or software-fused MRI and PET images. Data on distinctive utility of hybrid PET/MRI are rapidly emerging. There are potential competitive advantages of PET/MRI over PET/CT. In general, PET/MRI may be preferred over PET/CT where the unique features of MRI provide more robust imaging evaluation in certain clinical settings. The exact role and potential utility of simultaneous data acquisition in specific research and clinical settings will need to be defined. It may be that simultaneous PET/MRI will be best suited for clinical situations that are disease-specific, organ-specific, related to diseases of the children or in those patients undergoing repeated imaging for whom cumulative radiation dose must be kept as low as reasonably achievable. PET/MRI also offers interesting opportunities for use of dual modality probes. Upon clear definition of clinical utility, other important and practical issues related to business operational model, clinical workflow and reimbursement will also be resolved. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

MRI predictors of treatment response for perianal fistulizing Crohn disease in children and young adults.

PubMed

Shenoy-Bhangle, Anuradha; Nimkin, Katherine; Goldner, Dana; Bradley, William F; Israel, Esther J; Gee, Michael S

2014-01-01

Magnetic resonance imaging (MRI) is considered the imaging standard for diagnosis and characterization of perianal complications associated with Crohn disease in children and adults. To define MRI criteria that could act as potential predictors of treatment response in fistulizing Crohn disease in children, in order to guide more informed study interpretation. We performed a retrospective database query to identify all children and young adults with Crohn disease who underwent serial MRI studies for assessment of perianal symptoms between 2003 and 2010. We examined imaging features of perianal disease including fistula number, type and length, presence and size of associated abscess, and disease response/progression on follow-up MRI. We reviewed imaging studies and electronic medical records. Statistical analysis, including logistic regression, was performed to associate MR imaging features with treatment response and disease progression. We included 36 patients (22 male, 14 female; age range 8-21 years). Of these, 32 had a second MRI exam and 4 had clinical evidence of complete response, obviating the need for repeat imaging. Of the parameters analyzed, presence of abscess, type of fistula according to the Parks classification, and multiplicity were not predictors of treatment outcome. Maximum length of the dominant fistula and aggregate fistula length in the case of multiple fistulae were the best predictors of treatment outcome. Maximum fistula length <2.5 cm was a predictor of treatment response, while aggregate fistula length ≥2.5 cm was a predictor of disease progression. Perianal fistula length is an important imaging feature to assess on MRI of fistulizing Crohn disease.

A multi-layer MRI description of Parkinson's disease

NASA Astrophysics Data System (ADS)

La Rocca, M.; Amoroso, N.; Lella, E.; Bellotti, R.; Tangaro, S.

2017-09-01

Magnetic resonance imaging (MRI) along with complex network is currently one of the most widely adopted techniques for detection of structural changes in neurological diseases, such as Parkinson's Disease (PD). In this paper, we present a digital image processing study, within the multi-layer network framework, combining more classifiers to evaluate the informative power of the MRI features, for the discrimination of normal controls (NC) and PD subjects. We define a network for each MRI scan; the nodes are the sub-volumes (patches) the images are divided into and the links are defined using the Pearson's pairwise correlation between patches. We obtain a multi-layer network whose important network features, obtained with different feature selection methods, are used to feed a supervised multi-level random forest classifier which exploits this base of knowledge for accurate classification. Method evaluation has been carried out using T1 MRI scans of 354 individuals, including 177 PD subjects and 177 NC from the Parkinson's Progression Markers Initiative (PPMI) database. The experimental results demonstrate that the features obtained from multiplex networks are able to accurately describe PD patterns. Besides, also if a privileged scale for studying PD disease exists, exploring the informative content of more scales leads to a significant improvement of the performances in the discrimination between disease and healthy subjects. In particular, this method gives a comprehensive overview of brain regions statistically affected by the disease, an additional value to the presented study.

Functional Evaluation of Hidden Figures Object Analysis in Children with Autistic Disorder

ERIC Educational Resources Information Center

Malisza, Krisztina L.; Clancy, Christine; Shiloff, Deborah; Foreman, Derek; Holden, Jeanette; Jones, Cheryl; Paulson, K.; Summers, Randy; Yu, C. T.; Chudley, Albert E.

2011-01-01

Functional magnetic resonance imaging (fMRI) during performance of a hidden figures task (HFT) was used to compare differences in brain function in children diagnosed with autism disorder (AD) compared to children with attention-deficit/hyperactivity disorder (ADHD) and typical controls (TC). Overall greater functional MRI activity was observed in…

[Clinical value of MRI united-sequences examination in diagnosis and differentiation of morphological sub-type of hilar and extrahepatic big bile duct cholangiocarcinoma].

PubMed

Yin, Long-Lin; Song, Bin; Guan, Ying; Li, Ying-Chun; Chen, Guang-Wen; Zhao, Li-Ming; Lai, Li

2014-09-01

To investigate MRI features and associated histological and pathological changes of hilar and extrahepatic big bile duct cholangiocarcinoma with different morphological sub-types, and its value in differentiating between nodular cholangiocarcinoma (NCC) and intraductal growing cholangiocarcinoma (IDCC). Imaging data of 152 patients with pathologically confirmed hilar and extrahepatic big bile duct cholangiocarcinoma were reviewed, which included 86 periductal infiltrating cholangiocarcinoma (PDCC), 55 NCC, and 11 IDCC. Imaging features of the three morphological sub-types were compared. Each of the subtypes demonstrated its unique imaging features. Significant differences (P < 0.05) were found between NCC and IDCC in tumor shape, dynamic enhanced pattern, enhancement degree during equilibrium phase, multiplicity or singleness of tumor, changes in wall and lumen of bile duct at the tumor-bearing segment, dilatation of tumor upstream or downstream bile duct, and invasion of adjacent organs. Imaging features reveal tumor growth patterns of hilar and extrahepatic big bile duct cholangiocarcinoma. MRI united-sequences examination can accurately describe those imaging features for differentiation diagnosis.

Quantifying heterogeneity of lesion uptake in dynamic contrast enhanced MRI for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Karahaliou, A.; Vassiou, K.; Skiadopoulos, S.; Kanavou, T.; Yiakoumelos, A.; Costaridou, L.

2009-07-01

The current study investigates whether texture features extracted from lesion kinetics feature maps can be used for breast cancer diagnosis. Fifty five women with 57 breast lesions (27 benign, 30 malignant) were subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) on 1.5T system. A linear-slope model was fitted pixel-wise to a representative lesion slice time series and fitted parameters were used to create three kinetic maps (wash out, time to peak enhancement and peak enhancement). 28 grey level co-occurrence matrices features were extracted from each lesion kinetic map. The ability of texture features per map in discriminating malignant from benign lesions was investigated using a Probabilistic Neural Network classifier. Additional classification was performed by combining classification outputs of most discriminating feature subsets from the three maps, via majority voting. The combined scheme outperformed classification based on individual maps achieving area under Receiver Operating Characteristics curve 0.960±0.029. Results suggest that heterogeneity of breast lesion kinetics, as quantified by texture analysis, may contribute to computer assisted tissue characterization in DCE-MRI.

Effects of non-neuronal components for functional connectivity analysis from resting-state functional MRI toward automated diagnosis of schizophrenia

NASA Astrophysics Data System (ADS)

Kim, Junghoe; Lee, Jong-Hwan

2014-03-01

A functional connectivity (FC) analysis from resting-state functional MRI (rsfMRI) is gaining its popularity toward the clinical application such as diagnosis of neuropsychiatric disease. To delineate the brain networks from rsfMRI data, non-neuronal components including head motions and physiological artifacts mainly observed in cerebrospinal fluid (CSF), white matter (WM) along with a global brain signal have been regarded as nuisance variables in calculating the FC level. However, it is still unclear how the non-neuronal components can affect the performance toward diagnosis of neuropsychiatric disease. In this study, a systematic comparison of classification performance of schizophrenia patients was provided employing the partial correlation coefficients (CCs) as feature elements. Pair-wise partial CCs were calculated between brain regions, in which six combinatorial sets of nuisance variables were considered. The partial CCs were used as candidate feature elements followed by feature selection based on the statistical significance test between two groups in the training set. Once a linear support vector machine was trained using the selected features from the training set, the classification performance was evaluated using the features from the test set (i.e. leaveone- out cross validation scheme). From the results, the error rate using all non-neuronal components as nuisance variables (12.4%) was significantly lower than those using remaining combination of non-neuronal components as nuisance variables (13.8 ~ 20.0%). In conclusion, the non-neuronal components substantially degraded the automated diagnosis performance, which supports our hypothesis that the non-neuronal components are crucial in controlling the automated diagnosis performance of the neuropsychiatric disease using an fMRI modality.

Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory.

PubMed

Khazaee, Ali; Ebrahimzadeh, Ata; Babajani-Feremi, Abbas

2015-11-01

Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation. In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD. Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%. Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD. Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. Copyright © 2015 International Federation of Clinical Neurophysiology. All rights reserved.

Machine-learning in grading of gliomas based on multi-parametric magnetic resonance imaging at 3T.

PubMed

Citak-Er, Fusun; Firat, Zeynep; Kovanlikaya, Ilhami; Ture, Ugur; Ozturk-Isik, Esin

2018-06-15

The objective of this study was to assess the contribution of multi-parametric (mp) magnetic resonance imaging (MRI) quantitative features in the machine learning-based grading of gliomas with a multi-region-of-interests approach. Forty-three patients who were newly diagnosed as having a glioma were included in this study. The patients were scanned prior to any therapy using a standard brain tumor magnetic resonance (MR) imaging protocol that included T1 and T2-weighted, diffusion-weighted, diffusion tensor, MR perfusion and MR spectroscopic imaging. Three different regions-of-interest were drawn for each subject to encompass tumor, immediate tumor periphery, and distant peritumoral edema/normal. The normalized mp-MRI features were used to build machine-learning models for differentiating low-grade gliomas (WHO grades I and II) from high grades (WHO grades III and IV). In order to assess the contribution of regional mp-MRI quantitative features to the classification models, a support vector machine-based recursive feature elimination method was applied prior to classification. A machine-learning model based on support vector machine algorithm with linear kernel achieved an accuracy of 93.0%, a specificity of 86.7%, and a sensitivity of 96.4% for the grading of gliomas using ten-fold cross validation based on the proposed subset of the mp-MRI features. In this study, machine-learning based on multiregional and multi-parametric MRI data has proven to be an important tool in grading glial tumors accurately even in this limited patient population. Future studies are needed to investigate the use of machine learning algorithms for brain tumor classification in a larger patient cohort. Copyright © 2018. Published by Elsevier Ltd.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

PubMed

Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

2016-01-01

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Single trial decoding of belief decision making from EEG and fMRI data using independent components features

PubMed Central

Douglas, Pamela K.; Lau, Edward; Anderson, Ariana; Head, Austin; Kerr, Wesley; Wollner, Margalit; Moyer, Daniel; Li, Wei; Durnhofer, Mike; Bramen, Jennifer; Cohen, Mark S.

2013-01-01

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and fMRI data. Our results demonstrate that IC features outperformed features derived from event related spectral perturbations derived from any single spectral band, yet were similar to accuracy across all spectral bands combined. We compared our diagnostic IC spatial maps with our conventional general linear model (GLM) results, and found that informative ICs had significant spatial overlap with our GLM results, yet also revealed unique regions like amygdala that were not statistically significant in GLM analyses. Overall, these results suggest that ICs may yield a parsimonious feature set that can be used along with a decision tree structure for interpretation of features used in classifying complex cognitive processes such as belief and disbelief across both fMRI and EEG neuroimaging modalities. PMID:23914164

Comparative brain stem lesions on MRI of acute disseminated encephalomyelitis, neuromyelitis optica, and multiple sclerosis.

PubMed

Lu, Zhengqi; Zhang, Bingjun; Qiu, Wei; Kang, Zhuang; Shen, Liping; Long, Youming; Huang, Junqi; Hu, Xueqiang

2011-01-01

Brain stem lesions are common in patients with acute disseminated encephalomyelitis (ADEM), neuromyelitis optica (NMO), and multiple sclerosis (MS). To investigate comparative brain stem lesions on magnetic resonance imaging (MRI) among adult patients with ADEM, NMO, and MS. Sixty-five adult patients with ADEM (n = 17), NMO (n = 23), and MS (n = 25) who had brain stem lesions on MRI were enrolled. Morphological features of brain stem lesions among these diseases were assessed. Patients with ADEM had a higher frequency of midbrain lesions than did patients with NMO (94.1% vs. 17.4%, P<0.001) and MS (94.1% vs. 40.0%, P<0.001); patients with NMO had a lower frequency of pons lesions than did patients with MS (34.8% vs. 84.0%, P<0.001) and ADEM (34.8% vs. 70.6%, P = 0.025); and patients with NMO had a higher frequency of medulla oblongata lesions than did patients with ADEM (91.3% vs. 35.3%, P<0.001) and MS (91.3% vs. 36.0%, P<0.001). On the axial section of the brain stem, the majority (82.4%) of patients with ADEM showed lesions on the ventral part; the brain stem lesions in patients with NMO were typically located in the dorsal part (91.3%); and lesions in patients with MS were found in both the ventral (44.0%) and dorsal (56.0%) parts. The lesions in patients with ADEM (100%) and NMO (91.3%) had poorly defined margins, while lesions of patients with MS (76.0%) had well defined margins. Brain stem lesions in patients with ADEM were usually bilateral and symmetrical (82.4%), while lesions in patients with NMO (87.0%) and MS (92.0%) were asymmetrical or unilateral. Brain stem lesions showed various morphological features among adult patients with ADEM, NMO, and MS. The different lesion locations may be helpful in distinguishing these diseases.

Comparative Brain Stem Lesions on MRI of Acute Disseminated Encephalomyelitis, Neuromyelitis Optica, and Multiple Sclerosis

PubMed Central

Kang, Zhuang; Shen, Liping; Long, Youming; Huang, Junqi; Hu, Xueqiang

2011-01-01

Background Brain stem lesions are common in patients with acute disseminated encephalomyelitis (ADEM), neuromyelitis optica (NMO), and multiple sclerosis (MS). Objectives To investigate comparative brain stem lesions on magnetic resonance imaging (MRI) among adult patients with ADEM, NMO, and MS. Methods Sixty-five adult patients with ADEM (n = 17), NMO (n = 23), and MS (n = 25) who had brain stem lesions on MRI were enrolled. Morphological features of brain stem lesions among these diseases were assessed. Results Patients with ADEM had a higher frequency of midbrain lesions than did patients with NMO (94.1% vs. 17.4%, P<0.001) and MS (94.1% vs. 40.0%, P<0.001); patients with NMO had a lower frequency of pons lesions than did patients with MS (34.8% vs. 84.0%, P<0.001) and ADEM (34.8% vs. 70.6%, P = 0.025); and patients with NMO had a higher frequency of medulla oblongata lesions than did patients with ADEM (91.3% vs. 35.3%, P<0.001) and MS (91.3% vs. 36.0%, P<0.001). On the axial section of the brain stem, the majority (82.4%) of patients with ADEM showed lesions on the ventral part; the brain stem lesions in patients with NMO were typically located in the dorsal part (91.3%); and lesions in patients with MS were found in both the ventral (44.0%) and dorsal (56.0%) parts. The lesions in patients with ADEM (100%) and NMO (91.3%) had poorly defined margins, while lesions of patients with MS (76.0%) had well defined margins. Brain stem lesions in patients with ADEM were usually bilateral and symmetrical (82.4%), while lesions in patients with NMO (87.0%) and MS (92.0%) were asymmetrical or unilateral. Conclusions Brain stem lesions showed various morphological features among adult patients with ADEM, NMO, and MS. The different lesion locations may be helpful in distinguishing these diseases. PMID:21853047

Altered Cortico-Striatal–Thalamic Connectivity in Relation to Spatial Working Memory Capacity in Children with ADHD

PubMed Central

Mills, Kathryn L.; Bathula, Deepti; Dias, Taciana G. Costa; Iyer, Swathi P.; Fenesy, Michelle C.; Musser, Erica D.; Stevens, Corinne A.; Thurlow, Bria L.; Carpenter, Samuel D.; Nagel, Bonnie J.; Nigg, Joel T.; Fair, Damien A.

2012-01-01

Introduction: Attention deficit hyperactivity disorder (ADHD) captures a heterogeneous group of children, who are characterized by a range of cognitive and behavioral symptoms. Previous resting-state functional connectivity MRI (rs-fcMRI) studies have sought to understand the neural correlates of ADHD by comparing connectivity measurements between those with and without the disorder, focusing primarily on cortical–striatal circuits mediated by the thalamus. To integrate the multiple phenotypic features associated with ADHD and help resolve its heterogeneity, it is helpful to determine how specific circuits relate to unique cognitive domains of the ADHD syndrome. Spatial working memory has been proposed as a key mechanism in the pathophysiology of ADHD. Methods: We correlated the rs-fcMRI of five thalamic regions of interest (ROIs) with spatial span working memory scores in a sample of 67 children aged 7–11 years [ADHD and typically developing children (TDC)]. In an independent dataset, we then examined group differences in thalamo-striatal functional connectivity between 70 ADHD and 89 TDC (7–11 years) from the ADHD-200 dataset. Thalamic ROIs were created based on previous methods that utilize known thalamo-cortical loops and rs-fcMRI to identify functional boundaries in the thalamus. Results/Conclusion: Using these thalamic regions, we found atypical rs-fcMRI between specific thalamic groupings with the basal ganglia. To identify the thalamic connections that relate to spatial working memory in ADHD, only connections identified in both the correlational and comparative analyses were considered. Multiple connections between the thalamus and basal ganglia, particularly between medial and anterior dorsal thalamus and the putamen, were related to spatial working memory and also altered in ADHD. These thalamo-striatal disruptions may be one of multiple atypical neural and cognitive mechanisms that relate to the ADHD clinical phenotype. PMID:22291667

MRI anatomy of schizophrenia.

PubMed

McCarley, R W; Wible, C G; Frumin, M; Hirayasu, Y; Levitt, J J; Fischer, I A; Shenton, M E

1999-05-01

Structural magnetic resonance imaging (MRI) data have provided much evidence in support of our current view that schizophrenia is a brain disorder with altered brain structure, and consequently involving more than a simple disturbance in neurotransmission. This review surveys 118 peer-reviewed studies with control group from 1987 to May 1998. Most studies (81%) do not find abnormalities of whole brain/intracranial contents, while lateral ventricle enlargement is reported in 77%, and third ventricle enlargement in 67%. The temporal lobe was the brain parenchymal region with the most consistently documented abnormalities. Volume decreases were found in 62% of 37 studies of whole temporal lobe, and in 81% of 16 studies of the superior temporal gyrus (and in 100% with gray matter separately evaluated). Fully 77% of the 30 studies of the medial temporal lobe reported volume reduction in one or more of its constituent structures (hippocampus, amygdala, parahippocampal gyrus). Despite evidence for frontal lobe functional abnormalities, structural MRI investigations less consistently found abnormalities, with 55% describing volume reduction. It may be that frontal lobe volume changes are small, and near the threshold for MRI detection. The parietal and occipital lobes were much less studied; about half of the studies showed positive findings. Most studies of cortical gray matter (86%) found volume reductions were not diffuse, but more pronounced in certain areas. About two thirds of the studies of subcortical structures of thalamus, corpus callosum and basal ganglia (which tend to increase volume with typical neuroleptics), show positive findings, as do almost all (91%) studies of cavum septi pellucidi (CSP). Most data were consistent with a developmental model, but growing evidence was compatible also with progressive, neurodegenerative features, suggesting a "two-hit" model of schizophrenia, for which a cellular hypothesis is discussed. The relationship of clinical symptoms to MRI findings is reviewed, as is the growing evidence suggesting structural abnormalities differ in affective (bipolar) psychosis and schizophrenia.

An investigation into the relationship between vigabatrin, movement disorders, and brain magnetic resonance imaging abnormalities in children with infantile spasms.

PubMed

Fong, Choong Yi; Osborne, John P; Edwards, Stuart W; Hemingway, Cheryl; Hancock, Eleanor; Johnson, Anthony L; Kennedy, Colin R; Kneen, Rachel; Likeman, Marcus; Lux, Andrew L; Mordekar, Santosh R; Murugan, Velayutham; Newton, Richard W; Pike, Michael; Quinn, Michael; Spinty, Stefan; Vassallo, Grace; Verity, Christopher M; Whitney, Andrea; O'Callaghan, Finbar J K

2013-09-01

We aimed to investigate the relationship between movement disorders, changes on brain magnetic resonance imaging (MRI), and vigabatrin therapy in children with infantile spasms. Retrospective review and brain MRI analysis of children enrolled in the International Collaborative Infantile Spasms Study (ICISS) who developed a movement disorder on vigabatrin therapy. Comparisons were made with controls within ICISS who had no movement disorder. Ten of 124 infants had a movement disorder and in eight it had developed on vigabatrin therapy. Two had a movement disorder that resolved on dose-reduction of vigabatrin, one had improvement on withdrawing vigabatrin, two had resolution without any dose change, and in three it persisted despite vigabatrin withdrawal. The typical brain MRI changes associated with vigabatrin therapy were noted in two infants. Ten control infants were identified. Typical MRI changes noted with vigabatrin were noted in three controls. It is possible that in two out of eight cases, vigabatrin was associated with the development of a movement disorder. In six out of eight cases a causal relationship was less plausible. The majority of infants treated with vigabatrin did not develop a movement disorder. MRI changes associated with vigabatrin do not appear to be specifically related to the movement disorder. © 2013 Mac Keith Press.

Test-retest reliability of fMRI during nonverbal semantic decisions in moderate-severe nonfluent aphasia patients

PubMed Central

Kurland, Jacquie; Naeser, Margaret A.; Baker, Errol H.; Doron, Karl; Martin, Paula I.; Seekins, Heidi E.; Bogdan, Andrew; Renshaw, Perry; Yurgelun-Todd, Deborah

2005-01-01

Cortical reorganization in poststroke aphasia is not well understood. Few studies have investigated neural mechanisms underlying language recovery in severe aphasia patients, who are typically viewed as having a poor prognosis for language recovery. Although test-retest reliability is routinely demonstrated during collection of language data in single-subject aphasia research, this is rarely examined in fMRI studies investigating the underlying neural mechanisms in aphasia recovery. The purpose of this study was to acquire fMRI test-retest data examining semantic decisions both within and between two aphasia patients. Functional MRI was utilized to image individuals with chronic, moderate-severe nonfluent aphasia during nonverbal, yes/no button-box semantic judgments of iconic sentences presented in the Computer-assisted Visual Communication (C-ViC) program. We investigated the critical issue of intra-subject reliability by exploring similarities and differences in regions of activation during participants’ performance of identical tasks twice on the same day. Each participant demonstrated high intra-subject reliability, with response decrements typical of task familiarity. Differences between participants included greater left hemisphere perilesional activation in the individual with better response to C-ViC training. This study provides fMRI reliability in chronic nonfluent aphasia, and adds to evidence supporting differences in individual cortical reorganization in aphasia recovery. PMID:15706052

Magnetic Resonance Imaging Criteria for Thrombolysis in Hyperacute Cerebral Infarction

PubMed Central

AHMETGJEKAJ, ILIR; KABASHI-MUÇAJ, SERBEZE; LASCU, LUANA CORINA; KABASHI, ANTIGONA; BONDARI, A.; BONDARI, SIMONA; DEDUSHI-HOTI, KRESHNIKE; BIÇAKU, ARDIAN; SHATRI, JETON

2014-01-01

Purpose: Selection of patients with cerebral infarction for MRI that is suitable for thrombolytic therapy as an emerging application. Although the efficiency of the therapy with i.v. tissue plasminogen activator (tPA) within 3 hours after onset of symptoms has been proven in selected patients with CT, now these criteria are determined by MRI, as the data we gather are fast and accurate in the first hours. Material and methods: MRI screening in patients with acute cerebral infarction before application of thrombolytic therapy was done in a UCC Mannheim in Germany. Unlike trials with CT, MRI studies demonstrated the benefits of therapy up to 6 hours after the onset of symptoms. We studied 21 patients hospitalized in Clinic of Neuroradiology at University Clinical Centre in Mannheim-Germany. They all undergo brain MRI evaluation for stroke. This article reviews literature that has followed application of thrombolysis in patients with cerebral infarction based on MRI. Results: We have analyzed the MRI criteria for i.v. application of tPA at this University Centre. Alongside the personal viewpoints of clinicians, survey reveals a variety of clinical aspects and MRI features that are opened for further more exploration: therapeutic effects, the use of the MRI angiography, dynamics, and other. Conclusions: MRI is a tested imaging method for rapid evaluation of patients with hyperacute cerebral infarction, replacing the use of CT imaging and clinical features. MRI criteria for thrombolytic therapy are being applied in some cerebral vascular centres. In Kosovo, the application of thrombolytic therapy has not started yet. PMID:25729591

Atlas-Guided Segmentation of Vervet Monkey Brain MRI

PubMed Central

Fedorov, Andriy; Li, Xiaoxing; Pohl, Kilian M; Bouix, Sylvain; Styner, Martin; Addicott, Merideth; Wyatt, Chris; Daunais, James B; Wells, William M; Kikinis, Ron

2011-01-01

The vervet monkey is an important nonhuman primate model that allows the study of isolated environmental factors in a controlled environment. Analysis of monkey MRI often suffers from lower quality images compared with human MRI because clinical equipment is typically used to image the smaller monkey brain and higher spatial resolution is required. This, together with the anatomical differences of the monkey brains, complicates the use of neuroimage analysis pipelines tuned for human MRI analysis. In this paper we developed an open source image analysis framework based on the tools available within the 3D Slicer software to support a biological study that investigates the effect of chronic ethanol exposure on brain morphometry in a longitudinally followed population of male vervets. We first developed a computerized atlas of vervet monkey brain MRI, which was used to encode the typical appearance of the individual brain structures in MRI and their spatial distribution. The atlas was then used as a spatial prior during automatic segmentation to process two longitudinal scans per subject. Our evaluation confirms the consistency and reliability of the automatic segmentation. The comparison of atlas construction strategies reveals that the use of a population-specific atlas leads to improved accuracy of the segmentation for subcortical brain structures. The contribution of this work is twofold. First, we describe an image processing workflow specifically tuned towards the analysis of vervet MRI that consists solely of the open source software tools. Second, we develop a digital atlas of vervet monkey brain MRIs to enable similar studies that rely on the vervet model. PMID:22253661

Delineation of early brain development from fetuses to infants with diffusion MRI and beyond.

PubMed

Ouyang, Minhui; Dubois, Jessica; Yu, Qinlin; Mukherjee, Pratik; Huang, Hao

2018-04-12

Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders. Copyright © 2018 Elsevier Inc. All rights reserved.

Microstructure Imaging of Crossing (MIX) White Matter Fibers from diffusion MRI

PubMed Central

Farooq, Hamza; Xu, Junqian; Nam, Jung Who; Keefe, Daniel F.; Yacoub, Essa; Georgiou, Tryphon; Lenglet, Christophe

2016-01-01

Diffusion MRI (dMRI) reveals microstructural features of the brain white matter by quantifying the anisotropic diffusion of water molecules within axonal bundles. Yet, identifying features such as axonal orientation dispersion, density, diameter, etc., in complex white matter fiber configurations (e.g. crossings) has proved challenging. Besides optimized data acquisition and advanced biophysical models, computational procedures to fit such models to the data are critical. However, these procedures have been largely overlooked by the dMRI microstructure community and new, more versatile, approaches are needed to solve complex biophysical model fitting problems. Existing methods are limited to models assuming single fiber orientation, relevant to limited brain areas like the corpus callosum, or multiple orientations but without the ability to extract detailed microstructural features. Here, we introduce a new and versatile optimization technique (MIX), which enables microstructure imaging of crossing white matter fibers. We provide a MATLAB implementation of MIX, and demonstrate its applicability to general microstructure models in fiber crossings using synthetic as well as ex-vivo and in-vivo brain data. PMID:27982056

Development of PEGylated KMnF3 nanoparticles as a T1-weighted contrast agent: chemical synthesis, in vivo brain MR imaging, and accounting for high relaxivity.

PubMed

Liu, Zhi-jun; Song, Xiao-xia; Tang, Qun

2013-06-07

Magnetic nanoparticles consisting of manganese-based T1-weighted contrast agents have rapidly achieved clinical application, however low proton relaxivity impedes further development. In this report, by analyzing nanoparticles' surface oxidation states we propose the possible reason for the low r1 relaxivity of common MnO nanoparticles and develop PEGylated fluoroperovskite KMnF3 nanoparticles as new T1-weighted contrast agents, which exhibit the highest longitudinal relaxivity (r1 = 23.15 mM(-1) s(-1)) among all the reported manganese-based T1-weighted contrast agents. We, for the first time, illustrate a typical example showing that the surface oxidation states of metal ions exposed on the nanoparticles' surfaces are able to influence not only the optical, magnetic, electronic or catalytic properties but also water proton longitudinal relaxivity when applied as an MRI contrast agent. Cytotoxicity tests demonstrate that the PEGylated KMnF3 nanoparticles are free from toxicity. Further in vivo MRI experiments distinctively depict fine anatomical features in brain imaging at a low dose of 5 mg of Mn per kg and possible removal from the kidneys due to their small size and biocompatibility.

Ophthalmoplegic migraine.

PubMed

Bek, Semai; Genc, Gencer; Demirkaya, Seref; Eroglu, Erdal; Odabasi, Zeki

2009-05-01

According to the International Headache Society, ophthalmoplegic migraine is recurrent attacks of headache with migrainous characteristics associated with paresis of one or more ocular cranial nerves (commonly the third nerve) in the absence of any demonstrable intracranial lesion. We report a patient with typical clinical features of ophthalmoplegic migraine. A 21-year-old man had right frontal throbbing headaches recurring twice a year. His headache lasted for 1 to 5 days and was followed by slight drooping of his eyelid and double vision that lasted for approximately 3 months. On examination he had ptosis and adduction paralysis of the right eye. Brain MRI revealed a thickened, enhancing right oculomotor nerve. He was treated with methylprednisolone 1000 mg/d IV for 5 days. Only 2 weeks later, clinical improvement was observed and 3 months later the oculomotor nerve enhancement resolved. Ophthalmoplegic migraine has been considered to have a microvascular, ischemic etiology, but more recently it has been reclassified as a demyelinating condition affecting the oculomotor. To our knowledge, this is the first ophthalmoplegic migraine case presented pretreatment and post-treatment with clinical photographic documentation and an MRI showing enduring thickening of the oculomotor nerve although symptoms and contrast enhancement resolved.

Common neural systems associated with the recognition of famous faces and names: An event-related fMRI study

PubMed Central

Nielson, Kristy A.; Seidenberg, Michael; Woodard, John L.; Durgerian, Sally; Zhang, Qi; Gross, William L.; Gander, Amelia; Guidotti, Leslie M.; Antuono, Piero; Rao, Stephen M.

2010-01-01

Person recognition can be accomplished through several modalities (face, name, voice). Lesion, neurophysiology and neuroimaging studies have been conducted in an attempt to determine the similarities and differences in the neural networks associated with person identity via different modality inputs. The current study used event-related functional-MRI in 17 healthy participants to directly compare activation in response to randomly presented famous and non-famous names and faces (25 stimuli in each of the four categories). Findings indicated distinct areas of activation that differed for faces and names in regions typically associated with pre-semantic perceptual processes. In contrast, overlapping brain regions were activated in areas associated with the retrieval of biographical knowledge and associated social affective features. Specifically, activation for famous faces was primarily right lateralized and famous names were left lateralized. However, for both stimuli, similar areas of bilateral activity were observed in the early phases of perceptual processing. Activation for fame, irrespective of stimulus modality, activated an extensive left hemisphere network, with bilateral activity observed in the hippocampi, posterior cingulate, and middle temporal gyri. Findings are discussed within the framework of recent proposals concerning the neural network of person identification. PMID:20167415

Segmental Schwannomatosis of the Spine: Report of a Rare Case and Brief Review of Literature.

PubMed

Baruah, Ranjit Kumar; Bora, Suresh; Haque, Russel

2016-01-01

To report a case of segmental schwannomatosis involving the dorsal and lumbar spine and describe its excision as well as review of literature on schwannomatosis involving the spine. Schwannomas are nerve sheath tumours which usually occur as solitary lesions. Presence of multiple schwannomas suggests a genetic predisposition to tumorogenesis and possible association with neurofibromatosis. However, in very rare cases multiple schwannomas exist without typical features of neurofibromatosis and constitute a clinically and genetically distinct rare syndrome termed schwannomatosis. A 31-year-old female presented with low back pain with left lower limb radiculopathy and sensory deficit over the L4-L5 dermatome. Auditory and ophthalmologic examinations were normal. MRI showed two discrete intradural masses at D12-L2 and L3-L5. MRI of the brain was negative for any vestibular schwannoma. The tumours were excised discretely through a single midline incision to improve the symptoms. HPE of both the tumours revealed them to be schwannomas. Karyotyping from lymphocyte DNA revealed no abnormality. This is the 3rd case of schwannomatosis involving the dorsal and lumbar spine, in which excision of the tumours led to resolution of symptoms.

Parallel workflow tools to facilitate human brain MRI post-processing

PubMed Central

Cui, Zaixu; Zhao, Chenxi; Gong, Gaolang

2015-01-01

Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues. PMID:26029043

A hybrid method for classifying cognitive states from fMRI data.

PubMed

Parida, S; Dehuri, S; Cho, S-B; Cacha, L A; Poznanski, R R

2015-09-01

Functional magnetic resonance imaging (fMRI) makes it possible to detect brain activities in order to elucidate cognitive-states. The complex nature of fMRI data requires under-standing of the analyses applied to produce possible avenues for developing models of cognitive state classification and improving brain activity prediction. While many models of classification task of fMRI data analysis have been developed, in this paper, we present a novel hybrid technique through combining the best attributes of genetic algorithms (GAs) and ensemble decision tree technique that consistently outperforms all other methods which are being used for cognitive-state classification. Specifically, this paper illustrates the combined effort of decision-trees ensemble and GAs for feature selection through an extensive simulation study and discusses the classification performance with respect to fMRI data. We have shown that our proposed method exhibits significant reduction of the number of features with clear edge classification accuracy over ensemble of decision-trees.

Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods.

PubMed

Georgiadis, Pantelis; Cavouras, Dionisis; Kalatzis, Ioannis; Glotsos, Dimitris; Athanasiadis, Emmanouil; Kostopoulos, Spiros; Sifaki, Koralia; Malamas, Menelaos; Nikiforidis, George; Solomou, Ekaterini

2009-01-01

Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to "unseen" cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.

Contrast-enhanced MRI compared with the physical examination in the evaluation of disease activity in juvenile idiopathic arthritis.

PubMed

Hemke, Robert; Maas, Mario; van Veenendaal, Mira; Dolman, Koert M; van Rossum, Marion A J; van den Berg, J Merlijn; Kuijpers, Taco W

2014-02-01

To assess the value of magnetic resonance imaging (MRI) in discriminating between active and inactive juvenile idiopathic arthritis (JIA) patients and to compare physical examination outcomes with MRI outcomes in the assessment of disease status in JIA patients. Consecutive JIA patients with knee involvement were prospectively studied using an open-bore MRI. Imaging findings from 146 JIA patients were analysed (59.6% female; mean age, 12.9 years). Patients were classified as clinically active or inactive. MRI features were evaluated using the JAMRIS system, comprising validated scores for synovial hypertrophy, bone marrow oedema, cartilage lesions and bone erosions. Inter-reader reliability was good for all MRI features (intra-class correlation coefficient [ICC] = 0.87-0.94). No differences were found between the two groups regarding MRI scores of bone marrow oedema, cartilage lesions or bone erosions. Synovial hypertrophy scores differed significantly between groups (P = 0.016). Nonetheless, synovial hypertrophy was also present in 14 JIA patients (35.9%) with clinically inactive disease. Of JIA patients considered clinically active, 48.6% showed no signs of MRI-based synovitis. MRI can discriminate between clinically active and inactive JIA patients. However, physical examination is neither very sensitive nor specific in evaluating JIA disease activity compared with MRI. Subclinical synovitis was present in >35% of presumed clinically inactive patients. • MRI is sensitive for evaluating juvenile idiopathic arthritis (JIA) disease activity. • Contrast-enhanced MRI can distinguish clinically active and inactive JIA patients. • Subclinical synovitis is present in 35.9 % of presumed clinically inactive patients. • Physical examination is neither sensitive nor specific in evaluating JIA disease activity.

Multi-task linear programming discriminant analysis for the identification of progressive MCI individuals.

PubMed

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method.

Multi-Task Linear Programming Discriminant Analysis for the Identification of Progressive MCI Individuals

PubMed Central

Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang

2014-01-01

Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images). Experimental results show very promising performance of our proposed MLPD method. PMID:24820966

Optic Nerve Assessment Using 7-Tesla Magnetic Resonance Imaging

PubMed Central

Singh, Arun D.; Platt, Sean M.; Lystad, Lisa; Lowe, Mark; Oh, Sehong; Jones, Stephen E.; Alzahrani, Yahya; Plesec, Thomas

2016-01-01

Purpose The purpose of this study was to correlate high-resolution magnetic resonance imaging (MRI) and histologic findings in a case of juxtapapillary choroidal melanoma with clinical evidence of optic nerve invasion. Methods With institutional review board approval, an enucleated globe with choroidal melanoma and optic nerve invasion was imaged using a 7-tesla MRI followed by histopathologic evaluation. Results Optical coherence tomography, B-scan ultrasonography, and 1.5-tesla MRI of the orbit (1-mm sections) could not detect optic disc invasion. Ex vivo, 7-tesla MRI detected optic nerve invasion, which correlated with histopathologic features. Conclusions Our case demonstrates the potential to document the existence of optic nerve invasion in the presence of an intraocular tumor, a feature that has a major bearing on decision making, particularly for consideration of enucleation. PMID:27239461

[Central nervous system involvement in systemic diseases: Spectrum of MRI findings].

PubMed

Drier, A; Bonneville, F; Haroche, J; Amoura, Z; Dormont, D; Chiras, J

2010-12-01

Central nervous system (CNS) involvement in systemic disease (SD) is unusual. MRI features of such lesions are unfamiliar to most radiologists. The diagnosis of SD is still based on clinical features and laboratory findings but some characteristic MRI findings exist for each SD: micronodular leptomeningeal enhancement in sarcoidosis, diffuse or focal pachymeningeal involvement in Wegener disease, dentate nuclei and brain stem lesions in Langerhans cell histiocytosis, meningeal masses, dentate nuclei lesions and periarterial infiltration in Erdheim-Chester disease, meningeal masses in Rosai-Dorfman disease, veinular pontic lesions and cerebral vein thrombosis in Behçet, supratentorial microvascular lesions in lupus and antiphospholipid and Gougerot-Sjögren syndrome. In this work, we explain, describe and illustrate the most characteristic MRI findings for each disease. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Sex Differences and Autism: Brain Function during Verbal Fluency and Mental Rotation

PubMed Central

Minati, Ludovico; Baron-Cohen, Simon; Lombardo, Michael V.; Lai, Meng-Chuan; Walker, Anne; Howard, Dawn; Gray, Marcus A.; Harrison, Neil A.; Critchley, Hugo D.

