Dye-Enhanced Multimodal Confocal Imaging of Brain Cancers

NASA Astrophysics Data System (ADS)

Wirth, Dennis; Snuderl, Matija; Sheth, Sameer; Curry, William; Yaroslavsky, Anna

2011-04-01

Background and Significance: Accurate high resolution intraoperative detection of brain tumors may result in improved patient survival and better quality of life. The goal of this study was to evaluate dye enhanced multimodal confocal imaging for discriminating normal and cancerous brain tissue. Materials and Methods: Fresh thick brain specimens were obtained from the surgeries. Normal and cancer tissues were investigated. Samples were stained in methylene blue and imaged. Reflectance and fluorescence signals were excited at 658nm. Fluorescence emission and polarization were registered from 670 nm to 710 nm. The system provided lateral resolution of 0.6 μm and axial resolution of 7 μm. Normal and cancer specimens exhibited distinctively different characteristics. H&E histopathology was processed from each imaged sample. Results and Conclusions: The analysis of normal and cancerous tissues indicated clear differences in appearance in both the reflectance and fluorescence responses. These results confirm the feasibility of multimodal confocal imaging for intraoperative detection of small cancer nests and cells.

Robust generative asymmetric GMM for brain MR image segmentation.

PubMed

Ji, Zexuan; Xia, Yong; Zheng, Yuhui

2017-11-01

Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity inhomogeneity. In this paper, the RGAGMM

[Three-dimensional reconstruction of functional brain images].

PubMed

Inoue, M; Shoji, K; Kojima, H; Hirano, S; Naito, Y; Honjo, I

1999-08-01

We consider PET (positron emission tomography) measurement with SPM (Statistical Parametric Mapping) analysis to be one of the most useful methods to identify activated areas of the brain involved in language processing. SPM is an effective analytical method that detects markedly activated areas over the whole brain. However, with the conventional presentations of these functional brain images, such as horizontal slices, three directional projection, or brain surface coloring, makes understanding and interpreting the positional relationships among various brain areas difficult. Therefore, we developed three-dimensionally reconstructed images from these functional brain images to improve the interpretation. The subjects were 12 normal volunteers. The following three types of images were constructed: 1) routine images by SPM, 2) three-dimensional static images, and 3) three-dimensional dynamic images, after PET images were analyzed by SPM during daily dialog listening. The creation of images of both the three-dimensional static and dynamic types employed the volume rendering method by VTK (The Visualization Toolkit). Since the functional brain images did not include original brain images, we synthesized SPM and MRI brain images by self-made C++ programs. The three-dimensional dynamic images were made by sequencing static images with available software. Images of both the three-dimensional static and dynamic types were processed by a personal computer system. Our newly created images showed clearer positional relationships among activated brain areas compared to the conventional method. To date, functional brain images have been employed in fields such as neurology or neurosurgery, however, these images may be useful even in the field of otorhinolaryngology, to assess hearing and speech. Exact three-dimensional images based on functional brain images are important for exact and intuitive interpretation, and may lead to new developments in brain science. Currently, the

Non-invasive imaging of the levels and effects of glutathione on the redox status of mouse brain using electron paramagnetic resonance imaging

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

Emoto, Miho C.; Department of Neurology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido 060-8556; Matsuoka, Yuta

Glutathione (GSH) is the most abundant non-protein thiol that buffers reactive oxygen species in the brain. GSH does not reduce nitroxides directly, but in the presence of ascorbates, addition of GSH increases ascorbate-induced reduction of nitroxides. In this study, we used electron paramagnetic resonance (EPR) imaging and the nitroxide imaging probe, 3-methoxycarbonyl-2,2,5,5-tetramethyl-piperidine-1-oxyl (MCP), to non-invasively obtain spatially resolved redox data from mouse brains depleted of GSH with diethyl maleate compared to control. Based on the pharmacokinetics of the reduction reaction of MCP in the mouse heads, the pixel-based rate constant of its reduction reaction was calculated as an index ofmore » the redox status in vivo and mapped as a “redox map”. The obtained redox maps from control and GSH-depleted mouse brains showed a clear change in the brain redox status, which was due to the decreased levels of GSH in brains as measured by a biochemical assay. We observed a linear relationship between the reduction rate constant of MCP and the level of GSH for both control and GSH-depleted mouse brains. Using this relationship, the GSH level in the brain can be estimated from the redox map obtained with EPR imaging. - Highlights: • Redox status of glutathione-depleted mouse brain was examined with EPR imaging. • Redox status of mouse brain changed depending on glutathione (GSH) levels in brains. • Linear relationship between GSH levels and redox status in brains was found. • Using this relation, estimation of GSH levels in brains is possible from EPR images.« less

A high-resolution optical imaging system for obtaining the serial transverse section images of biologic tissue

NASA Astrophysics Data System (ADS)

Wu, Li; Zhang, Bin; Wu, Ping; Liu, Qian; Gong, Hui

2007-05-01

A high-resolution optical imaging system was designed and developed to obtain the serial transverse section images of the biologic tissue, such as the mouse brain, in which new knife-edge imaging technology, high-speed and high-sensitive line-scan CCD and linear air bearing stages were adopted and incorporated with an OLYMPUS microscope. The section images on the tip of the knife-edge were synchronously captured by the reflection imaging in the microscope while cutting the biologic tissue. The biologic tissue can be sectioned at interval of 250 nm with the same resolution of the transverse section images obtained in x and y plane. And the cutting job can be automatically finished based on the control program wrote specially in advance, so we save the mass labor of the registration of the vast images data. In addition, by using this system a larger sample can be cut than conventional ultramicrotome so as to avoid the loss of the tissue structure information because of splitting the tissue sample to meet the size request of the ultramicrotome.

Functional Brain Imaging

PubMed Central

2006-01-01

Executive Summary Objective The objective of this analysis is to review a spectrum of functional brain imaging technologies to identify whether there are any imaging modalities that are more effective than others for various brain pathology conditions. This evidence-based analysis reviews magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI) for the diagnosis or surgical management of the following conditions: Alzheimer’s disease (AD), brain tumours, epilepsy, multiple sclerosis (MS), and Parkinson’s disease (PD). Clinical Need: Target Population and Condition Alzheimer’s disease is a progressive, degenerative, neurologic condition characterized by cognitive impairment and memory loss. The Canadian Study on Health and Aging estimated that there will be 97,000 incident cases (about 60,000 women) of dementia (including AD) in Canada in 2006. In Ontario, there will be an estimated 950 new cases and 580 deaths due to brain cancer in 2006. Treatments for brain tumours include surgery and radiation therapy. However, one of the limitations of radiation therapy is that it damages tissue though necrosis and scarring. Computed tomography (CT) and magnetic resonance imaging (MRI) may not distinguish between radiation effects and resistant tissue, creating a potential role for functional brain imaging. Epilepsy is a chronic disorder that provokes repetitive seizures. In Ontario, the rate of epilepsy is estimated to be 5 cases per 1,000 people. Most people with epilepsy are effectively managed with drug therapy; but about 50% do not respond to drug therapy. Surgical resection of the seizure foci may be considered in these patients, and functional brain imaging may play a role in localizing the seizure foci. Multiple sclerosis is a progressive, inflammatory, demyelinating disease of the central nervous system (CNS). The cause of MS is unknown; however, it is thought to be

The Relationship between Neurite Density Measured with Confocal Microscopy in a Cleared Mouse Brain and Metrics Obtained from Diffusion Tensor and Diffusion Kurtosis Imaging

PubMed Central

Irie, Ryusuke; Kamagata, Koji; Kerever, Aurelien; Ueda, Ryo; Yokosawa, Suguru; Otake, Yosuke; Ochi, Hisaaki; Yoshizawa, Hidekazu; Hayashi, Ayato; Tagawa, Kazuhiko; Okazawa, Hitoshi; Takahashi, Kohske; Sato, Kanako; Hori, Masaaki; Arikawa-Hirasawa, Eri; Aoki, Shigeki