2012-01-01

Autism spectrum conditions (ASC) affect more males than females. This suggests that the neurobiology of autism: 1) may overlap with mechanisms underlying typical sex-differentiation or 2) alternately reflect sex-specificity in how autism is expressed in males and females. Here we used functional magnetic resonance imaging (fMRI) to test these alternate hypotheses. Fifteen men and fourteen women with Asperger syndrome (AS), and sixteen typically developing men and sixteen typically developing women underwent fMRI during performance of mental rotation and verbal fluency tasks. All groups performed the tasks equally well. On the verbal fluency task, despite equivalent task-performance, both males and females with AS showed enhanced activation of left occipitoparietal and inferior prefrontal activity compared to controls. During mental rotation, there was a significant diagnosis-by-sex interaction across occipital, temporal, parietal, middle frontal regions, with greater activation in AS males and typical females compared to AS females and typical males. These findings suggest a complex relationship between autism and sex that is differentially expressed in verbal and visuospatial domains. PMID:22701630

Sex differences and autism: brain function during verbal fluency and mental rotation.

PubMed

Beacher, Felix D C C; Radulescu, Eugenia; Minati, Ludovico; Baron-Cohen, Simon; Lombardo, Michael V; Lai, Meng-Chuan; Walker, Anne; Howard, Dawn; Gray, Marcus A; Harrison, Neil A; Critchley, Hugo D

2012-01-01

Autism spectrum conditions (ASC) affect more males than females. This suggests that the neurobiology of autism: 1) may overlap with mechanisms underlying typical sex-differentiation or 2) alternately reflect sex-specificity in how autism is expressed in males and females. Here we used functional magnetic resonance imaging (fMRI) to test these alternate hypotheses. Fifteen men and fourteen women with Asperger syndrome (AS), and sixteen typically developing men and sixteen typically developing women underwent fMRI during performance of mental rotation and verbal fluency tasks. All groups performed the tasks equally well. On the verbal fluency task, despite equivalent task-performance, both males and females with AS showed enhanced activation of left occipitoparietal and inferior prefrontal activity compared to controls. During mental rotation, there was a significant diagnosis-by-sex interaction across occipital, temporal, parietal, middle frontal regions, with greater activation in AS males and typical females compared to AS females and typical males. These findings suggest a complex relationship between autism and sex that is differentially expressed in verbal and visuospatial domains.

Magnetic resonance imaging features of dogs with incomplete recovery after acute, severe spinal cord injury

PubMed Central

Lewis, Melissa J.; Cohen, Eli B.; Olby, Natasha J.

2017-01-01

Study Design Retrospective case series Objectives Describe the magnetic resonance imaging (MRI) features of dogs chronically impaired after severe spinal cord injury (SCI) and investigate associations between imaging variables and residual motor function. Setting United States of America Methods Thoracolumbar MRI from dogs with incomplete recovery months to years after clinically complete (paralysis with loss of pain perception) thoracolumbar SCI were reviewed. Lesion features were described and quantified. Gait was quantified using an ordinal, open field scale (OFS). Associations between imaging features and gait scores, duration of injury (DOI) or SCI treatment were determined. Results 35 dogs were included. Median OFS was 2 (0–6), median DOI was 13 months (3–83) and intervertebral disc herniation was the most common diagnosis (n=27). Myelomalacia was the most common qualitative feature followed by cystic change; syringomyelia and fibrosis were uncommon. Lesion length corrected to L2 length (LL:L2) was variable (median LL:L2=3.5 (1.34–11.54)). Twenty-nine dogs had 100% maximum cross-sectional spinal cord compromise (MSCC) at the lesion epicenter and the length of 100% compromised area varied widely (median length 100% MSCC:L2=1.29 (0.39–7.64). Length 100% MSCC:L2 was associated with OFS (p=0.012). OFS was not associated with any qualitative features. DOI or treatment type were not associated with imaging features or lesion quantification. Conclusions Lesion characteristics on MRI in dogs with incomplete recovery after severe SCI were established. Length of 100% MSCC was associated with hind limb motor function. Findings demonstrate a spectrum of injury severity on MRI amongst severely affected dogs which is related to functional status. PMID:29057987

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-11-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer's Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)(2), a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. Copyright © 2014 Elsevier Inc. All rights reserved.

Predicting axillary lymph node metastasis from kinetic statistics of DCE-MRI breast images

NASA Astrophysics Data System (ADS)

Ashraf, Ahmed B.; Lin, Lilie; Gavenonis, Sara C.; Mies, Carolyn; Xanthopoulos, Eric; Kontos, Despina

2012-03-01

The presence of axillary lymph node metastases is the most important prognostic factor in breast cancer and can influence the selection of adjuvant therapy, both chemotherapy and radiotherapy. In this work we present a set of kinetic statistics derived from DCE-MRI for predicting axillary node status. Breast DCE-MRI images from 69 women with known nodal status were analyzed retrospectively under HIPAA and IRB approval. Axillary lymph nodes were positive in 12 patients while 57 patients had no axillary lymph node involvement. Kinetic curves for each pixel were computed and a pixel-wise map of time-to-peak (TTP) was obtained. Pixels were first partitioned according to the similarity of their kinetic behavior, based on TTP values. For every kinetic curve, the following pixel-wise features were computed: peak enhancement (PE), wash-in-slope (WIS), wash-out-slope (WOS). Partition-wise statistics for every feature map were calculated, resulting in a total of 21 kinetic statistic features. ANOVA analysis was done to select features that differ significantly between node positive and node negative women. Using the computed kinetic statistic features a leave-one-out SVM classifier was learned that performs with AUC=0.77 under the ROC curve, outperforming the conventional kinetic measures, including maximum peak enhancement (MPE) and signal enhancement ratio (SER), (AUCs of 0.61 and 0.57 respectively). These findings suggest that our DCE-MRI kinetic statistic features can be used to improve the prediction of axillary node status in breast cancer patients. Such features could ultimately be used as imaging biomarkers to guide personalized treatment choices for women diagnosed with breast cancer.

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2014-01-01

For the last decade, it has been shown that neuroimaging can be a potential tool for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), and also fusion of different modalities can further provide the complementary information to enhance diagnostic accuracy. Here, we focus on the problems of both feature representation and fusion of multimodal information from Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). To our best knowledge, the previous methods in the literature mostly used hand-crafted features such as cortical thickness, gray matter densities from MRI, or voxel intensities from PET, and then combined these multimodal features by simply concatenating into a long vector or transforming into a higher-dimensional kernel space. In this paper, we propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Specifically, we use Deep Boltzmann Machine (DBM)1, a deep network with a restricted Boltzmann machine as a building block, to find a latent hierarchical feature representation from a 3D patch, and then devise a systematic method for a joint feature representation from the paired patches of MRI and PET with a multimodal DBM. To validate the effectiveness of the proposed method, we performed experiments on ADNI dataset and compared with the state-of-the-art methods. In three binary classification problems of AD vs. healthy Normal Control (NC), MCI vs. NC, and MCI converter vs. MCI non-converter, we obtained the maximal accuracies of 95.35%, 85.67%, and 74.58%, respectively, outperforming the competing methods. By visual inspection of the trained model, we observed that the proposed method could hierarchically discover the complex latent patterns inherent in both MRI and PET. PMID:25042445

Radiologic Diagnosis of Spondylodiscitis, Role of Magnetic Resonance.

PubMed

Ramadani, Naser; Dedushi, Kreshnike; Kabashi, Serbeze; Mucaj, Sefedin

2017-03-01

Study aim is to report the Magnetic Resonance Imaging (MRI) features of acute and chronic spontaneous spondylodiscitis. 57 year old female, complaining of a fever and longstanding cervical pain worsened during physical therapy. MR images were acquired using superconductive magnet 1.5 T, with the following sequences: sagittal PD and T2 TSE, sagittal T1 SE, axial PD and T2 TSE (lumbar spine), axial T2 GRE (cervical spine). Axial and sagittal T1 SE after administration of (gadolinium DTPA). Examination was reviewed by three radiologists and compared to CT findings. Patient reported cervical pain associated with fever and minimal weight loss. Blood tests were normal except hyperglycemia (DM tip II). X Ray: vertebral destruction localized at C-4 and C-5: NECT: destruction of the C-4/C-5 vertebral bodies (ventral part). MRI: Low signal of the bone marrow on T1l images, which enhanced after Gd-DTPA administration and became intermediate or high on T2 images. The steady high signal intensity of the disk on T2 images and enhancement on T1 images is typical for an acute inflammatory process. Bone Scintigrafi results: Bone changes suspicious for metastasis. Whole body CT results: apart from spine, no other significant changes. MRI is the most sensitive technique for the diagnosis of spondylodiscitis in the acute phase and comparable to CT regarding chronial stage of the disease. The present imagining essay os aimed at showing the main magnetic resonance imaging findings of tuberculous discitis.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera.

PubMed

Clausner, Tommy; Dalal, Sarang S; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D . Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position.

MRI-based intelligence quotient (IQ) estimation with sparse learning.

PubMed

Wang, Liye; Wee, Chong-Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

2015-01-01

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

Photogrammetry-Based Head Digitization for Rapid and Accurate Localization of EEG Electrodes and MEG Fiducial Markers Using a Single Digital SLR Camera

PubMed Central

Clausner, Tommy; Dalal, Sarang S.; Crespo-García, Maité

2017-01-01

The performance of EEG source reconstruction has benefited from the increasing use of advanced head modeling techniques that take advantage of MRI together with the precise positions of the recording electrodes. The prevailing technique for registering EEG electrode coordinates involves electromagnetic digitization. However, the procedure adds several minutes to experiment preparation and typical digitizers may not be accurate enough for optimal source reconstruction performance (Dalal et al., 2014). Here, we present a rapid, accurate, and cost-effective alternative method to register EEG electrode positions, using a single digital SLR camera, photogrammetry software, and computer vision techniques implemented in our open-source toolbox, janus3D. Our approach uses photogrammetry to construct 3D models from multiple photographs of the participant's head wearing the EEG electrode cap. Electrodes are detected automatically or semi-automatically using a template. The rigid facial features from these photo-based models are then surface-matched to MRI-based head reconstructions to facilitate coregistration to MRI space. This method yields a final electrode coregistration error of 0.8 mm, while a standard technique using an electromagnetic digitizer yielded an error of 6.1 mm. The technique furthermore reduces preparation time, and could be extended to a multi-camera array, which would make the procedure virtually instantaneous. In addition to EEG, the technique could likewise capture the position of the fiducial markers used in magnetoencephalography systems to register head position. PMID:28559791

[Neuroradiological pattern of peripartum cerebro vascular disease medicating transfer to determine care unit].

PubMed

Lakhdar, Rim; Baffoun, Nader; Hammami, Nadia; Nagi, Sonia; Baccar, Kamel; Drissi, Syrine; Kaddour, Chokri

2012-03-01

Pregnancy and puerperium are considered a period of a high risk of stroke responsible in a part of the morbidity and mortality in women. Imaging is the pivotal tool to diagnostics and care. To investigate the clinical and imaging features cerebrovascular complications during pregnancy and in post partum period. We report a retrospective analysis of forty four patients (November 2002 - October 2010) admitted in the intensive car department of the national institute of neurology for cerebro-vascular complications during pregnancy and in post partum period. Cerebro-vascular imaging modalities included cerebral computed tomography (CCT) with and without contrast in 94% of cases, magnetic resonance imaging (MRI) in 30.6% of cases completed by venous angiography MRI in 27.2% of cases and angiography MRI of Willis polygon in 11.3% of cases and by cerebral angiography in 13.6% of cases. Posterior reversible encephalopathy syndrome (PRES) is diagnosed in 61.4 % of cases followed by meningo-cerebral haemorrhage (MCH) in 29.5% and finally cerebral venous thrombosis (CVT) and arterial ischemia in 4.5% of cases each one. The cerebro-vascular complications are revelled in 86.3 % of the cases during the postpartum and were associated with the eclampsia or preeclampsia in 90.9 % of the cases (n=40). CCT showed typical lesions of PRES in 23 patients. It confirms the presence of hematoma in the 13 patients with MCH and find hypodense lesion in one case with ischemic stroke. CCT show direct (delta sign) and indirect signs of CVT. MRI confirms the diagnostic of PRES, when done (11 of 12 cases) and show cortical sub cortical hyper signal on T2 and FLAIR and hypo signal on T1 sequences. MRI was normal in one case. It shows hemorrhagic lesion in the 2 cases of MCH, thrombosis in the cases of CVT and ischemic lesion in the cases of ischemic stroke. CCT and MRI done within 48 hours from admission were decisive for early diagnostic and for fast and adequate care. Early recognition of stroke in peri partum by cerebral imaging is of paramount importance for prompt diagnosis and treatment to improve maternal morbidity and mortality.

Perceiving Age and Gender in Unfamiliar Faces: An fMRI Study on Face Categorization

ERIC Educational Resources Information Center

Wiese, Holger; Kloth, Nadine; Gullmar, Daniel; Reichenbach, Jurgen R.; Schweinberger, Stefan R.

2012-01-01

Efficient processing of unfamiliar faces typically involves their categorization (e.g., into old vs. young or male vs. female). However, age and gender categorization may pose different perceptual demands. In the present study, we employed functional magnetic resonance imaging (fMRI) to compare the activity evoked during age vs. gender…

Neural Basis of Irony Comprehension in Children with Autism: The Role of Prosody and Context

ERIC Educational Resources Information Center

Wang, A. Ting; Lee, Susan S.; Sigman, Marian; Dapretto, Mirella

2006-01-01

While individuals with autism spectrum disorders (ASD) are typically impaired in interpreting the communicative intent of others, little is known about the neural bases of higher-level pragmatic impairments. Here, we used functional MRI (fMRI) to examine the neural circuitry underlying deficits in understanding irony in high-functioning children…

A rare cause of recalcitrant coccydynia: benign dermoid cyst masquerading as coccygeal pain.

PubMed

Gaike, Chandrasekhar V; Kanna, Rishi M; Shetty, Ajoy P; Rajasekaran, S

2016-05-01

Coccydynia is a common entity in orthopedic practice, and various etiologies have been described for it. However, benign dermoid cyst causing coccydynia has not yet been reported. A 20-year-old male presented with typical symptoms of coccydynia recalcitrant to conservative treatment for 2 years. Since pain interfered with his daily activities, magnetic resonance imaging was performed which showed a circumscribed precoccygeal cystic lesion. The patient underwent coccygectomy along with cyst excision. Histological examination revealed features of benign dermoid cyst. After surgery, the patient had excellent relief of his symptoms. The case report identifies that the treating surgeon should be aware of benign dermoid cyst as one of the treatable but rare causes of intractable coccydynia, and MRI should be performed in patients with persistent coccygeal pain.

Two sibs who are double heterozygotes for achondroplasia and pseudoachondroplastic dysplasia.

PubMed Central

Woods, C G; Rogers, J G; Mayne, V

1994-01-01

We report a family in which two sibs have both achondroplasia and pseudoachondroplastic dysplasia. The mother has achondroplasia and the father has pseudoachondroplastic dysplasia, which he had inherited from his father. Both children appeared typical of achondroplasia at birth. By 1 1/2 years they had developed a fixed lumbar kyphosis with gibbus and had additional x ray changes unusual for just achondroplasia and suggestive of pseudoachondroplastic dysplasia. Subsequently both children have shown characteristic features of both conditions and have grown less well than expected for achondroplasia. Radiographs show the striking synergistic effects of the two conditions. MRI in both sibs confirmed brain stem compression at the foramen magnum. This may be an important complication and should be actively sought in any double heterozygote. Images PMID:7966194

[Chronic Inflammatory Demyelinating Polyneuropathy].

PubMed

Balke, M; Wunderlich, G; Brunn, A; Fink, G R; Lehmann, H C

2016-12-01

Chronic inflammatory demyelinating polyneuropathy (CIDP) is a chronic progressive or relapsing autoimmune neuropathy with heterogeneous clinical presentation. Symptoms typically include symmetrical, proximal and/or distal paresis and sensory loss. Atypical CIDP variants are increasingly recognized, including subtypes with rapid onset as well as variants with pure sensory, focal or marked asymmetrical deficits. Diagnosis is established by compatible symptoms, characteristic electrophysiological features and cerebrospinal fluid analysis. In unequivocal cases, inflammatory infiltrates in sural nerve biopsy support the diagnosis. Recent studies suggest that diagnostic imaging techniques such as MRI and nerve ultrasound may become useful tools for establishing the diagnosis. First-line therapies include immunoglobulines, steroids, and plasmapheresis. Immunosuppressant agents and monoclonal antibodies are used in therapy-refractory cases or as cortison-saving agents. © Georg Thieme Verlag KG Stuttgart · New York.

Hemorrhage detection in MRI brain images using images features

NASA Astrophysics Data System (ADS)

Moraru, Luminita; Moldovanu, Simona; Bibicu, Dorin; Stratulat (Visan), Mirela

2013-11-01

The abnormalities appear frequently on Magnetic Resonance Images (MRI) of brain in elderly patients presenting either stroke or cognitive impairment. Detection of brain hemorrhage lesions in MRI is an important but very time-consuming task. This research aims to develop a method to extract brain tissue features from T2-weighted MR images of the brain using a selection of the most valuable texture features in order to discriminate between normal and affected areas of the brain. Due to textural similarity between normal and affected areas in brain MR images these operation are very challenging. A trauma may cause microstructural changes, which are not necessarily perceptible by visual inspection, but they could be detected by using a texture analysis. The proposed analysis is developed in five steps: i) in the pre-processing step: the de-noising operation is performed using the Daubechies wavelets; ii) the original images were transformed in image features using the first order descriptors; iii) the regions of interest (ROIs) were cropped from images feature following up the axial symmetry properties with respect to the mid - sagittal plan; iv) the variation in the measurement of features was quantified using the two descriptors of the co-occurrence matrix, namely energy and homogeneity; v) finally, the meaningful of the image features is analyzed by using the t-test method. P-value has been applied to the pair of features in order to measure they efficacy.

Ultra-low field T1 vs. T1rho at 3T and 7T: study of rotationally immobilized protein gels and animal brain tissues

NASA Astrophysics Data System (ADS)

Dong, Hui; Inglis, Ben; Barr, Ian; Clarke, John

2015-03-01

Clinical magnetic resonance imaging (MRI) machines operating in static fields of typically 1.5 T or 3 T can capture information on slow molecular dynamics utilizing the so-called T1rho technique. This technique, in which a radiofrequency (RF) spin-lock field is applied with microtesla amplitude, has been used, for example, to determine the onset time of stroke in studies on rats. The long RF pulse, however, may exceed the specific absorption rate (SAR) limit, putting subjects at risk. Ultra-low-field (ULF) MRI, based on Superconducting Quantum Interference Devices (SQUIDs), directly detects proton signals at a static magnetic field of typically 50-250 μT. Using our ULF MRI system with adjustable static field of typically 55 to 240 μT, we systematically measured the T1 and T2 dispersion profiles of rotationally immobilized protein gels (bovine serum albumin), ex vivo pig brains, and ex vivo rat brains with induced stroke. Comparing the ULF results with T1rho dispersion obtained at 3 T and 7 T, we find that the degree of protein immobilization determines the frequency-dependence of both T1 and T1rho. Furthermore, T1rho and ULF T1 show similar results for stroke, suggesting that ULF MRI may be used to image traumatic brain injury with negligible SAR. This research was supported by the Henry H. Wheeler, Jr. Brain Imaging Center and the Donaldson Trust.

Ocular dermoid in Pai Syndrome: A review.

PubMed

Tormey, Peter; Bilic Cace, Iva; Boyle, Michael A

2017-04-01

Pai Syndrome is a rare congenital malformation syndrome of unknown cause with hypertelorism, midline cleft lip, nasal and facial polyps, ocular anomalies and the presence of distinctive lipomas adjacent to the corpus callosum. Herein, we present an infant girl with Pai Syndrome diagnosed in the first week of life with typical facial findings and associated pericallosal lipoma identified on cranial ultrasound and brain MRI. These typical features identified included median cleft of the upper lip (in her case as a forme fruste) with a cleft alveolus and a mid-anterior alveolar process congenital polyp. In addition to these findings there was mild hypertelorism and an ocular abnormality on the right eye. An ophthalmology assessment on day 5 identified the ocular lesion as a limbal dermoid. Several ocular anomalies have been reported in association with Pai Syndrome, however, dermoids have not been frequently described in this Syndrome and not before in a limbal location. Increasing identification of previously unreported ocular abnormalities in Pai Syndrome may improve diagnosis and may prove useful in future work attempting to elucidate the aetiology of this rare syndrome. Copyright © 2017. Published by Elsevier Masson SAS.

Clinical use of MRI for the evaluation of acute appendicitis during pregnancy.

PubMed

Patel, Darshan; Fingard, Jordan; Winters, Sean; Low, Gavin

2017-07-01

The purpose of this study was to determine the diagnostic accuracy of MRI for detecting acute appendicitis in pregnancy in a multi-institution study involving general body MR readers with no specific expertise in MR imaging of the pregnant patient. Retrospective review of MRI examinations on PACS in 42 pregnant patients was evaluated for acute right lower quadrant pain. Three fellowship-trained general body radiologists analyzed the MRI examinations in consensus and attempted to localize the appendix, assess for features of appendicitis, and exclude alternative etiologies for the right lower quadrant pain. Of the 42 MRI examinations, the readers noted 6 cases of acute appendicitis, 16 cases of a normal appendix, and 20 cases involving non-visualization of the appendix but where there were no secondary features of acute appendicitis. Based on the surgical data and clinical follow-up, there were 3 true-positive cases, 3 false-positive cases, 34 true-negative cases, and 2 false-negative cases of acute appendicitis on MRI. This yielded an accuracy of 88.1%, sensitivity of 60%, specificity of 91.9%, positive predictive value of 50%, and negative predictive value of 94.4% for the detection of acute appendicitis in the pregnant patient on MRI. Alternative etiologies for the right lower quadrant pain on MRI included torsion of an ovarian dermoid in 1 case and pyelonephritis in 1 case. MRI is an excellent modality for excluding acute appendicitis in pregnant patients presenting with right lower quadrant pain.

Bayesian uncertainty quantification in linear models for diffusion MRI.

PubMed

Sjölund, Jens; Eklund, Anders; Özarslan, Evren; Herberthson, Magnus; Bånkestad, Maria; Knutsson, Hans

2018-03-29

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. Copyright © 2018 Elsevier Inc. All rights reserved.

Development of a Comprehensive Osteochondral Allograft MRI Scoring System (OCAMRISS) With Histopathologic, Micro–Computed Tomography, and Biomechanical Validation

PubMed Central

Pallante-Kichura, Andrea L.; Bae, Won C.; Du, Jiang; Statum, Sheronda; Wolfson, Tanya; Gamst, Anthony C.; Cory, Esther; Amiel, David; Bugbee, William D.; Sah, Robert L.; Chung, Christine B.

2014-01-01

Objective: To describe and apply a semiquantitative MRI scoring system for multifeature analysis of cartilage defect repair in the knee by osteochondral allografts and to correlate this scoring system with histopathologic, micro–computed tomography (µCT), and biomechanical reference standards using a goat repair model. Design: Fourteen adult goats had 2 osteochondral allografts implanted into each knee: one in the medial femoral condyle and one in the lateral trochlea. At 12 months, goats were euthanized and MRI was performed. Two blinded radiologists independently rated 9 primary features for each graft, including cartilage signal, fill, edge integration, surface congruity, calcified cartilage integrity, subchondral bone plate congruity, subchondral bone marrow signal, osseous integration, and presence of cystic changes. Four ancillary features of the joint were also evaluated, including opposing cartilage, meniscal tears, synovitis, and fat-pad scarring. Comparison was made with histologic and µCT reference standards as well as biomechanical measures. Interobserver agreement and agreement with reference standards was assessed. Cohen’s κ, Spearman’s correlation, and Kruskal-Wallis tests were used as appropriate. Results: There was substantial agreement (κ > 0.6, P < 0.001) for each MRI feature and with comparison against reference standards, except for cartilage edge integration (κ = 0.6). There was a strong positive correlation between MRI and reference standard scores (ρ = 0.86, P < 0.01). Osteochondral allograft MRI scoring system was sensitive to differences in outcomes between the types of allografts. Conclusions: We have described a comprehensive MRI scoring system for osteochondral allografts and have validated this scoring system with histopathologic and µCT reference standards as well as biomechanical indentation testing. PMID:24489999

Prognostic significance of MRI findings in patients with myxoid-round cell liposarcoma.

PubMed

Tateishi, Ukihide; Hasegawa, Tadashi; Beppu, Yasuo; Kawai, Akira; Satake, Mitsuo; Moriyama, Noriyuki

2004-03-01

The aims of this study were to determine the prognostic significance of MRI findings in patients with myxoid-round cell liposarcomas and to clarify which MRI features best indicate tumors with adverse clinical behavior. The initial MRI studies of 36 pathologically confirmed myxoid-round cell liposarcomas were retrospectively reviewed, and observations from this review were correlated with the histopathologic features. MR images were evaluated by two radiologists with agreement by consensus, and both univariate and multivariate analyses were conducted to evaluate survival with a median clinical follow-up of 33 months (range, 9-276 months). Statistically significant MRI findings that favored a diagnosis of intermediate- or high-grade tumor were large tumor size (> 10 cm), deeply situated tumor, tumor possessing irregular contours, absence of lobulation, absence of thin septa, presence of thick septa, absence of tumor capsule, high-intensity signal pattern, pronounced enhancement, and globular or nodular enhancement. Of these MRI findings, thin septa (p < 0.05), a tumor capsule (p < 0.01), and pronounced enhancement (p < 0.01) were associated significantly, according to univariate analysis, with overall survival. Multivariate analysis indicated that pronounced enhancement was associated significantly with overall survival (p < 0.05). Contrast-enhanced MRI findings can indicate a good or adverse prognosis in patients with myxoid-round cell liposarcomas.

[Magnetic resonance semiotics of prostate cancer according to the PI-RADS classification. The clinical diagnostic algorithm of a study].

PubMed

Korobkin, A S; Shariya, M A; Chaban, A S; Voskanvan, G A; Vinarov, A Z

2015-01-01

to elaborate the magnetic resonance imaging (MRI) signs of prostate cancer (PC) in accordance with the PI-RADS classification during multiparametric MRI (mpMRI). A total of 89 men aged 20 to 82 years were examined. A control group consisted of 8 (9%) healthy volunteers younger than 30 years of age with no urological history to obtain control images and MRI plots and 20 (22.5%) men aged 26-76 years, whose morphological changes were inflammatory and hyperplastic. The second age-matched group included 61 (68.5%) patients diagnosed with prostate cancer at morphological examination. A set of studies included digital rectal examination, serum prostate-specific antigen, and transrectal ultrasound-guided prostate biopsy. All the patients underwent prostate mpMRI applying a 3.0 T Achieva MRI scanner (Philips, the Netherlands). The patients have been found to have mpMRI signs that were typical of PC; its MRI semiotics according to the PI-RADS classification is presented. Each mpMRI procedure has been determined to be of importance and informative value in detecting PC. The comprehensive mpMRI approach to diagnosing PC improves the quality and diagnostic value of prostate MRI.

[Optimization of diagnosis indicator selection and inspection plan by 3.0T MRI in breast cancer].

PubMed

Jiang, Zhongbiao; Wang, Yunhua; He, Zhong; Zhang, Lejun; Zheng, Kai

2013-08-01

To optimize 3.0T MRI diagnosis indicator in breast cancer and to select the best MRI scan program. Totally 45 patients with breast cancers were collected, and another 35 patients with benign breast tumor served as the control group. All patients underwent 3.0T MRI, including T1- weighted imaging (T1WI), fat suppression of the T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), 1H magnetic resonance spectroscopy (1H-MRS) and dynamic contrast enhanced (DCE) sequence. With operation pathology results as the gold standard in the diagnosis of breast diseases, the pathological results of benign and malignant served as dependent variables, and the diagnostic indicators of MRI were taken as independent variables. We put all the indicators of MRI examination under Logistic regression analysis, established the Logistic model, and optimized the diagnosis indicators of MRI examination to further improve MRI scan of breast cancer. By Logistic regression analysis, some indicators were selected in the equation, including the edge feature of the tumor, the time-signal intensity curve (TIC) type and the apparent diffusion coefficient (ADC) value when b=500 s/mm2. The regression equation was Logit (P)=-21.936+20.478X6+3.267X7+ 21.488X3. Valuable indicators in the diagnosis of breast cancer are the edge feature of the tumor, the TIC type and the ADC value when b=500 s/mm2. Combining conventional MRI scan, DWI and dynamic enhanced MRI is a better examination program, while MRS is the complementary program when diagnosis is difficult.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parekh, V; Jacobs, MA

Purpose: Multiparametric radiological imaging is used for diagnosis in patients. Potentially extracting useful features specific to a patient’s pathology would be crucial step towards personalized medicine and assessing treatment options. In order to automatically extract features directly from multiparametric radiological imaging datasets, we developed an advanced unsupervised machine learning algorithm called the multidimensional imaging radiomics-geodesics(MIRaGe). Methods: Seventy-six breast tumor patients underwent 3T MRI breast imaging were used for this study. We tested the MIRaGe algorithm to extract features for classification of breast tumors into benign or malignant. The MRI parameters used were T1-weighted, T2-weighted, dynamic contrast enhanced MR imaging (DCE-MRI)more » and diffusion weighted imaging(DWI). The MIRaGe algorithm extracted the radiomics-geodesics features (RGFs) from multiparametric MRI datasets. This enable our method to learn the intrinsic manifold representations corresponding to the patients. To determine the informative RGF, a modified Isomap algorithm(t-Isomap) was created for a radiomics-geodesics feature space(tRGFS) to avoid overfitting. Final classification was performed using SVM. The predictive power of the RGFs was tested and validated using k-fold cross validation. Results: The RGFs extracted by the MIRaGe algorithm successfully classified malignant lesions from benign lesions with a sensitivity of 93% and a specificity of 91%. The top 50 RGFs identified as the most predictive by the t-Isomap procedure were consistent with the radiological parameters known to be associated with breast cancer diagnosis and were categorized as kinetic curve characterizing RGFs, wash-in rate characterizing RGFs, wash-out rate characterizing RGFs and morphology characterizing RGFs. Conclusion: In this paper, we developed a novel feature extraction algorithm for multiparametric radiological imaging. The results demonstrated the power of the MIRaGe algorithm at automatically discovering useful feature representations directly from the raw multiparametric MRI data. In conclusion, the MIRaGe informatics model provides a powerful tool with applicability in cancer diagnosis and a possibility of extension to other kinds of pathologies. NIH (P50CA103175, 5P30CA006973 (IRAT), R01CA190299, U01CA140204), Siemens Medical Systems (JHU-2012-MR-86-01) and Nivida Graphics Corporation.« less

Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI

NASA Astrophysics Data System (ADS)

Alomari, Raja'S.; Corso, Jason J.; Chaudhary, Vipin; Dhillon, Gurmeet

2010-03-01

Intervertebral disc herniation is a major reason for lower back pain (LBP), which is the second most common neurological ailment in the United States. Automation of herniated disc diagnosis reduces the large burden on radiologists who have to diagnose hundreds of cases each day using clinical MRI. We present a method for automatic diagnosis of lumbar disc herniation using appearance and shape features. We jointly use the intensity signal for modeling the appearance of herniated disc and the active shape model for modeling the shape of herniated disc. We utilize a Gibbs distribution for classification of discs using appearance and shape features. We use 33 clinical MRI cases of the lumbar area for training and testing both appearance and shape models. We achieve over 91% accuracy in detection of herniation in a cross-validation experiment with specificity of 91% and sensitivity of 94%.

A narrative overview of the current status of MRI of the hip and its relevance for osteoarthritis research - what we know, what has changed and where are we going?

PubMed

Crema, M D; Watts, G J; Guermazi, A; Kim, Y-J; Kijowski, R; Roemer, F W

2017-01-01

To review and discuss the role of magnetic resonance imaging (MRI) in the context of hip osteoarthritis (OA) research. The content of this narrative review, based on an extensive PubMed database research including English literature only, describes the advances in MRI of the hip joint and its potential usefulness in hip OA research, reviews the relevance of different MRI features in regard to symptomatic and structural progression in hip OA, and gives an outlook regarding future use of MRI in hip OA research endeavors. Recent technical advances have helped to overcome many of the past difficulties related to MRI assessment of hip OA. MRI-based morphologic scoring systems allow for detailed assessment of several hip joint tissues and, in combination with the recent advances in MRI, may increase reproducibility and sensitivity to change. Compositional MRI techniques may add to our understanding of disease onset and progression. Knowledge about imaging pitfalls and anatomical variants is crucial to avoid misinterpretation. In comparison to research on knee OA, the associations between MRI features and the incidence and progression of disease as well as with clinical symptoms have been little explored. Anatomic alterations of the hip joint as seen in femoro-acetabular impingement (FAI) seem to play a role in the onset and progression of structural damage. With the technical advances occurring in recent years, MRI may play a major role in investigating the natural history of hip OA and provide an improved method for assessment of the efficacy of new therapeutic approaches. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

MRI-guided Breast Biopsy: Outcomes and Impact on Patient Management

PubMed Central

Kamel, Ihab R; Macura, Katarzyna J

2014-01-01

Introduction The purpose of this study was to correlate the pathology results of magnetic resonance imaging (MRI)-guided breast biopsies at our institution to MRI findings and patient clinical history characteristics. The impact of MRI-guided breast biopsies on surgical management in patients with a new diagnosis of breast cancer was also assessed. Patients and Methods In this HIPAA-compliant study we retrospectively reviewed all MRI-guided breast biopsies performed 3/2006–5/2012. Clinical history, MRI features and pathology outcomes were reviewed. In patients undergoing breast MRI to evaluate extent of disease, any change in surgical management resulting from the MRI-guided biopsy was recorded. Statistical analysis included binary logistic regression and independent student’s t-test. Results Two-hundred fifteen lesions in 168 patients were included, of which 23 (10.7%) were malignant, 43 (20%) were high risk, and 149 (69.3%) were benign. No clinical characteristic was associated with malignancy in our cohort. MRI features associated with malignancy were: larger size (mean 2.6 cm versus 1.3 cm, p=0.046), washout kinetics (18% malignancy rate, p=0.02) and marked background parenchymal enhancement (40% malignancy rate, p-value <0.001 to 0.03). Nineteen (28%) of the 67 patients with a new diagnosis of breast cancer undergoing MRI-guided breast biopsy had a change in surgical management based on the biopsy result. Conclusions Malignancy rate was associated with lesion size, washout kinetics and marked background enhancement of the breast parenchyma but was not associated with any clinical history characteristics. Pre-operative MRI-guided breast biopsies changed surgical management in 28% of women with a new diagnosis of breast cancer. PMID:25499596

Manifold Regularized Multitask Feature Learning for Multimodality Disease Classification

PubMed Central

Jie, Biao; Zhang, Daoqiang; Cheng, Bo; Shen, Dinggang

2015-01-01

Multimodality based methods have shown great advantages in classification of Alzheimer’s disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis. PMID:25277605

Carotid Artery Disease and Stroke: Assessing Risk with Vessel Wall MRI

PubMed Central

Kerwin, William S.

2012-01-01

Although MRI is widely used to diagnose stenotic carotid arteries, it also detects characteristics of the atherosclerotic plaque itself, including its size, composition, and activity. These features are emerging as additional risk factors for stroke that can be feasibly acquired clinically. This paper summarizes the state of evidence for a clinical role for MRI of carotid atherosclerosis. PMID:23209940

Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study.

PubMed

Vidić, Igor; Egnell, Liv; Jerome, Neil P; Teruel, Jose R; Sjøbakk, Torill E; Østlie, Agnes; Fjøsne, Hans E; Bathen, Tone F; Goa, Pål Erik

2018-05-01

Diffusion-weighted MRI (DWI) is currently one of the fastest developing MRI-based techniques in oncology. Histogram properties from model fitting of DWI are useful features for differentiation of lesions, and classification can potentially be improved by machine learning. To evaluate classification of malignant and benign tumors and breast cancer subtypes using support vector machine (SVM). Prospective. Fifty-one patients with benign (n = 23) and malignant (n = 28) breast tumors (26 ER+, whereof six were HER2+). Patients were imaged with DW-MRI (3T) using twice refocused spin-echo echo-planar imaging with echo time / repetition time (TR/TE) = 9000/86 msec, 90 × 90 matrix size, 2 × 2 mm in-plane resolution, 2.5 mm slice thickness, and 13 b-values. Apparent diffusion coefficient (ADC), relative enhanced diffusivity (RED), and the intravoxel incoherent motion (IVIM) parameters diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f) were calculated. The histogram properties (median, mean, standard deviation, skewness, kurtosis) were used as features in SVM (10-fold cross-validation) for differentiation of lesions and subtyping. Accuracies of the SVM classifications were calculated to find the combination of features with highest prediction accuracy. Mann-Whitney tests were performed for univariate comparisons. For benign versus malignant tumors, univariate analysis found 11 histogram properties to be significant differentiators. Using SVM, the highest accuracy (0.96) was achieved from a single feature (mean of RED), or from three feature combinations of IVIM or ADC. Combining features from all models gave perfect classification. No single feature predicted HER2 status of ER + tumors (univariate or SVM), although high accuracy (0.90) was achieved with SVM combining several features. Importantly, these features had to include higher-order statistics (kurtosis and skewness), indicating the importance to account for heterogeneity. Our findings suggest that SVM, using features from a combination of diffusion models, improves prediction accuracy for differentiation of benign versus malignant breast tumors, and may further assist in subtyping of breast cancer. 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1205-1216. © 2017 International Society for Magnetic Resonance in Medicine.

Brain imaging in mitochondrial respiratory chain deficiency: combination of brain MRI features as a useful tool for genotype/phenotype correlations.

PubMed

Bricout, M; Grévent, D; Lebre, A S; Rio, M; Desguerre, I; De Lonlay, P; Valayannopoulos, V; Brunelle, F; Rötig, A; Munnich, A; Boddaert, N

2014-07-01

Mitochondrial diseases are characterised by a broad clinical and genetic heterogeneity that makes diagnosis difficult. Owing to the wide pattern of symptoms in mitochondrial disorders and the constantly growing number of disease genes, their genetic diagnosis is difficult and genotype/phenotype correlations remain elusive. Brain MRI appears as a useful tool for genotype/phenotype correlations. Here, we summarise the various combinations of MRI lesions observed in the most frequent mitochondrial respiratory chain deficiencies so as to direct molecular genetic test in patients at risk of such diseases. We believe that the combination of brain MRI features is of value to support respiratory chain deficiency and direct molecular genetic tests. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

MR imaging of the traumatic triangular fibrocartilaginous complex tear

PubMed Central

Griffith, James F.; Fung, Cindy S. Y.; Lee, Ryan K. L.; Tong, Cina S. L.; Wong, Clara W. Y.; Tse, Wing Lim; Ho, Pak Cheong

2017-01-01

Triangular fibrocartilage complex is a major stabilizer of the distal radioulnar joint (DRUJ). However, triangular fibrocartilage complex (TFCC) tear is difficult to be diagnosed on MRI for its intrinsic small and thin structure with complex anatomy. The purpose of this article is to review the anatomy of TFCC, state of art MRI imaging technique, normal appearance and features of tear on MRI according to the Palmar’s classification. Atypical tear and limitations of MRI in diagnosis of TFCC tear are also discussed. PMID:28932701

MRI texture analysis (MRTA) of T2-weighted images in Crohn's disease may provide information on histological and MRI disease activity in patients undergoing ileal resection.

PubMed

Makanyanga, Jesica; Ganeshan, Balaji; Rodriguez-Justo, Manuel; Bhatnagar, Gauraang; Groves, Ashley; Halligan, Steve; Miles, Ken; Taylor, Stuart A

2017-02-01

To associate MRI textural analysis (MRTA) with MRI and histological Crohn's disease (CD) activity. Sixteen patients (mean age 39.5 years, 9 male) undergoing MR enterography before ileal resection were retrospectively analysed. Thirty-six small (≤3 mm) ROIs were placed on T2-weighted images and location-matched histological acute inflammatory scores (AIS) measured. MRI activity (mural thickness, T2 signal, T1 enhancement) (CDA) was scored in large ROIs. MRTA features (mean, standard deviation, mean of positive pixels (MPP), entropy, kurtosis, skewness) were extracted using a filtration histogram technique. Spatial scale filtration (SSF) ranged from 2 to 5 mm. Regression (linear/logistic) tested associations between MRTA and AIS (small ROIs), and CDA/constituent parameters (large ROIs). Skewness (SSF = 2 mm) was associated with AIS [regression coefficient (rc) 4.27, p = 0.02]. Of 120 large ROI analyses (for each MRI, MRTA feature and SSF), 15 were significant. Entropy (SSF = 2, 3 mm) and kurtosis (SSF = 3 mm) were associated with CDA (rc 0.9, 1.0, -0.45, p = 0.006-0.01). Entropy and mean (SSF = 2-4 mm) were associated with T2 signal [odds ratio (OR) 2.32-3.16, p = 0.02-0.004], [OR 1.22-1.28, p = 0.03-0.04]. MPP (SSF = 2 mm) was associated with mural thickness (OR 0.91, p = 0.04). Kurtosis (SSF = 3 mm), standard deviation (SSF = 5 mm) were associated with decreased T1 enhancement (OR 0.59, 0.42, p = 0.004, 0.007). MRTA features may be associated with CD activity. • MR texture analysis features may be associated with Crohn's disease histological activity. • Texture analysis features may correlate with MR-dependent Crohn's disease activity scores. • The utility of MR texture analysis in Crohn's disease merits further investigation.

Evaluation of Blalock-Taussig shunts in newborns: value of oblique MRI planes.

PubMed

Kastler, B; Livolsi, A; Germain, P; Zöllner, G; Dietemann, J L

1991-01-01

Eight infants with systemic-pulmonary Blalock-Taussig shunts were evaluated by spin-echo ECG-gated MRI. Contrary to Echocardiography, MRI using coronal oblique projections successfully visualized all palliative shunts entirely in one single plane (including one carried out on a right aberrant subclavian artery). MRI allowed assessment of size, course and patency of the shunt, including pulmonary and subclavian insertion. The proximal portion of the pulmonary and subclavian arteries were also visualized. We conclude that MRI with axial scans completed by coronal oblique planes is a promising, non invasive method for imaging the anatomical features of Blalock-Taussig shunts.

Syntactic Priming and the Lexical Boost Effect during Sentence Production and Sentence Comprehension: An fMRI Study

ERIC Educational Resources Information Center

Segaert, Katrien; Kempen, Gerard; Petersson, Karl Magnus; Hagoort, Peter

2013-01-01

Behavioral syntactic priming effects during sentence comprehension are typically observed only if both the syntactic structure and lexical head are repeated. In contrast, during production syntactic priming occurs with structure repetition alone, but the effect is boosted by repetition of the lexical head. We used fMRI to investigate the neuronal…

Event-Related fMRI Studies of Episodic Encoding and Retrieval: Meta-Analyses Using Activation Likelihood Estimation

ERIC Educational Resources Information Center

Spaniol, Julia; Davidson, Patrick S. R.; Kim, Alice S. N.; Han, Hua; Moscovitch, Morris; Grady, Cheryl L.

2009-01-01

The recent surge in event-related fMRI studies of episodic memory has generated a wealth of information about the neural correlates of encoding and retrieval processes. However, interpretation of individual studies is hampered by methodological differences, and by the fact that sample sizes are typically small. We submitted results from studies of…

Are rheumatoid arthritis patients discernible from other early arthritis patients using 1.5T extremity magnetic resonance imaging? a large cross-sectional study.

PubMed

Stomp, Wouter; Krabben, Annemarie; van der Heijde, Désirée; Huizinga, Tom W J; Bloem, Johan L; van der Helm-van Mil, Annette H M; Reijnierse, Monique

2014-08-01

Magnetic resonance imaging (MRI) is increasingly used in rheumatoid arthritis (RA) research. A European League Against Rheumatism (EULAR) task force recently suggested that MRI can improve the certainty of RA diagnosis. Because this recommendation may reflect a tendency to use MRI in daily practice, thorough studies on the value of MRI are required. Thus far no large studies have evaluated the accuracy of MRI to differentiate early RA from other patients with early arthritis. We performed a large cross-sectional study to determine whether patients who are clinically classified with RA differ in MRI features compared to patients with other diagnoses. In our study, 179 patients presenting with early arthritis (median symptom duration 15.4 weeks) underwent 1.5T extremity MRI of unilateral wrist, metacarpophalangeal, and metatarsophalangeal joints according to our arthritis protocol, the foot without contrast. Images were scored according to OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging Scoring (RAMRIS) by 2 independent readers. Tenosynovitis was also assessed. The main outcome was fulfilling the 1987 American College of Rheumatology (ACR) criteria for RA. Test characteristics and areas under the receiver-operator-characteristic curves (AUC) were evaluated. In subanalyses, the 2010 ACR/EULAR criteria were used as outcome, and analyses were stratified for anticitrullinated protein antibodies (ACPA). The ACR 1987 criteria were fulfilled in 43 patients (24.0%). Patients with RA had higher scores for synovitis, tenosynovitis, and bone marrow edema (BME) than patients without RA (p < 0.05). ACPA-positive patients had more BME (median scores 6.5 vs. 4.25, p = 0.016) than ACPA-negative patients. For all MRI features, the predictive value for the presence of RA was low (< 50%). For all MRI features the AUC were < 0.70. Patients who fulfilled ACR/EULAR 2010 criteria but not ACR87 criteria for RA had less synovitis than patients who were positive for RA according to both sets of criteria (p = 0.029). Although patients with RA had higher scores of MRI inflammation and ACPA-positive patients had more BME, the severity of MRI inflammation assessed according to RAMRIS does not accurately differentiate patients with RA from other early arthritis patients.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

[Fragile X syndrome and white matter abnormalities: Case study of two brothers].

PubMed

Wallach, E; Bieth, E; Sevely, A; Cances, C

2017-03-01

Fragile X syndrome is the most usual cause of hereditary intellectual deficiency. Typical symptoms combine intellectual deficiency, social anxiety, intense emotional vigilance, and a characteristic facial dysmorphy. This is subsequent to a complete mutation of the FMR1 gene, considering a semidominant transmission linked to the unstable X. The expansion of the CGG triplet greater than 200 units combined with a high methylation pattern lead to a transcriptional silence of the FMR1 gene, and the protein product, the FMRP, is not synthesized. This protein is involved in synaptic plasticity. Brain MRI can show an increased volume of the caudate nucleus and hippocampus, combined with hypoplasia of the cerebellar vermis. Fragile X Associated Tremor Ataxia Syndrome (FXTAS) syndrome is a neurodegenerative disorder occurring in carriers of the premutation in FMR1. Brain MRI shows an increased T2 signal in the middle cerebellar peduncles. This syndrome is linked to a premutation in the FMR1 gene. We report here the case of two brothers presenting a typical fragile X symptomatology. Brain MRI showed hyperintensities of the middle cerebellar peduncles. Such MRI findings support the assumption of a genetic mosaicism. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Spatio-temporal texture (SpTeT) for distinguishing vulnerable from stable atherosclerotic plaque on dynamic contrast enhancement (DCE) MRI in a rabbit model

PubMed Central

Wan, Tao; Madabhushi, Anant; Phinikaridou, Alkystis; Hamilton, James A.; Hua, Ning; Pham, Tuan; Danagoulian, Jovanna; Kleiman, Ross; Buckler, Andrew J.

2014-01-01

Purpose: To develop a new spatio-temporal texture (SpTeT) based method for distinguishing vulnerable versus stable atherosclerotic plaques on DCE-MRI using a rabbit model of atherothrombosis. Methods: Aortic atherosclerosis was induced in 20 New Zealand White rabbits by cholesterol diet and endothelial denudation. MRI was performed before (pretrigger) and after (posttrigger) inducing plaque disruption with Russell's-viper-venom and histamine. Of the 30 vascular targets (segments) under histology analysis, 16 contained thrombus (vulnerable) and 14 did not (stable). A total of 352 voxel-wise computerized SpTeT features, including 192 Gabor, 36 Kirsch, 12 Sobel, 52 Haralick, and 60 first-order textural features, were extracted on DCE-MRI to capture subtle texture changes in the plaques over the course of contrast uptake. Different combinations of SpTeT feature sets, in which the features were ranked by a minimum-redundancy-maximum-relevance feature selection technique, were evaluated via a random forest classifier. A 500 iterative 2-fold cross validation was performed for discriminating the vulnerable atherosclerotic plaque and stable atherosclerotic plaque on per voxel basis. Four quantitative metrics were utilized to measure the classification results in separating between vulnerable and stable plaques. Results: The quantitative results show that the combination of five classes of SpTeT features can distinguish between vulnerable (disrupted plaques with an overlying thrombus) and stable plaques with the best AUC values of 0.9631 ± 0.0088, accuracy of 89.98% ± 0.57%, sensitivity of 83.71% ± 1.71%, and specificity of 94.55% ± 0.48%. Conclusions: Vulnerable and stable plaque can be distinguished by SpTeT based features. The SpTeT features, following validation on larger datasets, could be established as effective and reliable imaging biomarkers for noninvasively assessing atherosclerotic risk. PMID:24694153

Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model.

PubMed

Liu, Fang; Velikina, Julia V; Block, Walter F; Kijowski, Richard; Samsonov, Alexey A

2017-02-01

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexible representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplified treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed  ∼ 200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure.

Fast Realistic MRI Simulations Based on Generalized Multi-Pool Exchange Tissue Model

PubMed Central

Velikina, Julia V.; Block, Walter F.; Kijowski, Richard; Samsonov, Alexey A.

2017-01-01

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexibl representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplifie treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed ∼200× improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure. PMID:28113746

MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

NASA Astrophysics Data System (ADS)

Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

2014-03-01

An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

Interaction of MRI field gradients with the human body.

PubMed

Glover, P M

2009-11-07

In this review, the effects of low-frequency electromagnetic fields encountered specifically during magnetic resonance imaging (MRI) are examined. The primary biological effect at frequencies of between 100 and 5000 Hz (typical of MRI magnetic field gradient switching) is peripheral nerve stimulation, the result of which can be a mild tingling and muscle twitching to a sensation of pain. The models for nerve stimulation and how they are related to the rate of change of magnetic field are examined. The experimental measurements, and analytic and computational modelling work in this area are reviewed. The review concludes with a discussion of current regulation in this area and current practice as both are applied to MRI.

MRI-based quantification of Duchenne muscular dystrophy in a canine model

NASA Astrophysics Data System (ADS)

Wang, Jiahui; Fan, Zheng; Kornegay, Joe N.; Styner, Martin A.

2011-03-01

Duchenne muscular dystrophy (DMD) is a progressive and fatal X-linked disease caused by mutations in the DMD gene. Magnetic resonance imaging (MRI) has shown potential to provide non-invasive and objective biomarkers for monitoring disease progression and therapeutic effect in DMD. In this paper, we propose a semi-automated scheme to quantify MRI features of golden retriever muscular dystrophy (GRMD), a canine model of DMD. Our method was applied to a natural history data set and a hydrodynamic limb perfusion data set. The scheme is composed of three modules: pre-processing, muscle segmentation, and feature analysis. The pre-processing module includes: calculation of T2 maps, spatial registration of T2 weighted (T2WI) images, T2 weighted fat suppressed (T2FS) images, and T2 maps, and intensity calibration of T2WI and T2FS images. We then manually segment six pelvic limb muscles. For each of the segmented muscles, we finally automatically measure volume and intensity statistics of the T2FS images and T2 maps. For the natural history study, our results showed that four of six muscles in affected dogs had smaller volumes and all had higher mean intensities in T2 maps as compared to normal dogs. For the perfusion study, the muscle volumes and mean intensities in T2FS were increased in the post-perfusion MRI scans as compared to pre-perfusion MRI scans, as predicted. We conclude that our scheme successfully performs quantitative analysis of muscle MRI features of GRMD.

Prediction of outcome using pretreatment 18F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy.

PubMed

Lucia, François; Visvikis, Dimitris; Desseroit, Marie-Charlotte; Miranda, Omar; Malhaire, Jean-Pierre; Robin, Philippe; Pradier, Olivier; Hatt, Mathieu; Schick, Ulrike

2018-05-01

The aim of this study is to determine if radiomics features from 18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) images could contribute to prognoses in cervical cancer. One hundred and two patients (69 for training and 33 for testing) with locally advanced cervical cancer (LACC) receiving chemoradiotherapy (CRT) from 08/2010 to 12/2016 were enrolled in this study. 18 F-FDG PET/CT and MRI examination [T1, T2, T1C, diffusion-weighted imaging (DWI)] were performed for each patient before CRT. Primary tumor volumes were delineated with the fuzzy locally adaptive Bayesian algorithm in the PET images and with 3D Slicer™ in the MRI images. Radiomics features (intensity, shape, and texture) were extracted and their prognostic value was compared with clinical parameters for recurrence-free and locoregional control. In the training cohort, median follow-up was 3.0 years (range, 0.43-6.56 years) and relapse occurred in 36% of patients. In univariate analysis, FIGO stage (I-II vs. III-IV) and metabolic response (complete vs. non-complete) were probably associated with outcome without reaching statistical significance, contrary to several radiomics features from both PET and MRI sequences. Multivariate analysis in training test identified Grey Level Non Uniformity GLRLM in PET and Entropy GLCM in ADC maps from DWI MRI as independent prognostic factors. These had significantly higher prognostic power than clinical parameters, as evaluated in the testing cohort with accuracy of 94% for predicting recurrence and 100% for predicting lack of loco-regional control (versus ~50-60% for clinical parameters). In LACC treated with CRT, radiomics features such as EntropyGLCM and GLNUGLRLM from functional imaging DWI-MRI and PET, respectively, are independent predictors of recurrence and loco-regional control with significantly higher prognostic power than usual clinical parameters. Further research is warranted for their validation, which may justify more aggressive treatment in patients identified with high probability of recurrence.

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention.

PubMed

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-13

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features.

Utility of texture analysis for quantifying hepatic fibrosis on proton density MRI.

PubMed

Yu, HeiShun; Buch, Karen; Li, Baojun; O'Brien, Michael; Soto, Jorge; Jara, Hernan; Anderson, Stephan W

2015-11-01

To evaluate the potential utility of texture analysis of proton density maps for quantifying hepatic fibrosis in a murine model of hepatic fibrosis. Following Institutional Animal Care and Use Committee (IACUC) approval, a dietary model of hepatic fibrosis was used and 15 ex vivo murine liver tissues were examined. All images were acquired using a 30 mm bore 11.7T magnetic resonance imaging (MRI) scanner with a multiecho spin-echo sequence. A texture analysis was employed extracting multiple texture features including histogram-based, gray-level co-occurrence matrix-based (GLCM), gray-level run-length-based features (GLRL), gray level gradient matrix (GLGM), and Laws' features. Texture features were correlated with histopathologic and digital image analysis of hepatic fibrosis. Histogram features demonstrated very weak to moderate correlations (r = -0.29 to 0.51) with hepatic fibrosis. GLCM features correlation and contrast demonstrated moderate-to-strong correlations (r = -0.71 and 0.59, respectively) with hepatic fibrosis. Moderate correlations were seen between hepatic fibrosis and the GLRL feature short run low gray-level emphasis (SRLGE) (r = -0. 51). GLGM features demonstrate very weak to weak correlations with hepatic fibrosis (r = -0.27 to 0.09). Moderate correlations were seen between hepatic fibrosis and Laws' features L6 and L7 (r = 0.58). This study demonstrates the utility of texture analysis applied to proton density MRI in a murine liver fibrosis model and validates the potential utility of texture-based features for the noninvasive, quantitative assessment of hepatic fibrosis. © 2015 Wiley Periodicals, Inc.

Parametric dictionary learning for modeling EAP and ODF in diffusion MRI.

PubMed

Merlet, Sylvain; Caruyer, Emmanuel; Deriche, Rachid

2012-01-01

In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the ensemble average propagator (EAP) and the orientation distribution function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estimation as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and parametric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffusion features: the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.

Language laterality in autism spectrum disorder and typical controls: a functional, volumetric, and diffusion tensor MRI study.

PubMed

Knaus, Tracey A; Silver, Andrew M; Kennedy, Meaghan; Lindgren, Kristen A; Dominick, Kelli C; Siegel, Jeremy; Tager-Flusberg, Helen

2010-02-01

Language and communication deficits are among the core features of autism spectrum disorder (ASD). Reduced or reversed asymmetry of language has been found in a number of disorders, including ASD. Studies of healthy adults have found an association between language laterality and anatomical measures but this has not been systematically investigated in ASD. The goal of this study was to examine differences in gray matter volume of perisylvian language regions, connections between language regions, and language abilities in individuals with typical left lateralized language compared to those with atypical (bilateral or right) asymmetry of language functions. Fourteen adolescent boys with ASD and 20 typically developing adolescent boys participated, including equal numbers of left- and right-handed individuals in each group. Participants with typical left lateralized language activation had smaller frontal language region volume and higher fractional anisotropy of the arcuate fasciculus compared to the group with atypical language laterality, across both ASD and control participants. The group with typical language asymmetry included the most right-handed controls and fewest left-handers with ASD. Atypical language laterality was more prevalent in the ASD than control group. These findings support an association between laterality of language function and language region anatomy. They also suggest anatomical differences may be more associated with variation in language laterality than specifically with ASD. Language laterality therefore may provide a novel way of subdividing samples, resulting in more homogenous groups for research into genetic and neurocognitive foundations of developmental disorders. Copyright 2009 Elsevier Inc. All rights reserved.

Deformable templates guided discriminative models for robust 3D brain MRI segmentation.

PubMed

Liu, Cheng-Yi; Iglesias, Juan Eugenio; Tu, Zhuowen

2013-10-01

Automatically segmenting anatomical structures from 3D brain MRI images is an important task in neuroimaging. One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols. A deformable template is a type of generative model that attempts to explicitly match an input image with a template (atlas), and thus, they are robust against global intensity changes. On the other hand, discriminative models combine local image features to capture complex image patterns. In this paper, we propose a robust brain image segmentation algorithm that fuses together deformable templates and informative features. It takes advantage of the adaptation capability of the generative model and the classification power of the discriminative models. The proposed algorithm achieves both robustness and efficiency, and can be used to segment brain MRI images with large anatomical variations. We perform an extensive experimental study on four datasets of T1-weighted brain MRI data from different sources (1,082 MRI scans in total) and observe consistent improvement over the state-of-the-art systems.

MRI and clinical features of maple syrup urine disease: preliminary results in 10 cases.

PubMed

Cheng, Ailan; Han, Lianshu; Feng, Yun; Li, Huimin; Yao, Rong; Wang, Dengbin; Jin, Biao

2017-01-01

We aimed to evaluate the magnetic resonance imaging (MRI) and clinical features of maple syrup urine disease (MSUD). This retrospective study consisted of 10 MSUD patients confirmed by genetic testing. All patients underwent brain MRI. Phenotype, genotype, and areas of brain injury on MRI were retrospectively reviewed. Six patients (60%) had the classic form of MSUD with BCKDHB mutation, three patients (30%) had the intermittent form (two with BCKDHA mutations and one with DBT mutation), and one patient (10%) had the thiamine-responsive form with DBT mutation. On diffusion-weighted imaging, nine cases presented restricted diffusion in myelinated areas, and one intermittent case with DBT mutation was normal. The classic form of MSUD involved the basal ganglia in six cases; the cerebellum, mesencephalon, pons, and supratentorial area in five cases; and the thalamus in four cases, respectively. The intermittent form involved the cerebellum, pons, and supratentorial area in two cases. The thiamine-responsive form involved the basal ganglia and supratentorial area. Our preliminary results indicate that patients with MSUD presented more commonly in classic form with BCKDHB mutation and displayed extensive brain injury on MRI.

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

NASA Astrophysics Data System (ADS)

Vallières, M.; Freeman, C. R.; Skamene, S. R.; El Naqa, I.

2015-07-01

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

Automated prostate cancer localization without the need for peripheral zone extraction using multiparametric MRI.

PubMed

Liu, Xin; Yetik, Imam Samil

2011-06-01

Multiparametric magnetic resonance imaging (MRI) has been shown to have higher localization accuracy than transrectal ultrasound (TRUS) for prostate cancer. Therefore, automated cancer segmentation using multiparametric MRI is receiving a growing interest, since MRI can provide both morphological and functional images for tissue of interest. However, all automated methods to this date are applicable to a single zone of the prostate, and the peripheral zone (PZ) of the prostate needs to be extracted manually, which is a tedious and time-consuming job. In this paper, our goal is to remove the need of PZ extraction by incorporating the spatial and geometric information of prostate tumors with multiparametric MRI derived from T2-weighted MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI). In order to remove the need of PZ extraction, the authors propose a new method to incorporate the spatial information of the cancer. This is done by introducing a new feature called location map. This new feature is constructed by applying a nonlinear transformation to the spatial position coordinates of each pixel, so that the location map implicitly represents the geometric position of each pixel with respect to the prostate region. Then, this new feature is combined with multiparametric MR images to perform tumor localization. The proposed algorithm is applied to multiparametric prostate MRI data obtained from 20 patients with biopsy-confirmed prostate cancer. The proposed method which does not need the masks of PZ was found to have prostate cancer detection specificity of 0.84, sensitivity of 0.80 and dice coefficient value of 0.42. The authors have found that fusing the spatial information allows us to obtain tumor outline without the need of PZ extraction with a considerable success (better or similar performance to methods that require manual PZ extraction). Our experimental results quantitatively demonstrate the effectiveness of the proposed method, depicting that the proposed method has a slightly better or similar localization performance compared to methods which require the masks of PZ.

A quantitative link between face discrimination deficits and neuronal selectivity for faces in autism☆

PubMed Central

Jiang, Xiong; Bollich, Angela; Cox, Patrick; Hyder, Eric; James, Joette; Gowani, Saqib Ali; Hadjikhani, Nouchine; Blanz, Volker; Manoach, Dara S.; Barton, Jason J.S.; Gaillard, William D.; Riesenhuber, Maximilian

2013-01-01

Individuals with Autism Spectrum Disorder (ASD) appear to show a general face discrimination deficit across a range of tasks including social–emotional judgments as well as identification and discrimination. However, functional magnetic resonance imaging (fMRI) studies probing the neural bases of these behavioral differences have produced conflicting results: while some studies have reported reduced or no activity to faces in ASD in the Fusiform Face Area (FFA), a key region in human face processing, others have suggested more typical activation levels, possibly reflecting limitations of conventional fMRI techniques to characterize neuron-level processing. Here, we test the hypotheses that face discrimination abilities are highly heterogeneous in ASD and are mediated by FFA neurons, with differences in face discrimination abilities being quantitatively linked to variations in the estimated selectivity of face neurons in the FFA. Behavioral results revealed a wide distribution of face discrimination performance in ASD, ranging from typical performance to chance level performance. Despite this heterogeneity in perceptual abilities, individual face discrimination performance was well predicted by neural selectivity to faces in the FFA, estimated via both a novel analysis of local voxel-wise correlations, and the more commonly used fMRI rapid adaptation technique. Thus, face processing in ASD appears to rely on the FFA as in typical individuals, differing quantitatively but not qualitatively. These results for the first time mechanistically link variations in the ASD phenotype to specific differences in the typical face processing circuit, identifying promising targets for interventions. PMID:24179786

High-field fMRI unveils orientation columns in humans.

PubMed

Yacoub, Essa; Harel, Noam; Ugurbil, Kâmil

2008-07-29

Functional (f)MRI has revolutionized the field of human brain research. fMRI can noninvasively map the spatial architecture of brain function via localized increases in blood flow after sensory or cognitive stimulation. Recent advances in fMRI have led to enhanced sensitivity and spatial accuracy of the measured signals, indicating the possibility of detecting small neuronal ensembles that constitute fundamental computational units in the brain, such as cortical columns. Orientation columns in visual cortex are perhaps the best known example of such a functional organization in the brain. They cannot be discerned via anatomical characteristics, as with ocular dominance columns. Instead, the elucidation of their organization requires functional imaging methods. However, because of insufficient sensitivity, spatial accuracy, and image resolution of the available mapping techniques, thus far, they have not been detected in humans. Here, we demonstrate, by using high-field (7-T) fMRI, the existence and spatial features of orientation- selective columns in humans. Striking similarities were found with the known spatial features of these columns in monkeys. In addition, we found that a larger number of orientation columns are devoted to processing orientations around 90 degrees (vertical stimuli with horizontal motion), whereas relatively similar fMRI signal changes were observed across any given active column. With the current proliferation of high-field MRI systems and constant evolution of fMRI techniques, this study heralds the exciting prospect of exploring unmapped and/or unknown columnar level functional organizations in the human brain.

Estimation of trabecular bone parameters in children from multisequence MRI using texture-based regression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lekadir, Karim, E-mail: karim.lekadir@upf.edu; Hoogendoorn, Corné; Armitage, Paul

Purpose: This paper presents a statistical approach for the prediction of trabecular bone parameters from low-resolution multisequence magnetic resonance imaging (MRI) in children, thus addressing the limitations of high-resolution modalities such as HR-pQCT, including the significant exposure of young patients to radiation and the limited applicability of such modalities to peripheral bones in vivo. Methods: A statistical predictive model is constructed from a database of MRI and HR-pQCT datasets, to relate the low-resolution MRI appearance in the cancellous bone to the trabecular parameters extracted from the high-resolution images. The description of the MRI appearance is achieved between subjects by usingmore » a collection of feature descriptors, which describe the texture properties inside the cancellous bone, and which are invariant to the geometry and size of the trabecular areas. The predictive model is built by fitting to the training data a nonlinear partial least square regression between the input MRI features and the output trabecular parameters. Results: Detailed validation based on a sample of 96 datasets shows correlations >0.7 between the trabecular parameters predicted from low-resolution multisequence MRI based on the proposed statistical model and the values extracted from high-resolution HRp-QCT. Conclusions: The obtained results indicate the promise of the proposed predictive technique for the estimation of trabecular parameters in children from multisequence MRI, thus reducing the need for high-resolution radiation-based scans for a fragile population that is under development and growth.« less

BlochSolver: A GPU-optimized fast 3D MRI simulator for experimentally compatible pulse sequences

NASA Astrophysics Data System (ADS)

Kose, Ryoichi; Kose, Katsumi

2017-08-01

A magnetic resonance imaging (MRI) simulator, which reproduces MRI experiments using computers, has been developed using two graphic-processor-unit (GPU) boards (GTX 1080). The MRI simulator was developed to run according to pulse sequences used in experiments. Experiments and simulations were performed to demonstrate the usefulness of the MRI simulator for three types of pulse sequences, namely, three-dimensional (3D) gradient-echo, 3D radio-frequency spoiled gradient-echo, and gradient-echo multislice with practical matrix sizes. The results demonstrated that the calculation speed using two GPU boards was typically about 7 TFLOPS and about 14 times faster than the calculation speed using CPUs (two 18-core Xeons). We also found that MR images acquired by experiment could be reproduced using an appropriate number of subvoxels, and that 3D isotropic and two-dimensional multislice imaging experiments for practical matrix sizes could be simulated using the MRI simulator. Therefore, we concluded that such powerful MRI simulators are expected to become an indispensable tool for MRI research and development.

Development of an apparatus and methodology for conducting functional magnetic resonance imaging (fMRI) with pharmacological stimuli in conscious rhesus monkeys.

PubMed

Murnane, Kevin Sean; Howell, Leonard Lee

2010-08-15

Functional magnetic resonance imaging (fMRI) is a technique with significant potential to advance our understanding of multiple brain systems. However, when human subjects undergo fMRI studies they are typically conscious whereas pre-clinical fMRI studies typically utilize anesthesia, which complicates comparisons across studies. Therefore, we have developed an apparatus suitable for imaging conscious rhesus monkeys. In order to minimize subject stress and spatial motion, each subject was acclimated to the necessary procedures over several months. The effectiveness of this process was then evaluated, in fully trained subjects, by quantifying objective physiological measures. These physiological metrics were stable both within and across sessions and did not differ from when these same subjects were immobilized using standard primate handling procedures. Subject motion and blood oxygenation level dependent (BOLD) fMRI measurements were then evaluated by scanning subjects under three different conditions: the absence of stimulation, presentation of a visual stimulus, or administration of intravenous (i.v.) cocaine (0.3mg/kg). Spatial motion differed neither by condition nor along the three principal axes. In addition, maximum translational and rotational motion never exceeded one half of the voxel size (0.75 mm) or 1.5 degrees, respectively. Furthermore, the localization of changes in blood oxygenation closely matched those reported in previous studies using similar stimuli. These findings document the feasibility of fMRI data collection in conscious rhesus monkeys using these procedures and allow for the further study of the neural effects of psychoactive drugs. (c) 2010 Elsevier B.V. All rights reserved.

Visual search for feature conjunctions: an fMRI study comparing alcohol-related neurodevelopmental disorder (ARND) to ADHD.

PubMed

O'Conaill, Carrie R; Malisza, Krisztina L; Buss, Joan L; Bolster, R Bruce; Clancy, Christine; de Gervai, Patricia Dreessen; Chudley, Albert E; Longstaffe, Sally

2015-01-01

Alcohol-related neurodevelopmental disorder (ARND) falls under the umbrella of fetal alcohol spectrum disorder (FASD). Diagnosis of ARND is difficult because individuals do not demonstrate the characteristic facial features associated with fetal alcohol syndrome (FAS). While attentional problems in ARND are similar to those found in attention-deficit/hyperactivity disorder (ADHD), the underlying impairment in attention pathways may be different. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) was conducted at 3 T. Sixty-three children aged 10 to 14 years diagnosed with ARND, ADHD, and typically developing (TD) controls performed a single-feature and a feature-conjunction visual search task. Dorsal and ventral attention pathways were activated during both attention tasks in all groups. Significantly greater activation was observed in ARND subjects during a single-feature search as compared to TD and ADHD groups, suggesting ARND subjects require greater neural recruitment to perform this simple task. ARND subjects appear unable to effectively use the very efficient automatic perceptual 'pop-out' mechanism employed by TD and ADHD groups during presentation of the disjunction array. By comparison, activation was lower in ARND compared to TD and ADHD subjects during the more difficult conjunction search task as compared to the single-feature search. Analysis of DTI data using tract-based spatial statistics (TBSS) showed areas of significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the right inferior longitudinal fasciculus (ILF) in ARND compared to TD subjects. Damage to the white matter of the ILF may compromise the ventral attention pathway and may require subjects to use the dorsal attention pathway, which is associated with effortful top-down processing, for tasks that should be automatic. Decreased functional activity in the right temporoparietal junction (TPJ) of ARND subjects may be due to a reduction in the white matter tract's ability to efficiently convey information critical to performance of the attention tasks. Limited activation patterns in ARND suggest problems in information processing along the ventral frontoparietal attention pathway. Poor integrity of the ILF, which connects the functional components of the ventral attention network, in ARND subjects may contribute to the attention deficits characteristic of the disorder.

Multiclass feature selection for improved pediatric brain tumor segmentation

NASA Astrophysics Data System (ADS)

Ahmed, Shaheen; Iftekharuddin, Khan M.

2012-03-01

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Creutzfeldt-Jakob Disease as a Cause of Cognitive Decline and Seizures in the Elderly: Diagnostic Pointers and Strategy for Investigation

PubMed Central

Williams, R.; Cresswell, F.; McClure, M.; Lane, R.

2011-01-01

Cognitive decline affects one in twenty people over the age of 65. There is often a paucity of clues as to the underlying pathology, and while the diagnosis will usually prove to be either Alzheimer's disease or vascular dementia, there may be clinical features suggesting rarer alternatives. This case of a 71-year-old lady with a 3-month history of progressive cognitive decline illustrates clinical features suggestive of Creutzfeltd-Jakob disease such as rapid decline in conscious level and myoclonic jerking. Diagnosis was confirmed by 3 means: (1) Electroencephalogram demonstrating periodic sharp wave complexes, (2) MRI brain showing cortical ribboning and high signal in the caudate nucleus, and (3) presence of protein S100 and protein14-3-3 in the cerebrospinal fluid. Postmortem brain histology confirmed a typical spongiform encephalopathy. Establishing an underlying aetiology is dementia is important not only for prognostic reasons but in order to detect potentially reversible causes. In cases of an atypical dementing illness our proposed investigations may assist in confirming or excluding underlying Creutzfeltd-Jakob disease. PMID:22194754

Cold tuberculous abscess identified by FDG PET.

PubMed

Yago, Yuzo; Yukihiro, Masashi; Kuroki, Hirofumi; Katsuragawa, Yuzo; Kubota, Kazuo

2005-09-01

We report FDG PET of two cases of cold abscess due to Mycobacterium tuberculosis. Case 1 had colon cancer; FDG PET showed high FDG uptake in the colon lesion and low uptake in the inguinal lesion. The latter was a tuberculous cold abscess confirmed by CT/MRI and biopsy. Case 2 received radiotherapy for lung cancer and presented with suspected vertebral metastasis. Further studies revealed tuberculosis of the vertebra and a tuberculous cold abscess in the iliopsoas muscle. FDG PET showed moderate uptake in the third lumbar spine and low uptake in the abscess center of iliopsoas lesion. Both tuberculous cold abscesses showed moderate FDG uptake in the capsule and low uptake in the center. These features are unique compared with non-tuberculous abscess and typical tuberculosis lesions, which are characterized by high FDG uptake. Pathologically, tuberculous cold abscess is not accompanied by active inflammatory reaction. Our findings suggested that the FDG uptake by tuberculous lesion varies according to the grade of inflammatory activity. The new diagnostic features of tuberculous cold abscess may be useful in the evaluation of such lesions by FDG PET.

Relationship of compartment-specific structural knee status at baseline with change in cartilage morphology: a prospective observational study using data from the osteoarthritis initiative

PubMed Central

Eckstein, Felix; Wirth, Wolfgang; Hudelmaier, Martin I; Maschek, Susanne; Hitzl, Wolfgang; Wyman, Bradley T; Nevitt, Michael; Hellio Le Graverand, Marie-Pierre; Hunter, David

2009-01-01

Introduction The aim was to investigate the relationship of cartilage loss (change in medial femorotibial cartilage thickness measured with magnetic resonance imaging (MRI)) with compartment-specific baseline radiographic findings and MRI cartilage morphometry features, and to identify which baseline features can be used for stratification of fast progressors. Methods An age and gender stratified subsample of the osteoarthritis (OA) initiative progression subcohort (79 women; 77 men; age 60.9 ± 9.9 years; body mass index (BMI) 30.3 ± 4.7) with symptomatic, radiographic OA in at least one knee was studied. Baseline fixed flexion radiographs were read centrally and adjudicated, and cartilage morphometry was performed at baseline and at one year follow-up from coronal FLASH 3 Tesla MR images of the right knee. Results Osteophyte status at baseline was not associated with medial cartilage loss. Knees with medial joint space narrowing tended to show higher rates of change than those without, but the relationship was not statistically significant. Knees with medial femoral subchondral bone sclerosis (radiography), medial denuded subchondral bone areas (MRI), and low cartilage thickness (MRI) at baseline displayed significantly higher cartilage loss than those without, both with and without adjusting for age, sex, and BMI. Participants with denuded subchondral bone showed a standardized response mean of up to -0.64 versus -0.33 for the entire subcohort. Conclusions The results indicate that radiographic and MRI cartilage morphometry features suggestive of advanced disease appear to be associated with greater cartilage loss. These features may be suited for selecting patients with a higher likelihood of fast progression in studies that attempt to demonstrate the cartilage-preserving effect of disease-modifying osteoarthritis drugs. PMID:19534783

Agreement among Magnetic Resonance Imaging/Magnetic Resonance Cholangiopancreatography (MRI-MRCP) and Endoscopic Ultrasound (EUS) in the evaluation of morphological features of Branch Duct Intraductal Papillary Mucinous Neoplasm (BD-IPMN).