2018-01-01

Purpose: Diffusional kurtosis imaging (DKI) enables sensitive measurement of tissue microstructure by quantifying the non-Gaussian diffusion of water. Although DKI is widely applied in many situations, histological correlation with DKI analysis is lacking. The purpose of this study was to determine the relationship between DKI metrics and neurite density measured using confocal microscopy of a cleared mouse brain. Methods: One thy-1 yellow fluorescent protein 16 mouse was deeply anesthetized and perfusion fixation was performed. The brain was carefully dissected out and whole-brain MRI was performed using a 7T animal MRI system. DKI and diffusion tensor imaging (DTI) data were obtained. After the MRI scan, brain sections were prepared and then cleared using aminoalcohols (CUBIC). Confocal microscopy was performed using a two-photon confocal microscope with a laser. Forty-eight ROIs were set on the caudate putamen, seven ROIs on the anterior commissure, and seven ROIs on the ventral hippocampal commissure on the confocal microscopic image and a corresponding MR image. In each ROI, histological neurite density and the metrics of DKI and DTI were calculated. The correlations between diffusion metrics and neurite density were analyzed using Pearson correlation coefficient analysis. Results: Mean kurtosis (MK) (P = 5.2 × 10−9, r = 0.73) and radial kurtosis (P = 2.3 × 10−9, r = 0.74) strongly correlated with neurite density in the caudate putamen. The correlation between fractional anisotropy (FA) and neurite density was moderate (P = 0.0030, r = 0.42). In the anterior commissure and the ventral hippocampal commissure, neurite density and FA are very strongly correlated (P = 1.3 × 10−5, r = 0.90). MK in these areas were very high value and showed no significant correlation (P = 0.48). Conclusion: DKI accurately reflected neurite density in the area with crossing fibers, potentially allowing evaluation of complex microstructures. PMID:29213008

The Relationship between Neurite Density Measured with Confocal Microscopy in a Cleared Mouse Brain and Metrics Obtained from Diffusion Tensor and Diffusion Kurtosis Imaging.

PubMed

Irie, Ryusuke; Kamagata, Koji; Kerever, Aurelien; Ueda, Ryo; Yokosawa, Suguru; Otake, Yosuke; Ochi, Hisaaki; Yoshizawa, Hidekazu; Hayashi, Ayato; Tagawa, Kazuhiko; Okazawa, Hitoshi; Takahashi, Kohske; Sato, Kanako; Hori, Masaaki; Arikawa-Hirasawa, Eri; Aoki, Shigeki

2018-04-10

Diffusional kurtosis imaging (DKI) enables sensitive measurement of tissue microstructure by quantifying the non-Gaussian diffusion of water. Although DKI is widely applied in many situations, histological correlation with DKI analysis is lacking. The purpose of this study was to determine the relationship between DKI metrics and neurite density measured using confocal microscopy of a cleared mouse brain. One thy-1 yellow fluorescent protein 16 mouse was deeply anesthetized and perfusion fixation was performed. The brain was carefully dissected out and whole-brain MRI was performed using a 7T animal MRI system. DKI and diffusion tensor imaging (DTI) data were obtained. After the MRI scan, brain sections were prepared and then cleared using aminoalcohols (CUBIC). Confocal microscopy was performed using a two-photon confocal microscope with a laser. Forty-eight ROIs were set on the caudate putamen, seven ROIs on the anterior commissure, and seven ROIs on the ventral hippocampal commissure on the confocal microscopic image and a corresponding MR image. In each ROI, histological neurite density and the metrics of DKI and DTI were calculated. The correlations between diffusion metrics and neurite density were analyzed using Pearson correlation coefficient analysis. Mean kurtosis (MK) (P = 5.2 × 10 -9 , r = 0.73) and radial kurtosis (P = 2.3 × 10 -9 , r = 0.74) strongly correlated with neurite density in the caudate putamen. The correlation between fractional anisotropy (FA) and neurite density was moderate (P = 0.0030, r = 0.42). In the anterior commissure and the ventral hippocampal commissure, neurite density and FA are very strongly correlated (P = 1.3 × 10 -5 , r = 0.90). MK in these areas were very high value and showed no significant correlation (P = 0.48). DKI accurately reflected neurite density in the area with crossing fibers, potentially allowing evaluation of complex microstructures.

Cerenkov and radioluminescence imaging of brain tumor specimens during neurosurgery

NASA Astrophysics Data System (ADS)

Spinelli, Antonello Enrico; Schiariti, Marco P.; Grana, Chiara M.; Ferrari, Mahila; Cremonesi, Marta; Boschi, Federico

2016-05-01

We presented the first example of Cerenkov luminescence imaging (CLI) and radioluminescence imaging (RLI) of human tumor specimens. A patient with a brain meningioma localized in the left parietal region was injected with 166 MBq of Y90-DOTATOC the day before neurosurgery. The specimens of the tumor removed during surgery were imaged using both CLI and RLI using an optical imager prototype developed in our laboratory. The system is based on a cooled electron multiplied charge coupled device coupled with an f/0.95 17-mm C-mount lens. We showed for the first time the possibility of obtaining CLI and RLI images of fresh human brain tumor specimens removed during neurosurgery.

A brain imaging repository of normal structural MRI across the life course: Brain Images of Normal Subjects (BRAINS).

PubMed

Job, Dominic E; Dickie, David Alexander; Rodriguez, David; Robson, Andrew; Danso, Sammy; Pernet, Cyril; Bastin, Mark E; Boardman, James P; Murray, Alison D; Ahearn, Trevor; Waiter, Gordon D; Staff, Roger T; Deary, Ian J; Shenkin, Susan D; Wardlaw, Joanna M

2017-01-01

The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks. BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81years of age, from 7 projects in 3 centres. Additional completed and ongoing studies of normal individuals from 1st to 10th decades are in preparation and will be included as they become available. BRAINS holds several MRI structural sequences, including T1, T2, T2* and fluid attenuated inversion recovery (FLAIR), available in DICOM (http://dicom.nema.org/); in future Diffusion Tensor Imaging (DTI) will be added where available. Images are linked to a wide range of 'textual data', such as age, medical history, physiological measures (e.g. blood pressure), medication use, cognitive ability, and perinatal information for pre/post-natal subjects. The imagebank can be searched to include or exclude ranges of these variables to create better estimates of 'what is normal' at different ages. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Automatic labeling of MR brain images through extensible learning and atlas forests.

PubMed

Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

2017-12-01

Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic

Antisense imaging of gene expression in the brain in vivo

NASA Astrophysics Data System (ADS)

Shi, Ningya; Boado, Ruben J.; Pardridge, William M.

2000-12-01

Antisense radiopharmaceuticals could be used to image gene expression in the brain in vivo, should these polar molecules be made transportable through the blood-brain barrier. The present studies describe an antisense imaging agent comprised of an iodinated peptide nucleic acid (PNA) conjugated to a monoclonal antibody to the rat transferrin receptor by using avidin-biotin technology. The PNA was a 16-mer antisense to the sequence around the methionine initiation codon of the luciferase mRNA. C6 rat glioma cells were permanently transfected with a luciferase expression plasmid, and C6 experimental brain tumors were developed in adult rats. The expression of the luciferase transgene in the tumors in vivo was confirmed by measurement of luciferase enzyme activity in the tumor extract. The [125I]PNA conjugate was injected intravenously in anesthetized animals with brain tumors and killed 2 h later for frozen sectioning of brain and film autoradiography. No image of the luciferase gene expression was obtained after the administration of either the unconjugated antiluciferase PNA or a PNA conjugate that was antisense to the mRNA of a viral transcript. In contrast, tumors were imaged in all rats administered the [125I]PNA that was antisense to the luciferase sequence and was conjugated to the targeting antibody. In conclusion, these studies demonstrate gene expression in the brain in vivo can be imaged with antisense radiopharmaceuticals that are conjugated to a brain drug-targeting system.

Brain Tumor Image Segmentation in MRI Image

NASA Astrophysics Data System (ADS)

Peni Agustin Tjahyaningtijas, Hapsari

2018-04-01

Brain tumor segmentation plays an important role in medical image processing. Treatment of patients with brain tumors is highly dependent on early detection of these tumors. Early detection of brain tumors will improve the patient’s life chances. Diagnosis of brain tumors by experts usually use a manual segmentation that is difficult and time consuming because of the necessary automatic segmentation. Nowadays automatic segmentation is very populer and can be a solution to the problem of tumor brain segmentation with better performance. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. this paper, we focus on the recent trend of automatic segmentation in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of full automatic segmentaion are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed.