PubMed

Uribarri-Gonzalez, Laura; Keane, Margaret G; Pereira, Stephen P; Iglesias-García, Julio; Dominguez-Muñoz, J Enrique; Lariño-Noia, Jose

2018-03-01

To evaluate the agreement between the imaging modalities MRI-MRCP and EUS in cystic lesions of the pancreas which were thought to be a BD-IPMN. Multicenter retrospective study included all patients between 2010 and 2015 with a suspected BD-IPMN who underwent an EUS and MRI-MRCP within 6 months or less of each other. Location, number, size, worrisome features and high-risk stigmata were evaluated. Interobserver agreement was evaluated by Kappa score. 173 patients were included (97 UHSC, 76 UCLH-RFH), mean age 65 (range 25-87 years), 66 males. When comparing both modalities there was good agreement for the location of the cyst. The median lesion size was larger by MRI-MRCP than EUS although it was not significant. With regards to worrisome features, there was moderate agreement for main PD of 5-9 mm and abrupt change (k = 0.45 and 0.52). Fair agreement was seen for the cyst wall thickening (k = 0.25). No agreement was seen between the presence of non-enhanced mural nodules or lymphadenopathy (k < 0). With regards to high-risk stigmata, poor agreement was obtained for the detection of an enhanced solid component (k = 0.12). No agreement was observed for main PD > 10 mm (k < 0). In this multicentre study of patients with a BD-IPMN under active surveillance, most disagreement between these modalities was seen in the proximal pancreas. There was generally only minimal concordance between the imaging findings of EUS and MRI-MRCP for the detection of high-risk stigmata and worrisome features. Copyright © 2018 IAP and EPC. All rights reserved.

Association of BRCA Mutation Types, Imaging Features, and Pathologic Findings in Patients With Breast Cancer With BRCA1 and BRCA2 Mutations.

PubMed

Ha, Su Min; Chae, Eun Young; Cha, Joo Hee; Kim, Hak Hee; Shin, Hee Jung; Choi, Woo Jung

2017-10-01

The purpose of this study is to retrospectively evaluate the relationships between the BRCA mutation types, imaging features, and pathologic findings of breast cancers in BRCA1 and BRCA2 mutation carriers. We identified patients with breast cancer with BRCA gene mutations from January 2000 to December 2014. After excluding patients who underwent lesion excision before MRI, 99 BRCA1 and 103 BRCA2 lesions in 187 women (mean age, 39.7 and 40.4 years, respectively) were enrolled. Mammographic, sonographic, and MRI scans were reviewed according to the BI-RADS lexicon (5th edition). Pathologic data were reviewed, including the immunohistochemistry findings. The relationships between the BRCA mutations and both imaging and pathologic findings were analyzed. The distribution of molecular subtypes of tumors significantly differed by the mutation type. BRCA1 tumors were associated with the triple-negative subtype, whereas BRCA2 tumors were associated with the luminal B subtype (p = 0.002). At MRI, breast cancers with BRCA1 mutations exhibited a circumscribed margin (p = 0.032) and rim enhancement (p = 0.013). No significant differences in mass shape or kinetic features were observed at MRI. Cancers in BRCA1 mutation carriers tended to develop in the posterior location in the breast (p = 0.034). At mammography, no significant difference in the prevalence of calcifications was observed according to the mutation type. At sonography, BRCA1 lesions were found to be associated with posterior acoustic enhancement (p < 0.0001). Breast cancers with BRCA1 mutations tend to exhibit benign morphologic features at MRI, mammography, and sonography, compared with BRCA2 mutations. Lesion location may represent another difference on imaging among various genetic phenotypes.

Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

PubMed

Sørensen, Lauge; Nielsen, Mads

2018-05-15

The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

Estimation of cardiac motion in cine-MRI sequences by correlation transform optical flow of monogenic features distance

NASA Astrophysics Data System (ADS)

Gao, Bin; Liu, Wanyu; Wang, Liang; Liu, Zhengjun; Croisille, Pierre; Delachartre, Philippe; Clarysse, Patrick

2016-12-01

Cine-MRI is widely used for the analysis of cardiac function in clinical routine, because of its high soft tissue contrast and relatively short acquisition time in comparison with other cardiac MRI techniques. The gray level distribution in cardiac cine-MRI is relatively homogenous within the myocardium, and can therefore make motion quantification difficult. To ensure that the motion estimation problem is well posed, more image features have to be considered. This work is inspired by a method previously developed for color image processing. The monogenic signal provides a framework to estimate the local phase, orientation, and amplitude, of an image, three features which locally characterize the 2D intensity profile. The independent monogenic features are combined into a 3D matrix for motion estimation. To improve motion estimation accuracy, we chose the zero-mean normalized cross-correlation as a matching measure, and implemented a bilateral filter for denoising and edge-preservation. The monogenic features distance is used in lieu of the color space distance in the bilateral filter. Results obtained from four realistic simulated sequences outperformed two other state of the art methods even in the presence of noise. The motion estimation errors (end point error) using our proposed method were reduced by about 20% in comparison with those obtained by the other tested methods. The new methodology was evaluated on four clinical sequences from patients presenting with cardiac motion dysfunctions and one healthy volunteer. The derived strain fields were analyzed favorably in their ability to identify myocardial regions with impaired motion.

A Novel Feature-Map Based ICA Model for Identifying the Individual, Intra/Inter-Group Brain Networks across Multiple fMRI Datasets.

PubMed

Wang, Nizhuan; Chang, Chunqi; Zeng, Weiming; Shi, Yuhu; Yan, Hongjie

2017-01-01

Independent component analysis (ICA) has been widely used in functional magnetic resonance imaging (fMRI) data analysis to evaluate functional connectivity of the brain; however, there are still some limitations on ICA simultaneously handling neuroimaging datasets with diverse acquisition parameters, e.g., different repetition time, different scanner, etc. Therefore, it is difficult for the traditional ICA framework to effectively handle ever-increasingly big neuroimaging datasets. In this research, a novel feature-map based ICA framework (FMICA) was proposed to address the aforementioned deficiencies, which aimed at exploring brain functional networks (BFNs) at different scales, e.g., the first level (individual subject level), second level (intragroup level of subjects within a certain dataset) and third level (intergroup level of subjects across different datasets), based only on the feature maps extracted from the fMRI datasets. The FMICA was presented as a hierarchical framework, which effectively made ICA and constrained ICA as a whole to identify the BFNs from the feature maps. The simulated and real experimental results demonstrated that FMICA had the excellent ability to identify the intergroup BFNs and to characterize subject-specific and group-specific difference of BFNs from the independent component feature maps, which sharply reduced the size of fMRI datasets. Compared with traditional ICAs, FMICA as a more generalized framework could efficiently and simultaneously identify the variant BFNs at the subject-specific, intragroup, intragroup-specific and intergroup levels, implying that FMICA was able to handle big neuroimaging datasets in neuroscience research.

[Different aspects of magnetic resonance imaging of muscles between dermatomyositis and polymyositis].

PubMed

Miranda, Sofia Silveira de Castro; Alvarenga, Daniel; Rodrigues, João Carlos; Shinjo, Samuel Katsuyuki

2014-01-01

Although dermatomyositis (DM) and polymyositis (PM) share many clinical features in common, they have distinct pathophysiological and histological features. It is possible that these distinctions reflect also macroscopically, for example, in muscle alterations seen in magnetic resonance images (MRI). To compare simultaneously the MRI of various muscle compartments of the thighs of adult DM and PM. The present study is a cross-sectional that included, between 2010 and 2013, 11 newly diagnosed DM and 11 PM patients (Bohan and Peter's criteria, 1975), with clinical and laboratory activity. They were valued at RM thighs, T1 and T2 with fat suppression, 1.5 T MRI scanner sequences. The mean age at the time of MRI, the time between onset of symptoms and the realization of the MRI distribution of sex and drug therapy were comparable between the two groups (p>0.050). Concerning the MRI, muscle edema was significantly found in DM, and mainly in the proximal region of the muscles. The area of fat replacement was found predominantly in PM. The partial fat replacement area occurred mainly in the medial and distal region, whereas the total fat replacement area occurred mainly in the distal muscles. There was no area of muscle fibrosis. DM and PM have different characteristics on MRI muscles, alike pathophysiological and histological distinctions. Copyright © 2014 Elsevier Editora Ltda. All rights reserved.

Early classification of Alzheimer's disease using hippocampal texture from structural MRI

NASA Astrophysics Data System (ADS)

Zhao, Kun; Ding, Yanhui; Wang, Pan; Dou, Xuejiao; Zhou, Bo; Yao, Hongxiang; An, Ningyu; Zhang, Yongxin; Zhang, Xi; Liu, Yong

2017-03-01

Convergent evidence has been collected to support that Alzheimer's disease (AD) is associated with reduction in hippocampal volume based on anatomical magnetic resonance imaging (MRI) and impaired functional connectivity based on functional MRI. Radiomics texture analysis has been previously successfully used to identify MRI biomarkers of several diseases, including AD, mild cognitive impairment and multiple sclerosis. In this study, our goal was to determine if MRI hippocampal textures, including the intensity, shape, texture and wavelet features, could be served as an MRI biomarker of AD. For this purpose, the texture marker was trained and evaluated from MRI data of 48 AD and 39 normal samples. The result highlights the presence of hippocampal texture abnormalities in AD, and the possibility that texture may serve as a neuroimaging biomarker for AD.

Visual feature extraction from voxel-weighted averaging of stimulus images in 2 fMRI studies.

PubMed

Hart, Corey B; Rose, William J

2013-11-01

Multiple studies have provided evidence for distributed object representation in the brain, with several recent experiments leveraging basis function estimates for partial image reconstruction from fMRI data. Using a novel combination of statistical decomposition, generalized linear models, and stimulus averaging on previously examined image sets and Bayesian regression of recorded fMRI activity during presentation of these data sets, we identify a subset of relevant voxels that appear to code for covarying object features. Using a technique we term "voxel-weighted averaging," we isolate image filters that these voxels appear to implement. The results, though very cursory, appear to have significant implications for hierarchical and deep-learning-type approaches toward the understanding of neural coding and representation.

An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques

PubMed Central

Sui, Jing; Adali, Tülay; Pearlson, Godfrey D.; Calhoun, Vince D.

2013-01-01

Extraction of relevant features from multitask functional MRI (fMRI) data in order to identify potential biomarkers for disease, is an attractive goal. In this paper, we introduce a novel feature-based framework, which is sensitive and accurate in detecting group differences (e.g. controls vs. patients) by proposing three key ideas. First, we integrate two goal-directed techniques: coefficient-constrained independent component analysis (CC-ICA) and principal component analysis with reference (PCA-R), both of which improve sensitivity to group differences. Secondly, an automated artifact-removal method is developed for selecting components of interest derived from CC-ICA, with an average accuracy of 91%. Finally, we propose a strategy for optimal feature/component selection, aiming to identify optimal group-discriminative brain networks as well as the tasks within which these circuits are engaged. The group-discriminating performance is evaluated on 15 fMRI feature combinations (5 single features and 10 joint features) collected from 28 healthy control subjects and 25 schizophrenia patients. Results show that a feature from a sensorimotor task and a joint feature from a Sternberg working memory (probe) task and an auditory oddball (target) task are the top two feature combinations distinguishing groups. We identified three optimal features that best separate patients from controls, including brain networks consisting of temporal lobe, default mode and occipital lobe circuits, which when grouped together provide improved capability in classifying group membership. The proposed framework provides a general approach for selecting optimal brain networks which may serve as potential biomarkers of several brain diseases and thus has wide applicability in the neuroimaging research community. PMID:19457398

Methods for Acquiring Structural MRI Data in Very Young Children with Autism without the Use of Sedation

ERIC Educational Resources Information Center

Nordahl, Christine Wu; Simon, Tony J.; Zierhut, Cynthia; Solomon, Marjorie; Rogers, Sally J.; Amaral, David G.

2008-01-01

We describe a protocol with which we achieved a 93% success rate in acquiring high quality MRI scans without the use of sedation in 2.5-4.5 year old children with autism, developmental delays, and typical development. Our main strategy was to conduct MRIs during natural nocturnal sleep in the evenings after the child's normal bedtime.…

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Saha, Ashirbani, E-mail: as698@duke.edu; Grimm, La

Purpose: To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI. Methods: Four readers annotated breast tumors from the MRI examinations of 50 patients from one institution using a bounding box to indicate a tumor. All of the annotated tumors were biopsy proven cancers. The similarity of bounding boxes was analyzed using Dice coefficients. An automatic tumor segmentation algorithm was used to segment tumors from the readers’ annotations. The segmented tumors were then compared between readersmore » using Dice coefficients as the similarity metric. Cases showing high interobserver variability (average Dice coefficient <0.8) after segmentation were analyzed by a panel of radiologists to identify the reasons causing the low level of agreement. Furthermore, an imaging feature, quantifying tumor and breast tissue enhancement dynamics, was extracted from each segmented tumor for a patient. Pearson’s correlation coefficients were computed between the features for each pair of readers to assess the effect of the annotation on the feature values. Finally, the authors quantified the extent of variation in feature values caused by each of the individual reasons for low agreement. Results: The average agreement between readers in terms of the overlap (Dice coefficient) of the bounding box was 0.60. Automatic segmentation of tumor improved the average Dice coefficient for 92% of the cases to the average value of 0.77. The mean agreement between readers expressed by the correlation coefficient for the imaging feature was 0.96. Conclusions: There is a moderate variability between readers when identifying the rectangular outline of breast tumors on MRI. This variability is alleviated by the automatic segmentation of the tumors. Furthermore, the moderate interobserver variability in terms of the bounding box does not translate into a considerable variability in terms of assessment of enhancement dynamics. The authors propose some additional ways to further reduce the interobserver variability.« less

The potential of multiparametric MRI of the breast

PubMed Central

Pinker, Katja; Helbich, Thomas H

2017-01-01

MRI is an essential tool in breast imaging, with multiple established indications. Dynamic contrast-enhanced MRI (DCE-MRI) is the backbone of any breast MRI protocol and has an excellent sensitivity and good specificity for breast cancer diagnosis. DCE-MRI provides high-resolution morphological information, as well as some functional information about neoangiogenesis as a tumour-specific feature. To overcome limitations in specificity, several other functional MRI parameters have been investigated and the application of these combined parameters is defined as multiparametric MRI (mpMRI) of the breast. MpMRI of the breast can be performed at different field strengths (1.5–7 T) and includes both established (diffusion-weighted imaging, MR spectroscopic imaging) and novel MRI parameters (sodium imaging, chemical exchange saturation transfer imaging, blood oxygen level-dependent MRI), as well as hybrid imaging with positron emission tomography (PET)/MRI and different radiotracers. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the underlying oncogenic processes of cancer development and progression and can provide additional specificity. This article will review the current and emerging functional parameters for mpMRI of the breast for improved diagnostic accuracy in breast cancer. PMID:27805423

SU-F-R-42: Association of Radiomic and Metabolic Tumor Volumes in Radiation Treatment of Glioblastoma Multiforme

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lopez, C; Nagornaya, N; Parra, N

Purpose: High-throughput extraction of imaging and metabolomic quantitative features from MRI and MR Spectroscopy Imaging (MRSI) of Glioblastoma Multiforme (GBM) results in tens of variables per patient. In radiotherapy (RT) of GBM, the relevant metabolic tumor volumes (MTVs) are related to aberrant levels of N-acetyl Aspartate (NAA) and Choline (Cho). Corresponding Clinical Target Volumes (CTVs) for RT planning are based on Contrast Enhancing T1-weighted MRI (CE-T1w) and T2-weighted/Fluid Attenuated Inversion Recovery (FLAIR) MRI. The objective is to build a framework for investigation of associations between imaging, CTV, and MTV features better understanding of the underlying information in the CTVs andmore » dependencies between these volumes. Methods: Necrotic portions, enhancing lesion and edema were manually contoured on T1w/T2w images for 17 GBM patients. CTVs and MTVs for NAA (MTV{sub NAA}) and Cho (MTV{sub Cho}) were constructed. Tumors were scored categorically for ten semantic imaging traits by neuroradiologist. All features were investigated for redundancy. Two-way correlations between imaging and RT/MTV features were visualized as heat maps. Associations between MTV{sub NAA}, MTV{sub Cho} and imaging features were studied using Spearman correlation. Results: 39 imaging features were computed per patient. Half of the imaging traits were replaced with automatically extracted continuous variables. 21 features were extracted from MTVs/CTVs. There were a high number (43) of significant correlations of imaging with CTVs/MTV{sub NAA} while very few (10) significant correlations were with CTVs/MTV{sub Cho}. MTV{sub NAA} was found to be closely associated with MRI volumes, MTV{sub Cho} remains elusive for characterization with imaging. Conclusion: A framework for investigation of co-dependency between MRI and RT/metabolic features is established. A series of semantic imaging traits were replaced with automatically extracted continuous variables. The approach will allow for exploration of relationships between sizes and intersection of imaging features of tumors, RT volumes, metabolite concentrations and comparing those to therapy outcome, quality of life evaluation and overall survival rate. This publication was supported by Grant 10BN03 from Bankhead Coley Cancer Research Program, R01EB000822, R01EB016064, and R01CA172210 from the National Institutes of Health, and Indo-US Science & Technology Forum award #20-2009. Bhaswati Roy received financial assistance from University Grant Commission, New Delhi, India.« less

Examination of neural systems sub-serving facebook "addiction".

PubMed

Turel, Ofir; He, Qinghua; Xue, Gui; Xiao, Lin; Bechara, Antoine

2014-12-01

Because addictive behaviors typically result from violated homeostasis of the impulsive (amygdala-striatal) and inhibitory (prefrontal cortex) brain systems, this study examined whether these systems sub-serve a specific case of technology-related addiction, namely Facebook "addiction." Using a go/no-go paradigm in functional MRI settings, the study examined how these brain systems in 20 Facebook users (M age = 20.3 yr., SD = 1.3, range = 18-23) who completed a Facebook addiction questionnaire, responded to Facebook and less potent (traffic sign) stimuli. The findings indicated that at least at the examined levels of addiction-like symptoms, technology-related "addictions" share some neural features with substance and gambling addictions, but more importantly they also differ from such addictions in their brain etiology and possibly pathogenesis, as related to abnormal functioning of the inhibitory-control brain system.

Time-frequency analysis of pediatric murmurs

NASA Astrophysics Data System (ADS)

Lombardo, Joseph S.; Blodgett, Lisa A.; Rosen, Ron S.; Najmi, Amir-Homayoon; Thompson, W. Reid

1998-05-01

Technology has provided many new tools to assist in the diagnosis of pathologic conditions of the heart. Echocardiography, Ultrafast CT, and MRI are just a few. While these tools are a valuable resource, they are typically too expensive, large and complex in operation for use in rural, homecare, and physician's office settings. Recent advances in computer performance, miniaturization, and acoustic signal processing, have yielded new technologies that when applied to heart sounds can provide low cost screening for pathologic conditions. The short duration and transient nature of these signals requires processing techniques that provide high resolution in both time and frequency. Short-time Fourier transforms, Wigner distributions, and wavelet transforms have been applied to signals form hearts with various pathologic conditions. While no single technique provides the ideal solution, the combination of tools provides a good representation of the acoustic features of the pathologies selected.

It isn't always caviar

PubMed Central

Flammer Anikpeh, Yvonne; Grimm, Felix; Lindenblatt, Nicole; Zinkernagel, Annelies

2014-01-01

A 47-year-old HIV-positive woman presented with fever and a painful swollen right forearm. Clinical presentation and MRI were suggestive for a necrotising fasciitis. Surgical exploration revealed small transparent cystic bodies resembling white caviar, which were identified by their typical morphological features as larval stages (cysticerci) of Taenia crassiceps. Molecular methods, using sequence analysis of the small subunit rRNA gene, definitively confirmed T crassiceps. T crassiceps (Cestodea: Taeniidae) is a tapeworm found in the intestines of red foxes and dogs in the Northern Hemisphere. Human infections are rare and appear to depend on the host's immunocompetence. The eight published cases could not clarify the mode of infection but discuss ingestion of teniid eggs or penetration through a cutaneous wound. The optimal treatment remains unclear. We describe a detailed and successful treatment strategy including extensive surgical interventions, prolonged anthelmintic and antiretroviral treatment. PMID:24692370

It isn't always caviar.

PubMed

Flammer Anikpeh, Yvonne; Grimm, Felix; Lindenblatt, Nicole; Zinkernagel, Annelies

2014-04-01

A 47-year-old HIV-positive woman presented with fever and a painful swollen right forearm. Clinical presentation and MRI were suggestive for a necrotising fasciitis. Surgical exploration revealed small transparent cystic bodies resembling white caviar, which were identified by their typical morphological features as larval stages (cysticerci) of Taenia crassiceps. Molecular methods, using sequence analysis of the small subunit rRNA gene, definitively confirmed T crassiceps. T crassiceps (Cestodea: Taeniidae) is a tapeworm found in the intestines of red foxes and dogs in the Northern Hemisphere. Human infections are rare and appear to depend on the host's immunocompetence. The eight published cases could not clarify the mode of infection but discuss ingestion of teniid eggs or penetration through a cutaneous wound. The optimal treatment remains unclear. We describe a detailed and successful treatment strategy including extensive surgical interventions, prolonged anthelmintic and antiretroviral treatment.

A semi-supervised Support Vector Machine model for predicting the language outcomes following cochlear implantation based on pre-implant brain fMRI imaging.

PubMed

Tan, Lirong; Holland, Scott K; Deshpande, Aniruddha K; Chen, Ye; Choo, Daniel I; Lu, Long J

2015-12-01

We developed a machine learning model to predict whether or not a cochlear implant (CI) candidate will develop effective language skills within 2 years after the CI surgery by using the pre-implant brain fMRI data from the candidate. The language performance was measured 2 years after the CI surgery by the Clinical Evaluation of Language Fundamentals-Preschool, Second Edition (CELF-P2). Based on the CELF-P2 scores, the CI recipients were designated as either effective or ineffective CI users. For feature extraction from the fMRI data, we constructed contrast maps using the general linear model, and then utilized the Bag-of-Words (BoW) approach that we previously published to convert the contrast maps into feature vectors. We trained both supervised models and semi-supervised models to classify CI users as effective or ineffective. Compared with the conventional feature extraction approach, which used each single voxel as a feature, our BoW approach gave rise to much better performance for the classification of effective versus ineffective CI users. The semi-supervised model with the feature set extracted by the BoW approach from the contrast of speech versus silence achieved a leave-one-out cross-validation AUC as high as 0.97. Recursive feature elimination unexpectedly revealed that two features were sufficient to provide highly accurate classification of effective versus ineffective CI users based on our current dataset. We have validated the hypothesis that pre-implant cortical activation patterns revealed by fMRI during infancy correlate with language performance 2 years after cochlear implantation. The two brain regions highlighted by our classifier are potential biomarkers for the prediction of CI outcomes. Our study also demonstrated the superiority of the semi-supervised model over the supervised model. It is always worthwhile to try a semi-supervised model when unlabeled data are available.

Muscle MRI in female carriers of dystrophinopathy.

PubMed

Tasca, G; Monforte, M; Iannaccone, E; Laschena, F; Ottaviani, P; Silvestri, G; Masciullo, M; Mirabella, M; Servidei, S; Ricci, E

2012-09-01

Duchenne muscular dystrophy carriers represent a rare condition that needs to be recognized because of the possible implications for prenatal diagnosis. Muscle biopsy is currently the diagnostic instrument of choice in sporadic patients. We wanted to verify whether muscle magnetic resonance imaging (MRI) could identify a pattern of involvement suggestive of this condition and whether it was similar to that reported in Duchenne and Becker muscular dystrophy. Evaluation of pelvic and lower limb MRI scans of 12 dystrophinopathy carriers was performed. We found a frequent involvement of the quadratus femoris, gluteus maximus and medius, biceps femoris long head, adductor magnus, vasti and paraspinal muscles, whilst the popliteus, iliopsoas, recti abdominis, sartorius, and gracilis were relatively spared. Asymmetry was a major feature on MRI; it could be detected significantly more often than with sole clinical examination and even in patients without weakness. The pattern we describe here is similar to that reported in Duchenne and Becker muscular dystrophy, although asymmetry represents a major distinctive feature. Muscle MRI was more sensitive than clinical examination for detecting single muscle involvement and asymmetry. Further studies are needed to verify the consistency of this pattern in larger cohorts and to assess whether muscle MRI can improve diagnostic accuracy in carriers with normal dystrophin staining on muscle biopsy. © 2012 The Author(s) European Journal of Neurology © 2012 EFNS.

MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

PubMed Central

Wang, Liye; Wee, Chong-Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

2015-01-01

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge. PMID:25822851

Radiologic Diagnosis of Spondylodiscitis, Role of Magnetic Resonance

PubMed Central

Ramadani, Naser; Dedushi, Kreshnike; Kabashi, Serbeze; Mucaj, Sefedin

2017-01-01

Introduction: Study aim is to report the Magnetic Resonance Imaging (MRI) features of acute and chronic spontaneous spondylodiscitis. Case report: 57 year old female, complaining of a fever and longstanding cervical pain worsened during physical therapy. Methods: MR images were acquired using superconductive magnet 1.5 T, with the following sequences: sagittal PD and T2 TSE, sagittal T1 SE, axial PD and T2 TSE (lumbar spine), axial T2 GRE (cervical spine). Axial and sagittal T1 SE after administration of (gadolinium DTPA). Examination was reviewed by three radiologists and compared to CT findings. Results: Patient reported cervical pain associated with fever and minimal weight loss. Blood tests were normal except hyperglycemia (DM tip II). X Ray: vertebral destruction localized at C-4 and C-5: NECT: destruction of the C-4/C-5 vertebral bodies (ventral part). MRI: Low signal of the bone marrow on T1l images, which enhanced after Gd-DTPA administration and became intermediate or high on T2 images. The steady high signal intensity of the disk on T2 images and enhancement on T1 images is typical for an acute inflammatory process. Bone Scintigrafi results: Bone changes suspicious for metastasis. Whole body CT results: apart from spine, no other significant changes. Conclusion: MRI is the most sensitive technique for the diagnosis of spondylodiscitis in the acute phase and comparable to CT regarding chronial stage of the disease. The present imagining essay os aimed at showing the main magnetic resonance imaging findings of tuberculous discitis. PMID:28484299

Clinical characteristics of hypertensive encephalopathy in pediatric patients

PubMed Central

Ahn, Chang Hoon; Han, Seung-A; Kong, Young Hwa

2017-01-01

Purpose The aim of this study was to assess the clinical characteristics of hypertensive encephalopathy according to the underlying etiologies in children. Methods We retrospectively evaluated 33 pediatric patients who were diagnosed as having hypertensive encephalopathy in Chonbuk National University Children's Hospital. Among the patients, 18 were excluded because of incomplete data or because brain magnetic resonance imaging (MRI) was not performed. Finally, 17 patients were enrolled and divided into a renal-origin hypertension group and a non-renal-origin hypertension group according to the underlying cause. We compared the clinical features and brain MRI findings between the 2 groups. Results The renal group included renal artery stenosis (4), acute poststreptococcal glomerulonephritis (2), lupus nephritis (2), and acute renal failure (1); the nonrenal group included essential hypertension (4), pheochromocytoma (2), thyrotoxicosis (1), and acute promyelocytic leukemia (1). The mean systolic blood pressure of the renal group (172.5±36.9 mmHg) was higher than that of the nonrenal group (137.1±11.1 mmHg, P<0.05). Seizure was the most common neurologic symptom, especially in the renal group (P<0.05). Posterior reversible encephalopathy syndrome (PRES), which is the most typical finding of hypertensive encephalopathy, was found predominantly in the renal group as compared with the nonrenal group (66.6% vs. 12.5%, P<0.05). Conclusion We conclude that the patients with renal-origin hypertension had a more severe clinical course than those with non-renal-origin hypertension. Furthermore, the renal-origin group was highly associated with PRES on brain MRI. PMID:29042869

Morphologic Features of Magnetic Resonance Imaging as a Surrogate of Capsular Contracture in Breast Cancer Patients With Implant-based Reconstructions.

PubMed

Tyagi, Neelam; Sutton, Elizabeth; Hunt, Margie; Zhang, Jing; Oh, Jung Hun; Apte, Aditya; Mechalakos, James; Wilgucki, Molly; Gelb, Emily; Mehrara, Babak; Matros, Evan; Ho, Alice

2017-02-01

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI). We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores. UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P<.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79). The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction. Copyright © 2016 Elsevier Inc. All rights reserved.

The impracticality of MRI for the diagnosis of atypical penile fracture in the emergency setting.

PubMed

Maurice, Matthew J; Spirnak, J Patrick

2014-05-01

We report the case of a patient who presented to the emergency department with a history suspicious for penile fracture without typical physical exam findings. A small penile fracture was present on MRI, but the diagnosis was missed, and surgery was withheld owing to this misinformation. Despite its technical accuracy, MRI may be impractical for the diagnosis of penile fracture in the emergency setting. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Review: Magnetic resonance imaging techniques in ophthalmology

PubMed Central

Fagan, Andrew J.

2012-01-01

Imaging the eye with magnetic resonance imaging (MRI) has proved difficult due to the eye’s propensity to move involuntarily over typical imaging timescales, obscuring the fine structure in the eye due to the resulting motion artifacts. However, advances in MRI technology help to mitigate such drawbacks, enabling the acquisition of high spatiotemporal resolution images with a variety of contrast mechanisms. This review aims to classify the MRI techniques used to date in clinical and preclinical ophthalmologic studies, describing the qualitative and quantitative information that may be extracted and how this may inform on ocular pathophysiology. PMID:23112569

Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain.

PubMed

Latha, Manohar; Kavitha, Ganesan

2018-02-03

Schizophrenia (SZ) is a psychiatric disorder that especially affects individuals during their adolescence. There is a need to study the subanatomical regions of SZ brain on magnetic resonance images (MRI) based on morphometry. In this work, an attempt was made to analyze alterations in structure and texture patterns in images of the SZ brain using the level-set method and Laws texture features. T1-weighted MRI of the brain from Center of Biomedical Research Excellence (COBRE) database were considered for analysis. Segmentation was carried out using the level-set method. Geometrical and Laws texture features were extracted from the segmented brain stem, corpus callosum, cerebellum, and ventricle regions to analyze pattern changes in SZ. The level-set method segmented multiple brain regions, with higher similarity and correlation values compared with an optimized method. The geometric features obtained from regions of the corpus callosum and ventricle showed significant variation (p < 0.00001) between normal and SZ brain. Laws texture feature identified a heterogeneous appearance in the brain stem, corpus callosum and ventricular regions, and features from the brain stem were correlated with Positive and Negative Syndrome Scale (PANSS) score (p < 0.005). A framework of geometric and Laws texture features obtained from brain subregions can be used as a supplement for diagnosis of psychiatric disorders.

Hepatic perivascular epithelioid cell tumor (PEComa): dynamic CT, MRI, ultrasonography, and pathologic features--analysis of 7 cases and review of the literature.

PubMed

Tan, Yan; Xiao, En-hua

2012-10-01

To evaluate the dynamic CT, MRI, ultrasonography, and pathologic features of hepatic perivascular epithelioid cell tumor (PEComa), improving the understanding and diagnosis of the tumor. A retrospective analysis of CT, MRI, ultrasonography, and pathologic features of 7 hepatic PEComas diagnosed by pathology during 1st January 2005 to 1st September 2011 in our hospital. The performance of dynamic CT, MRI, and ultrasonography revealed that lesions were regular masses with well-defined borders, the maximum diameters were 2.5-8.5 cm (mean = 4 cm), density was homogeneous, contrast-enhanced CT and MRI showed the lesions were significantly and heterogeneously enhanced on arterial phase, less enhanced on portal venous phase, and slightly hypodense on delayed phase. One patient had multiple hepatic lesions and had delayed enhancement. There were no backgrounds of hepatitis and cirrhosis, enlarged lymph nodes, or distant metastases. Pathology showed the gross appearance of the tumor was smooth. Tumor cells were round or polygonal, with clear boundaries and clear membranes, and had abundant translucent cytoplasm. Nuclei were round, with medium size. Tumor cells were epithelial-like cells and arranged in dense sheets. Immunohistochemistry showed that most of them were positive in HMB45 and MelanA, S-100, SMA, while negative in CgA, Syn, CK, CD117, CD10, and CD34. Dynamic CT, MRI, ultrasonography, and pathology of PEComa had some characteristics of benign tumor's performance. Enhanced scan showed PEComa quickly enhanced on arterial phase and enhanced less on portal venous phase. Knowing these characteristics could help to improve the understanding and diagnosis of hepatic PEComa.

Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

PubMed

Ren, Jiliang; Yuan, Ying; Wu, Yingwei; Tao, Xiaofeng

2018-05-02

The overlap of morphological feature and mean ADC value restricted clinical application of MRI in the differential diagnosis of orbital lymphoma and idiopathic orbital inflammatory pseudotumor (IOIP). In this paper, we aimed to retrospectively evaluate the combined diagnostic value of conventional magnetic resonance imaging (MRI) and whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in the differentiation of the two lesions. In total, 18 patients with orbital lymphoma and 22 patients with IOIP were included, who underwent both conventional MRI and diffusion weighted imaging before treatment. Conventional MRI features and histogram parameters derived from ADC maps, including mean ADC (ADC mean ), median ADC (ADC median ), skewness, kurtosis, 10th, 25th, 75th and 90th percentiles of ADC (ADC 10 , ADC 25 , ADC 75 , ADC 90 ) were evaluated and compared between orbital lymphoma and IOIP. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating. Differential model was built upon the selected variables and receiver operating characteristic (ROC) analysis was also performed to determine the differential ability of the model. Multivariate logistic regression showed ADC 10 (P = 0.023) and involvement of orbit preseptal space (P = 0.029) were the most promising indexes in the discrimination of orbital lymphoma and IOIP. The logistic model defined by ADC 10 and involvement of orbit preseptal space was built, which achieved an AUC of 0.939, with sensitivity of 77.30% and specificity of 94.40%. Conventional MRI feature of involvement of orbit preseptal space and ADC histogram parameter of ADC 10 are valuable in differential diagnosis of orbital lymphoma and IOIP.

Detailed magnetic resonance imaging features of a case series of primary gliosarcoma.

PubMed

Sampaio, Luísa; Linhares, Paulo; Fonseca, José

2017-12-01

Objective We aimed to characterise the magnetic resonance imaging (MRI) features of a case series of primary gliosarcoma, with the inclusion of diffusion-weighted imaging and perfusion imaging with dynamic susceptibility contrast MRI. Materials and methods We conducted a retrospective study of cases of primary gliosarcoma from the Pathology Department database from January 2006 to December 2014. Clinical and demographic data were obtained. Two neuroradiologists, blinded to diagnosis, assessed tumour location, signal intensity in T1 and T2-weighted images, pattern of enhancement, diffusion-weighted imaging and dynamic susceptibility contrast MRI studies on preoperative MRI. Results Seventeen patients with primary gliosarcomas had preoperative MRI study: seven men and 10 women, with a mean age of 59 years (range 27-74). All lesions were well demarcated, supratentorial and solitary (frontal n = 5, temporal n = 4, parietal n = 3); 13 tumours abutted the dural surface (8/13 with dural enhancement); T1 and T2-weighted imaging patterns were heterogeneous and the majority of lesions (12/17) showed a rim-like enhancement pattern with focal nodularities/irregular thickness. Restricted diffusion (mean apparent diffusion coefficient values 0.64 × 10 -3 mm 2 /s) in the more solid/thick components was present in eight out of 11 patients with diffusion-weighted imaging study. Dynamic susceptibility contrast MRI study ( n = 8) consistently showed hyperperfusion in non-necrotic/cystic components on relative cerebral volume maps. Conclusions The main distinguishing features of primary gliosarcoma are supratentorial and peripheral location, well-defined boundaries and a rim-like pattern of enhancement with an irregular thick wall. Diffusion-weighted imaging and relative cerebral volume map analysis paralleled primary gliosarcoma with high-grade gliomas, thus proving helpful in differential diagnosis.

MRI detection of soleus muscle injuries in professional football players.

PubMed

Pezzotta, G; Querques, G; Pecorelli, A; Nani, R; Sironi, S

2017-11-01

To describe magnetic resonance imaging (MRI) characteristics of soleus muscle injuries in symptomatic professional football players stratified according to both the Munich consensus statement and the British Athletics Muscle Injury Classification (BAMIC), and to investigate the association between specific MRI features and the "return to play" (RTP). Professional football players with an episode of acute posterior calf pain and impaired function, subsequent to sports activity, underwent ultrasound followed by MRI examination reviewed by two different radiologists with more than 10 years of experience in the musculoskeletal system. MRI features and RTP outcome were evaluated for all types of injuries. During a 36-month period, a total of 20 professional football players were evaluated. According to the Munich consensus, 11 were type 3A, 8 were type 3B, and 1 was type 4, whereas according to the BAMIC, 11 lesions were considered grade 1, 4 grade 2, 4 grade 3, and 1 grade 4. RTP data were available for all patients (mean 3.3 ± 1.6 weeks). Both the Munich consensus and the BAMIC correlated with RTP (Spearman correlation = 0.982 and p < 0.0001 and 0.886 and p < 0.0001 respectively). Extension of edema was an independent prognostic factor for RTP in two different models of multivariate regression analysis (p = 0.044 model A; p = 0.031 model B). The Munich consensus and BAMIC grading systems are useful tools for defining the patient's prognosis and proper rehabilitation time after injury. The MRI feature that we should carefully look for is the extension of edema, as it seems to significantly affect the RTP.

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

PubMed Central

Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L.; Rueckert, Daniel; Thiran, Jean-Philippe

2017-01-01

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. PMID:28350816

Megalencephalic leukoencephalopathy with subcortical cysts: Characterization of disease variants.