Magnetic resonance imaging of the fetal brain.

PubMed

Tee, L Mf; Kan, E Yl; Cheung, J Cy; Leung, W C

2016-06-01

This review covers the recent literature on fetal brain magnetic resonance imaging, with emphasis on techniques, advances, common indications, and safety. We conducted a search of MEDLINE for articles published after 2010. The search terms used were "(fetal OR foetal OR fetus OR foetus) AND (MR OR MRI OR [magnetic resonance]) AND (brain OR cerebral)". Consensus statements from major authorities were also included. As a result, 44 relevant articles were included and formed the basis of this review. One major challenge is fetal motion that is largely overcome by ultra-fast sequences. Currently, single-shot fast spin-echo T2-weighted imaging remains the mainstay for motion resistance and anatomical delineation. Recently, a snap-shot inversion recovery sequence has enabled robust T1-weighted images to be obtained, which is previously a challenge for standard gradient-echo acquisitions. Fetal diffusion-weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopy are also being developed. With multiplanar capabilities, superior contrast resolution and field of view, magnetic resonance imaging does not have the limitations of sonography, and can provide additional important information. Common indications include ventriculomegaly, callosum and posterior fossa abnormalities, and twin complications. There are safety concerns about magnetic resonance-induced heating and acoustic damage but current literature showed no conclusive evidence of deleterious fetal effects. The American College of Radiology guideline states that pregnant patients can be accepted to undergo magnetic resonance imaging at any stage of pregnancy if risk-benefit ratio to patients warrants that the study be performed. Magnetic resonance imaging of the fetal brain is a safe and powerful adjunct to sonography in prenatal diagnosis. It can provide additional information that aids clinical management, prognostication, and counselling.

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.

Non-invasive imaging of the levels and effects of glutathione on the redox status of mouse brain using electron paramagnetic resonance imaging.

PubMed

Emoto, Miho C; Matsuoka, Yuta; Yamada, Ken-Ichi; Sato-Akaba, Hideo; Fujii, Hirotada G

2017-04-15

Glutathione (GSH) is the most abundant non-protein thiol that buffers reactive oxygen species in the brain. GSH does not reduce nitroxides directly, but in the presence of ascorbates, addition of GSH increases ascorbate-induced reduction of nitroxides. In this study, we used electron paramagnetic resonance (EPR) imaging and the nitroxide imaging probe, 3-methoxycarbonyl-2,2,5,5-tetramethyl-piperidine-1-oxyl (MCP), to non-invasively obtain spatially resolved redox data from mouse brains depleted of GSH with diethyl maleate compared to control. Based on the pharmacokinetics of the reduction reaction of MCP in the mouse heads, the pixel-based rate constant of its reduction reaction was calculated as an index of the redox status in vivo and mapped as a "redox map". The obtained redox maps from control and GSH-depleted mouse brains showed a clear change in the brain redox status, which was due to the decreased levels of GSH in brains as measured by a biochemical assay. We observed a linear relationship between the reduction rate constant of MCP and the level of GSH for both control and GSH-depleted mouse brains. Using this relationship, the GSH level in the brain can be estimated from the redox map obtained with EPR imaging. Copyright © 2017 Elsevier Inc. All rights reserved.

Brain tumor image segmentation using kernel dictionary learning.

PubMed

Jeon Lee; Seung-Jun Kim; Rong Chen; Herskovits, Edward H

2015-08-01

Automated brain tumor image segmentation with high accuracy and reproducibility holds a big potential to enhance the current clinical practice. Dictionary learning (DL) techniques have been applied successfully to various image processing tasks recently. In this work, kernel extensions of the DL approach are adopted. Both reconstructive and discriminative versions of the kernel DL technique are considered, which can efficiently incorporate multi-modal nonlinear feature mappings based on the kernel trick. Our novel discriminative kernel DL formulation allows joint learning of a task-driven kernel-based dictionary and a linear classifier using a K-SVD-type algorithm. The proposed approaches were tested using real brain magnetic resonance (MR) images of patients with high-grade glioma. The obtained preliminary performances are competitive with the state of the art. The discriminative kernel DL approach is seen to reduce computational burden without much sacrifice in performance.

Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging.

PubMed

Mohseni Salehi, Seyed Sadegh; Erdogmus, Deniz; Gholipour, Ali

2017-11-01

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic

Brain tumor classification of microscopy images using deep residual learning

NASA Astrophysics Data System (ADS)

Ishikawa, Yota; Washiya, Kiyotada; Aoki, Kota; Nagahashi, Hiroshi

2016-12-01

The crisis rate of brain tumor is about one point four in ten thousands. In general, cytotechnologists take charge of cytologic diagnosis. However, the number of cytotechnologists who can diagnose brain tumors is not sufficient, because of the necessity of highly specialized skill. Computer-Aided Diagnosis by computational image analysis may dissolve the shortage of experts and support objective pathological examinations. Our purpose is to support a diagnosis from a microscopy image of brain cortex and to identify brain tumor by medical image processing. In this study, we analyze Astrocytes that is a type of glia cell of central nerve system. It is not easy for an expert to discriminate brain tumor correctly since the difference between astrocytes and low grade astrocytoma (tumors formed from Astrocyte) is very slight. In this study, we present a novel method to segment cell regions robustly using BING objectness estimation and to classify brain tumors using deep convolutional neural networks (CNNs) constructed by deep residual learning. BING is a fast object detection method and we use pretrained BING model to detect brain cells. After that, we apply a sequence of post-processing like Voronoi diagram, binarization, watershed transform to obtain fine segmentation. For classification using CNNs, a usual way of data argumentation is applied to brain cells database. Experimental results showed 98.5% accuracy of classification and 98.2% accuracy of segmentation.

Quantitative analysis of brain magnetic resonance imaging for hepatic encephalopathy

NASA Astrophysics Data System (ADS)

Syh, Hon-Wei; Chu, Wei-Kom; Ong, Chin-Sing

1992-06-01

High intensity lesions around ventricles have recently been observed in T1-weighted brain magnetic resonance images for patients suffering hepatic encephalopathy. The exact etiology that causes magnetic resonance imaging (MRI) gray scale changes has not been totally understood. The objective of our study was to investigate, through quantitative means, (1) the amount of changes to brain white matter due to the disease process, and (2) the extent and distribution of these high intensity lesions, since it is believed that the abnormality may not be entirely limited to the white matter only. Eleven patients with proven haptic encephalopathy and three normal persons without any evidence of liver abnormality constituted our current data base. Trans-axial, sagittal, and coronal brain MRI were obtained on a 1.5 Tesla scanner. All processing was carried out on a microcomputer-based image analysis system in an off-line manner. Histograms were decomposed into regular brain tissues and lesions. Gray scale ranges coded as lesion were then brought back to original images to identify distribution of abnormality. Our results indicated the disease process involved pallidus, mesencephalon, and subthalamic regions.

Functional photoacoustic imaging to observe regional brain activation induced by cocaine hydrochloride

NASA Astrophysics Data System (ADS)

Jo, Janggun; Yang, Xinmai

2011-09-01

Photoacoustic microscopy (PAM) was used to detect small animal brain activation in response to drug abuse. Cocaine hydrochloride in saline solution was injected into the blood stream of Sprague Dawley rats through tail veins. The rat brain functional change in response to the injection of drug was then monitored by the PAM technique. Images in the coronal view of the rat brain at the locations of 1.2 and 3.4 mm posterior to bregma were obtained. The resulted photoacoustic (PA) images showed the regional changes in the blood volume. Additionally, the regional changes in blood oxygenation were also presented. The results demonstrated that PA imaging is capable of monitoring regional hemodynamic changes induced by drug abuse.