PubMed

Hamilton, Eline M C; Tekturk, Pinar; Cialdella, Fia; van Rappard, Diane F; Wolf, Nicole I; Yalcinkaya, Cengiz; Çetinçelik, Ümran; Rajaee, Ahmad; Kariminejad, Ariana; Paprocka, Justyna; Yapici, Zuhal; Bošnjak, Vlatka Mejaški; van der Knaap, Marjo S

2018-04-17

To provide an overview of clinical and MRI characteristics of the different variants of the leukodystrophy megalencephalic leukoencephalopathy with subcortical cysts (MLC) and identify possible differentiating features. We performed an international multi-institutional, cross-sectional observational study of the clinical and MRI characteristics in patients with genetically confirmed MLC. Clinical information was obtained by questionnaires for physicians and retrospective chart review. We included 204 patients with classic MLC, 187 of whom had recessive mutations in MLC1 (MLC1 variant) and 17 in GLIALCAM (MLC2A variant) and 38 patients with remitting MLC caused by dominant GLIALCAM mutations (MLC2B variant). We observed a relatively wide variability in neurologic disability among patients with classic MLC. No clinical differences could be identified between patients with MLC1 and MLC2A. Patients with MLC2B invariably had a milder phenotype with preservation of motor function, while intellectual disability and autism were relatively frequent. Systematic MRI review revealed no MRI features that distinguish between MLC1 and MLC2A. Radiologic improvement was observed in all patients with MLC2B and also in 2 patients with MLC1. In MRIs obtained in the early disease stage, absence of signal abnormalities of the posterior limb of the internal capsule and cerebellar white matter and presence of only rarefied subcortical white matter instead of true subcortical cysts were suggestive of MLC2B. Clinical and MRI features did not distinguish between classic MLC with MLC1 or GLIALCAM mutations. Absence of signal abnormalities of the internal capsule and cerebellar white matter are MRI findings that point to the remitting phenotype. Copyright © 2018 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612

A Radio-genomics Approach for Identifying High Risk Estrogen Receptor-positive Breast Cancers on DCE-MRI: Preliminary Results in Predicting OncotypeDX Risk Scores

PubMed Central

Wan, Tao; Bloch, B. Nicolas; Plecha, Donna; Thompson, CheryI L.; Gilmore, Hannah; Jaffe, Carl; Harris, Lyndsay; Madabhushi, Anant

2016-01-01

To identify computer extracted imaging features for estrogen receptor (ER)-positive breast cancers on dynamic contrast en-hanced (DCE)-MRI that are correlated with the low and high OncotypeDX risk categories. We collected 96 ER-positivebreast lesions with low (<18, N = 55) and high (>30, N = 41) OncotypeDX recurrence scores. Each lesion was quantitatively charac-terize via 6 shape features, 3 pharmacokinetics, 4 enhancement kinetics, 4 intensity kinetics, 148 textural kinetics, 5 dynamic histogram of oriented gradient (DHoG), and 6 dynamic local binary pattern (DLBP) features. The extracted features were evaluated by a linear discriminant analysis (LDA) classifier in terms of their ability to distinguish low and high OncotypeDX risk categories. Classification performance was evaluated by area under the receiver operator characteristic curve (Az). The DHoG and DLBP achieved Az values of 0.84 and 0.80, respectively. The 6 top features identified via feature selection were subsequently combined with the LDA classifier to yield an Az of 0.87. The correlation analysis showed that DHoG (ρ = 0.85, P < 0.001) and DLBP (ρ = 0.83, P < 0.01) were significantly associated with the low and high risk classifications from the OncotypeDX assay. Our results indicated that computer extracted texture features of DCE-MRI were highly correlated with the high and low OncotypeDX risk categories for ER-positive cancers. PMID:26887643

Non-Traumatic Myositis Ossificans in the Lumbosacral Paravertebral Muscle

PubMed Central

Jung, DaeYoung; Roh, Ji Hyeon

2013-01-01

Myositis ossificans (MO) is a benign condition of non-neoplastic heterotopic bone formation in the muscle or soft tissue. Trauma plays a role in the development of MO, thus, non-traumatic MO is very rare. Although MO may occur anywhere in the body, it is rarely seen in the lumbosacral paravertebral muscle (PVM). Herein, we report a case of non-traumatic MO in the lumbosacral PVM. A 42-year-old man with no history of trauma was referred to our hospital for pain in the low back, left buttock, and left thigh. On physical examination, a slightly tender, hard, and fixed mass was palpated in the left lumbosacral PVM. Computed tomography showed a calcified mass within the left lumbosacral PVM. Magnetic resonance imaging (MRI) showed heterogeneous high signal intensity in T1- and T2-weighted image, and no enhancement of the mass was found in the postcontrast T1-weighted MRI. The lack of typical imaging features required an open biopsy, and MO was confirmed. MO should be considered in the differential diagnosis when the imaging findings show a mass involving PVM. When it is difficult to distinguish MO from soft tissue or bone malignancy by radiology, it is necessary to perform a biopsy to confirm the diagnosis. PMID:23908707

Learning-dependent plasticity with and without training in the human brain.

PubMed

Zhang, Jiaxiang; Kourtzi, Zoe

2010-07-27

Long-term experience through development and evolution and shorter-term training in adulthood have both been suggested to contribute to the optimization of visual functions that mediate our ability to interpret complex scenes. However, the brain plasticity mechanisms that mediate the detection of objects in cluttered scenes remain largely unknown. Here, we combine behavioral and functional MRI (fMRI) measurements to investigate the human-brain mechanisms that mediate our ability to learn statistical regularities and detect targets in clutter. We show two different routes to visual learning in clutter with discrete brain plasticity signatures. Specifically, opportunistic learning of regularities typical in natural contours (i.e., collinearity) can occur simply through frequent exposure, generalize across untrained stimulus features, and shape processing in occipitotemporal regions implicated in the representation of global forms. In contrast, learning to integrate discontinuities (i.e., elements orthogonal to contour paths) requires task-specific training (bootstrap-based learning), is stimulus-dependent, and enhances processing in intraparietal regions implicated in attention-gated learning. We propose that long-term experience with statistical regularities may facilitate opportunistic learning of collinear contours, whereas learning to integrate discontinuities entails bootstrap-based training for the detection of contours in clutter. These findings provide insights in understanding how long-term experience and short-term training interact to shape the optimization of visual recognition processes.

Isolated double adrenocorticotropic hormone-secreting pituitary adenomas: A case report and review of the literature

PubMed Central

PU, JIUJUN; WANG, ZHIMING; ZHOU, HUI; ZHONG, AILING; JIN, KAI; RUAN, LUNLIANG; YANG, GANG

2016-01-01

Only a few cases of double or multiple pituitary adenomas have previously been reported in the literature; however, isolated double adrenocorticotropic hormone (ACTH)-secreting pituitary adenomas are even more rare. The present study reports a rare case of a 50-year-old female patient who presented with typical clinical features of Cushing's disease and was diagnosed with isolated double ACTH-secreting pituitary adenomas. Endocrinological examination revealed an ACTH-producing pituitary adenoma, and preoperative magnetic resonance imaging (MRI) demonstrated a microadenoma with a lower intensity on the right side of the pituitary gland. The patient underwent endoscopic endonasal transsphenoidal surgery, which revealed another pituitary tumor in the left side of the pituitary gland. The two, clearly separated, pituitary adenomas identified in the same gland were completely resected. Immunohistochemistry and pathology revealed that the clearly separated double pituitary adenomas were positive for ACTH, thyroid-stimulating, growth and prolactin hormones. Postoperatively, the levels of ACTH and cortisol hormone decreased rapidly. The case reported in the present study is considerably rare, due to the presence of a second pituitary adenoma in the same gland, which was not detected by preoperative MRI scan, but was noticed during surgery. Intraoperative evaluation may be important in the identification of double or multiple pituitary adenomas. PMID:27347184

Familial temporal lobe epilepsy due to focal cortical dysplasia type IIIa.

PubMed

Fabera, Petr; Krijtova, Hana; Tomasek, Martin; Krysl, David; Zamecnik, Josef; Mohapl, Milan; Jiruska, Premysl; Marusic, Petr

2015-09-01

Focal cortical dysplasia (FCD) represents a common cause of refractory epilepsy. It is considered a sporadic disorder, but its occasional familial occurrence suggests the involvement of genetic mechanisms. Siblings with intractable epilepsy were referred for epilepsy surgery evaluation. Both patients were examined using video-EEG monitoring, MRI examination and PET imaging. They underwent left anteromedial temporal lobe resection. Electroclinical features pointed to left temporal lobe epilepsy and MRI examination revealed typical signs of left-sided hippocampal sclerosis and increased white matter signal intensity in the left temporal pole. PET examination confirmed interictal hypometabolism in the left temporal lobe. Histopathological examination of resected tissue demonstrated the presence FCD type IIIa, i.e. hippocampal sclerosis and focal cortical dysplasia in the left temporal pole. We present a unique case of refractory mesial temporal lobe epilepsy in siblings, characterized by an identical clinical profile and histopathology of FCD type IIIa, who were successfully treated by epilepsy surgery. The presence of such a high concordance between the clinical and morphological data, together with the occurrence of epilepsy and febrile seizures in three generations of the family pedigree points towards a possible genetic nature of the observed FCD type IIIa. Copyright © 2015 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.

High resolution magnetic resonance imaging in pathogenesis diagnosis of single lenticulostriate infarction with nonstenotic middle cerebral artery, a retrospective study.

PubMed

Sun, Li-Li; Li, Zhong-Hao; Tang, Wen-Xiong; Liu, Lei; Chang, Fei-Yan; Zhang, Xue-Bin; Ye, Wei-Jie; Lu, Shuo; Liu, Zun-Jing; Zhu, Xian-Jin

2018-04-25

It is usually difficult to identify stroke pathogenesis for single lenticulostriate infarction with nonstenotic middle cerebral artery (MCA). Our aim is to differentiate the two pathogeneses, non-branch atheromatous small vessel disease and branch atheromatous disease (BAD) by high-resolution magnetic resonance imaging (HR-MRI). Thirty-two single lenticulostriate infarction patients with nonstenotic MCA admitted to the China-Japan Friendship Hospital from December 2014 to August 2017 were enrolled for retrospective analysis. National Institutes of Health Stroke Scale (NIHSS), modified Rankin Scale (mRS), atherosclerotic risk factors, imaging features, and the characteristic of MCA vessel wall in HR-MRI were evaluated. MCA plaques were detected in 15(46.9%) patients which implied BAD and 8 of 15 (53.3%) patients had plaques location in upper dorsal side of the vessel wall. Patients with HR-MRI identified plaques had a significantly larger infarction lesion length (1.95 ± 0.86 cm versus 1.38 ± 0.55 cm; P = 0.031) and larger lesion volume (2.95 ± 3.94 cm 3 versus 0.90 ± 0.94 cm 3 ; P = 0.027) than patients without plaques. Patients with HR-MRI identified plaques had a significant higher percentage of proximal lesions than patients without plaques (P = 0.055). However, according to the location of MCA plaques, there were no significant differences in terms of imaging features, NIHSS and mRS. We demonstrated high frequency of MCA atheromatous plaques visualized in single lenticulostriate infarction patients with nonstenotic MCA by using HR-MRI. Patients with HR-MRI identified plaque presented larger infarction lesions and more proximal lesions than patients without plaque, which were consistent with imaging features of BAD. HR-MRI is an important and effective tool for identifying stroke etiology in patients with nonstenotic MCA.

The value of DCE-MRI in assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas.

PubMed

Yuan, Su Juan; Qiao, Tian Kui; Qiang, Jin Wei; Cai, Song Qi; Li, Ruo Kun

2017-09-26

To investigate dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for assessing histopathological and molecular biological features in induced rat epithelial ovarian carcinomas (EOCs). 7,12-dimethylbenz[A]anthracene (DMBA) was applied to induce EOCs in situ in 46 SD rats. Conventional MRI and DCE-MRI were performed to evaluate the morphology and perfusion features of the tumors, including the time-signal intensity curve (TIC), volume transfer constant (K trans ), rate constant (K ep ), extravascular extracellular space volume ratio (V e ) and initial area under the curve (IAUC). DCE-MRI parameters were correlated with histological grade, microvascular density (MVD), vascular endothelial growth factor (VEGF) and fraction of Ki67-positive cells and the serum level of cancer antigen 125 (CA125). Thirty-five of the 46 rats developed EOCs. DCE-MRI showed type III TIC more frequently than type II (29/35 vs. 6/35, p < 0.001) in EOCs. The two types of TIC of tumors had significant differences in the histological grade, MVD, expression of VEGF and Ki67, and the serum level of CA125 (all p < 0.01). K trans , K ep and IAUC values showed significant differences in different histological grades in overall and pairwise comparisons except for IAUC in grade 2 vs. grade 3 (all p < 0.01). There was no significant difference in V e values among the three grade groups (p > 0.05). K trans , K ep and IAUC values were positively correlated with MVD, VEGF and Ki67 expression (all p < 0.01). V e was not significantly correlated with MVD, VEGF expression, Ki67 expression and the CA125 level (all p > 0.05). TIC types and perfusion parameters of DCE-MRI can reflect tumor grade, angiogenesis and cell proliferation to some extent, thereby helping treatment planning and predicting prognosis.

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex

PubMed Central

Summerfield, Christopher; Egner, Tobias

2016-01-01

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might “spread” from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. SIGNIFICANCE STATEMENT We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features. PMID:27810936

MRI features of extramedullary myeloma.

PubMed

Tirumani, Sree Harsha; Shinagare, Atul B; Jagannathan, Jyothi P; Krajewski, Katherine M; Munshi, Nikhil C; Ramaiya, Nikhil H

2014-04-01

The purpose of this study was to describe the MRI features of extramedullary myeloma and to evaluate the role of MRI in extramedullary myeloma. The cases of 28 patients (15 men, 13 women; mean age, 57.53 years; range, 34-83 years) with extramedullary myeloma who underwent MRI at one institution from January 2004 through December 2012 were retrospectively identified through an electronic search of an institutional radiology database. Two radiologists reviewed images from 44 MRI examinations in consensus to document the morphologic, signal-intensity, and enhancement characteristics of extramedullary myeloma. Electronic medical records were reviewed to document the indication for MRI and subsequent management of extramedullary myeloma. A total of 72 sites of extramedullary myeloma were noted, most commonly the paraspinal-epidural location (28/72, 39%). Two radiologic patterns were identified: lesions contiguous with bone (n = 44) and lesions noncontiguous with bone (n = 28). Lesions contiguous with bone were larger (p = 0.001; Student t test). Of 28 paraspinal-epidural lesions, 13 compressed the cord. Compared with skeletal muscle, most of the lesions were hypointense to isointense on T1-weighted images (67/72, 93.1%) and isointense to hyperintense on T2-weighted images (62/72, 86.1%). Lesions noncontiguous with bone were more often hypointense on T2-weighted images (8/28 vs 2/44; p = 0.006; Fisher exact test). Neurologic symptoms prompted MRI in most cases (n = 32/44). MRI was helpful in management by radiotherapy and surgery (19/28). Extramedullary myeloma can be contiguous or noncontiguous with bone. Lesions contiguous with bone are larger, often occur in a paraspinal or epidural location, and can cause cord compression. Lesions noncontiguous with bone can be T2 hypointense. MRI helps in treatment planning.

3D variational brain tumor segmentation on a clustered feature set

NASA Astrophysics Data System (ADS)

Popuri, Karteek; Cobzas, Dana; Jagersand, Martin; Shah, Sirish L.; Murtha, Albert

2009-02-01

Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect and deform nearby tissue. Our work addresses these last two difficult problems. We use the available MRI modalities (T1, T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches, we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue. We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major structures in the brain. Our method shows good results on these test cases.

Enhancing insight in scientific problem solving by highlighting the functional features of prototypes: an fMRI study.

PubMed

Hao, Xin; Cui, Shuai; Li, Wenfu; Yang, Wenjing; Qiu, Jiang; Zhang, Qinglin

2013-10-09

Insight can be the first step toward creating a groundbreaking product. As evident in anecdotes and major inventions in history, heuristic events (heuristic prototypes) prompted inventors to acquire insight when solving problems. Bionic imitation in scientific innovation is an example of this kind of problem solving. In particular, heuristic prototypes (e.g., the lotus effect; the very high water repellence exhibited by lotus leaves) help solve insight problems (e.g., non-stick surfaces). We speculated that the biological functional feature of prototypes is a critical factor in inducing insightful scientific problem solving. In this functional magnetic resonance imaging (fMRI) study, we selected scientific innovation problems and utilized "learning prototypes-solving problems" two-phase paradigm to test the supposition. We also explored its neural mechanisms. Functional MRI data showed that the activation of the middle temporal gyrus (MTG, BA 37) and the middle occipital gyrus (MOG, BA 19) were associated with the highlighted functional feature condition. fMRI data also indicated that the MTG (BA 37) could be responsible for the semantic processing of functional features and for the formation of novel associations based on related functions. In addition, the MOG (BA 19) could be involved in the visual imagery of formation and application of function association between the heuristic prototype and problem. Our findings suggest that both semantic processing and visual imagery could be crucial components underlying scientific problem solving. © 2013 Elsevier B.V. All rights reserved.

MR imaging features and staging of neuroendocrine carcinomas of the uterine cervix with pathological correlations.

PubMed

Duan, Xiaohui; Ban, Xiaohua; Zhang, Xiang; Hu, Huijun; Li, Guozhao; Wang, Dongye; Wang, Charles Qian; Zhang, Fang; Shen, Jun

2016-12-01

To determine MR imaging features and staging accuracy of neuroendocrine carcinomas (NECs) of the uterine cervix with pathological correlations. Twenty-six patients with histologically proven NECs, 60 patients with squamous cell carcinomas (SCCs), and 30 patients with adenocarcinomas of the uterine cervix were included. The clinical data, pathological findings, and MRI findings were reviewed retrospectively. MRI features of cervical NECs, SCCs, and adenocarcinomas were compared, and MRI staging of cervical NECs was compared with the pathological staging. Cervical NECs showed a higher tendency toward a homogeneous signal intensity on T2-weighted imaging and a homogeneous enhancement pattern, as well as a lower ADC value of tumour and a higher incidence of lymphadenopathy, compared with SCCs and adenocarcinomas (P < 0.05). An ADC value cutoff of 0.90 × 10 -3  mm 2 /s was robust for differentiation between cervical NECs and other cervical cancers, with a sensitivity of 63.3 % and a specificity of 95 %. In 21 patients who underwent radical hysterectomy and lymphadenectomy, the overall accuracy of tumour staging by MR imaging was 85.7 % with reference to pathology staging. Homogeneous lesion texture and low ADC value are likely suggestive features of cervical NECs and MR imaging is reliable for the staging of cervical NECs. • Cervical NECs show a tendency of lesion homogeneity and lymphadenopathy • Low ADC values are found in cervical NECs • MRI is an accurate imaging modality for the cervical NEC staging.

Differentiation between benign and malignant palatal tumors using conventional MRI: a retrospective analysis of 130 cases.

PubMed

Zheng, Yingyan; Xiao, Zebin; Zhang, Hua; She, Dejun; Lin, Xuehua; Lin, Yu; Cao, Dairong

2018-04-01

To evaluate the discriminative value of conventional magnetic resonance imaging between benign and malignant palatal tumors. Conventional magnetic resonance imaging features of 130 patients with palatal tumors confirmed by histopathologic examination were retrospectively reviewed. Clinical data and imaging findings were assessed between benign and malignant tumors and between benign and low-grade malignant salivary gland tumors. The variables that were significant in differentiating benign from malignant lesions were further identified using logistic regression analysis. Moreover, imaging features of each common palatal histologic entity were statistically analyzed with the rest of the tumors to define their typical imaging features. Older age, partially defined and ill-defined margins, and absence of a capsule were highly suggestive of malignant palatal tumors, especially ill-defined margins (β = 6.400). The precision in determining malignant palatal tumors achieved a sensitivity of 92.8% and a specificity of 85.6%. In addition, irregular shape, ill-defined margins, lack of a capsule, perineural spread, and invasion of surrounding structures were more often associated with low-grade malignant salivary gland tumors. Conventional magnetic resonance imaging is useful for differentiating benign from malignant palatal tumors as well as benign salivary gland tumors from low-grade salivary gland malignancies. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI.

PubMed

Adler, Daniel H; Pluta, John; Kadivar, Salmon; Craige, Caryne; Gee, James C; Avants, Brian B; Yushkevich, Paul A

2014-01-01

Recently, there has been a growing effort to analyze the morphometry of hippocampal subfields using both in vivo and postmortem magnetic resonance imaging (MRI). However, given that boundaries between subregions of the hippocampal formation (HF) are conventionally defined on the basis of microscopic features that often lack discernible signature in MRI, subfield delineation in MRI literature has largely relied on heuristic geometric rules, the validity of which with respect to the underlying anatomy is largely unknown. The development and evaluation of such rules are challenged by the limited availability of data linking MRI appearance to microscopic hippocampal anatomy, particularly in three dimensions (3D). The present paper, for the first time, demonstrates the feasibility of labeling hippocampal subfields in a high resolution volumetric MRI dataset based directly on microscopic features extracted from histology. It uses a combination of computational techniques and manual post-processing to map subfield boundaries from a stack of histology images (obtained with 200μm spacing and 5μm slice thickness; stained using the Kluver-Barrera method) onto a postmortem 9.4Tesla MRI scan of the intact, whole hippocampal formation acquired with 160μm isotropic resolution. The histology reconstruction procedure consists of sequential application of a graph-theoretic slice stacking algorithm that mitigates the effects of distorted slices, followed by iterative affine and diffeomorphic co-registration to postmortem MRI scans of approximately 1cm-thick tissue sub-blocks acquired with 200μm isotropic resolution. These 1cm blocks are subsequently co-registered to the MRI of the whole HF. Reconstruction accuracy is evaluated as the average displacement error between boundaries manually delineated in both the histology and MRI following the sequential stages of reconstruction. The methods presented and evaluated in this single-subject study can potentially be applied to multiple hippocampal tissue samples in order to construct a histologically informed MRI atlas of the hippocampal formation. © 2013 Elsevier Inc. All rights reserved.

Breast MRI radiogenomics: Current status and research implications.

PubMed

Grimm, Lars J

2016-06-01

Breast magnetic resonance imaging (MRI) radiogenomics is an emerging area of research that has the potential to directly influence clinical practice. Clinical MRI scanners today are capable of providing excellent temporal and spatial resolution, which allows extraction of numerous imaging features via human extraction approaches or complex computer vision algorithms. Meanwhile, advances in breast cancer genetics research has resulted in the identification of promising genes associated with cancer outcomes. In addition, validated genomic signatures have been developed that allow categorization of breast cancers into distinct molecular subtypes as well as predict the risk of cancer recurrence and response to therapy. Current radiogenomics research has been directed towards exploratory analysis of individual genes, understanding tumor biology, and developing imaging surrogates to genetic analysis with the long-term goal of developing a meaningful tool for clinical care. The background of breast MRI radiogenomics research, image feature extraction techniques, approaches to radiogenomics research, and promising areas of investigation are reviewed. J. Magn. Reson. Imaging 2016;43:1269-1278. © 2015 Wiley Periodicals, Inc.

Advanced magnetic resonance imaging of neurodegenerative diseases.

PubMed

Agosta, Federica; Galantucci, Sebastiano; Filippi, Massimo

2017-01-01

Magnetic resonance imaging (MRI) is playing an increasingly important role in the study of neurodegenerative diseases, delineating the structural and functional alterations determined by these conditions. Advanced MRI techniques are of special interest for their potential to characterize the signature of each neurodegenerative condition and aid both the diagnostic process and the monitoring of disease progression. This aspect will become crucial when disease-modifying (personalized) therapies will be established. MRI techniques are very diverse and go from the visual inspection of MRI scans to more complex approaches, such as manual and automatic volume measurements, diffusion tensor MRI, and functional MRI. All these techniques allow us to investigate the different features of neurodegeneration. In this review, we summarize the most recent advances concerning the use of MRI in some of the most important neurodegenerative conditions, putting an emphasis on the advanced techniques.

Multiparametric Breast MRI of Breast Cancer

PubMed Central

Rahbar, Habib; Partridge, Savannah C.

2015-01-01

Synopsis Breast MRI has increased in popularity over the past two decades due to evidence for its high sensitivity for cancer detection. Current clinical MRI approaches rely on the use of a dynamic contrast enhanced (DCE-MRI) acquisition that facilitates morphologic and semi-quantitative kinetic assessments of breast lesions. The use of more functional and quantitative parameters, such as pharmacokinetic features from high temporal resolution DCE-MRI, apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) on diffusion weighted MRI, and choline concentrations on MR spectroscopy, hold promise to broaden the utility of MRI and improve its specificity. However, due to wide variations in approach among centers for measuring these parameters and the considerable technical challenges, robust multicenter data supporting their routine use is not yet available, limiting current applications of many of these tools to research purposes. PMID:26613883

Selective Audiovisual Semantic Integration Enabled by Feature-Selective Attention

PubMed Central

Li, Yuanqing; Long, Jinyi; Huang, Biao; Yu, Tianyou; Wu, Wei; Li, Peijun; Fang, Fang; Sun, Pei

2016-01-01

An audiovisual object may contain multiple semantic features, such as the gender and emotional features of the speaker. Feature-selective attention and audiovisual semantic integration are two brain functions involved in the recognition of audiovisual objects. Humans often selectively attend to one or several features while ignoring the other features of an audiovisual object. Meanwhile, the human brain integrates semantic information from the visual and auditory modalities. However, how these two brain functions correlate with each other remains to be elucidated. In this functional magnetic resonance imaging (fMRI) study, we explored the neural mechanism by which feature-selective attention modulates audiovisual semantic integration. During the fMRI experiment, the subjects were presented with visual-only, auditory-only, or audiovisual dynamical facial stimuli and performed several feature-selective attention tasks. Our results revealed that a distribution of areas, including heteromodal areas and brain areas encoding attended features, may be involved in audiovisual semantic integration. Through feature-selective attention, the human brain may selectively integrate audiovisual semantic information from attended features by enhancing functional connectivity and thus regulating information flows from heteromodal areas to brain areas encoding the attended features. PMID:26759193

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy.

PubMed

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N; Styner, Martin A

2013-09-01

Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD. Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features. The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.

Biomechanical factors and physical examination findings in osteoarthritis of the knee: associations with tissue abnormalities assessed by conventional radiography and high-resolution 3.0 Tesla magnetic resonance imaging.

PubMed

Knoop, Jesper; Dekker, Joost; Klein, Jan-Paul; van der Leeden, Marike; van der Esch, Martin; Reiding, Dick; Voorneman, Ramon E; Gerritsen, Martijn; Roorda, Leo D; Steultjens, Martijn P M; Lems, Willem F

2012-10-05

We aimed to explore the associations between knee osteoarthritis (OA)-related tissue abnormalities assessed by conventional radiography (CR) and by high-resolution 3.0 Tesla magnetic resonance imaging (MRI), as well as biomechanical factors and findings from physical examination in patients with knee OA. This was an explorative cross-sectional study of 105 patients with knee OA. Index knees were imaged using CR and MRI. Multiple features from CR and MRI (cartilage, osteophytes, bone marrow lesions, effusion and synovitis) were related to biomechanical factors (quadriceps and hamstrings muscle strength, proprioceptive accuracy and varus-valgus laxity) and physical examination findings (bony tenderness, crepitus, bony enlargement and palpable warmth), using multivariable regression analyses. Quadriceps weakness was associated with cartilage integrity, effusion, synovitis (all detected by MRI) and CR-detected joint space narrowing. Knee joint laxity was associated with MRI-detected cartilage integrity, CR-detected joint space narrowing and osteophyte formation. Multiple tissue abnormalities including cartilage integrity, osteophytes and effusion, but only those detected by MRI, were found to be associated with physical examination findings such as crepitus. We observed clinically relevant findings, including a significant association between quadriceps weakness and both effusion and synovitis, detected by MRI. Inflammation was detected in over one-third of the participants, emphasizing the inflammatory component of OA and a possible important role for anti-inflammatory therapies in knee OA. In general, OA-related tissue abnormalities of the knee, even those detected by MRI, were found to be discordant with biomechanical and physical examination features.

MRI in patients with inflammatory bowel disease

PubMed Central

Gee, Michael S.; Harisinghani, Mukesh G.

2011-01-01

Inflammatory bowel disease (IBD) affects approximately 1.4 million people in North America and, because of its typical early age of onset and episodic disease course, IBD patients often undergo numerous imaging studies over the course of their lifetimes. CT has become the standard imaging modality for assessment of IBD patients because of its widespread availability, rapid image acquisition, and ability to evaluate intraluminal and extraluminal disease. However, repetitive CT imaging has been associated with a significant ionizing radiation risk to patients, making MRI an appealing alternative IBD imaging modality. Pelvic MRI is currently the imaging gold standard for detecting perianal disease, while recent studies indicate that MRI bowel-directed techniques (enteroclysis, enterography, colonography) can accurately evaluate bowel inflammation in IBD. With recent technical innovations leading to faster and higher resolution body MRI, the role of MRI in IBD evaluation is likely to continue to expand. Future applications include surveillance imaging, detection of mural fibrosis, and early assessment of therapy response. PMID:21512607

Intratumor heterogeneity of DCE-MRI reveals Ki-67 proliferation status in breast cancer

NASA Astrophysics Data System (ADS)

Cheng, Hu; Fan, Ming; Zhang, Peng; Liu, Bin; Shao, Guoliang; Li, Lihua

2018-03-01

Breast cancer is a highly heterogeneous disease both biologically and clinically, and certain pathologic parameters, i.e., Ki67 expression, are useful in predicting the prognosis of patients. The aim of the study is to identify intratumor heterogeneity of breast cancer for predicting Ki-67 proliferation status in estrogen receptor (ER)-positive breast cancer patients. A dataset of 77 patients was collected who underwent dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) examination. Of these patients, 51 were high-Ki-67 expression and 26 were low-Ki-67 expression. We partitioned the breast tumor into subregions using two methods based on the values of time to peak (TTP) and peak enhancement rate (PER). Within each tumor subregion, image features were extracted including statistical and morphological features from DCE-MRI. The classification models were applied on each region separately to assess whether the classifiers based on features extracted from various subregions features could have different performance for prediction. An area under a receiver operating characteristic curve (AUC) was computed using leave-one-out cross-validation (LOOCV) method. The classifier using features related with moderate time to peak achieved best performance with AUC of 0.826 than that based on the other regions. While using multi-classifier fusion method, the AUC value was significantly (P=0.03) increased to 0.858+/-0.032 compare to classifier with AUC of 0.778 using features from the entire tumor. The results demonstrated that features reflect heterogeneity in intratumoral subregions can improve the classifier performance to predict the Ki-67 proliferation status than the classifier using features from entire tumor alone.

Maturation of language networks in children: A systematic review of 22years of functional MRI.

PubMed

Weiss-Croft, Louise J; Baldeweg, Torsten

2015-12-01

Understanding how language networks change during childhood is important for theories of cognitive development and for identifying the neural causes of language impairment. Despite this, there is currently little systematic evidence regarding the typical developmental trajectory for language from the field of neuroimaging. We reviewed functional MRI (fMRI) studies published between 1992 and 2014, and quantified the evidence for age-related changes in localisation and lateralisation of fMRI activation in the language network (excluding the cerebellum and subcortical regions). Although age-related changes differed according to task type and input modality, we identified four consistent findings concerning the typical maturation of the language system. First, activation in core semantic processing regions increases with age. Second, activation in lower-level sensory and motor regions increases with age as activation in higher-level control regions reduces. We suggest that this reflects increased automaticity of language processing as children become more proficient. Third, the posterior cingulate cortex and precuneus (regions associated with the default mode network) show increasing attenuation across childhood and adolescence. Finally, language lateralisation is established by approximately 5years of age. Small increases in leftward lateralisation are observed in frontal regions, but these are tightly linked to performance. Copyright © 2015 Elsevier Inc. All rights reserved.

Resting-state fMRI data reflects default network activity rather than null data: A defense of commonly employed methods to correct for multiple comparisons.

PubMed

Slotnick, Scott D

2017-07-01

Analysis of functional magnetic resonance imaging (fMRI) data typically involves over one hundred thousand independent statistical tests; therefore, it is necessary to correct for multiple comparisons to control familywise error. In a recent paper, Eklund, Nichols, and Knutsson used resting-state fMRI data to evaluate commonly employed methods to correct for multiple comparisons and reported unacceptable rates of familywise error. Eklund et al.'s analysis was based on the assumption that resting-state fMRI data reflect null data; however, their 'null data' actually reflected default network activity that inflated familywise error. As such, Eklund et al.'s results provide no basis to question the validity of the thousands of published fMRI studies that have corrected for multiple comparisons or the commonly employed methods to correct for multiple comparisons.

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

PubMed Central

Guan, Hao; Liu, Tao; Jiang, Jiyang; Tao, Dacheng; Zhang, Jicong; Niu, Haijun; Zhu, Wanlin; Wang, Yilong; Cheng, Jian; Kochan, Nicole A.; Brodaty, Henry; Sachdev, Perminder; Wen, Wei

2017-01-01

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73–85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment. PMID:29085292

Pseudo CT estimation from MRI using patch-based random forest

NASA Astrophysics Data System (ADS)

Yang, Xiaofeng; Lei, Yang; Shu, Hui-Kuo; Rossi, Peter; Mao, Hui; Shim, Hyunsuk; Curran, Walter J.; Liu, Tian

2017-02-01

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.

Dengue encephalitis with predominant cerebellar involvement: report of eight cases with MR and CT imaging features.

PubMed

Hegde, Vinay; Aziz, Zarina; Kumar, Sharath; Bhat, Maya; Prasad, Chandrajit; Gupta, A K; Netravathi, M; Saini, Jitender

2015-03-01

CNS dengue infection is a rare condition and the pattern of brain involvement has not been well described. We report the MR imaging (MRI) features in eight cases of dengue encephalitis. We retrospectively searched cases of dengue encephalitis in which imaging was performed. Eight cases (three men, five women; age range: 8-42 years) diagnosed with dengue encephalitis were included in the study. MR studies were performed on 3-T and 1.5-T MR clinical systems. Two neuroradiologists retrospectively reviewed the MR images and analysed the type of lesions, as well as their distribution and imaging features. All eight cases exhibited MRI abnormalities and the cerebellum was involved in all cases. In addition, MRI signal changes were also noted in the brainstem, thalamus, basal ganglia, internal capsule, insula, mesial temporal lobe, and cortical and cerebral white matter. Areas of susceptibility, diffusion restriction, and patchy post-contrast enhancement were the salient imaging features in our cohort of cases. A pattern of symmetrical cerebellar involvement and presence of microbleeds/haemorrhage may serve as a useful imaging marker and may help in the diagnosis of dengue encephalitis.

Sponyloarthritis features forecasting the presence of HLA-B27 or sacroiliitis on magnetic resonance imaging in patients with suspected axial spondyloarthritis: results from a cross-sectional study in the ESPeranza Cohort.

PubMed

Navarro-Compán, Victoria; de Miguel, Eugenio; van der Heijde, Désirée; Landewé, Robert; Almodóvar, Raquel; Montilla, Carlos; Beltrán, Emma; Zarco, Pedro

2015-09-23

Chronic back pain (CBP) is frequently the presenting symptom in patients with suspected axial spondyloarthritis (axSpA). Presence of sacroiliitis on magnetic-resonance-imaging (MRI) or HLA-B27 adds to diagnostic certainty. However, these costly tests cannot be applied in all patients with CBP. This study aims to investigate which SpA features increase the likelihood of a positive HLA-B27 or positive MRI of the sacroiliac-joints (MRI-SI) in patients with suspected axSpA. Data from 665 patients with CBP within the ESPeranza Programme were analysed. Diagnostic utility measures (LR+, LR-) for a positive MRI-SI or HLA-B27 were calculated for various definitions of inflammatory back pain (IBP), their separate items and for other SpA features. Pretest probabilityies of a positive result was 41% for MRI-SI and 40% for HLA-B27. For a positive MRI-SI result the most useful IBP characteristic was alternating buttock pain (LR + =2.6). Among the IBP-criteria, fulfillment of the 'ASAS criteria' (LR + =2.1) was most contributory. Interestingly, the addition of alternating buttock pain to the Calin/ASAS-IBP criteria (LR + =6.0 and 5.5, respectively) or the addition of awakening at second half of night to the Calin-IBP criteria (LR + =5.5) increased the pre-test probability of MRI-sacroiliitis from 41% to 79-80%. Dactylitis (LR + =4.1) and inflammatory bowel disease (IBD) (LR + =6.4) increased this probability to 73% and 81%, respectively. To forecast HLA-B27 positivity, awakening at the second half of the night, fulfillment of the ASAS-IBP definition and uveitis were the most useful, but only marginally predictive (LR + = 1.3, 1,6 and 2.6, respectively). If patients with suspected axial SpA have either (1) IBP according to Calin/ASAS definition plus alternating buttock pain, or (2) IBP according to Calin definition plus awakening at night, or (3) dactylitis or 4) IBD, the probability of finding a positive MRI-SI increases significantly.

MR imaging guidance for minimally invasive procedures

NASA Astrophysics Data System (ADS)

Wong, Terence Z.; Kettenbach, Joachim; Silverman, Stuart G.; Schwartz, Richard B.; Morrison, Paul R.; Kacher, Daniel F.; Jolesz, Ferenc A.

1998-04-01

Image guidance is one of the major challenges common to all minimally invasive procedures including biopsy, thermal ablation, endoscopy, and laparoscopy. This is essential for (1) identifying the target lesion, (2) planning the minimally invasive approach, and (3) monitoring the therapy as it progresses. MRI is an ideal imaging modality for this purpose, providing high soft tissue contrast and multiplanar imaging, capability with no ionizing radiation. An interventional/surgical MRI suite has been developed at Brigham and Women's Hospital which provides multiplanar imaging guidance during surgery, biopsy, and thermal ablation procedures. The 0.5T MRI system (General Electric Signa SP) features open vertical access, allowing intraoperative imaging to be performed. An integrated navigational system permits near real-time control of imaging planes, and provides interactive guidance for positioning various diagnostic and therapeutic probes. MR imaging can also be used to monitor cryotherapy as well as high temperature thermal ablation procedures sing RF, laser, microwave, or focused ultrasound. Design features of the interventional MRI system will be discussed, and techniques will be described for interactive image acquisition and tracking of interventional instruments. Applications for interactive and near-real-time imaging will be presented as well as examples of specific procedures performed using MRI guidance.

MRI and clinical features of maple syrup urine disease: preliminary results in 10 cases

PubMed Central

Cheng, Ailan; Han, Lianshu; Feng, Yun; Li, Huimin; Yao, Rong; Wang, Dengbin; Jin, Biao

2017-01-01

PURPOSE We aimed to evaluate the magnetic resonance imaging (MRI) and clinical features of maple syrup urine disease (MSUD). METHODS This retrospective study consisted of 10 MSUD patients confirmed by genetic testing. All patients underwent brain MRI. Phenotype, genotype, and areas of brain injury on MRI were retrospectively reviewed. RESULTS Six patients (60%) had the classic form of MSUD with BCKDHB mutation, three patients (30%) had the intermittent form (two with BCKDHA mutations and one with DBT mutation), and one patient (10%) had the thiamine-responsive form with DBT mutation. On diffusion-weighted imaging, nine cases presented restricted diffusion in myelinated areas, and one intermittent case with DBT mutation was normal. The classic form of MSUD involved the basal ganglia in six cases; the cerebellum, mesencephalon, pons, and supratentorial area in five cases; and the thalamus in four cases, respectively. The intermittent form involved the cerebellum, pons, and supratentorial area in two cases. The thiamine-responsive form involved the basal ganglia and supratentorial area. CONCLUSION Our preliminary results indicate that patients with MSUD presented more commonly in classic form with BCKDHB mutation and displayed extensive brain injury on MRI. PMID:28830848

Neoadjuvant radiation in primary extremity liposarcoma: correlation of MRI features with histopathology.