Comparing three-dimensional serial optical coherence tomography histology to MRI imaging in the entire mouse brain

NASA Astrophysics Data System (ADS)

Castonguay, Alexandre; Lefebvre, Joël; Pouliot, Philippe; Lesage, Frédéric

2018-01-01

An automated serial histology setup combining optical coherence tomography (OCT) imaging with vibratome sectioning was used to image eight wild type mouse brains. The datasets resulted in thousands of volumetric tiles resolved at a voxel size of (4.9×4.9×6.5) μm3 stitched back together to give a three-dimensional map of the brain from which a template OCT brain was obtained. To assess deformation caused by tissue sectioning, reconstruction algorithms, and fixation, OCT datasets were compared to both in vivo and ex vivo magnetic resonance imaging (MRI) imaging. The OCT brain template yielded a highly detailed map of the brain structure, with a high contrast in white matter fiber bundles and was highly resemblant to the in vivo MRI template. Brain labeling using the Allen brain framework showed little variation in regional brain volume among imaging modalities with no statistical differences. The high correspondence between the OCT template brain and its in vivo counterpart demonstrates the potential of whole brain histology to validate in vivo imaging.

Nuclear emission-based imaging in the study of brain function

NASA Astrophysics Data System (ADS)

Sossi, Vesna

2016-09-01

Nuclear emission - based imaging has been used in medicine for decades either in the form of Single Photon Emission Computerized Tomography (SPECT) or Positron Emission Tomography (PET). Both techniques are based on radiolabelling molecules of biological interest (radiotracers) with either a gamma (SPECT) or a positron (PET) emitting radionuclide. By detecting radiation from the radiolabels and reconstructing the acquired data it is possible to form an image of the radiotracer distribution in the body and thus obtain information on the biological process that the radiotracer is tagging. While most of the clinical applications of PET are in oncology, where the glucose analogue 18F-flurodeoxyglocose (FDG) is the most commonly used radiotracer, the importance of PET imaging for brain applications is rapidly increasing. Numerous radiotracers exist that can tag different neurotransmitter systems as well as abnormal protein aggregations that are known to underlie several brain diseases: amyloid deposition, a characteristic of Alzheimer's, and, more recently, tau deposition, which is deemed abnormal not only in dementia, but also in Parkinson's syndrome and traumatic brain injury. Imaging has shown that may brain diseases start decades before clinical symptoms, in part explaining the difficulty of developing adequate treatments. This talk will briefly summarize the role of PET imaging in the study of neurodegeneration and discuss the upcoming hybrid PET/MRI imaging instrumentation. NSERC, CIHR, MJFF.

Enhancement of brain tumor MR images based on intuitionistic fuzzy sets

NASA Astrophysics Data System (ADS)

Deng, Wankai; Deng, He; Cheng, Lifang

2015-12-01

Brain tumor is one of the most fatal cancers, especially high-grade gliomas are among the most deadly. However, brain tumor MR images usually have the disadvantages of low resolution and contrast when compared with the optical images. Consequently, we present a novel adaptive intuitionistic fuzzy enhancement scheme by combining a nonlinear fuzzy filtering operation with fusion operators, for the enhancement of brain tumor MR images in this paper. The presented scheme consists of the following six steps: Firstly, the image is divided into several sub-images. Secondly, for each sub-image, object and background areas are separated by a simple threshold. Thirdly, respective intuitionistic fuzzy generators of object and background areas are constructed based on the modified restricted equivalence function. Fourthly, different suitable operations are performed on respective membership functions of object and background areas. Fifthly, the membership plane is inversely transformed into the image plane. Finally, an enhanced image is obtained through fusion operators. The comparison and evaluation of enhancement performance demonstrate that the presented scheme is helpful to determine the abnormal functional areas, guide the operation, judge the prognosis, and plan the radiotherapy by enhancing the fine detail of MR images.

Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

PubMed Central

Guo, Bing-bing; Zheng, Xiao-lin; Lu, Zhen-gang; Wang, Xing; Yin, Zheng-qin; Hou, Wen-sheng; Meng, Ming

2015-01-01

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only “see” pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex (the implant area) is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine (LSVM) as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern. PMID:26692860

Functional connectivity in the mouse brain imaged by B-mode photoacoustic microscopy

NASA Astrophysics Data System (ADS)

Nasiriavanaki, Mohammadreza; Xing, Wenxin; Xia, Jun; Wang, Lihong V.

2014-03-01

The increasing use of mouse models for human brain disease studies, coupled with the fact that existing functional imaging modalities cannot be easily applied to mice, presents an emerging need for a new functional imaging modality. Utilizing acoustic-resolution photoacoustic microscopy (AR-PAM), we imaged spontaneous cerebral hemodynamic fluctuations and their associated functional connections in the mouse brain. The images were acquired noninvasively in B-scan mode with a fast frame rate, a large field of view, and a high spatial resolution. At a location relative to the bregma 0, correlations were investigated inter-hemispherically between bilaterally homologous regions, as well as intra-hemispherically within the same functional regions. The functional connectivity in different functional regions was studied. The locations of these regions agreed well with the Paxinos mouse brain atlas. The functional connectivity map obtained in this study can then be used in the investigation of brain disorders such as stroke, Alzheimer's, schizophrenia, multiple sclerosis, autism, and epilepsy. Our experiments show that photoacoustic microscopy is capable to detect connectivities between different functional regions in B-scan mode, promising a powerful functional imaging modality for future brain research.

Imaging of Traumatic Brain Injury.

PubMed

Bodanapally, Uttam K; Sours, Chandler; Zhuo, Jiachen; Shanmuganathan, Kathirkamanathan

2015-07-01

Imaging plays an important role in the management of patients with traumatic brain injury (TBI). Computed tomography (CT) is the first-line imaging technique allowing rapid detection of primary structural brain lesions that require surgical intervention. CT also detects various deleterious secondary insults allowing early medical and surgical management. Serial imaging is critical to identifying secondary injuries. MR imaging is indicated in patients with acute TBI when CT fails to explain neurologic findings. However, MR imaging is superior in patients with subacute and chronic TBI and also predicts neurocognitive outcome. Copyright © 2015 Elsevier Inc. All rights reserved.

Imaging the Alzheimer Brain

PubMed Central

Ashford, J. Wesson; Salehi, Ahmad; Furst, Ansgar; Bayley, Peter; Frisoni, Giovanni B.; Jack, Clifford R.; Sabri, Osama; Adamson, Maheen M.; Coburn, Kerry L.; Olichney, John; Schuff, Norbert; Spielman, Daniel; Edland, Steven D.; Black, Sandra; Rosen, Allyson; Kennedy, David; Weiner, Michael; Perry, George

2013-01-01

This supplement to the Journal of Alzheimer's Disease contains more than half of the chapters from The Handbook of Imaging the Alzheimer Brain, which was first presented at the International Conference on Alzheimer's Disease in Paris, in July, 2011. While the Handbook contains 27 chapters that are modified articles from 2009, 2010, and 2011 issues of the Journal of Alzheimer's Disease, this supplement contains the 31 new chapters of that book and an introductory article drawn from the introductions to each section of the book. The Handbook was designed to provide a multilevel overview of the full field of brain imaging related to Alzheimer's disease (AD). The Handbook, as well as this supplement, contains both reviews of the basic concepts of imaging, the latest developments in imaging, and various discussions and perspectives of the problems of the field and promising directions. The Handbook was designed to be useful for students and clinicians interested in AD as well as scientists studying the brain and pathology related to AD. PMID:21971448

Brain MR imaging at ultra-low radiofrequency power.