PubMed

Wortman, Jeremy R; Tirumani, Sree Harsha; Tirumani, Harika; Shinagare, Atul B; Jagannathan, Jyothi P; Hornick, Jason L; Ramaiya, Nikhil H

2016-05-01

To evaluate MRI features of response of primary extremity liposarcoma (LPS) to neoadjuvant radiation therapy (RT) with histopathologic correlation. In this IRB-approved study including 125 patients with extremity LPS treated with neoadjuvant RT from 2000 to 2013, MRI of the primary tumour in 18 patients (5 pleomorphic LPS, 13 myxoid LPS) before and after RT were reviewed by two radiologists by consensus. Histopathology of the surgical specimens was reviewed by a pathologist with expertise in sarcomas. In the pleomorphic LPS cohort, 3/5 tumours increased in size; 3/5 decreased in enhancing component; and 3/5 increased in peritumoral oedema, intratumoral haemorrhage, and necrosis. In the myxoid LPS cohort, 12/13 tumours decreased in size, 8/13 decreased in enhancing component, and 5/13 increased in internal fat following RT. Histopathology showed ≥50% residual tumour in 1/5 pleomorphic LPS and 2/13 myxoid LPS. Hyalinization/necrosis of ≥75% was noted in 4/5 pleomorphic LPS and 11/13 myxoid LPS. Cytodifferentiation was noted in 1/5 pleomorphic and 9/13 myxoid LPS. While pleomorphic LPS showed an increase in size, peritumoral oedema, intratumoral haemorrhage, and necrosis on MRI following neoadjuvant RT, myxoid LPS showed a decrease in size and enhancement with an increase in internal fat. • Pleomorphic LPS commonly increase in size and necrosis on MRI following RT. • Myxoid LPS commonly decrease in size and enhancement on MRI following RT. • Myxoid LPS often increase in fatty component on MRI following RT.

Cerebral microbleeds, cognitive impairment, and MRI in patients with diabetes mellitus.

PubMed

Zhou, Hong; Yang, Juan; Xie, Peihan; Dong, Yulan; You, Yong; Liu, Jincai

2017-07-01

Cerebral microbleeds (CMBs), a typical imaging manifestation marker of sporadic cerebral small vessel disease, play a critical role in vascular cognitive impairment, which is often accompanied by diabetes mellitus (DM). Hence, CMBs may, in part, be responsible for the occurrence and development of cognitive impairment in patients with diabetes. Novel magnetic resonance imaging (MRI) sequences, such as susceptibility-weighted imaging and T2*-weighted gradient-echo, have the capability of noninvasively revealing CMBs in the brain. Moreover, a correlation between CMBs and cognitive impairment in patients with diabetes has been suggested in applications of functional MRI (fMRI). Since pathological changes in the brain occur prior to observable decline in cognitive function, neuroimaging may help predict the progression of cognitive impairment in diabetic patients. In this article, we review the detection of CMBs using MRI in diabetic patients exhibiting cognitive impairment. Future studies should emphasize the development and establishment of a novel MRI protocol, including fMRI, for diabetic patients with cognitive impairment to detect CMBs. A reliable MRI protocol would also be helpful in understanding the pathological mechanisms of cognitive impairment in this important patient population. Copyright © 2017. Published by Elsevier B.V.

Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)/MRI for Lung Cancer Staging.

PubMed

Ohno, Yoshiharu; Koyama, Hisanobu; Lee, Ho Yun; Yoshikawa, Takeshi; Sugimura, Kazuro

2016-07-01

Tumor, lymph node, and metastasis (TNM) classification of lung cancer is typically performed with the TNM staging system, as recommended by the Union Internationale Contre le Cancer (UICC), the American Joint Committee on Cancer (AJCC), and the International Association for the Study of Lung Cancer (IASLC). Radiologic examinations for TNM staging of lung cancer patients include computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography with 2-[fluorine-18] fluoro-2-deoxy-D-glucose (FDG-PET), and FDG-PET combined with CT (FDG-PET/CT) and are used for pretherapeutic assessments. Recent technical advances in MR systems, application of fast and parallel imaging and/or introduction of new MR techniques, and utilization of contrast media have markedly improved the diagnostic utility of MRI in this setting. In addition, FDG-PET can be combined or fused with MRI (PET/MRI) for clinical practice. This review article will focus on these recent advances in MRI as well as on PET/MRI for lung cancer staging, in addition to a discussion of their potential and limitations for routine clinical practice in comparison with other modalities such as CT, FDG-PET, and PET/CT.

Investigation of undersampling and reconstruction algorithm dependence on respiratory correlated 4D-MRI for online MR-guided radiation therapy

NASA Astrophysics Data System (ADS)

Mickevicius, Nikolai J.; Paulson, Eric S.

2017-04-01

The purpose of this work is to investigate the effects of undersampling and reconstruction algorithm on the total processing time and image quality of respiratory phase-resolved 4D MRI data. Specifically, the goal is to obtain quality 4D-MRI data with a combined acquisition and reconstruction time of five minutes or less, which we reasoned would be satisfactory for pre-treatment 4D-MRI in online MRI-gRT. A 3D stack-of-stars, self-navigated, 4D-MRI acquisition was used to scan three healthy volunteers at three image resolutions and two scan durations. The NUFFT, CG-SENSE, SPIRiT, and XD-GRASP reconstruction algorithms were used to reconstruct each dataset on a high performance reconstruction computer. The overall image quality, reconstruction time, artifact prevalence, and motion estimates were compared. The CG-SENSE and XD-GRASP reconstructions provided superior image quality over the other algorithms. The combination of a 3D SoS sequence and parallelized reconstruction algorithms using computing hardware more advanced than those typically seen on product MRI scanners, can result in acquisition and reconstruction of high quality respiratory correlated 4D-MRI images in less than five minutes.

Primary central nervous system lymphoma and glioblastoma differentiation based on conventional magnetic resonance imaging by high-throughput SIFT features.

PubMed

Chen, Yinsheng; Li, Zeju; Wu, Guoqing; Yu, Jinhua; Wang, Yuanyuan; Lv, Xiaofei; Ju, Xue; Chen, Zhongping

2018-07-01

Due to the totally different therapeutic regimens needed for primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM), accurate differentiation of the two diseases by noninvasive imaging techniques is important for clinical decision-making. Thirty cases of PCNSL and 66 cases of GBM with conventional T1-contrast magnetic resonance imaging (MRI) were analyzed in this study. Convolutional neural networks was used to segment tumor automatically. A modified scale invariant feature transform (SIFT) method was utilized to extract three-dimensional local voxel arrangement information from segmented tumors. Fisher vector was proposed to normalize the dimension of SIFT features. An improved genetic algorithm (GA) was used to extract SIFT features with PCNSL and GBM discrimination ability. The data-set was divided into a cross-validation cohort and an independent validation cohort by the ratio of 2:1. Support vector machine with the leave-one-out cross-validation based on 20 cases of PCNSL and 44 cases of GBM was employed to build and validate the differentiation model. Among 16,384 high-throughput features, 1356 features show significant differences between PCNSL and GBM with p < 0.05 and 420 features with p < 0.001. A total of 496 features were finally chosen by improved GA algorithm. The proposed method produces PCNSL vs. GBM differentiation with an area under the curve (AUC) curve of 99.1% (98.2%), accuracy 95.3% (90.6%), sensitivity 85.0% (80.0%) and specificity 100% (95.5%) on the cross-validation cohort (and independent validation cohort). Since the local voxel arrangement characterization provided by SIFT features, proposed method produced more competitive PCNSL and GBM differentiation performance by using conventional MRI than methods based on advanced MRI.

Can responses to basic non-numerical visual features explain neural numerosity responses?

PubMed

Harvey, Ben M; Dumoulin, Serge O

2017-04-01

Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs. Copyright © 2017 Elsevier Inc. All rights reserved.

A Cinematic Magnetic Resonance Imaging Study of Milk of Magnesia Laxative and an Antiflatulent Diet to Reduce Intrafraction Prostate Motion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nichol, Alan M.; Warde, Padraig R.; Lockwood, Gina A.

Purpose: To determine the reduction of prostate motion during a typical radiotherapy (RT) fraction from a bowel regimen comprising an antiflatulent diet and daily milk of magnesia. Methods and Materials: Forty-two patients with T1c-T2c prostate cancer voided the bladder and rectum before three cinematic magnetic resonance imaging scans obtained every 9 s for 9 min in a vacuum immobilization device. The MRIs were at baseline without bowel regimen (MRI-BL), before CT planning with bowel regimen (MRI-CT), and before a randomly assigned RT fraction (1-42) with bowel regimen (MRI-RT). A single observer tracked displacement of the posterior midpoint (PM) of themore » prostate. The primary endpoints were comparisons of the proportion of time that the PM was displaced >3 mm (PTPM3) from its initial position, and the secondary endpoints were comparisons of the reduction of initial rectal area, with and without the bowel regimen. Results: The mean rectal area was: 13.5 cm{sup 2} at MRI-BL, 12.7 cm{sup 2} at MRI-CT, and 12.3 cm{sup 2} at MRI-RT (MRI-BL vs. MRI-CT, p = 0.11; MRI-BL vs. MRI-CT, p = 0.07). Moving rectal gas alone (56%) and moving gas and stool (18%) caused 74% of intrafraction prostate motion. The PTPM3 was 11.3% at MRI-BL, 4.8% at MRI-CT, and 12.0% at MRI-RT (MRI-BL vs. MRI-CT, p = 0.12; MRI-BL vs. MRI-RT, p = 0.89). Conclusion: For subjects voiding their rectum before imaging, an antiflatulent diet and milk of magnesia laxative did not significantly reduce initial rectal area or intrafraction prostate motion.« less

TU-AB-202-06: Quantitative Evaluation of Deformable Image Registration in MRI-Guided Adaptive Radiation Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mooney, K; Zhao, T; Green, O

Purpose: To assess the performance of the deformable image registration algorithm used for MRI-guided adaptive radiation therapy using image feature analysis. Methods: MR images were collected from five patients treated on the MRIdian (ViewRay, Inc., Oakwood Village, OH), a three head Cobalt-60 therapy machine with an 0.35 T MR system. The images were acquired immediately prior to treatment with a uniform 1.5 mm resolution. Treatment sites were as follows: head/neck, lung, breast, stomach, and bladder. Deformable image registration was performed using the ViewRay software between the first fraction MRI and the final fraction MRI, and the DICE similarity coefficient (DSC)more » for the skin contours was reported. The SIFT and Harris feature detection and matching algorithms identified point features in each image separately, then found matching features in the other image. The target registration error (TRE) was defined as the vector distance between matched features on the two image sets. Each deformation was evaluated based on comparison of average TRE and DSC. Results: Image feature analysis produced between 2000–9500 points for evaluation on the patient images. The average (± standard deviation) TRE for all patients was 3.3 mm (±3.1 mm), and the passing rate of TRE<3 mm was 60% on the images. The head/neck patient had the best average TRE (1.9 mm±2.3 mm) and the best passing rate (80%). The lung patient had the worst average TRE (4.8 mm±3.3 mm) and the worst passing rate (37.2%). DSC was not significantly correlated with either TRE (p=0.63) or passing rate (p=0.55). Conclusions: Feature matching provides a quantitative assessment of deformable image registration, with a large number of data points for analysis. The TRE of matched features can be used to evaluate the registration of many objects throughout the volume, whereas DSC mainly provides a measure of gross overlap. We have a research agreement with ViewRay Inc.« less

Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chowdhury, Najeeb; Toth, Robert; Chappelow, Jonathan

2012-04-15

Purpose: Prostate gland segmentation is a critical step in prostate radiotherapy planning, where dose plans are typically formulated on CT. Pretreatment MRI is now beginning to be acquired at several medical centers. Delineation of the prostate on MRI is acknowledged as being significantly simpler to perform, compared to delineation on CT. In this work, the authors present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model (SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This framework is particularly relevant in scenarios where accurate boundary delineations ofmore » the SOI on one of the modalities may not be readily available, or difficult to obtain, for training a SSM. In this work the authors apply the LSSM in the context of multimodal prostate segmentation for radiotherapy planning, where the prostate is concurrently segmented on MRI and CT. Methods: The framework comprises a number of logically connected steps. The first step utilizes multimodal registration of MRI and CT to map 2D boundary delineations of the prostate from MRI onto corresponding CT images, for a set of training studies. Hence, the scheme obviates the need for expert delineations of the gland on CT for explicitly constructing a SSM for prostate segmentation on CT. The delineations of the prostate gland on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the building of the LSSM. In order to perform concurrent prostate MRI and CT segmentation using the LSSM, the authors employ a region-based level set approach where the authors deform the evolving prostate boundary to simultaneously fit to MRI and CT images in which voxels are classified to be either part of the prostate or outside the prostate. The classification is facilitated by using a combination of MRI-CT probabilistic spatial atlases and a random forest classifier, driven by gradient and Haar features. Results: The authors acquire a total of 20 MRI-CT patient studies and use the leave-one-out strategy to train and evaluate four different LSSMs. First, a fusion-based LSSM (fLSSM) is built using expert ground truth delineations of the prostate on MRI alone, where the ground truth for the gland on CT is obtained via coregistration of the corresponding MRI and CT slices. The authors compare the fLSSM against another LSSM (xLSSM), where expert delineations of the gland on both MRI and CT are employed in the model building; xLSSM representing the idealized LSSM. The authors also compare the fLSSM against an exclusive CT-based SSM (ctSSM), built from expert delineations of the gland on CT alone. In addition, two LSSMs trained using trainee delineations (tLSSM) on CT are compared with the fLSSM. The results indicate that the xLSSM, tLSSMs, and the fLSSM perform equivalently, all of them out-performing the ctSSM. Conclusions: The fLSSM provides an accurate alternative to SSMs that require careful expert delineations of the SOI that may be difficult or laborious to obtain. Additionally, the fLSSM has the added benefit of providing concurrent segmentations of the SOI on multiple imaging modalities.« less

Detection of Focal Cortical Dysplasia Lesions in MRI Using Textural Features

NASA Astrophysics Data System (ADS)

Loyek, Christian; Woermann, Friedrich G.; Nattkemper, Tim W.

Focal cortical dysplasia (FCD) is a frequent cause of medically refractory partial epilepsy. The visual identification of FCD lesions on magnetic resonance images (MRI) is a challenging task in standard radiological analysis. Quantitative image analysis which tries to assist in the diagnosis of FCD lesions is an active field of research. In this work we investigate the potential of different texture features, in order to explore to what extent they are suitable for detecting lesional tissue. As a result we can show first promising results based on segmentation and texture classification.

Identification of neural connectivity signatures of autism using machine learning

PubMed Central

Deshpande, Gopikrishna; Libero, Lauren E.; Sreenivasan, Karthik R.; Deshpande, Hrishikesh D.; Kana, Rajesh K.

2013-01-01

Alterations in interregional neural connectivity have been suggested as a signature of the pathobiology of autism. There have been many reports of functional and anatomical connectivity being altered while individuals with autism are engaged in complex cognitive and social tasks. Although disrupted instantaneous correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the causal influence of a brain area on another (effective connectivity) is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind (ToM) in 15 high-functioning adolescents and adults with autism and 15 typically developing control participants. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. The mean time series, extracted from 18 activated regions of interest, were processed using a multivariate autoregressive model (MVAR) to obtain the causality matrices for each of the 30 participants. These causal connectivity weights, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant's group membership (autism or control). We found a maximum classification accuracy of 95.9% with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between autism and control groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point toward the fact that alterations in causal connectivity in the brain in ASD could serve as a potential non-invasive neuroimaging signature for autism. PMID:24151458

[Lipomatosis of nerve: a clinicopathologic analysis of 15 cases].

PubMed

MAO, Rong-jun; YANG, Ke-fei; WANG, Jian

2011-03-01

To study the clinicopathologic features of lipomatosis of nerve (NLS). The clinical, radiologic and pathologic features were analyzed in 15 cases of NLS. There were a total of 10 males and 5 females. The age of patients ranged from 4 to 42 years (mean age = 22.4 years). Eleven cases were located in the upper limbs and 4 cases in the lower limbs. The median nerve was the most common involved nerve. The patients typically presented before 30 years of age (often at birth or in early childhood) with a soft and slowly enlarging mass in the limb, with or without accompanying motor and sensory deficits. Some cases also had macrodactyly and carpal tunnel syndrome. MRI showed the presence of fatty tissue between nerve fascicles, resembling coaxial cable in axial plane and assuming a spaghetti-like appearance in coronal plane. On gross examination, the affected nerve was markedly increased in length and diameter. It consisted of a diffusely enlarged greyish-yellow lobulated fusiform beaded mass within the epineural sheath. Histologically, the epineurium was infiltrated by fibrofatty tissue which separated, surrounded and compressed the usually normal-appearing nerve fascicles, resulting in perineural septation of nerve fascicles and microfascicle formation. The infiltration sometimes resulted in concentric arrangement of perineural cells and pseudo-onion bulb-like hypertrophic changes. The perineurial cells might proliferate, with thickening of collagen fibers, degeneration and atrophic changes of nerve bundles. Immunohistochemical study showed that the nerve fibers expressed S-100 protein, neurofilament and CD56 (weak). The endothelial cells and dendritic fibers were highlighted by CD34. The intravascular smooth muscle cells were positive for muscle-specific actin. NLS is a rare benign soft tissue tumor of peripheral nerve. The MRI findings are characteristic. A definitive diagnosis can be made with histologic examination of tissue biopsy.

Pattern Recognition Approaches for Breast Cancer DCE-MRI Classification: A Systematic Review.

PubMed

Fusco, Roberta; Sansone, Mario; Filice, Salvatore; Carone, Guglielmo; Amato, Daniela Maria; Sansone, Carlo; Petrillo, Antonella

2016-01-01

We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), and Bayesian classifiers (BC), and features used for classification are described. The findings of a systematic review of 26 studies are presented. The sensitivity and specificity are respectively 91 and 83 % for ANN, 85 and 82 % for SVM, 96 and 85 % for LDA, 92 and 87 % for TC, and 82 and 85 % for BC. The sensitivity and specificity are respectively 82 and 74 % for dynamic features, 93 and 60 % for morphological features, 88 and 81 % for textural features, 95 and 86 % for a combination of dynamic and morphological features, and 88 and 84 % for a combination of dynamic, morphological, and other features. LDA and TC have the best performance. A combination of dynamic and morphological features gives the best performance.

Multiparametric imaging of brain hemodynamics and function using gas-inhalation MRI.

PubMed

Liu, Peiying; Welch, Babu G; Li, Yang; Gu, Hong; King, Darlene; Yang, Yihong; Pinho, Marco; Lu, Hanzhang

2017-02-01

Diagnosis and treatment monitoring of cerebrovascular diseases routinely require hemodynamic imaging of the brain. Current methods either only provide part of the desired information or require the injection of multiple exogenous agents. In this study, we developed a multiparametric imaging scheme for the imaging of brain hemodynamics and function using gas-inhalation MRI. The proposed technique uses a single MRI scan to provide simultaneous measurements of baseline venous cerebral blood volume (vCBV), cerebrovascular reactivity (CVR), bolus arrival time (BAT), and resting-state functional connectivity (fcMRI). This was achieved with a novel, concomitant O 2 and CO 2 gas inhalation paradigm, rapid MRI image acquisition with a 9.3min BOLD sequence, and an advanced algorithm to extract multiple hemodynamic information from the same dataset. In healthy subjects, CVR and vCBV values were 0.23±0.03%/mmHg and 0.0056±0.0006%/mmHg, respectively, with a strong correlation (r=0.96 for CVR and r=0.91 for vCBV) with more conventional, separate acquisitions that take twice the scan time. In patients with Moyamoya syndrome, CVR in the stenosis-affected flow territories (typically anterior-cerebral-artery, ACA, and middle-cerebral-artery, MCA, territories) was significantly lower than that in posterior-cerebral-artery (PCA), which typically has minimal stenosis, flow territories (0.12±0.06%/mmHg vs. 0.21±0.05%/mmHg, p<0.001). BAT of the gas bolus was significantly longer (p=0.008) in ACA/MCA territories, compared to PCA, and the maps were consistent with the conventional contrast-enhanced CT perfusion method. FcMRI networks were robustly identified from the gas-inhalation MRI data after factoring out the influence of CO 2 and O 2 on the signal time course. The spatial correspondence between the gas-data-derived fcMRI maps and those using a separate, conventional fcMRI scan was excellent, showing a spatial correlation of 0.58±0.17 and 0.64±0.20 for default mode network and primary visual network, respectively. These findings suggest that advanced gas-inhalation MRI provides reliable measurements of multiple hemodynamic parameters within a clinically acceptable imaging time and is suitable for patient examinations. Copyright © 2016 Elsevier Inc. All rights reserved.

Multiparametric imaging of brain hemodynamics and function using gas-inhalation MRI

PubMed Central

Liu, Peiying; Welch, Babu G.; Li, Yang; Gu, Hong; King, Darlene; Yang, Yihong; Pinho, Marco; Lu, Hanzhang

2016-01-01

Diagnosis and treatment monitoring of cerebrovascular diseases routinely require hemodynamic imaging of the brain. Current methods either only provide part of the desired information or require the injection of multiple exogenous agents. In this study, we developed a multiparametric imaging scheme for the imaging of brain hemodynamics and function using gas-inhalation MRI. The proposed technique uses a single MRI scan to provide simultaneous measurements of baseline venous cerebral blood volume (vCBV), cerebrovascular reactivity (CVR), bolus arrival time (BAT), and resting-state functional connectivity (fcMRI). This was achieved with a novel, concomitant O2 and CO2 gas inhalation paradigm, rapid MRI image acquisition with a 9.3 min BOLD sequence, and an advanced algorithm to extract multiple hemodynamic information from the same dataset. In healthy subjects, CVR and vCBV values were 0.23±0.03 %/mmHg and 0.0056±0.0006 %/mmHg, respectively, with a strong correlation (r=0.96 for CVR and r=0.91 for vCBV) with more conventional, separate acquisitions that take twice the scan time. In patients with Moyamoya syndrome, CVR in the stenosis-affected flow territories (typically anterior-cerebral-artery, ACA, and middle-cerebral-artery, MCA, territories) was significantly lower than that in posterior-cerebral-artery (PCA), which typically has minimal stenosis, flow territories (0.12±0.06 %/mmHg vs. 0.21±0.05 %/mmHg, p<0.001). BAT of the gas bolus was significantly longer (p=0.008) in ACA/MCA territories, compared to PCA, and the maps were consistent with the conventional contrast-enhanced CT perfusion method. FcMRI networks were robustly identified from the gas-inhalation MRI data after factoring out the influence of CO2 and O2 on the signal time course. The spatial correspondence between the gas-data-derived fcMRI maps and those using a separate, conventional fcMRI scan was excellent, showing a spatial correlation of 0.58±0.17 and 0.64±0.20 for default mode network and primary visual network, respectively. These findings suggest that advanced gas-inhalation MRI provides reliable measurements of multiple hemodynamic parameters within a clinically acceptable imaging time and is suitable for patient examinations. PMID:27693197

Multiclass fMRI data decoding and visualization using supervised self-organizing maps.

PubMed

Hausfeld, Lars; Valente, Giancarlo; Formisano, Elia

2014-08-01

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches). Copyright © 2014. Published by Elsevier Inc.

Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females

PubMed Central

Swinnen, Stephan P.; Wenderoth, Nicole

2016-01-01

Autism spectrum disorders (ASD) are far more prevalent in males than in females. Little is known however about the differential neural expression of ASD in males and females. We used a resting-state fMRI-dataset comprising 42 males/42 females with ASD and 75 male/75 female typical-controls to examine whether autism-related alterations in intrinsic functional connectivity are similar or different in males and females, and particularly whether alterations reflect ‘neural masculinization’, as predicted by the Extreme Male Brain theory. Males and females showed a differential neural expression of ASD, characterized by highly consistent patterns of hypo-connectivity in males with ASD (compared to typical males), and hyper-connectivity in females with ASD (compared to typical females). Interestingly, patterns of hyper-connectivity in females with ASD reflected a shift towards the (high) connectivity levels seen in typical males (neural masculinization), whereas patterns of hypo-connectivity observed in males with ASD reflected a shift towards the (low) typical feminine connectivity patterns (neural feminization). Our data support the notion that ASD is a disorder of sexual differentiation rather than a disorder characterized by masculinization in both genders. Future work is needed to identify underlying factors such as sex hormonal alterations that drive these sex-specific neural expressions of ASD. PMID:26989195

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments

PubMed Central

Pereira, Francisco; Botvinick, Matthew; Detre, Greg

2012-01-01

In this paper we show that a corpus of a few thousand Wikipedia articles about concrete or visualizable concepts can be used to produce a low-dimensional semantic feature representation of those concepts. The purpose of such a representation is to serve as a model of the mental context of a subject during functional magnetic resonance imaging (fMRI) experiments. A recent study [19] showed that it was possible to predict fMRI data acquired while subjects thought about a concrete concept, given a representation of those concepts in terms of semantic features obtained with human supervision. We use topic models on our corpus to learn semantic features from text in an unsupervised manner, and show that those features can outperform those in [19] in demanding 12-way and 60-way classification tasks. We also show that these features can be used to uncover similarity relations in brain activation for different concepts which parallel those relations in behavioral data from human subjects. PMID:23243317

Prediction of individual brain maturity using fMRI.

PubMed

Dosenbach, Nico U F; Nardos, Binyam; Cohen, Alexander L; Fair, Damien A; Power, Jonathan D; Church, Jessica A; Nelson, Steven M; Wig, Gagan S; Vogel, Alecia C; Lessov-Schlaggar, Christina N; Barnes, Kelly Anne; Dubis, Joseph W; Feczko, Eric; Coalson, Rebecca S; Pruett, John R; Barch, Deanna M; Petersen, Steven E; Schlaggar, Bradley L

2010-09-10

Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features

PubMed Central

Liu, Zhiya; Song, Xiaohong; Seger, Carol A.

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting. PMID:26274332

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features.

PubMed

Liu, Zhiya; Song, Xiaohong; Seger, Carol A

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting.

Working memory network alterations in high-functioning adolescents with an autism spectrum disorder.

PubMed

Barendse, Evelien M; Schreuder, Lisanne J; Thoonen, Geert; Hendriks, Marc P H; Kessels, Roy P C; Backes, Walter H; Aldenkamp, Albert P; Jansen, Jacobus F A

2018-02-01

People with autism spectrum disorder (ASD) typically have deficits in the working memory (WM) system. WM is found to be an essential chain in successfully navigating in the social world. We hypothesize that brain networks for WM have an altered network integrity in ASD compared to controls. Thirteen adolescents (one female) with autistic disorder (n = 1), Asperger's disorder (n = 7), or pervasive developmental disorder not otherwise specified (n = 5), and 13 typically developing healthy control adolescents (one female) participated in this study. Functional magnetic resonance imaging (MRI) was performed using an n-back task and in resting state. The analysis of the behavioral data revealed deficits in WM performance in ASD, but only when tested to the limit. Adolescents with ASD showed lower binary global efficiency in the WM network than the healthy control group with n-back and resting-state data. This correlated with diagnostic scores for total problems, reciprocity, and language. Adolescents with higher-functioning autism have difficulty with the WM system, which is typically compensated. Functional MRI markers of brain network organization in ASD are related to characteristics of autism as represented in diagnostic scores. Therefore, functional MRI provides neuronal correlates for memory difficulties in adolescents with ASD. © 2017 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

SU-F-R-32: Evaluation of MRI Acquisition Parameter Variations On Texture Feature Extraction Using ACR Phantom

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Y; Wang, J; Wang, C

Purpose: To investigate the sensitivity of classic texture features to variations of MRI acquisition parameters. Methods: This study was performed on American College of Radiology (ACR) MRI Accreditation Program Phantom. MR imaging was acquired on a GE 750 3T scanner with XRM explain gradient, employing a T1-weighted images (TR/TE=500/20ms) with the following parameters as the reference standard: number of signal average (NEX) = 1, matrix size = 256×256, flip angle = 90°, slice thickness = 5mm. The effect of the acquisition parameters on texture features with and without non-uniformity correction were investigated respectively, while all the other parameters were keptmore » as reference standard. Protocol parameters were set as follows: (a). NEX = 0.5, 2 and 4; (b).Phase encoding steps = 128, 160 and 192; (c). Matrix size = 128×128, 192×192 and 512×512. 32 classic texture features were generated using the classic gray level run length matrix (GLRLM) and gray level co-occurrence matrix (GLCOM) from each image data set. Normalized range ((maximum-minimum)/mean) was calculated to determine variation among the scans with different protocol parameters. Results: For different NEX, 31 out of 32 texture features’ range are within 10%. For different phase encoding steps, 31 out of 32 texture features’ range are within 10%. For different acquisition matrix size without non-uniformity correction, 14 out of 32 texture features’ range are within 10%; for different acquisition matrix size with non-uniformity correction, 16 out of 32 texture features’ range are within 10%. Conclusion: Initial results indicated that those texture features that range within 10% are less sensitive to variations in T1-weighted MRI acquisition parameters. This might suggest that certain texture features might be more reliable to be used as potential biomarkers in MR quantitative image analysis.« less

SU-D-207B-04: Morphological Features of MRI as a Correlate of Capsular Contracture in Breast Cancer Patients with Implant-Based Reconstructions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tyagi, N; Sutton, E; Hunt, M

Purpose: Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. The goal of this study was to identify image-based correlates of CC using MRI imaging in breast cancer patients who received both MRI and clinical evaluation following reconstructive surgery. Methods: We analyzed a retrospective dataset of 50 patients who had both a diagnostic MR and a plastic surgeon’s evaluations of CC score (Baker’s score) within a six month period following mastectomy and reconstructive surgery. T2w sagittal MRIs (TR/TE = 3500/102 ms, slice thickness = 4 mm) were used for morphological shape features (roundness, eccentricity,more » solidity, extent and ratio-length) and histogram features (median, skewness and kurtosis) of the implant and the pectoralis muscle overlying the implant. Implant and pectoralis muscles were segmented in 3D using Computation Environment for Radiological Research (CERR) and shape and histogram features were calculated as a function of Baker’s score. Results: Shape features such as roundness and eccentricity were statistically significant in differentiating grade 1 and grade 2 (p = 0.009; p = 0.06) as well as grade 1 and grade 3 CC (p = 0.001; p = 0.006). Solidity and extent were statistically significant in differentiating grade 1 and grade 3 CC (p = 0.04; p = 0.04). Ratio-length was statistically significant in differentiating all grades of CC except grade 2 and grade 3 that showed borderline significance (p = 0.06). The muscle thickness, median intensity and kurtosis were significant in differentiating between grade 1 and grade 3 (p = 0.02), grade 1 and grade 2 (p = 0.03) and grade 1 and grade 3 (p = 0.01) respectively. Conclusion: Morphological shape features described on MR images were associated with the severity of CC. MRI may be important in objectively evaluating outcomes in breast cancer patients who undergo implant reconstruction.« less

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Effects of Spatial and Feature Attention on Disparity-Rendered Structure-From-Motion Stimuli in the Human Visual Cortex

PubMed Central

Ip, Ifan Betina; Bridge, Holly; Parker, Andrew J.

2014-01-01

An important advance in the study of visual attention has been the identification of a non-spatial component of attention that enhances the response to similar features or objects across the visual field. Here we test whether this non-spatial component can co-select individual features that are perceptually bound into a coherent object. We combined human psychophysics and functional magnetic resonance imaging (fMRI) to demonstrate the ability to co-select individual features from perceptually coherent objects. Our study used binocular disparity and visual motion to define disparity structure-from-motion (dSFM) stimuli. Although the spatial attention system induced strong modulations of the fMRI response in visual regions, the non-spatial system’s ability to co-select features of the dSFM stimulus was less pronounced and variable across subjects. Our results demonstrate that feature and global feature attention effects are variable across participants, suggesting that the feature attention system may be limited in its ability to automatically select features within the attended object. Careful comparison of the task design suggests that even minor differences in the perceptual task may be critical in revealing the presence of global feature attention. PMID:24936974

Use of MRI in Differentiation of Papillary Renal Cell Carcinoma Subtypes: Qualitative and Quantitative Analysis.

PubMed

Doshi, Ankur M; Ream, Justin M; Kierans, Andrea S; Bilbily, Matthew; Rusinek, Henry; Huang, William C; Chandarana, Hersh

2016-03-01

The purpose of this study was to determine whether qualitative and quantitative MRI feature analysis is useful for differentiating type 1 from type 2 papillary renal cell carcinoma (PRCC). This retrospective study included 21 type 1 and 17 type 2 PRCCs evaluated with preoperative MRI. Two radiologists independently evaluated various qualitative features, including signal intensity, heterogeneity, and margin. For the quantitative analysis, a radiology fellow and a medical student independently drew 3D volumes of interest over the entire tumor on T2-weighted HASTE images, apparent diffusion coefficient parametric maps, and nephrographic phase contrast-enhanced MR images to derive first-order texture metrics. Qualitative and quantitative features were compared between the groups. For both readers, qualitative features with greater frequency in type 2 PRCC included heterogeneous enhancement, indistinct margin, and T2 heterogeneity (all, p < 0.035). Indistinct margins and heterogeneous enhancement were independent predictors (AUC, 0.822). Quantitative analysis revealed that apparent diffusion coefficient, HASTE, and contrast-enhanced entropy were greater in type 2 PRCC (p < 0.05; AUC, 0.682-0.716). A combined quantitative and qualitative model had an AUC of 0.859. Qualitative features within the model had interreader concordance of 84-95%, and the quantitative data had intraclass coefficients of 0.873-0.961. Qualitative and quantitative features can help discriminate between type 1 and type 2 PRCC. Quantitative analysis may capture useful information that complements the qualitative appearance while benefiting from high interobserver agreement.

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Magnetic resonance imaging: a useful tool to distinguish between keratocystic odontogenic tumours and odontogenic cysts.

PubMed

Probst, F A; Probst, M; Pautke, Ch; Kaltsi, E; Otto, S; Schiel, S; Troeltzsch, M; Ehrenfeld, M; Cornelius, C P; Müller-Lisse, U G

2015-03-01

In contrast to odontogenic cysts, keratocystic odontogenic tumours often recur and require more aggressive surgical treatment, so we tried to find features that distinguished between them on magnetic resonance imaging (MRI). Without knowing the diagnosis, two radiologists reviewed intensity (low, intermediate, or high) and homogeneity (homogeneous or heterogeneous) of signals in short-tau-inversion-recovery (STIR), T1- and T2-weighted, and fat-suppressed, contrast-enhanced MRI in 20 consecutive patients with oval, radiolucent lesions of the mandible on panoramic radiography, and who were subsequently confirmed histopathologically to have either an odontogenic cyst or a keratocystic odontogenic tumour (n=10 in each group). Fisher's exact test was statistically significant at p<0.05. Delineation of a contrast-enhanced wall of a cyst with high signal intensity distinguished odontogenic cysts (9/10 and 8/10, respectively) from keratocystic odontogenic tumours (3/10, p=0.02, and 1/10, p=0.01, respectively). One radiologist found odontogenic cysts were more likely to be homogeneous on unenhanced T1-weighted images (odontogenic cysts 9/10, keratocystic odontogenic tumours 3/10, p=0.02) and one on contrast-enhanced MRI, when the cyst wall was enhanced (odontogenic cysts 7/9, keratocystic odontogenic tumours 0/3, p=0.01). There were no other significant distinguishing features on MRI. In conclusion, the signal intensity of the enhanced wall seems to be a feature on contrast-enhanced MRI that differentiates odontogenic cysts from keratocystic odontogenic tumours. Copyright © 2014 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Prediction of response to neoadjuvant chemotherapy in breast cancer: a radiomic study

NASA Astrophysics Data System (ADS)

Wu, Guolin; Fan, Ming; Zhang, Juan; Zheng, Bin; Li, Lihua

2017-03-01

Breast cancer is one of the most malignancies among women in worldwide. Neoadjuvant Chemotherapy (NACT) has gained interest and is increasingly used in treatment of breast cancer in recent years. Therefore, it is necessary to find a reliable non-invasive assessment and prediction method which can evaluate and predict the response of NACT. Recent studies have highlighted the use of MRI for predicting response to NACT. In addition, molecular subtype could also effectively identify patients who are likely have better prognosis in breast cancer. In this study, a radiomic analysis were performed, by extracting features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and immunohistochemistry (IHC) to determine subtypes. A dataset with fifty-seven breast cancer patients were included, all of them received preoperative MRI examination. Among them, 47 patients had complete response (CR) or partial response (PR) and 10 had stable disease (SD) to chemotherapy based on the RECIST criterion. A total of 216 imaging features including statistical characteristics, morphology, texture and dynamic enhancement were extracted from DCE-MRI. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.923 (P = 0.0002) in leave-one-out crossvalidation. The performance of the classifier increased to 0.960, 0.950 and 0.936 when status of HER2, Luminal A and Luminal B subtypes were added into the statistic model, respectively. The results of this study demonstrated that IHC determined molecular status combined with radiomic features from DCE-MRI could be used as clinical marker that is associated with response to NACT.

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

NASA Astrophysics Data System (ADS)

Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

2017-09-01

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma

NASA Astrophysics Data System (ADS)

Beig, Niha; Patel, Jay; Prasanna, Prateek; Partovi, Sasan; Varadan, Vinay; Madabhushi, Anant; Tiwari, Pallavi

2017-03-01

Glioblastoma Multiforme (GBM) is a highly aggressive brain tumor with a median survival of 14 months. Hypoxia is a hallmark trait in GBM that is known to be associated with angiogenesis, tumor growth, and resistance to conventional therapy, thereby limiting treatment options for GBM patients. There is thus an urgent clinical need for non-invasively capturing tumor hypoxia in GBM towards identifying a subset of patients who would likely benefit from anti-angiogenic therapies (bevacizumab) in the adjuvant setting. In this study, we employed radiomic descriptors to (a) capture molecular variations of tumor hypoxia on routine MRI that are otherwise not appreciable; and (b) employ the radiomic correlates of hypoxia to discriminate patients with short-term survival (STS, overall survival (OS) < 7 months), mid-term survival (MTS) (7 months16 months). A total of 97 studies (25 STS, 36 MTS, 36 LTS) with Gadolinium T1-contrast (Gd-T1c), T2w, and FLAIR protocols with their corresponding gene expression profiles were obtained from the cancer genome atlas (TCGA) database. For each MRI study, necrotic, enhancing tumor, and edematous regions were segmented by an expert. A total of 30 radiomic descriptors (i.e. Haralick, Laws energy, Gabor) were extracted from every region across all three MRI protocols. By performing unsupervised clustering of the expression profile of hypoxia associated genes, a "low", "medium", or "high" index was defined for every study. Spearman correlation was then used to identify the most significantly correlated MRI features with the hypoxia index for every study. These features were further used to categorize each study as STS, MTS, and LTS using Kaplan-Meier (KM) analysis. Our results revealed that the most significant features (p < 0.05) were identified as Laws energy and Haralick features that capture image heterogeneity on FLAIR and Gd-T1w sequences. We also found these radiomic features to be significantly associated with survival, distinguishing MTS from LTS (p=.005) and STS from LTS (p=.0008).