PubMed

Sarkar, Subhendra N; Alsop, David C; Madhuranthakam, Ananth J; Busse, Reed F; Robson, Philip M; Rofsky, Neil M; Hackney, David B

2011-05-01

To explore the lower limits for radiofrequency (RF) power-induced specific absorption rate (SAR) achievable at 1.5 T for brain magnetic resonance (MR) imaging without loss of tissue signal or contrast present in high-SAR clinical imaging in order to create a potentially viable MR method at ultra-low RF power to image tissues containing implanted devices. An institutional review board-approved HIPAA-compliant prospective MR study design was used, with written informed consent from all subjects prior to MR sessions. Seven healthy subjects were imaged prospectively at 1.5 T with ultra-low-SAR optimized three-dimensional (3D) fast spin-echo (FSE) and fluid-attenuated inversion-recovery (FLAIR) T2-weighted sequences and an ultra-low-SAR 3D spoiled gradient-recalled acquisition in the steady state T1-weighted sequence. Corresponding high-SAR two-dimensional (2D) clinical sequences were also performed. In addition to qualitative comparisons, absolute signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) for multicoil, parallel imaging acquisitions were generated by using a Monte Carlo method for quantitative comparison between ultra-low-SAR and high-SAR results. There were minor to moderate differences in the absolute tissue SNR and CNR values and in qualitative appearance of brain images obtained by using ultra-low-SAR and high-SAR techniques. High-SAR 2D T2-weighted imaging produced slightly higher SNR, while ultra-low-SAR 3D technique not only produced higher SNR for T1-weighted and FLAIR images but also higher CNRs for all three sequences for most of the brain tissues. The 3D techniques adopted here led to a decrease in the absorbed RF power by two orders of magnitude at 1.5 T, and still the image quality was preserved within clinically acceptable imaging times. RSNA, 2011

Real-time reconstruction of three-dimensional brain surface MR image using new volume-surface rendering technique

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

Watanabe, T.; Momose, T.; Oku, S.

It is essential to obtain realistic brain surface images, in which sulci and gyri are easily recognized, when examining the correlation between functional (PET or SPECT) and anatomical (MRI) brain studies. The volume rendering technique (VRT) is commonly employed to make three-dimensional (3D) brain surface images. This technique, however, takes considerable time to make only one 3D image. Therefore it has not been practical to make the brain surface images in arbitrary directions on a real-time basis using ordinary work stations or personal computers. The surface rendering technique (SRT), on the other hand, is much less computationally demanding, but themore » quality of resulting images is not satisfactory for our purpose. A new computer algorithm has been developed to make 3D brain surface MR images very quickly using a volume-surface rendering technique (VSRT), in which the quality of resulting images is comparable to that of VRT and computation time to SRT. In VSRT the process of volume rendering is done only once to the direction of the normal vector of each surface point, rather than each time a new view point is determined as in VRT. Subsequent reconstruction of the 3D image uses a similar algorithm to that of SRT. Thus we can obtain brain surface MR images of sufficient quality viewed from any direction on a real-time basis using an easily available personal computer (Macintosh Quadra 800). The calculation time to make a 3D image is less than 1 sec. in VSRT, while that is more than 15 sec. in the conventional VRT. The difference of resulting image quality between VSRT and VRT is almost imperceptible. In conclusion, our new technique for real-time reconstruction of 3D brain surface MR image is very useful and practical in the functional and anatomical correlation study.« less

Advantages in functional imaging of the brain.

PubMed

Mier, Walter; Mier, Daniela

2015-01-01

As neuronal pathologies cause only minor morphological alterations, molecular imaging techniques are a prerequisite for the study of diseases of the brain. The development of molecular probes that specifically bind biochemical markers and the advances of instrumentation have revolutionized the possibilities to gain insight into the human brain organization and beyond this-visualize structure-function and brain-behavior relationships. The review describes the development and current applications of functional brain imaging techniques with a focus on applications in psychiatry. A historical overview of the development of functional imaging is followed by the portrayal of the principles and applications of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), two key molecular imaging techniques that have revolutionized the ability to image molecular processes in the brain. We conclude that the juxtaposition of PET and fMRI in hybrid PET/MRI scanners enhances the significance of both modalities for research in neurology and psychiatry and might pave the way for a new area of personalized medicine.

Compact and mobile high resolution PET brain imager

DOEpatents

Majewski, Stanislaw [Yorktown, VA; Proffitt, James [Newport News, VA

2011-02-08

A brain imager includes a compact ring-like static PET imager mounted in a helmet-like structure. When attached to a patient's head, the helmet-like brain imager maintains the relative head-to-imager geometry fixed through the whole imaging procedure. The brain imaging helmet contains radiation sensors and minimal front-end electronics. A flexible mechanical suspension/harness system supports the weight of the helmet thereby allowing for patient to have limited movements of the head during imaging scans. The compact ring-like PET imager enables very high resolution imaging of neurological brain functions, cancer, and effects of trauma using a rather simple mobile scanner with limited space needs for use and storage.

Cognition in action: imaging brain/body dynamics in mobile humans.

PubMed

Gramann, Klaus; Gwin, Joseph T; Ferris, Daniel P; Oie, Kelvin; Jung, Tzyy-Ping; Lin, Chin-Teng; Liao, Lun-De; Makeig, Scott

2011-01-01

We have recently developed a mobile brain imaging method (MoBI), that allows for simultaneous recording of brain and body dynamics of humans actively behaving in and interacting with their environment. A mobile imaging approach was needed to study cognitive processes that are inherently based on the use of human physical structure to obtain behavioral goals. This review gives examples of the tight coupling between human physical structure with cognitive processing and the role of supraspinal activity during control of human stance and locomotion. Existing brain imaging methods for actively behaving participants are described and new sensor technology allowing for mobile recordings of different behavioral states in humans is introduced. Finally, we review recent work demonstrating the feasibility of a MoBI system that was developed at the Swartz Center for Computational Neuroscience at the University of California, San Diego, demonstrating the range of behavior that can be investigated with this method.

Fueling and imaging brain activation

PubMed Central

Dienel, Gerald A

2012-01-01

Metabolic signals are used for imaging and spectroscopic studies of brain function and disease and to elucidate the cellular basis of neuroenergetics. The major fuel for activated neurons and the models for neuron–astrocyte interactions have been controversial because discordant results are obtained in different experimental systems, some of which do not correspond to adult brain. In rats, the infrastructure to support the high energetic demands of adult brain is acquired during postnatal development and matures after weaning. The brain's capacity to supply and metabolize glucose and oxygen exceeds demand over a wide range of rates, and the hyperaemic response to functional activation is rapid. Oxidative metabolism provides most ATP, but glycolysis is frequently preferentially up-regulated during activation. Underestimation of glucose utilization rates with labelled glucose arises from increased lactate production, lactate diffusion via transporters and astrocytic gap junctions, and lactate release to blood and perivascular drainage. Increased pentose shunt pathway flux also causes label loss from C1 of glucose. Glucose analogues are used to assay cellular activities, but interpretation of results is uncertain due to insufficient characterization of transport and phosphorylation kinetics. Brain activation in subjects with low blood-lactate levels causes a brain-to-blood lactate gradient, with rapid lactate release. In contrast, lactate flooding of brain during physical activity or infusion provides an opportunistic, supplemental fuel. Available evidence indicates that lactate shuttling coupled to its local oxidation during activation is a small fraction of glucose oxidation. Developmental, experimental, and physiological context is critical for interpretation of metabolic studies in terms of theoretical models. PMID:22612861

Generating Text from Functional Brain Images

PubMed Central

Pereira, Francisco; Detre, Greg; Botvinick, Matthew

2011-01-01

Recent work has shown that it is possible to take brain images acquired during viewing of a scene and reconstruct an approximation of the scene from those images. Here we show that it is also possible to generate text about the mental content reflected in brain images. We began with images collected as participants read names of concrete items (e.g., “Apartment’’) while also seeing line drawings of the item named. We built a model of the mental semantic representation of concrete concepts from text data and learned to map aspects of such representation to patterns of activation in the corresponding brain image. In order to validate this mapping, without accessing information about the items viewed for left-out individual brain images, we were able to generate from each one a collection of semantically pertinent words (e.g., “door,” “window” for “Apartment’’). Furthermore, we show that the ability to generate such words allows us to perform a classification task and thus validate our method quantitatively. PMID:21927602

Prenatal Brain MR Imaging: Reference Linear Biometric Centiles between 20 and 24 Gestational Weeks.

PubMed

Conte, G; Milani, S; Palumbo, G; Talenti, G; Boito, S; Rustico, M; Triulzi, F; Righini, A; Izzo, G; Doneda, C; Zolin, A; Parazzini, C

2018-05-01

Evaluation of biometry is a fundamental step in prenatal brain MR imaging. While different studies have reported reference centiles for MR imaging biometric data of fetuses in the late second and third trimesters of gestation, no one has reported them in fetuses in the early second trimester. We report centiles of normal MR imaging linear biometric data of a large cohort of fetal brains within 24 weeks of gestation. From the data bases of 2 referral centers of fetal medicine, accounting for 3850 examinations, we retrospectively collected 169 prenatal brain MR imaging examinations of singleton pregnancies, between 20 and 24 weeks of gestational age, with normal brain anatomy at MR imaging and normal postnatal neurologic development. To trace the reference centiles, we used the CG-LMS method. Reference biometric centiles for the developing structures of the cerebrum, cerebellum, brain stem, and theca were obtained. The overall interassessor agreement was adequate for all measurements. Reference biometric centiles of the brain structures in fetuses between 20 and 24 weeks of gestational age may be a reliable tool in assessing fetal brain development. © 2018 by American Journal of Neuroradiology.

Functional brain imaging and bioacoustics in the Bottlenose dolphins, Tursiops truncatus

NASA Astrophysics Data System (ADS)

Ridgway, Sam; Finneran, James; Carder, Donald; van Bonn, William; Smith, Cynthia; Houser, Dorian; Mattrey, Robert; Hoh, Carl

2003-10-01

The dolphin brain is the central processing computer for a complex and effective underwater echolocation and communication system. Until now, it has not been possible to study or diagnose disorders of the dolphin brain employing modern functional imaging methods like those used in human medicine. Our most recent studies employ established methods such as behavioral tasks, physiological observations, and computed tomography (CT) and, for the first time, single photon emission computed tomography (SPECT), and positron emission tomography (PET). Trained dolphins slide out of their enclosure on to a mat and are transported by trainers and veterinarians to the laboratory for injection of a ligand. Following ligand injection, brief experiments include trained vocal responses to acoustic, visual, or tactile stimuli. We have used the ligand technetium (Tc-99m) biscisate (Neurolite) to image circulatory flow by SPECT. Fluro-deoxy-d-glucose (18-F-FDG) has been employed to image brain metabolism with PET. Veterinarians carefully monitored dolphins during and after the procedure. Through these methods, we have demonstrated that functional imaging can be employed safely and productively with dolphins to obtain valuable information on brain structure and function for medical and research purposes. Hemispheric differences and variations in flow and metabolism in different brain areas will be shown.

Resting-state functional connectivity imaging of the mouse brain using photoacoustic tomography

NASA Astrophysics Data System (ADS)

Nasiriavanaki, Mohammadreza; Xia, Jun; Wan, Hanlin; Bauer, Adam Q.; Culver, Joseph P.; Wang, Lihong V.

2014-03-01

Resting-state functional connectivity (RSFC) imaging is an emerging neuroimaging approach that aims to identify spontaneous cerebral hemodynamic fluctuations and their associated functional connections. Clinical studies have demonstrated that RSFC is altered in brain disorders such as stroke, Alzheimer's, autism, and epilepsy. However, conventional neuroimaging modalities cannot easily be applied to mice, the most widely used model species for human brain disease studies. For instance, functional magnetic resonance imaging (fMRI) of mice requires a very high magnetic field to obtain a sufficient signal-to-noise ratio and spatial resolution. Functional connectivity mapping with optical intrinsic signal imaging (fcOIS) is an alternative method. Due to the diffusion of light in tissue, the spatial resolution of fcOIS is limited, and experiments have been performed using an exposed skull preparation. In this study, we show for the first time, the use of photoacoustic computed tomography (PACT) to noninvasively image resting-state functional connectivity in the mouse brain, with a large field of view and a high spatial resolution. Bilateral correlations were observed in eight regions, as well as several subregions. These findings agreed well with the Paxinos mouse brain atlas. This study showed that PACT is a promising, non-invasive modality for small-animal functional brain imaging.

A novel method for fast imaging of brain function, non-invasively, with light

NASA Astrophysics Data System (ADS)

Chance, Britton; Anday, Endla; Nioka, Shoko; Zhou, Shuoming; Hong, Long; Worden, Katherine; Li, C.; Murray, T.; Ovetsky, Y.; Pidikiti, D.; Thomas, R.

1998-05-01

Imaging of the human body by any non-invasive technique has been an appropriate goal of physics and medicine, and great success has been obtained with both Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) in brain imaging. Non-imaging responses to functional activation using near infrared spectroscopy of brain (fNIR) obtained in 1993 (Chance, et al. [1]) and in 1994 (Tamura, et al. [2]) are now complemented with images of pre-frontal and parietal stimulation in adults and pre-term neonates in this communication (see also [3]). Prior studies used continuous [4], pulsed [3] or modulated [5] light. The amplitude and phase cancellation of optical patterns as demonstrated for single source detector pairs affords remarkable sensitivity of small object detection in model systems [6]. The methods have now been elaborated with multiple source detector combinations (nine sources, four detectors). Using simple back projection algorithms it is now possible to image sensorimotor and cognitive activation of adult and pre- and full-term neonate human brain function in times < 30 sec and with two dimensional resolutions of < 1 cm in two dimensional displays. The method can be used in evaluation of adult and neonatal cerebral dysfunction in a simple, portable and affordable method that does not require immobilization, as contrasted to MRI and PET.

Unwarping confocal microscopy images of bee brains by nonrigid registration to a magnetic resonance microscopy image.

PubMed

Rohlfing, Torsten; Schaupp, Frank; Haddad, Daniel; Brandt, Robert; Haase, Axel; Menzel, Randolf; Maurer, Calvin R

2005-01-01

Confocal microscopy (CM) is a powerful image acquisition technique that is well established in many biological applications. It provides 3-D acquisition with high spatial resolution and can acquire several different channels of complementary image information. Due to the specimen extraction and preparation process, however, the shapes of imaged objects may differ considerably from their in vivo appearance. Magnetic resonance microscopy (MRM) is an evolving variant of magnetic resonance imaging, which achieves microscopic resolutions using a high magnetic field and strong magnetic gradients. Compared to CM imaging, MRM allows for in situ imaging and is virtually free of geometrical distortions. We propose to combine the advantages of both methods by unwarping CM images using a MRM reference image. Our method incorporates a sequence of image processing operators applied to the MRM image, followed by a two-stage intensity-based registration to compute a nonrigid coordinate transformation between the CM images and the MRM image. We present results obtained using CM images from the brains of 20 honey bees and a MRM image of an in situ bee brain. Copyright 2005 Society of Photo-Optical Instrumentation Engineers.

Rat brain imaging using full field optical coherence microscopy with short multimode fiber probe

NASA Astrophysics Data System (ADS)

Sato, Manabu; Saito, Daisuke; Kurotani, Reiko; Abe, Hiroyuki; Kawauchi, Satoko; Sato, Shunichi; Nishidate, Izumi

2017-02-01

We demonstrated FF OCM(full field optical coherence microscopy) using an ultrathin forward-imaging SMMF (short multimode fiber) probe of 50 μm core diameter, 125 μm diameter, and 7.4 mm length, which is a typical graded-index multimode fiber for optical communications. The axial resolution was measured to be 2.20 μm, which is close to the calculated axial resolution of 2.06 μm. The lateral resolution was evaluated to be 4.38 μm using a test pattern. Assuming that the FWHM of the contrast is the DOF (depth of focus), the DOF of the signal is obtained at 36 μm and that of the OCM is 66 μm. The contrast of the OCT images was 6.1 times higher than that of the signal images due to the coherence gate. After an euthanasia the rat brain was resected and cut at 2.6mm tail from Bregma. Contacting SMMF to the primary somatosensory cortex and the agranular insular cortex of ex vivo brain, OCM images of the brain were measured 100 times with 2μm step. 3D OCM images of the brain were measured, and internal structure information was obtained. The feasibility of an SMMF as an ultrathin forward-imaging probe in full-field OCM has been demonstrated.

Fully automated rodent brain MR image processing pipeline on a Midas server: from acquired images to region-based statistics.