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy

PubMed Central

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N.; Styner, Martin A.

2015-01-01

Purpose Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semi-automated system to quantify MRI biomarkers of GRMD. Methods Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using two competing schemes: 1) standard, limited muscle range segmentation and 2) semi-automatic full muscle segmentation. We then performed pre-processing, including: intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted (T2w) and T2-weighted fat suppressed (T2fs) images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume and intensity statistics over MRI biomarker maps, and statistical image texture features. Results The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation shows significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. Conclusion The experimental results demonstrated that this quantification tool can reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients. PMID:23299128

Good outcome of brain stem progressive multifocal leukoencephalopathy in an immunosuppressed renal transplant patient: Importance of early detection and rapid immune reconstitution.

PubMed

Sauer, Roland; Gölitz, Philipp; Jacobi, Johannes; Schwab, Stefan; Linker, Ralf A; Lee, De-Hyung

2017-04-15

Progressive multifocal leukoencephalopathy (PML) is a rare, opportunistic and often fatal disease of the CNS which may occur under immunosuppression in transplant patients. Brain stem PML is associated with a particularly bad prognosis. Here, we present a case of a renal transplant patient treated with mycophenolate mofetil (MMF) and tacrolimus who developed brain stem PML with limb ataxia, dysarthria and dysphagia. Diagnosis was established by typical MRI features and detection of JCV-DNA in the CSF. Immune reconstitution after stopping MMF and tacrolimus led to a complete and sustained remission of symptoms with improvement of the brain stem lesion over a follow-up over 20months. In summary, early detection of PML and consequent treatment may improve neurological outcomes even in brain stem disease with a notorious bad prognosis. Copyright © 2017 Elsevier B.V. All rights reserved.

[MELAS syndrome as a differential diagnosis of ischemic stroke].

PubMed

Finsterer, J

2009-01-01

Mitochondrial encephalomyopathy, lactacidosis and stroke-like episode (MELAS) syndrome is a phenotypically and genetically heterogeneous mitochondrial disorder with a clinical onset between the first and third decade. The clinical hallmark is the stroke-like-episode, which mimicks ischemic stroke but is usually transient and non-disabling in nature. The morphological equivalent on MRI is a T2-hyperintensity, predominantly over the temporo-parieto-occipital region, not confined to a vascular territory, which is also hyperintense on diffusion weighted imaging and on apparent diffusion coefficient sequences (vasogenic edema, stroke-like lesion). Additional features include seizures, cognitive decline, psychosis, lactic acidosis, migraine, visual impairment, hearing loss, short stature, diabetes, or myopathy. Muscle biopsy typically shows ragged-red fibers, COX-negative fibers, SDH hyperreactivity, and abnormally shaped mitochondria with paracristalline inclusions. The diagnosis is confirmed by demonstration of a biochemical respiratory chain defect or one of the disease-causing mutations, of which 80 % affect the mitochondrial tRNALeu gene.

Toward Dysfunctional Connectivity: A Review of Neuroimaging Findings in Pediatric Major Depressive Disorder

PubMed Central

Hulvershorn, Leslie; Cullen, Kathryn; Anand, Amit

2011-01-01

Child and adolescent psychiatric neuroimaging research typically lags behind similar advances in adult disorders. While the pediatric depression imaging literature is less developed, a recent surge in interest has created the need for a synthetic review of this work. Major findings from pediatric volumetric and functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI) and resting state functional connectivity studies converge to implicate a corticolimbic network of key areas that work together to mediate the task of emotion regulation. Imaging the brain of children and adolescents with unipolar depression began with volumetric studies of isolated brain regions that served to identify key prefrontal, cingulate and limbic nodes of depression-related circuitry elucidated from more recent advances in DTI and functional connectivity imaging. Systematic review of these studies preliminarily suggests developmental differences between findings in youth and adults, including prodromal neurobiological features, along with some continuity across development. PMID:21901425

Anatomic Correlates of Stereotypies in Frontotemporal Lobar Degeneration

PubMed Central

Josephs, Keith A.; Whitwell, Jennifer L.; Jack, Clifford R.

2009-01-01

Stereotypies are common in frontotemporal lobar degeneration (FTLD) however the anatomical correlates of stereotypies are unknown. We therefore set out to compare patterns of grey matter volume loss in FTLD subjects with and without stereotypies. Subjects with a diagnosis of FTLD that met international consensus criteria were prospectively recruited and separated into those with and without stereotypies. MRI and cognitive measures were obtained and voxel-based morphometry was used to assess the patterns of grey matter volume loss in those with and without stereotypies, compared to a group of age-and gender-matched controls. Demographic and clinical features were similar between subjects with and without stereotypies. FTLD subjects with stereotypies had greater volume loss in the striatum compared to those without stereotypies. Those without stereotypies showed a more widespread and typical pattern of cortical frontotemporal loss. Stereotypies in FTLD are therefore associated with a greater proportion of striatal to cortical volume loss than those without stereotypies. PMID:17574708

Typical and atypical neurodevelopment for face specialization: An fMRI study

PubMed Central

Joseph, Jane E.; Zhu, Xun; Gundran, Andrew; Davies, Faraday; Clark, Jonathan D.; Ruble, Lisa; Glaser, Paul; Bhatt, Ramesh S.

2014-01-01

Individuals with Autism Spectrum Disorder (ASD) and their relatives process faces differently from typically developed (TD) individuals. In an fMRI face-viewing task, TD and undiagnosed sibling (SIB) children (5–18 years) showed face specialization in the right amygdala and ventromedial prefrontal cortex (vmPFC), with left fusiform and right amygdala face specialization increasing with age in TD subjects. SIBs showed extensive antero-medial temporal lobe activation for faces that was not present in any other group, suggesting a potential compensatory mechanism. In ASD, face specialization was minimal but increased with age in the right fusiform and decreased with age in the left amygdala, suggesting atypical development of a frontal-amygdala-fusiform system which is strongly linked to detecting salience and processing facial information. PMID:25479816

IDH mutation assessment of glioma using texture features of multimodal MR images

NASA Astrophysics Data System (ADS)

Zhang, Xi; Tian, Qiang; Wu, Yu-Xia; Xu, Xiao-Pan; Li, Bao-Juan; Liu, Yi-Xiong; Liu, Yang; Lu, Hong-Bing

2017-03-01

Purpose: To 1) find effective texture features from multimodal MRI that can distinguish IDH mutant and wild status, and 2) propose a radiomic strategy for preoperatively detecting IDH mutation patients with glioma. Materials and Methods: 152 patients with glioma were retrospectively included from the Cancer Genome Atlas. Corresponding T1-weighted image before- and post-contrast, T2-weighted image and fluid-attenuation inversion recovery image from the Cancer Imaging Archive were analyzed. Specific statistical tests were applied to analyze the different kind of baseline information of LrGG patients. Finally, 168 texture features were derived from multimodal MRI per patient. Then the support vector machine-based recursive feature elimination (SVM-RFE) and classification strategy was adopted to find the optimal feature subset and build the identification models for detecting the IDH mutation. Results: Among 152 patients, 92 and 60 were confirmed to be IDH-wild and mutant, respectively. Statistical analysis showed that the patients without IDH mutation was significant older than patients with IDH mutation (p<0.01), and the distribution of some histological subtypes was significant different between IDH wild and mutant groups (p<0.01). After SVM-RFE, 15 optimal features were determined for IDH mutation detection. The accuracy, sensitivity, specificity, and AUC after SVM-RFE and parameter optimization were 82.2%, 85.0%, 78.3%, and 0.841, respectively. Conclusion: This study presented a radiomic strategy for noninvasively discriminating IDH mutation of patients with glioma. It effectively incorporated kinds of texture features from multimodal MRI, and SVM-based classification strategy. Results suggested that features selected from SVM-RFE were more potential to identifying IDH mutation. The proposed radiomics strategy could facilitate the clinical decision making in patients with glioma.

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

PubMed

Gatos, Ilias; Tsantis, Stavros; Karamesini, Maria; Spiliopoulos, Stavros; Karnabatidis, Dimitris; Hazle, John D; Kagadis, George C

2017-07-01

To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American Association of Physicists in Medicine.

Diagnostic imaging features of normal anal sacs in dogs and cats.

PubMed

Jung, Yechan; Jeong, Eunseok; Park, Sangjun; Jeong, Jimo; Choi, Ul Soo; Kim, Min-Su; Kim, Namsoo; Lee, Kichang

2016-09-30

This study was conducted to provide normal reference features for canine and feline anal sacs using ultrasound, low-field magnetic resonance imaging (MRI) and radiograph contrast as diagnostic imaging tools. A total of ten clinically normal beagle dogs and eight clinically normally cats were included. General radiography with contrast, ultrasonography and low-field MRI scans were performed. The visualization of anal sacs, which are located at distinct sites in dogs and cats, is possible with a contrast study on radiography. Most surfaces of the anal sacs tissue, occasionally appearing as a hyperechoic thin line, were surrounded by the hypoechoic external sphincter muscle on ultrasonography. The normal anal sac contents of dogs and cats had variable echogenicity. Signals of anal sac contents on low-field MRI varied in cats and dogs, and contrast medium using T1-weighted images enhanced the anal sac walls more obviously than that on ultrasonography. In conclusion, this study provides the normal features of anal sacs from dogs and cats on diagnostic imaging. Further studies including anal sac evaluation are expected to investigate disease conditions.

Diagnostic imaging features of normal anal sacs in dogs and cats

PubMed Central

Jung, Yechan; Jeong, Eunseok; Park, Sangjun; Jeong, Jimo; Choi, Ul Soo; Kim, Min-Su; Kim, Namsoo

2016-01-01

This study was conducted to provide normal reference features for canine and feline anal sacs using ultrasound, low-field magnetic resonance imaging (MRI) and radiograph contrast as diagnostic imaging tools. A total of ten clinically normal beagle dogs and eight clinically normally cats were included. General radiography with contrast, ultrasonography and low-field MRI scans were performed. The visualization of anal sacs, which are located at distinct sites in dogs and cats, is possible with a contrast study on radiography. Most surfaces of the anal sacs tissue, occasionally appearing as a hyperechoic thin line, were surrounded by the hypoechoic external sphincter muscle on ultrasonography. The normal anal sac contents of dogs and cats had variable echogenicity. Signals of anal sac contents on low-field MRI varied in cats and dogs, and contrast medium using T1-weighted images enhanced the anal sac walls more obviously than that on ultrasonography. In conclusion, this study provides the normal features of anal sacs from dogs and cats on diagnostic imaging. Further studies including anal sac evaluation are expected to investigate disease conditions. PMID:26645338

Conventional MRI features for predicting the clinical outcome of patients with invasive placenta

PubMed Central

Chen, Ting; Xu, Xiao-Quan; Shi, Hai-Bin; Yang, Zheng-Qiang; Zhou, Xin; Pan, Yi

2017-01-01

PURPOSE We aimed to evaluate whether morphologic magnetic resonance imaging (MRI) features could help to predict the maternal outcome after uterine artery embolization (UAE)-assisted cesarean section (CS) in patients with invasive placenta previa. METHODS We retrospectively reviewed the MRI data of 40 pregnant women who have undergone UAE-assisted cesarean section due to suspected high risk of massive hemorrhage caused by invasive placenta previa. Patients were divided into two groups based on the maternal outcome (good-outcome group: minor hemorrhage and uterus preserved; poor-outcome group: significant hemorrhage or emergency hysterectomy). Morphologic MRI features were compared between the two groups. Multivariate logistic regression analysis was used to identify the most valuable variables, and predictive value of the identified risk factor was determined. RESULTS Low signal intensity bands on T2-weighted imaging (P < 0.001), placenta percreta (P = 0.011), and placental cervical protrusion sign (P = 0.002) were more frequently observed in patients with poor outcome. Low signal intensity bands on T2-weighted imaging was the only significant predictor of poor maternal outcome in multivariate analysis (P = 0.020; odds ratio, 14.79), with 81.3% sensitivity and 84.3% specificity. CONCLUSION Low signal intensity bands on T2-weighted imaging might be a predictor of poor maternal outcome after UAE-assisted cesarean section in patients with invasive placenta previa. PMID:28345524

Is There a Role for MRI in Plantar Heel Pain.

PubMed

Fazal, Muhammad Ali; Tsekes, Demetris; Baloch, Irshad

2018-06-01

There is an increasing trend to investigate plantar heel pain with magnetic resonance imaging (MRI) scan though plantar fasciitis is the most common cause. The purpose of our study was to evaluate the role of MRI in patients presenting with plantar heel pain. Case notes and MRI scans of 141 patients with a clinical diagnosis of plantar fasciitis were reviewed retrospectively. There were 98 females and 43 males patients. Fourteen patients had bilateral symptoms. Average age for male patients was 51 years (range = 26-78 years), and for female patients the average age was 52 years (range = 29-76 years). A total of 121 feet had MRI features suggestive of plantar fasciitis. MRI was normal in 32 feet. There was one case of stress fracture of calcaneus and another of a heel fibroma diagnosed on MRI scan. In our study, MRI scan was normal in 20.7% of the cases; 1.3% had a diagnosis other than plantar fasciitis but no sinister pathology. We therefore conclude that MRI scan is not routinely indicated and key is careful clinical assessment. Therapeutic, Level IV: Retrospective, Case series.

MRI correlates of alien leg-like phenomenon in corticobasal degeneration.

PubMed

Hu, William T; Josephs, Keith A; Ahlskog, J Eric; Shin, Cheolsu; Boeve, Bradley F; Witte, Robert J

2005-07-01

We describe the clinical and neuroradiologic correlates in two patients with the clinical picture of CBD and alien leg phenomena. The MRI brain scan in both had unique focal abnormalities in the corresponding leg area of the homunculus that may be the substrate for the alien limb features. Copyright 2005 Movement Disorder Society.

Normal Rates of Neuroradiological Findings in Children with High Functioning Autism

ERIC Educational Resources Information Center

Vasa, Roma A.; Ranta, Marin; Huisman, Thierry A. G. M.; Pinto, Pedro S.; Tillman, Rachael M.; Mostofsky, Stewart H.

2012-01-01

Magnetic resonance imaging (MRI) has been used to analyze highly specific volumetric and morphological features of the brains of individuals with autism spectrum disorder (ASD). To date, there are few comprehensive studies examining the prevalence of neuroradiologic findings seen on routine MRI scans in children with ASD. This study examined the…

High-resolution 3-T MRI of the triangular fibrocartilage complex in the wrist: injury pattern and MR features.

PubMed

Zhan, Huili; Zhang, Huibo; Bai, Rongjie; Qian, Zhanhua; Liu, Yue; Zhang, Heng; Yin, Yuming

2017-12-01

To investigate if using high-resolution 3-T MRI can identify additional injuries of the triangular fibrocartilage complex (TFCC) beyond the Palmer classification. Eighty-six patients with surgically proven TFCC injury were included in this study. All patients underwent high-resolution 3-T MRI of the injured wrist. The MR imaging features of TFCC were analyzed according to the Palmer classification. According to the Palmer classification, 69 patients could be classified as having Palmer injuries (52 had traumatic tears and 17 had degenerative tears). There were 17 patients whose injuries could not be classified according to the Palmer classification: 13 had volar or dorsal capsular TFC detachment and 4 had a horizontal tear of the articular disk. Using high-resolution 3-T MRI, we have not only found all the TFCC injuries described in the Palmer classification, additional injury types were found in this study, including horizontal tear of the TFC and capsular TFC detachment. We propose the modified Palmer classification and add the injury types that were not included in the original Palmer classification.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math.

PubMed

Raizada, Rajeev D S; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D; Ansari, Daniel; Kuhl, Patricia K

2010-05-15

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain-behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain-behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain-behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Non-alcoholic fatty liver disease and subclinical atherosclerosis: A comparison of metabolically- versus genetically-driven excess fat hepatic storage.

PubMed

Di Costanzo, Alessia; D'Erasmo, Laura; Polimeni, Licia; Baratta, Francesco; Coletta, Paola; Di Martino, Michele; Loffredo, Lorenzo; Perri, Ludovica; Ceci, Fabrizio; Montali, Anna; Girelli, Gabriella; De Masi, Bruna; Angeloni, Antonio; Catalano, Carlo; Maranghi, Marianna; Del Ben, Maria; Angelico, Francesco; Arca, Marcello

2017-02-01

Non-alcoholic fatty liver disease (NAFLD) is frequently associated with atherosclerosis. However, it is unclear whether this association is related to excess fat liver storage per se or to metabolic abnormalities that typically accompany NAFLD. To investigate this, we compared individuals with hepatic steatosis driven by metabolic disturbances to those with hepatic steatosis associated with the rs738409 GG genotype in the patatin-like phospholipase domain-containing 3 gene (PNPLA3). Carotid intima-media thickness (CIMT), as a surrogate marker of subclinical atherosclerosis, was measured in 83 blood donors with the mutant GG genotype (group G), 100 patients with features of metabolic syndrome (MetS) but the wildtype CC genotype (group M), and 74 blood donors with the wildtype CC genotype (controls). Fatty liver was evaluated by ultrasonography and hepatic fat fraction (HFF) was measured using magnetic resonance (MRS/MRI) in 157 subjects. Compared with group G and controls, group M subjects were older and had increased adiposity indices, dyslipidemia, insulin resistance and elevated transaminase levels (all p < 0.05). They also had a more fatty liver on both ultrasonography and MRS/MRI. After adjustment for confounders (including severity of hepatic steatosis), the median CIMT in group M (0.84 [0.70-0.95] mm) was significantly greater than that in group G (0.66 [0.55-0.74] mm; p < 0.001), which was similar to that in controls (0.70 [0.64-0.81] mm). Results were similar in the subgroup evaluated using MRS/MRI. Excess liver fat accumulation appeared to increase the burden of subclinical atherosclerosis only when it is associated with metabolic abnormalities. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Linking brain-wide multivoxel activation patterns to behaviour: Examples from language and math

PubMed Central

Raizada, Rajeev D.S.; Tsao, Feng-Ming; Liu, Huei-Mei; Holloway, Ian D.; Ansari, Daniel; Kuhl, Patricia K.

2010-01-01

A key goal of cognitive neuroscience is to find simple and direct connections between brain and behaviour. However, fMRI analysis typically involves choices between many possible options, with each choice potentially biasing any brain–behaviour correlations that emerge. Standard methods of fMRI analysis assess each voxel individually, but then face the problem of selection bias when combining those voxels into a region-of-interest, or ROI. Multivariate pattern-based fMRI analysis methods use classifiers to analyse multiple voxels together, but can also introduce selection bias via data-reduction steps as feature selection of voxels, pre-selecting activated regions, or principal components analysis. We show here that strong brain–behaviour links can be revealed without any voxel selection or data reduction, using just plain linear regression as a classifier applied to the whole brain at once, i.e. treating each entire brain volume as a single multi-voxel pattern. The brain–behaviour correlations emerged despite the fact that the classifier was not provided with any information at all about subjects' behaviour, but instead was given only the neural data and its condition-labels. Surprisingly, more powerful classifiers such as a linear SVM and regularised logistic regression produce very similar results. We discuss some possible reasons why the very simple brain-wide linear regression model is able to find correlations with behaviour that are as strong as those obtained on the one hand from a specific ROI and on the other hand from more complex classifiers. In a manner which is unencumbered by arbitrary choices, our approach offers a method for investigating connections between brain and behaviour which is simple, rigorous and direct. PMID:20132896

Clinical presentation of anti-N-methyl-d-aspartate receptor and anti-voltage-gated potassium channel complex antibodies in children: A series of 24 cases.

PubMed

Konuskan, Bahadir; Yildirim, Mirac; Topaloglu, Haluk; Erol, Ilknur; Oztoprak, Ulkuhan; Tan, Huseyin; Gocmen, Rahsan; Anlar, Banu

2018-01-01

The symptomatology and paraclinical findings of antibody-mediated encephalitis, a relatively novel disorder, are still being characterized in adults and children. A high index of suspicion is needed in order to identify these cases among children presenting with various neurological symptoms. The aim of this study is to examine the clinical, demographic and laboratory findings and outcome of children with anti-NMDAR and anti-VGKC encephalitis for any typical or distinctive features. Cases diagnosed with anti-N-Methyl d-aspartate receptor (NMDAR) and anti-voltage gated potassium channel (VGKC) antibody-mediated encephalopathy in four major child neurology centers are described. In four years, 16 children with NMDAR and 8 children with VGKC antibody-associated disease were identified in the participating centers. The most frequent initial manifestation consisted of generalized seizures and cognitive symptoms in both groups. Movement abnormalities were frequent in anti-NMDAR patients and autonomic symptoms, in anti-VGKC patients. Cerebrospinal fluid (CSF) protein, cell count and IgG index were normal in 9/15 anti-NMDAR and 5/8 anti-VGKC patients tested. EEG and MRI findings were usually nonspecific and non-contributory. The rate and time of recovery was not related to age, sex, acute or subacute onset, antibody type, MRI, EEG or CSF results. Treatment within 3 months of onset was associated with normal neurological outcome. Our results suggest anti-NMDAR and VGKC encephalopathies mostly present with non-focal neurological symptoms longer than 3 weeks. In contrast with adult cases, routine CSF testing, MRI and EEG did not contribute to the diagnosis in this series. Copyright © 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.

Utility of MRI versus tumor markers for post-treatment surveillance of marker-positive CNS germ cell tumors.

PubMed

Cheung, Victoria; Segal, Devorah; Gardner, Sharon L; Zagzag, David; Wisoff, Jeffrey H; Allen, Jeffrey C; Karajannis, Matthias A

2016-09-01

Patients with marker-positive central nervous system (CNS) germ cell tumors are typically monitored for tumor recurrence with both tumor markers (AFP and b-hCG) and MRI. We hypothesize that the recurrence of these tumors will always be accompanied by an elevation in tumor markers, and that surveillance MRI may not be necessary. We retrospectively identified 28 patients with CNS germ cell tumors treated at our institution that presented with an elevated serum or cerebrospinal fluid (CSF) tumor marker at the time of diagnosis. We then identified those who had a tumor recurrence after having been in remission and whether each recurrence was detected via MRI changes, elevated tumor markers, or both. Four patients suffered a tumor recurrence. Only one patient had simultaneously elevated tumor markers and MRI evidence of recurrence. Two patients had evidence of recurrence on MRI without corresponding elevations in serum or CSF tumor markers. One patient had abnormal tumor markers with no evidence of recurrence on MRI until 6 months later. We conclude that in patients with marker-positive CNS germ cell tumors who achieve complete remission, continued surveillance imaging in addition to measurement of tumor markers is indicated to detect recurrences.

Urea cycle disorders: brain MRI and neurological outcome.

PubMed

Bireley, William R; Van Hove, Johan L K; Gallagher, Renata C; Fenton, Laura Z

2012-04-01

Urea cycle disorders encompass several enzyme deficiencies that can result in cerebral damage, with a wide clinical spectrum from asymptomatic to severe. The goal of this study was to correlate brain MRI abnormalities in urea cycle disorders with clinical neurological sequelae to evaluate whether MRI abnormalities can assist in guiding difficult treatment decisions. We performed a retrospective chart review of patients with urea cycle disorders and symptomatic hyperammonemia. Brain MRI images were reviewed for abnormalities that correlated with severity of clinical neurological sequelae. Our case series comprises six urea cycle disorder patients, five with ornithine transcarbamylase deficiency and one with citrullinemia type 1. The observed trend in distribution of brain MRI abnormalities as the severity of neurological sequelae increased was the peri-insular region first, extending into the frontal, parietal, temporal and, finally, the occipital lobes. There was thalamic restricted diffusion in three children with prolonged hyperammonemia. Prior to death, this site is typically reported to be spared in urea cycle disorders. The pattern and extent of brain MRI abnormalities correlate with clinical neurological outcome in our case series. This suggests that brain MRI abnormalities may assist in determining prognosis and helping clinicians with subsequent treatment decisions.

Improved explanation of human intelligence using cortical features with second order moments and regression.

PubMed

Park, Hyunjin; Yang, Jin-ju; Seo, Jongbum; Choi, Yu-yong; Lee, Kun-ho; Lee, Jong-min

2014-04-01

Cortical features derived from magnetic resonance imaging (MRI) provide important information to account for human intelligence. Cortical thickness, surface area, sulcal depth, and mean curvature were considered to explain human intelligence. One region of interest (ROI) of a cortical structure consisting of thousands of vertices contained thousands of measurements, and typically, one mean value (first order moment), was used to represent a chosen ROI, which led to a potentially significant loss of information. We proposed a technological improvement to account for human intelligence in which a second moment (variance) in addition to the mean value was adopted to represent a chosen ROI, so that the loss of information would be less severe. Two computed moments for the chosen ROIs were analyzed with partial least squares regression (PLSR). Cortical features for 78 adults were measured and analyzed in conjunction with the full-scale intelligence quotient (FSIQ). Our results showed that 45% of the variance of the FSIQ could be explained using the combination of four cortical features using two moments per chosen ROI. Our results showed improvement over using a mean value for each ROI, which explained 37% of the variance of FSIQ using the same set of cortical measurements. Our results suggest that using additional second order moments is potentially better than using mean values of chosen ROIs for regression analysis to account for human intelligence. Copyright © 2014 Elsevier Ltd. All rights reserved.

MRI appearance of posterior cruciate ligament tears.

PubMed

Rodriguez, William; Vinson, Emily N; Helms, Clyde A; Toth, Alison P

2008-10-01

There is little in the radiology literature regarding the MRI appearance of a torn posterior cruciate ligament (PCL). The purpose of this study was to describe the MRI appearance of surgically proven PCL tears and to emphasize previously unreported signs. The PCL is usually injured as the result of stretching deformation; on MRI, the ligament maintains continuity as a single structure with apparent thickening. On sagittal T2-weighted images, an anteroposterior diameter of 7 mm or more is highly suggestive of a torn PCL. Increased intrasubstance signal intensity in the PCL on proton-density images with lower signal intensity on T2-weighted images is another common feature.

Characteristics of early MRI in children and adolescents with vanishing white matter.

PubMed

van der Lei, Hannemieke D; Steenweg, Marjan E; Barkhof, Frederik; de Grauw, Ton; d'Hooghe, Marc; Morton, Richard; Shah, Siddharth; Wolf, Nicole; van der Knaap, Marjo S

2012-02-01

MRI in vanishing white matter typically shows diffuse abnormality of the cerebral white matter, which becomes increasingly rarefied and cystic. We investigated the MRI characteristics preceding this stage. In a retrospective observational study, we evaluated all available MRIs in our database of DNA-confirmed VWM patients and selected MRIs without diffuse cerebral white matter abnormalities and without signs of rarefaction or cystic degeneration in patients below 20 years of age. A previously established scoring list was used to evaluate the MRIs. An MRI of seven patients fulfilled the criteria. All had confluent and symmetrical abnormalities in the periventricular and bordering deep white matter. In young patients, myelination was delayed. The inner rim of the corpus callosum was affected in all patients. In early stages of VWM, MRI does not necessarily display diffuse cerebral white matter involvement and rarefaction or cystic degeneration. If the MRI abnormalities do not meet the criteria for VWM, it helps to look at the corpus callosum. If the inner rim (the callosal-septal interface) is affected, VWM should be considered. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Big Data Approaches for the Analysis of Large-Scale fMRI Data Using Apache Spark and GPU Processing: A Demonstration on Resting-State fMRI Data from the Human Connectome Project

PubMed Central

Boubela, Roland N.; Kalcher, Klaudius; Huf, Wolfgang; Našel, Christian; Moser, Ewald

2016-01-01

Technologies for scalable analysis of very large datasets have emerged in the domain of internet computing, but are still rarely used in neuroimaging despite the existence of data and research questions in need of efficient computation tools especially in fMRI. In this work, we present software tools for the application of Apache Spark and Graphics Processing Units (GPUs) to neuroimaging datasets, in particular providing distributed file input for 4D NIfTI fMRI datasets in Scala for use in an Apache Spark environment. Examples for using this Big Data platform in graph analysis of fMRI datasets are shown to illustrate how processing pipelines employing it can be developed. With more tools for the convenient integration of neuroimaging file formats and typical processing steps, big data technologies could find wider endorsement in the community, leading to a range of potentially useful applications especially in view of the current collaborative creation of a wealth of large data repositories including thousands of individual fMRI datasets. PMID:26778951

Differentiation of Glioblastoma and Lymphoma Using Feature Extraction and Support Vector Machine.

PubMed

Yang, Zhangjing; Feng, Piaopiao; Wen, Tian; Wan, Minghua; Hong, Xunning

2017-01-01

Differentiation of glioblastoma multiformes (GBMs) and lymphomas using multi-sequence magnetic resonance imaging (MRI) is an important task that is valuable for treatment planning. However, this task is a challenge because GBMs and lymphomas may have a similar appearance in MRI images. This similarity may lead to misclassification and could affect the treatment results. In this paper, we propose a semi-automatic method based on multi-sequence MRI to differentiate these two types of brain tumors. Our method consists of three steps: 1) the key slice is selected from 3D MRIs and region of interests (ROIs) are drawn around the tumor region; 2) different features are extracted based on prior clinical knowledge and validated using a t-test; and 3) features that are helpful for classification are used to build an original feature vector and a support vector machine is applied to perform classification. In total, 58 GBM cases and 37 lymphoma cases are used to validate our method. A leave-one-out crossvalidation strategy is adopted in our experiments. The global accuracy of our method was determined as 96.84%, which indicates that our method is effective for the differentiation of GBM and lymphoma and can be applied in clinical diagnosis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Differential diagnosis of ventriculomegaly and brainstem kinking on fetal MRI.

PubMed

Amir, Tali; Poretti, Andrea; Boltshauser, Eugen; Huisman, Thierry A G M

2016-01-01

Fetal ventriculomegaly is a common and frequently leading neuroimaging finding in complex brain malformations. Here we report on pre- and postnatal neuroimaging findings in three fetuses with prenatal ventriculomegaly and brainstem kinking. We aim to identify key neuroimaging features that may allow the prenatal differentiation between diseases associated with fetal ventriculomegaly and brainstem kinking. All pre- and postnatal magnetic resonance imaging (MRI) data were qualitatively evaluated for infra- and supratentorial abnormalities. Data about clinical features and genetic findings were collected from clinical histories. In all three patients, fetal MRI showed ventriculomegaly and brainstem kinking. In two patients, postnatal MRI also showed supratentorial migration abnormalities and eye abnormalities were found. In these children, the diagnosis of α-dystroglycanopathy was genetically confirmed. In the third patient, basal ganglia had an abnormal shape on MRI suggesting a tubulinopathy. The differential diagnosis of prenatal ventriculomegaly and brainstem kinking includes α-dystroglycanopathies, X-linked hydrocephalus due to mutations in L1CAM, and tubulinopathies. The prenatal differentiation between these diseases may be difficult. The presence of ocular abnormalities on prenatal neuroimaging may favor α-dystroglycanopathies, while dysplastic basal ganglia may suggest a tubulinopathy. However, in some patients the final differentiation between these diseases is possible only postnatally. Copyright © 2015 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Nonlesional atypical mesial temporal epilepsy

PubMed Central

Alexopoulos, Andreas V.; Busch, Robyn M.; Wehner, Tim; Nair, Dileep; Bingaman, William E.; Najm, Imad M.

2013-01-01

Objective: Misleading manifestations of common epilepsy syndromes might account for some epilepsy surgery failures, thus we sought to characterize patients with difficult to diagnose (atypical) mesial temporal lobe epilepsy (mTLE). Methods: We retrospectively reviewed our surgical database over 12 years to identify patients who underwent a standard anterior temporal lobectomy after undergoing intracranial EEG (ICEEG) evaluation with a combination of depth and subdural electrodes. We carefully studied electroclinical manifestations, neuroimaging data, neuropsychological findings, and indications for ICEEG. Results: Of 835 patients who underwent anterior temporal lobectomy, 55 were investigated with ICEEG. Ten of these had atypical mTLE features and were not considered to have mTLE preoperatively. All of them had Engel class I outcome for 3 to 7 years (median 3.85). Five reported uncommon auras, and 3 had no auras. Scalp-EEG and nuclear imaging studies failed to provide adequate localization. None had MRI evidence of hippocampal sclerosis. However, ICEEG demonstrated exclusive mesial temporal seizure onset in all patients. Clues suggesting the possibility of mTLE were typical auras when present, anterior temporal epileptiform discharges or ictal patterns, small hippocampi, asymmetrical or ipsilateral temporal hypometabolism on PET, anterior temporal hyperperfusion on ictal SPECT, and asymmetry of memory scores. Histopathology revealed hippocampal sclerosis in 6 patients and gliosis in 2. Conclusions: Atypical electroclinical presentation may be deceptive in some patients with mTLE. We emphasize the importance of searching for typical mTLE features to guide ICEEG study of mesial temporal structures in such patients, who may otherwise mistakenly undergo extramesial temporal resections or be denied surgery. PMID:24174582

Compressed Sensing for fMRI: Feasibility Study on the Acceleration of Non-EPI fMRI at 9.4T

PubMed Central

Kim, Seong-Gi; Ye, Jong Chul

2015-01-01

Conventional functional magnetic resonance imaging (fMRI) technique known as gradient-recalled echo (GRE) echo-planar imaging (EPI) is sensitive to image distortion and degradation caused by local magnetic field inhomogeneity at high magnetic fields. Non-EPI sequences such as spoiled gradient echo and balanced steady-state free precession (bSSFP) have been proposed as an alternative high-resolution fMRI technique; however, the temporal resolution of these sequences is lower than the typically used GRE-EPI fMRI. One potential approach to improve the temporal resolution is to use compressed sensing (CS). In this study, we tested the feasibility of k-t FOCUSS—one of the high performance CS algorithms for dynamic MRI—for non-EPI fMRI at 9.4T using the model of rat somatosensory stimulation. To optimize the performance of CS reconstruction, different sampling patterns and k-t FOCUSS variations were investigated. Experimental results show that an optimized k-t FOCUSS algorithm with acceleration by a factor of 4 works well for non-EPI fMRI at high field under various statistical criteria, which confirms that a combination of CS and a non-EPI sequence may be a good solution for high-resolution fMRI at high fields. PMID:26413503

PSP-CBS with Dopamine Deficiency in a Female with a FMR1 Premutation.

PubMed

Paucar, Martin; Beniaminov, Stanislav; Paslawski, Wojciech; Svenningsson, Per

2016-10-01

Premutations in the fragile X mental retardation 1 (FMR1) gene cause fragile X-associated tremor/ataxia syndrome (FXTAS) and FMR1-related primary ovarian insufficiency (POI). Female FMR1 premutation carriers rarely develop motor features. Dual pathology is an emerging phenomenon among FMR1 premutation carriers. Here, we describe a family affected by FMR1-related disorders in which the female index case has developed a rapidly progressive and disabling syndrome of atypical parkinsonism. This syndrome consists of early onset postural instability, echolalia, dystonia, and varying types of apraxia like early onset orobuccal apraxia and oculomotor apraxia. She has also developed supranuclear gaze palsy, increased latency of saccade initiation, and slow saccades. These features are compatible with progressive supranuclear palsy (PSP) of a corticobasal syndrome (CBS) variant. Imaging displays a marked reduction of presynaptic dopaminergic uptake and cerebrospinal fluid analysis showed reduced dopamine metabolism; however, the patient is unresponsive to levodopa. Midbrain atrophy ("hummingbird sign") and mild cerebellar atrophy were found on brain MRI. Her father was affected by a typical FXTAS presentation but also displayed dopamine deficiency along with the hummingbird sign. The mechanisms by which FMR1 premutations predispose to atypical parkinsonism and dopamine deficiency await further elucidation.

Surface-Constrained Volumetric Brain Registration Using Harmonic Mappings

PubMed Central

Joshi, Anand A.; Shattuck, David W.; Thompson, Paul M.; Leahy, Richard M.

2015-01-01

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRI). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the inter-subject alignment of expert-labeled sub-cortical structures after registration. PMID:18092736

Comparative Study of SVM Methods Combined with Voxel Selection for Object Category Classification on fMRI Data

PubMed Central

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-01-01

Background Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Methodology/Principal Findings Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. Conclusions/Significance The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice. PMID:21359184

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

PubMed

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

PubMed

Yang, Xiaofeng; Wu, Ning; Cheng, Guanghui; Zhou, Zhengyang; Yu, David S; Beitler, Jonathan J; Curran, Walter J; Liu, Tian

2014-12-01

To develop an automated magnetic resonance imaging (MRI) parotid segmentation method to monitor radiation-induced parotid gland changes in patients after head and neck radiation therapy (RT). The proposed method combines the atlas registration method, which captures the global variation of anatomy, with a machine learning technology, which captures the local statistical features, to automatically segment the parotid glands from the MRIs. The segmentation method consists of 3 major steps. First, an atlas (pre-RT MRI and manually contoured parotid gland mask) is built for each patient. A hybrid deformable image registration is used to map the pre-RT MRI to the post-RT MRI, and the transformation is applied to the pre-RT parotid volume. Second, the kernel support vector machine (SVM) is trained with the subject-specific atlas pair consisting of multiple features (intensity, gradient, and others) from the aligned pre-RT MRI and the transformed parotid volume. Third, the well-trained kernel SVM is used to differentiate the parotid from surrounding tissues in the post-RT MRIs by statistically matching multiple texture features. A longitudinal study of 15 patients undergoing head and neck RT was conducted: baseline MRI was acquired prior to RT, and the post-RT MRIs were acquired at 3-, 6-, and 12-month follow-up examinations. The resulting segmentations were compared with the physicians' manual contours. Successful parotid segmentation was achieved for all 15 patients (42 post-RT MRIs). The average percentage of volume differences between the automated segmentations and those of the physicians' manual contours were 7.98% for the left parotid and 8.12% for the right parotid. The average volume overlap was 91.1% ± 1.6% for the left parotid and 90.5% ± 2.4% for the right parotid. The parotid gland volume reduction at follow-up was 25% at 3 months, 27% at 6 months, and 16% at 12 months. We have validated our automated parotid segmentation algorithm in a longitudinal study. This segmentation method may be useful in future studies to address radiation-induced xerostomia in head and neck radiation therapy. Copyright © 2014 Elsevier Inc. All rights reserved.