PubMed

Budin, Francois; Hoogstoel, Marion; Reynolds, Patrick; Grauer, Michael; O'Leary-Moore, Shonagh K; Oguz, Ipek

2013-01-01

Magnetic resonance imaging (MRI) of rodent brains enables study of the development and the integrity of the brain under certain conditions (alcohol, drugs etc.). However, these images are difficult to analyze for biomedical researchers with limited image processing experience. In this paper we present an image processing pipeline running on a Midas server, a web-based data storage system. It is composed of the following steps: rigid registration, skull-stripping, average computation, average parcellation, parcellation propagation to individual subjects, and computation of region-based statistics on each image. The pipeline is easy to configure and requires very little image processing knowledge. We present results obtained by processing a data set using this pipeline and demonstrate how this pipeline can be used to find differences between populations.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

PubMed

Pereira, Sergio; Pinto, Adriano; Alves, Victor; Silva, Carlos A

2016-05-01

Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required; however, the large spatial and structural variability among brain tumors make automatic segmentation a challenging problem. In this paper, we propose an automatic segmentation method based on Convolutional Neural Networks (CNN), exploring small 3 ×3 kernels. The use of small kernels allows designing a deeper architecture, besides having a positive effect against overfitting, given the fewer number of weights in the network. We also investigated the use of intensity normalization as a pre-processing step, which though not common in CNN-based segmentation methods, proved together with data augmentation to be very effective for brain tumor segmentation in MRI images. Our proposal was validated in the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013), obtaining simultaneously the first position for the complete, core, and enhancing regions in Dice Similarity Coefficient metric (0.88, 0.83, 0.77) for the Challenge data set. Also, it obtained the overall first position by the online evaluation platform. We also participated in the on-site BRATS 2015 Challenge using the same model, obtaining the second place, with Dice Similarity Coefficient metric of 0.78, 0.65, and 0.75 for the complete, core, and enhancing regions, respectively.

Nanoparticle-assisted-multiphoton microscopy for in vivo brain imaging of mice

NASA Astrophysics Data System (ADS)

Qian, Jun

2015-03-01

Neuro/brain study has attracted much attention during past few years, and many optical methods have been utilized in order to obtain accurate and complete neural information inside the brain. Relying on simultaneous absorption of two or more near-infrared photons by a fluorophore, multiphoton microscopy can achieve deep tissue penetration and efficient light detection noninvasively, which makes it very suitable for thick-tissue and in vivo bioimaging. Nanoparticles possess many unique optical and chemical properties, such as anti-photobleaching, large multiphoton absorption cross-section, and high stability in biological environment, which facilitates their applications in long-term multiphoton microscopy as contrast agents. In this paper, we will introduce several typical nanoparticles (e.g. organic dye doped polymer nanoparticles and gold nanorods) with high multiphoton fluorescence efficiency. We further applied them in two- and three-photon in vivo functional brain imaging of mice, such as brain-microglia imaging, 3D architecture reconstruction of brain blood vessel, and blood velocity measurement.

Feasibility study: real-time 3-D ultrasound imaging of the brain.

PubMed

Smith, Stephen W; Chu, Kengyeh; Idriss, Salim F; Ivancevich, Nikolas M; Light, Edward D; Wolf, Patrick D

2004-10-01

We tested the feasibility of real-time, 3-D ultrasound (US) imaging in the brain. The 3-D scanner uses a matrix phased-array transducer of 512 transmit channels and 256 receive channels operating at 2.5 MHz with a 15-mm diameter footprint. The real-time system scans a 65 degrees pyramid, producing up to 30 volumetric scans per second, and features up to five image planes as well as 3-D rendering, 3-D pulsed-wave and color Doppler. In a human subject, the real-time 3-D scans produced simultaneous transcranial horizontal (axial), coronal and sagittal image planes and real-time volume-rendered images of the gross anatomy of the brain. In a transcranial sheep model, we obtained real-time 3-D color flow Doppler scans and perfusion images using bolus injection of contrast agents into the internal carotid artery.

Imaging the Working Brain.

ERIC Educational Resources Information Center

Swithenby, S. J.

1996-01-01

Very sensitive SQUID (superconducting quantum interference device) detectors are used in the technique known as magnetoencephalography to provide dynamic images of the brain. This can help our fundamental understanding of the way the brain works and may be of particular use in treating disorders such as epilepsy. (Author/MKR)

Brain medical image diagnosis based on corners with importance-values.

PubMed

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection

Brain tumor segmentation using holistically nested neural networks in MRI images.

PubMed

Zhuge, Ying; Krauze, Andra V; Ning, Holly; Cheng, Jason Y; Arora, Barbara C; Camphausen, Kevin; Miller, Robert W

2017-10-01

Gliomas are rapidly progressive, neurologically devastating, largely fatal brain tumors. Magnetic resonance imaging (MRI) is a widely used technique employed in the diagnosis and management of gliomas in clinical practice. MRI is also the standard imaging modality used to delineate the brain tumor target as part of treatment planning for the administration of radiation therapy. Despite more than 20 yr of research and development, computational brain tumor segmentation in MRI images remains a challenging task. We are presenting a novel method of automatic image segmentation based on holistically nested neural networks that could be employed for brain tumor segmentation of MRI images. Two preprocessing techniques were applied to MRI images. The N4ITK method was employed for correction of bias field distortion. A novel landmark-based intensity normalization method was developed so that tissue types have a similar intensity scale in images of different subjects for the same MRI protocol. The holistically nested neural networks (HNN), which extend from the convolutional neural networks (CNN) with a deep supervision through an additional weighted-fusion output layer, was trained to learn the multiscale and multilevel hierarchical appearance representation of the brain tumor in MRI images and was subsequently applied to produce a prediction map of the brain tumor on test images. Finally, the brain tumor was obtained through an optimum thresholding on the prediction map. The proposed method was evaluated on both the Multimodal Brain Tumor Image Segmentation (BRATS) Benchmark 2013 training datasets, and clinical data from our institute. A dice similarity coefficient (DSC) and sensitivity of 0.78 and 0.81 were achieved on 20 BRATS 2013 training datasets with high-grade gliomas (HGG), based on a two-fold cross-validation. The HNN model built on the BRATS 2013 training data was applied to ten clinical datasets with HGG from a locally developed database. DSC and sensitivity of

Ex vivo micro-CT imaging of murine brain models using non-ionic iodinated contrast

NASA Astrophysics Data System (ADS)

Salas Bautista, N.; Martínez-Dávalos, A.; Rodríguez-Villafuerte, M.; Murrieta-Rodríguez, T.; Manjarrez-Marmolejo, J.; Franco-Pérez, J.; Calvillo-Velasco, M. E.

2014-11-01

Preclinical investigation of brain tumors is frequently carried out by means of intracranial implantation of brain tumor xenografts or allografts, with subsequent analysis of tumor growth using conventional histopathology. However, very little has been reported on the use contrast-enhanced techniques in micro-CT imaging for the study of malignant brain tumors in small animal models. The aim of this study has been to test a protocol for ex vivo imaging of murine brain models of glioblastoma multiforme (GBM) after treatment with non-ionic iodinated solution, using an in-house developed laboratory micro-CT. We have found that the best compromise between acquisition time and image quality is obtained using a 50 kVp, 0.5 mAs, 1° angular step on a 360 degree orbit acquisition protocol, with 70 μm reconstructed voxel size using the Feldkamp algorithm. With this parameters up to 4 murine brains can be scanned in tandem in less than 15 minutes. Image segmentation and analysis of three sample brains allowed identifying tumor volumes as small as 0.4 mm3.

Imaging human brain cyto- and myelo-architecture with quantitative OCT (Conference Presentation)

NASA Astrophysics Data System (ADS)

Boas, David A.; Wang, Hui; Konukoglu, Ender; Fischl, Bruce; Sakadzic, Sava; Magnain, Caroline V.

2017-02-01

No current imaging technology allows us to directly and without significant distortion visualize the microscopic and defining anatomical features of the human brain. Ex vivo histological techniques can yield exquisite planar images, but the cutting, mounting and staining that are required components of this type of imaging induce distortions that are different for each slice, introducing cross-slice differences that prohibit true 3D analysis. We are overcoming this issue by utilizing Optical Coherence Tomography (OCT) with the goal to image whole human brain cytoarchitectural and laminar properties with potentially 3.5 µm resolution in block-face without the need for exogenous staining. From the intrinsic scattering contrast of the brain tissue, OCT gives us images that are comparable to Nissl stains, but without the distortions introduced in standard histology as the OCT images are acquired from the block face prior to slicing and thus without the need for subsequent staining and mounting. We have shown that laminar and cytoarchitectural properties of the brain can be characterized with OCT just as well as with Nissl staining. We will present our recent advances to improve the axial resolution while maintaining contrast; improvements afforded by speckle reduction procedures; and efforts to obtain quantitative maps of the optical scattering coefficient, an intrinsic property of the tissue.