Data-driven mapping of hypoxia-related tumor heterogeneity using DCE-MRI and OE-MRI.

PubMed

Featherstone, Adam K; O'Connor, James P B; Little, Ross A; Watson, Yvonne; Cheung, Sue; Babur, Muhammad; Williams, Kaye J; Matthews, Julian C; Parker, Geoff J M

2018-04-01

Previous work has shown that combining dynamic contrast-enhanced (DCE)-MRI and oxygen-enhanced (OE)-MRI binary enhancement maps can identify tumor hypoxia. The current work proposes a novel, data-driven method for mapping tissue oxygenation and perfusion heterogeneity, based on clustering DCE/OE-MRI data. DCE-MRI and OE-MRI were performed on nine U87 (glioblastoma) and seven Calu6 (non-small cell lung cancer) murine xenograft tumors. Area under the curve and principal component analysis features were calculated and clustered separately using Gaussian mixture modelling. Evaluation metrics were calculated to determine the optimum feature set and cluster number. Outputs were quantitatively compared with a previous non data-driven approach. The optimum method located six robustly identifiable clusters in the data, yielding tumor region maps with spatially contiguous regions in a rim-core structure, suggesting a biological basis. Mean within-cluster enhancement curves showed physiologically distinct, intuitive kinetics of enhancement. Regions of DCE/OE-MRI enhancement mismatch were located, and voxel categorization agreed well with the previous non data-driven approach (Cohen's kappa = 0.61, proportional agreement = 0.75). The proposed method locates similar regions to the previous published method of binarization of DCE/OE-MRI enhancement, but renders a finer segmentation of intra-tumoral oxygenation and perfusion. This could aid in understanding the tumor microenvironment and its heterogeneity. Magn Reson Med 79:2236-2245, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

Heterogeneity wavelet kinetics from DCE-MRI for classifying gene expression based breast cancer recurrence risk.

PubMed

Mahrooghy, Majid; Ashraf, Ahmed B; Daye, Dania; Mies, Carolyn; Feldman, Michael; Rosen, Mark; Kontos, Despina

2013-01-01

Breast tumors are heterogeneous lesions. Intra-tumor heterogeneity presents a major challenge for cancer diagnosis and treatment. Few studies have worked on capturing tumor heterogeneity from imaging. Most studies to date consider aggregate measures for tumor characterization. In this work we capture tumor heterogeneity by partitioning tumor pixels into subregions and extracting heterogeneity wavelet kinetic (HetWave) features from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to obtain the spatiotemporal patterns of the wavelet coefficients and contrast agent uptake from each partition. Using a genetic algorithm for feature selection, and a logistic regression classifier with leave one-out cross validation, we tested our proposed HetWave features for the task of classifying breast cancer recurrence risk. The classifier based on our features gave an ROC AUC of 0.78, outperforming previously proposed kinetic, texture, and spatial enhancement variance features which give AUCs of 0.69, 0.64, and 0.65, respectively.

Multidimensional Compressed Sensing MRI Using Tensor Decomposition-Based Sparsifying Transform

PubMed Central

Yu, Yeyang; Jin, Jin; Liu, Feng; Crozier, Stuart

2014-01-01

Compressed Sensing (CS) has been applied in dynamic Magnetic Resonance Imaging (MRI) to accelerate the data acquisition without noticeably degrading the spatial-temporal resolution. A suitable sparsity basis is one of the key components to successful CS applications. Conventionally, a multidimensional dataset in dynamic MRI is treated as a series of two-dimensional matrices, and then various matrix/vector transforms are used to explore the image sparsity. Traditional methods typically sparsify the spatial and temporal information independently. In this work, we propose a novel concept of tensor sparsity for the application of CS in dynamic MRI, and present the Higher-order Singular Value Decomposition (HOSVD) as a practical example. Applications presented in the three- and four-dimensional MRI data demonstrate that HOSVD simultaneously exploited the correlations within spatial and temporal dimensions. Validations based on cardiac datasets indicate that the proposed method achieved comparable reconstruction accuracy with the low-rank matrix recovery methods and, outperformed the conventional sparse recovery methods. PMID:24901331

New Findings, Classification and Long-Term Follow-Up Study Based on MRI Characterization of Brainstem Encephalitis Induced by Enterovirus 71

PubMed Central

Wen, Feiqiu; Huang, Wenxian; Gan, Yungen; Zeng, Weibin; Chen, Ranran; He, Yanxia; Wang, Yonker; Liu, Zaiyi; Liang, Changhong; Wong, Kelvin K. L.

2016-01-01

Background To report the diversity of MRI features of brainstem encephalitis (BE) induced by Enterovirus 71. This is supported by implementation and testing of our new classification scheme in order to improve the diagnostic level on this specific disease. Methods Neuroimaging of 91 pediatric patients who got EV71 related BE were hospitalized between March, 2010 to October, 2012, were analyzed retrospectively. All patients underwent pre- and post-contrast MRI scan. Thereafter, 31 patients were randomly called back for follow-up MRI study during December 2013 to August 2014. The MRI signal patterns of BE primary lesion were analyzed and classified according to MR signal alteration at various disease stages. Findings in fatal and non-fatal cases were compared, and according to the MRI scan time point during the course of this disease, the patients’ conditions were classified as 1) acute stage, 2) convalescence stage, 3) post mortem stage, and 4) long term follow-up study. Results 103 patients were identified. 11 patients did not undergo MRI, as they died within 48 hours. One patient died on 14th day without MR imaging. 2 patients had postmortem MRI. Medical records and imaging were reviewed in the 91 patients, aged 4 months to 12 years, and two cadavers who have had MRI scan. At acute stage: the most frequent pattern (40 patients) was foci of prolonged T1 and T2 signal, with (15) or without (25) contrast enhancement. We observed a novel pattern in 4 patients having foci of low signal intensity on T2WI, with contrast enhancement. Another pattern in 10 patients having foci of contrast enhancement without abnormalities in T1WI or T2WI weighted images. Based on 2 cases, the entire medulla and pons had prolonged T1 and T2 signal, and 2 of our postmortem cases demonstrated the same pattern. At convalescence stage, the pattern observed in 4 patients was foci of prolonged T1 and T2 signal without contrast enhancement. Follow-up MR study of 31 cases showed normal in 26 cases, and demonstrated foci of prolonged T1 and T2 signal with hyper-intensity on FLAIR in 3 cases, or of prolonged T1 and T2 signal with hypo-intensity on FLAIR in 2 cases. Most importantly, MR findings of each case were thoroughly investigated and classified according to phases and MRI signal alteration. Conclusions This study has provided enhanced and useful information for the MRI features of BE induced by EV71, apart from common practice established by previous reports. In addition, a classification scheme that summarizes all types of features based on the MRI signal at the four different stages of the disease would be helpful to improve the diagnostic level. PMID:27798639

New Findings, Classification and Long-Term Follow-Up Study Based on MRI Characterization of Brainstem Encephalitis Induced by Enterovirus 71.

PubMed

Zeng, Hongwu; Wen, Feiqiu; Huang, Wenxian; Gan, Yungen; Zeng, Weibin; Chen, Ranran; He, Yanxia; Wang, Yonker; Liu, Zaiyi; Liang, Changhong; Wong, Kelvin K L

2016-01-01

To report the diversity of MRI features of brainstem encephalitis (BE) induced by Enterovirus 71. This is supported by implementation and testing of our new classification scheme in order to improve the diagnostic level on this specific disease. Neuroimaging of 91 pediatric patients who got EV71 related BE were hospitalized between March, 2010 to October, 2012, were analyzed retrospectively. All patients underwent pre- and post-contrast MRI scan. Thereafter, 31 patients were randomly called back for follow-up MRI study during December 2013 to August 2014. The MRI signal patterns of BE primary lesion were analyzed and classified according to MR signal alteration at various disease stages. Findings in fatal and non-fatal cases were compared, and according to the MRI scan time point during the course of this disease, the patients' conditions were classified as 1) acute stage, 2) convalescence stage, 3) post mortem stage, and 4) long term follow-up study. 103 patients were identified. 11 patients did not undergo MRI, as they died within 48 hours. One patient died on 14th day without MR imaging. 2 patients had postmortem MRI. Medical records and imaging were reviewed in the 91 patients, aged 4 months to 12 years, and two cadavers who have had MRI scan. At acute stage: the most frequent pattern (40 patients) was foci of prolonged T1 and T2 signal, with (15) or without (25) contrast enhancement. We observed a novel pattern in 4 patients having foci of low signal intensity on T2WI, with contrast enhancement. Another pattern in 10 patients having foci of contrast enhancement without abnormalities in T1WI or T2WI weighted images. Based on 2 cases, the entire medulla and pons had prolonged T1 and T2 signal, and 2 of our postmortem cases demonstrated the same pattern. At convalescence stage, the pattern observed in 4 patients was foci of prolonged T1 and T2 signal without contrast enhancement. Follow-up MR study of 31 cases showed normal in 26 cases, and demonstrated foci of prolonged T1 and T2 signal with hyper-intensity on FLAIR in 3 cases, or of prolonged T1 and T2 signal with hypo-intensity on FLAIR in 2 cases. Most importantly, MR findings of each case were thoroughly investigated and classified according to phases and MRI signal alteration. This study has provided enhanced and useful information for the MRI features of BE induced by EV71, apart from common practice established by previous reports. In addition, a classification scheme that summarizes all types of features based on the MRI signal at the four different stages of the disease would be helpful to improve the diagnostic level.

Isolated rhomboencephalosynapsis - a rare cerebellar anomaly.

PubMed

Paprocka, Justyna; Jamroz, Ewa; Scieszka, Ewa; Kluczewska, Ewa

2012-01-01

Rhomboencephalosynapsis (RES, RS) is a unique entity usually recognized in infancy based on neuroimaging. Cerebellar fusion and absence of cerebellar vermis is often associated with supratentorial findings. Since now there are about 50 cases described worldwide, with approximately 36 patients diagnosed by MRI. The authors present the first in Poland case of this uncommon malformation and review the literature. The authors describe a 28-month-old-girl with microcephaly and proper psychomotor development. The family history was unrelevant. Based on MRI the congenital malformation of posterior fossa-rhombencephalosynapsis was confirmed Presented patient is a typical example of MRI usefulness especially in patients with RES. RES symptoms are mild and that is why the diagnosis is usually made only in adulthood.

Cross-sectional anatomy, computed tomography and magnetic resonance imaging of the head of common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba).

PubMed

Alonso-Farré, J M; Gonzalo-Orden, M; Barreiro-Vázquez, J D; Barreiro-Lois, A; André, M; Morell, M; Llarena-Reino, M; Monreal-Pawlowsky, T; Degollada, E

2015-02-01

Computed tomography (CT) and low-field magnetic resonance imaging (MRI) were used to scan seven by-caught dolphin cadavers, belonging to two species: four common dolphins (Delphinus delphis) and three striped dolphins (Stenella coeruleoalba). CT and MRI were obtained with the animals in ventral recumbency. After the imaging procedures, six dolphins were frozen at -20°C and sliced in the same position they were examined. Not only CT and MRI scans, but also cross sections of the heads were obtained in three body planes: transverse (slices of 1 cm thickness) in three dolphins, sagittal (5 cm thickness) in two dolphins and dorsal (5 cm thickness) in two dolphins. Relevant anatomical structures were identified and labelled on each cross section, obtaining a comprehensive bi-dimensional topographical anatomy guide of the main features of the common and the striped dolphin head. Furthermore, the anatomical cross sections were compared with their corresponding CT and MRI images, allowing an imaging identification of most of the anatomical features. CT scans produced an excellent definition of the bony and air-filled structures, while MRI allowed us to successfully identify most of the soft tissue structures in the dolphin's head. This paper provides a detailed anatomical description of the head structures of common and striped dolphins and compares anatomical cross sections with CT and MRI scans, becoming a reference guide for the interpretation of imaging studies. © 2014 Blackwell Verlag GmbH.

Co-registration of In-Vivo Human MRI Brain Images to Postmortem Histological Microscopic Images

PubMed Central

Singh, M.; Rajagopalan, A.; Kim, T.-S.; Hwang, D.; Chui, H.; Zhang, X.-L.; Lee, A.-Y.; Zarow, C.

2009-01-01

Certain features such as small vascular lesions seen in human MRI are detected reliably only in postmortem histological samples by microscopic imaging. Co-registration of these microscopically detected features to their corresponding locations in the in-vivo images would be of great benefit to understanding the MRI signatures of specific diseases. Using non-linear Polynomial transformation, we report a method to co-register in-vivo MRIs to microscopic images of histological samples drawn off the postmortem brain. The approach utilizes digital photographs of postmortem slices as an intermediate reference to co-register the MRIs to microscopy. The overall procedure is challenging due to gross structural deformations in the postmortem brain during extraction and subsequent distortions in the histological preparations. Hemispheres of the brain were co-registered separately to mitigate these effects. Approaches relying on matching single-slices, multiple-slices and entire volumes in conjunction with different similarity measures suggested that using four slices at a time in combination with two sequential measures, Pearson correlation coefficient followed by mutual information, produced the best MRI-postmortem co-registration according to a voxel mismatch count. The accuracy of the overall registration was evaluated by measuring the 3D Euclidean distance between the locations of microscopically identified lesions on postmortem slices and their MRI-postmortem co-registered locations. The results show a mean 3D displacement of 5.1 ± 2.0 mm between the in-vivo MRI and microscopically determined locations for 21 vascular lesions in 11 subjects. PMID:19169415

Predicting clinical decline in progressive agrammatic aphasia and apraxia of speech.

PubMed

Whitwell, Jennifer L; Weigand, Stephen D; Duffy, Joseph R; Clark, Heather M; Strand, Edythe A; Machulda, Mary M; Spychalla, Anthony J; Senjem, Matthew L; Jack, Clifford R; Josephs, Keith A

2017-11-28

To determine whether baseline clinical and MRI features predict rate of clinical decline in patients with progressive apraxia of speech (AOS). Thirty-four patients with progressive AOS, with AOS either in isolation or in the presence of agrammatic aphasia, were followed up longitudinally for up to 4 visits, with clinical testing and MRI at each visit. Linear mixed-effects regression models including all visits (n = 94) were used to assess baseline clinical and MRI variables that predict rate of worsening of aphasia, motor speech, parkinsonism, and behavior. Clinical predictors included baseline severity and AOS type. MRI predictors included baseline frontal, premotor, motor, and striatal gray matter volumes. More severe parkinsonism at baseline was associated with faster rate of decline in parkinsonism. Patients with predominant sound distortions (AOS type 1) showed faster rates of decline in aphasia and motor speech, while patients with segmented speech (AOS type 2) showed faster rates of decline in parkinsonism. On MRI, we observed trends for fastest rates of decline in aphasia in patients with relatively small left, but preserved right, Broca area and precentral cortex. Bilateral reductions in lateral premotor cortex were associated with faster rates of decline of behavior. No associations were observed between volumes and decline in motor speech or parkinsonism. Rate of decline of each of the 4 clinical features assessed was associated with different baseline clinical and regional MRI predictors. Our findings could help improve prognostic estimates for these patients. © 2017 American Academy of Neurology.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI

NASA Astrophysics Data System (ADS)

Pei, Linmin; Reza, Syed M. S.; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M.

2017-03-01

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. To model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

Improved brain tumor segmentation by utilizing tumor growth model in longitudinal brain MRI.

PubMed

Pei, Linmin; Reza, Syed M S; Li, Wei; Davatzikos, Christos; Iftekharuddin, Khan M

2017-02-11

In this work, we propose a novel method to improve texture based tumor segmentation by fusing cell density patterns that are generated from tumor growth modeling. In order to model tumor growth, we solve the reaction-diffusion equation by using Lattice-Boltzmann method (LBM). Computational tumor growth modeling obtains the cell density distribution that potentially indicates the predicted tissue locations in the brain over time. The density patterns is then considered as novel features along with other texture (such as fractal, and multifractal Brownian motion (mBm)), and intensity features in MRI for improved brain tumor segmentation. We evaluate the proposed method with about one hundred longitudinal MRI scans from five patients obtained from public BRATS 2015 data set, validated by the ground truth. The result shows significant improvement of complete tumor segmentation using ANOVA analysis for five patients in longitudinal MR images.

The MRI features of placental adhesion disorder—a pictorial review

PubMed Central

Teixidor Vinas, Mireia; Whitby, Elspeth

2016-01-01

Placental adhesion disorder (PAD) comprises placenta accreta, increta and percreta lesions; these are classified according to the depth of uterine invasion. Although PAD is considered a rare condition, its incidence has increased 10-fold in the last 50 years. Ultrasound is the primary imaging modality for the assessment of the placenta and in the majority of cases, it is sufficient for diagnosis; however, when ultrasound findings are suspicious or inconclusive, MRI is recommended as an adjunct imaging technique. Numerous MRI features of PAD have been described, including dark intraplacental bands, disorganized intraplacental vascularity and abnormal uterine bulging. This pictorial review describes and illustrates these characteristics and discusses their implications in planning delivery. In addition, we present a series of “pitfall” cases to aid the interpreting radiologist and discuss management of PAD. PAD is a clinical and diagnostic challenge that is encountered with increasing frequency, requiring a cohesive multidisciplinary approach to its management. PMID:27355318

Designing Image Operators for MRI-PET Image Fusion of the Brain

NASA Astrophysics Data System (ADS)

Márquez, Jorge; Gastélum, Alfonso; Padilla, Miguel A.

2006-09-01

Our goal is to obtain images combining in a useful and precise way the information from 3D volumes of medical imaging sets. We address two modalities combining anatomy (Magnetic Resonance Imaging or MRI) and functional information (Positron Emission Tomography or PET). Commercial imaging software offers image fusion tools based on fixed blending or color-channel combination of two modalities, and color Look-Up Tables (LUTs), without considering the anatomical and functional character of the image features. We used a sensible approach for image fusion taking advantage mainly from the HSL (Hue, Saturation and Luminosity) color space, in order to enhance the fusion results. We further tested operators for gradient and contour extraction to enhance anatomical details, plus other spatial-domain filters for functional features corresponding to wide point-spread-function responses in PET images. A set of image-fusion operators was formulated and tested on PET and MRI acquisitions.

Complementary aspects of diffusion imaging and fMRI; I: structure and function.

PubMed

Mulkern, Robert V; Davis, Peter E; Haker, Steven J; Estepar, Raul San Jose; Panych, Lawrence P; Maier, Stephan E; Rivkin, Michael J

2006-05-01

Studying the intersection of brain structure and function is an important aspect of modern neuroscience. The development of magnetic resonance imaging (MRI) over the last 25 years has provided new and powerful tools for the study of brain structure and function. Two tools in particular, diffusion imaging and functional MRI (fMRI), are playing increasingly important roles in elucidating the complementary aspects of brain structure and function. In this work, we review basic technical features of diffusion imaging and fMRI for studying the integrity of white matter structural components and for determining the location and extent of cortical activation in gray matter, respectively. We then review a growing body of literature in which the complementary aspects of diffusion imaging and fMRI, applied as separate examinations but analyzed in tandem, have been exploited to enhance our knowledge of brain structure and function.

Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics.

PubMed

Bahrami, Naeim; Hartman, Stephen J; Chang, Yu-Hsuan; Delfanti, Rachel; White, Nathan S; Karunamuni, Roshan; Seibert, Tyler M; Dale, Anders M; Hattangadi-Gluth, Jona A; Piccioni, David; Farid, Nikdokht; McDonald, Carrie R

2018-06-02

Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging. Sixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes. Patients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status. Quantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.

Magnetic resonance imaging features of Great Danes with and without clinical signs of cervical spondylomyelopathy

PubMed Central

Martin-Vaquero, Paula; da Costa, Ronaldo C.

2014-01-01

Objective To characterize and compare the MRI morphological features of the cervical vertebral column of Great Danes with and without clinical signs of cervical spondylomyelopathy (CSM). Design Prospective cohort study. Animals 30 Great Danes (15 clinically normal and 15 CSM-affected). Procedures All dogs underwent MRI of the cervical vertebral column (C2–3 through T1–2). Features evaluated included sites of subarachnoid space compression, spinal cord compression, or both; degree, cause, and direction of compression; MRI signal changes of the spinal cord; articular process (facet) joint characteristics; internal vertebral venous plexus visibility; and presence of extradural synovial cysts as well as presence and degree of intervertebral disk degeneration and foraminal stenosis. Results Clinically normal and CSM-affected dogs had 11 and 61 compressive sites, respectively, detected with MRI. All CSM-affected dogs had ≥ 1 site of spinal cord compression. No signal changes were observed in spinal cords of normal dogs, whereas 14 sites of hyperintensity were found in 9 CSM-affected dogs. Foraminal stenosis was present in 11 clinically normal and all CSM-affected dogs. The number of stenotic foraminal sites was significantly greater in the CSM-affected group, and severe stenosis appeared to be more common in this group than in the clinically normal group. Significant differences were identified between clinically normal and CSM-affected dogs with regard to amount of synovial fluid evident, regularity of articular surfaces, degree of articular process joint proliferation, and internal vertebral venous plexus visibility. Conclusions and Clinical Relevance Abnormalities were detected with MRI in several clinically normal Great Danes. Severe spinal cord compression, number of stenotic foraminal sites, and signal changes within the spinal cord distinguished CSM-affected from clinically normal Great Danes. PMID:25075822

Magnetic resonance imaging features of Great Danes with and without clinical signs of cervical spondylomyelopathy.

PubMed

Martin-Vaquero, Paula; da Costa, Ronaldo C

2014-08-15

To characterize and compare the MRI morphological features of the cervical vertebral column of Great Danes with and without clinical signs of cervical spondylomyelopathy (CSM). Prospective cohort study. 30 Great Danes (15 clinically normal and 15 CSM-affected). All dogs underwent MRI of the cervical vertebral column (C2-3 through T1-2). Features evaluated included sites of subarachnoid space compression, spinal cord compression, or both; degree, cause, and direction of compression; MRI signal changes of the spinal cord; articular process (facet) joint characteristics; internal vertebral venous plexus visibility; and presence of extradural synovial cysts as well as presence and degree of intervertebral disk degeneration and foraminal stenosis. Clinically normal and CSM-affected dogs had 11 and 61 compressive sites, respectively, detected with MRI. All CSM-affected dogs had ≥ 1 site of spinal cord compression. No signal changes were observed in spinal cords of normal dogs, whereas 14 sites of hyperintensity were found in 9 CSM-affected dogs. Foraminal stenosis was present in 11 clinically normal and all CSM-affected dogs. The number of stenotic foraminal sites was significantly greater in the CSM-affected group, and severe stenosis appeared to be more common in this group than in the clinically normal group. Significant differences were identified between clinically normal and CSM-affected dogs with regard to amount of synovial fluid evident, regularity of articular surfaces, degree of articular process joint proliferation, and internal vertebral venous plexus visibility. Abnormalities were detected with MRI in several clinically normal Great Danes. Severe spinal cord compression, number of stenotic foraminal sites, and signal changes within the spinal cord distinguished CSM-affected from clinically normal Great Danes.

Methodologies for semiquantitative evaluation of hip osteoarthritis by magnetic resonance imaging: approaches based on the whole organ and focused on active lesions.

PubMed

Jaremko, Jacob L; Lambert, Robert G W; Zubler, Veronika; Weber, Ulrich; Loeuille, Damien; Roemer, Frank W; Cibere, Jolanda; Pianta, Marcus; Gracey, David; Conaghan, Philip; Ostergaard, Mikkel; Maksymowych, Walter P

2014-02-01

As a wider variety of therapeutic options for osteoarthritis (OA) becomes available, there is an increasing need to objectively evaluate disease severity on magnetic resonance imaging (MRI). This is more technically challenging at the hip than at the knee, and as a result, few systematic scoring systems exist. The OMERACT (Outcome Measures in Rheumatology) filter of truth, discrimination, and feasibility can be used to validate image-based scoring systems. Our objective was (1) to review the imaging features relevant to the assessment of severity and progression of hip OA; and (2) to review currently used methods to grade these features in existing hip OA scoring systems. A systematic literature review was conducted. MEDLINE keyword search was performed for features of arthropathy (such as hip + bone marrow edema or lesion, synovitis, cyst, effusion, cartilage, etc.) and scoring system (hip + OA + MRI + score or grade), with a secondary manual search for additional references in the retrieved publications. Findings relevant to the severity of hip OA include imaging markers associated with inflammation (bone marrow lesion, synovitis, effusion), structural damage (cartilage loss, osteophytes, subchondral cysts, labral tears), and predisposing geometric factors (hip dysplasia, femoral-acetabular impingement). Two approaches to the semiquantitative assessment of hip OA are represented by Hip OA MRI Scoring System (HOAMS), a comprehensive whole organ assessment of nearly all findings, and the Hip Inflammation MRI Scoring System (HIMRISS), which selectively scores only active lesions (bone marrow lesion, synovitis/effusion). Validation is presently confined to limited assessment of reliability. Two methods for semiquantitative assessment of hip OA on MRI have been described and validation according to the OMERACT Filter is limited to evaluation of reliability.

Which patellofemoral joint imaging features are associated with patellofemoral pain? Systematic review and meta-analysis.

PubMed

Drew, B T; Redmond, A C; Smith, T O; Penny, F; Conaghan, P G

2016-02-01

To review the association between patellofemoral joint (PFJ) imaging features and patellofemoral pain (PFP). A systematic review of the literature from AMED, CiNAHL, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, PEDro, EMBASE and SPORTDiscus was undertaken from their inception to September 2014. Studies were eligible if they used magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US) or X-ray (XR) to compare PFJ features between a PFP group and an asymptomatic control group in people <45 years of age. A pooled meta-analysis was conducted and data was interpreted using a best evidence synthesis. Forty studies (all moderate to high quality) describing 1043 people with PFP and 839 controls were included. Two features were deemed to have a large standardised mean difference (SMD) based on meta-analysis: an increased MRI bisect offset at 0° knee flexion under load (0.99; 95% CI: 0.49, 1.49) and an increased CT congruence angle at 15° knee flexion, both under load (1.40 95% CI: 0.04, 2.76) and without load (1.24; 95% CI: 0.37, 2.12). A medium SMD was identified for MRI patella tilt and patellofemoral contact area. Limited evidence was found to support the association of other imaging features with PFP. A sensitivity analysis showed an increase in the SMD for patella bisect offset at 0° knee flexion (1.91; 95% CI: 1.31, 2.52) and patella tilt at 0° knee flexion (0.99; 95% CI: 0.47, 1.52) under full weight bearing. Certain PFJ imaging features were associated with PFP. Future interventional strategies may be targeted at these features. CRD 42014009503. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

4D tumor centroid tracking using orthogonal 2D dynamic MRI: Implications for radiotherapy planning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tryggestad, Erik; Flammang, Aaron; Shea, Steven M.

2013-09-15

Purpose: Current pretreatment, 4D imaging techniques are suboptimal in that they sample breathing motion over a very limited “snapshot” in time. Heretofore, long-duration, 4D motion characterization for radiotherapy planning, margin optimization, and validation have been impractical for safety reasons, requiring invasive markers imaged under x-ray fluoroscopy. To characterize 3D tumor motion and associated variability over durations more consistent with treatments, the authors have developed a practical dynamic MRI (dMRI) technique employing two orthogonal planes acquired in a continuous, interleaved fashion.Methods: 2D balanced steady-state free precession MRI was acquired continuously over 9–14 min at approximately 4 Hz in three healthy volunteersmore » using a commercial 1.5 T system; alternating orthogonal imaging planes (sagittal, coronal, sagittal, etc.) were employed. The 2D in-plane pixel resolution was 2 × 2 mm{sup 2} with a 5 mm slice profile. Simultaneous with image acquisition, the authors monitored a 1D surrogate respiratory signal using a device available with the MRI system. 2D template matching-based anatomic feature registration, or tracking, was performed independently in each orientation. 4D feature tracking at the raw frame rate was derived using spline interpolation.Results: Tracking vascular features in the lung for two volunteers and pancreatic features in one volunteer, the authors have successfully demonstrated this method. Registration error, defined here as the difference between the sagittal and coronal tracking result in the SI direction, ranged from 0.7 to 1.6 mm (1σ) which was less than the acquired image resolution. Although the healthy volunteers were instructed to relax and breathe normally, significantly variable respiration was observed. To demonstrate potential applications of this technique, the authors subsequently explored the intrafraction stability of hypothetical tumoral internal target volumes and 3D spatial probability distribution functions. The surrogate respiratory information allowed the authors to show how this technique can be used to study correlations between internal and external (surrogate) information over these prolonged durations. However, compared against the gold standard of the time stamps in the dMRI frames, the temporal synchronization of the surrogate 1D respiratory information was shown to be likely unreliable.Conclusions: The authors have established viability of a novel and practical pretreatment, 4D tumor centroid tracking method employing a commercially available dynamic MRI sequence. Further developments from the vendor are likely needed to provide a reliably synchronized surrogate 1D respiratory signal, which will likely broaden the utility of this method in the pretreatment radiotherapy planning context.« less

TU-CD-BRB-09: Prediction of Chemo-Radiation Outcome for Rectal Cancer Based On Radiomics of Tumor Clinical Characteristics and Multi-Parametric MRI

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nie, K; Yue, N; Shi, L

2015-06-15

Purpose: To evaluate the tumor clinical characteristics and quantitative multi-parametric MR imaging features for prediction of response to chemo-radiation treatment (CRT) in locally advanced rectal cancer (LARC). Methods: Forty-three consecutive patients (59.7±6.9 years, from 09/2013 – 06/2014) receiving neoadjuvant CRT followed by surgery were enrolled. All underwent MRI including anatomical T1/T2, Dynamic Contrast Enhanced (DCE)-MRI and Diffusion-Weighted MRI (DWI) prior to the treatment. A total of 151 quantitative features, including morphology/Gray Level Co-occurrence Matrix (GLCM) texture from T1/T2, enhancement kinetics and the voxelized distribution from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, along with clinical information (carcinoembryonic antigen CEA level,more » TNM staging etc.), were extracted for each patient. Response groups were separated based on down-staging, good response and pathological complete response (pCR) status. Logistic regression analysis (LRA) was used to select the best predictors to classify different groups and the predictive performance were calculated using receiver operating characteristic (ROC) analysis. Results: Individual imaging category or clinical charateristics might yield certain level of power in assessing the response. However, the combined model outperformed than any category alone in prediction. With selected features as Volume, GLCM AutoCorrelation (T2), MaxEnhancementProbability (DCE-MRI), and MeanADC (DWI), the down-staging prediciton accuracy (area under the ROC curve, AUC) could be 0.95, better than individual tumor metrics with AUC from 0.53–0.85. While for the pCR prediction, the best set included CEA (clinical charateristics), Homogeneity (DCE-MRI) and MeanADC (DWI) with an AUC of 0.89, more favorable compared to conventional tumor metrics with an AUC ranging from 0.511–0.79. Conclusion: Through a systematic analysis of multi-parametric MR imaging features, we are able to build models with improved predictive value over conventional imaging or clinical metrics. This is encouraging, suggesting the wealth of imaging radiomics should be further explored to help tailor the treatment into the era of personalized medicine. This work is supported by the National Science Foundation of China (NSFC Grant No. 81201091), National High Technology Research and Development Program of China (863 program, Grant No. 2015AA020917), and Fund Project for Excellent Abroad Scholar Personnel in Science and Technology.« less

Voxel-based automated detection of focal cortical dysplasia lesions using diffusion tensor imaging and T2-weighted MRI data.

PubMed

Wang, Yanming; Zhou, Yawen; Wang, Huijuan; Cui, Jin; Nguchu, Benedictor Alexander; Zhang, Xufei; Qiu, Bensheng; Wang, Xiaoxiao; Zhu, Mingwang

2018-05-21

The aim of this study was to automatically detect focal cortical dysplasia (FCD) lesions in patients with extratemporal lobe epilepsy by relying on diffusion tensor imaging (DTI) and T2-weighted magnetic resonance imaging (MRI) data. We implemented an automated classifier using voxel-based multimodal features to identify gray and white matter abnormalities of FCD in patient cohorts. In addition to the commonly used T2-weighted image intensity feature, DTI-based features were also utilized. A Gaussian processes for machine learning (GPML) classifier was tested on 12 patients with FCD (8 with histologically confirmed FCD) scanned at 1.5 T and cross-validated using a leave-one-out strategy. Moreover, we compared the multimodal GPML paradigm's performance with that of single modal GPML and classical support vector machine (SVM). Our results demonstrated that the GPML performance on DTI-based features (mean AUC = 0.63) matches with the GPML performance on T2-weighted image intensity feature (mean AUC = 0.64). More promisingly, GPML yielded significantly improved performance (mean AUC = 0.76) when applying DTI-based features to multimodal paradigm. Based on the results, it can also be clearly stated that the proposed GPML strategy performed better and is robust to unbalanced dataset contrary to SVM that performed poorly (AUC = 0.69). Therefore, the GPML paradigm using multimodal MRI data containing DTI modality has promising result towards detection of the FCD lesions and provides an effective direction for future researches. Copyright © 2018 Elsevier Inc. All rights reserved.

The Physics and Mathematics of MRI

NASA Astrophysics Data System (ADS)

Ansorge, Richard; Graves, Martin

2016-10-01

Magnetic Resonance Imaging is a very important clinical imaging tool. It combines different fields of physics and engineering in a uniquely complex way. MRI is also surprisingly versatile, `pulse sequences' can be designed to yield many different types of contrast. This versatility is unique to MRI. This short book gives both an in depth account of the methods used for the operation and construction of modern MRI systems and also the principles of sequence design and many examples of applications. An important additional feature of this book is the detailed discussion of the mathematical principles used in building optimal MRI systems and for sequence design. The mathematical discussion is very suitable for undergraduates attending medical physics courses. It is also more complete than usually found in alternative books for physical scientists or more clinically orientated works.

Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females.

PubMed

Alaerts, Kaat; Swinnen, Stephan P; Wenderoth, Nicole

2016-06-01

Autism spectrum disorders (ASD) are far more prevalent in males than in females. Little is known however about the differential neural expression of ASD in males and females. We used a resting-state fMRI-dataset comprising 42 males/42 females with ASD and 75 male/75 female typical-controls to examine whether autism-related alterations in intrinsic functional connectivity are similar or different in males and females, and particularly whether alterations reflect 'neural masculinization', as predicted by the Extreme Male Brain theory. Males and females showed a differential neural expression of ASD, characterized by highly consistent patterns of hypo-connectivity in males with ASD (compared to typical males), and hyper-connectivity in females with ASD (compared to typical females). Interestingly, patterns of hyper-connectivity in females with ASD reflected a shift towards the (high) connectivity levels seen in typical males (neural masculinization), whereas patterns of hypo-connectivity observed in males with ASD reflected a shift towards the (low) typical feminine connectivity patterns (neural feminization). Our data support the notion that ASD is a disorder of sexual differentiation rather than a disorder characterized by masculinization in both genders. Future work is needed to identify underlying factors such as sex hormonal alterations that drive these sex-specific neural expressions of ASD. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Under-reactive but easily distracted: An fMRI investigation of attentional capture in autism spectrum disorder.

PubMed

Keehn, Brandon; Nair, Aarti; Lincoln, Alan J; Townsend, Jeanne; Müller, Ralph-Axel

2016-02-01

For individuals with autism spectrum disorder (ASD), salient behaviorally-relevant information often fails to capture attention, while subtle behaviorally-irrelevant details commonly induce a state of distraction. The present study used functional magnetic resonance imaging (fMRI) to investigate the neurocognitive networks underlying attentional capture in sixteen high-functioning children and adolescents with ASD and twenty-one typically developing (TD) individuals. Participants completed a rapid serial visual presentation paradigm designed to investigate activation of attentional networks to behaviorally-relevant targets and contingent attention capture by task-irrelevant distractors. In individuals with ASD, target stimuli failed to trigger bottom-up activation of the ventral attentional network and the cerebellum. Additionally, the ASD group showed no differences in behavior or occipital activation associated with contingent attentional capture. Rather, results suggest that to-be-ignored distractors that shared either task-relevant or irrelevant features captured attention in ASD. Results indicate that individuals with ASD may be under-reactive to behaviorally-relevant stimuli, unable to filter irrelevant information, and that both top-down and bottom-up attention networks function atypically in ASD. Lastly, deficits in target-related processing were associated with autism symptomatology, providing further support for the hypothesis that non-social attentional processes and their neurofunctional underpinnings may play a significant role in the development of sociocommunicative impairments in ASD. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Clinical characteristics of 138 Chinese female patients with idiopathic hypogonadotropic hypogonadism

PubMed Central

Tang, Rui-yi; Ma, Miao; Lin, Shou-qing; Zhang, Yi-wen; Wang, Ya-ping

2017-01-01

Objective To evaluate the clinical features of Chinese women with idiopathic hypogonadotropic hypogonadism (IHH). Methods We retrospectively reviewed the clinical characteristics, laboratory and imaging findings, therapeutic management and fertility outcomes of 138 women with IHH. All patients had been treated and followed up at an academic medical centre during 1990–2016. Results Among the 138 patients, 82 patients (59.4%) were diagnosed with normosmic IHH and 56 patients (40.6%) were diagnosed with Kallmann syndrome (KS). The patients with IHH experienced occasional menses (4.3%), spontaneous thelarche (45.7%) or spontaneous pubarche (50.7%). Women with thelarche had a higher percentage of pubarche (P < 0.001) and higher gonadotropin concentrations (P < 0.01). Olfactory bulb/sulci abnormalities were found during the magnetic resonance imaging (MRI) of all patients with KS. Most patients with IHH had osteopenia and low bone age. Among the 16 women who received gonadotropin-releasing hormone treatment, ovulation induction or assisted reproductive technology, the clinical pregnancy rate was 81.3% and the live birth rate was 68.8%. Conclusions The present study revealed that the phenotypic spectrum of women with IHH is broader than typical primary amenorrhoea with no secondary sexual development, including occasional menses, spontaneous thelarche or pubarche. MRI of the olfactory system can facilitate the diagnosis of KS. Pregnancy can be achieved after receiving appropriate treatment. PMID:29018155

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.

PubMed

Shen, Guohua; Zhang, Jing; Wang, Mengxing; Lei, Du; Yang, Guang; Zhang, Shanmin; Du, Xiaoxia

2014-06-01

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement. © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

SPIRAL-SPRITE: a rapid single point MRI technique for application to porous media.

PubMed

Szomolanyi, P; Goodyear, D; Balcom, B; Matheson, D

2001-01-01

This study presents the application of a new, rapid, single point MRI technique which samples k space with spiral trajectories. The general principles of the technique are outlined along with application to porous concrete samples, solid pharmaceutical tablets and gas phase imaging. Each sample was chosen to highlight specific features of the method.