Synchrotron radiation imaging is a powerful tool to image brain microvasculature.

PubMed

Zhang, Mengqi; Peng, Guanyun; Sun, Danni; Xie, Yuanyuan; Xia, Jian; Long, Hongyu; Hu, Kai; Xiao, Bo

2014-03-01

Synchrotron radiation (SR) imaging is a powerful experimental tool for micrometer-scale imaging of microcirculation in vivo. This review discusses recent methodological advances and findings from morphological investigations of cerebral vascular networks during several neurovascular pathologies. In particular, it describes recent developments in SR microangiography for real-time assessment of the brain microvasculature under various pathological conditions in small animal models. It also covers studies that employed SR-based phase-contrast imaging to acquire 3D brain images and provide detailed maps of brain vasculature. In addition, a brief introduction of SR technology and current limitations of SR sources are described in this review. In the near future, SR imaging could transform into a common and informative imaging modality to resolve subtle details of cerebrovascular function.

The role of image registration in brain mapping

PubMed Central

Toga, A.W.; Thompson, P.M.

2008-01-01

Image registration is a key step in a great variety of biomedical imaging applications. It provides the ability to geometrically align one dataset with another, and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or across time. Registration algorithms also enable the pooling and comparison of experimental findings across laboratories, the construction of population-based brain atlases, and the creation of systems to detect group patterns in structural and functional imaging data. We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain. PMID:19890483

Wavelet-domain de-noising of OCT images of human brain malignant glioma

NASA Astrophysics Data System (ADS)

Dolganova, I. N.; Aleksandrova, P. V.; Beshplav, S.-I. T.; Chernomyrdin, N. V.; Dubyanskaya, E. N.; Goryaynov, S. A.; Kurlov, V. N.; Reshetov, I. V.; Potapov, A. A.; Tuchin, V. V.; Zaytsev, K. I.

2018-04-01

We have proposed a wavelet-domain de-noising technique for imaging of human brain malignant glioma by optical coherence tomography (OCT). It implies OCT image decomposition using the direct fast wavelet transform, thresholding of the obtained wavelet spectrum and further inverse fast wavelet transform for image reconstruction. By selecting both wavelet basis and thresholding procedure, we have found an optimal wavelet filter, which application improves differentiation of the considered brain tissue classes - i.e. malignant glioma and normal/intact tissue. Namely, it allows reducing the scattering noise in the OCT images and retaining signal decrement for each tissue class. Therefore, the observed results reveals the wavelet-domain de-noising as a prospective tool for improved characterization of biological tissue using the OCT.

Synchrotron radiation imaging is a powerful tool to image brain microvasculature

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

Zhang, Mengqi; Sun, Danni; Xie, Yuanyuan

2014-03-15

Synchrotron radiation (SR) imaging is a powerful experimental tool for micrometer-scale imaging of microcirculation in vivo. This review discusses recent methodological advances and findings from morphological investigations of cerebral vascular networks during several neurovascular pathologies. In particular, it describes recent developments in SR microangiography for real-time assessment of the brain microvasculature under various pathological conditions in small animal models. It also covers studies that employed SR-based phase-contrast imaging to acquire 3D brain images and provide detailed maps of brain vasculature. In addition, a brief introduction of SR technology and current limitations of SR sources are described in this review. Inmore » the near future, SR imaging could transform into a common and informative imaging modality to resolve subtle details of cerebrovascular function.« less

Evaluation of MLACF based calculated attenuation brain PET imaging for FDG patient studies

NASA Astrophysics Data System (ADS)

Bal, Harshali; Panin, Vladimir Y.; Platsch, Guenther; Defrise, Michel; Hayden, Charles; Hutton, Chloe; Serrano, Benjamin; Paulmier, Benoit; Casey, Michael E.

2017-04-01

Calculating attenuation correction for brain PET imaging rather than using CT presents opportunities for low radiation dose applications such as pediatric imaging and serial scans to monitor disease progression. Our goal is to evaluate the iterative time-of-flight based maximum-likelihood activity and attenuation correction factors estimation (MLACF) method for clinical FDG brain PET imaging. FDG PET/CT brain studies were performed in 57 patients using the Biograph mCT (Siemens) four-ring scanner. The time-of-flight PET sinograms were acquired using the standard clinical protocol consisting of a CT scan followed by 10 min of single-bed PET acquisition. Images were reconstructed using CT-based attenuation correction (CTAC) and used as a gold standard for comparison. Two methods were compared with respect to CTAC: a calculated brain attenuation correction (CBAC) and MLACF based PET reconstruction. Plane-by-plane scaling was performed for MLACF images in order to fix the variable axial scaling observed. The noise structure of the MLACF images was different compared to those obtained using CTAC and the reconstruction required a higher number of iterations to obtain comparable image quality. To analyze the pooled data, each dataset was registered to a standard template and standard regions of interest were extracted. An SUVr analysis of the brain regions of interest showed that CBAC and MLACF were each well correlated with CTAC SUVrs. A plane-by-plane error analysis indicated that there were local differences for both CBAC and MLACF images with respect to CTAC. Mean relative error in the standard regions of interest was less than 5% for both methods and the mean absolute relative errors for both methods were similar (3.4%  ±  3.1% for CBAC and 3.5%  ±  3.1% for MLACF). However, the MLACF method recovered activity adjoining the frontal sinus regions more accurately than CBAC method. The use of plane-by-plane scaling of MLACF images was found to be a

Advanced Pediatric Brain Imaging Research and Training Program

DTIC Science & Technology

2013-10-01

diffusion tensor imaging and perfusion ( arterial spin labeling) MRI data and to relate measures of global and regional brain microstructural organization...AD_________________ Award Number: W81XWH-11-2-0198 TITLE: Advanced Pediatric Brain Imaging...September 2013 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Advanced Pediatric Brain Imaging Research and Training Program 5b. GRANT NUMBER W81XWH

MR Imaging Applications in Mild Traumatic Brain Injury: An Imaging Update

PubMed Central

Wu, Xin; Kirov, Ivan I.; Gonen, Oded; Ge, Yulin; Grossman, Robert I.

2016-01-01

Mild traumatic brain injury (mTBI), also commonly referred to as concussion, affects millions of Americans annually. Although computed tomography is the first-line imaging technique for all traumatic brain injury, it is incapable of providing long-term prognostic information in mTBI. In the past decade, the amount of research related to magnetic resonance (MR) imaging of mTBI has grown exponentially, partly due to development of novel analytical methods, which are applied to a variety of MR techniques. Here, evidence of subtle brain changes in mTBI as revealed by these techniques, which are not demonstrable by conventional imaging, will be reviewed. These changes can be considered in three main categories of brain structure, function, and metabolism. Macrostructural and microstructural changes have been revealed with three-dimensional MR imaging, susceptibility-weighted imaging, diffusion-weighted imaging, and higher order diffusion imaging. Functional abnormalities have been described with both task-mediated and resting-state blood oxygen level–dependent functional MR imaging. Metabolic changes suggesting neuronal injury have been demonstrated with MR spectroscopy. These findings improve understanding of the true impact of mTBI and its pathogenesis. Further investigation may eventually lead to improved diagnosis, prognosis, and management of this common and costly condition. © RSNA, 2016 PMID:27183405

Brain extraction from normal and pathological images: A joint PCA/Image-Reconstruction approach.

PubMed

Han, Xu; Kwitt, Roland; Aylward, Stephen; Bakas, Spyridon; Menze, Bjoern; Asturias, Alexander; Vespa, Paul; Van Horn, John; Niethammer, Marc

2018-08-01

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and

Connectome imaging for mapping human brain pathways

PubMed Central

Shi, Y; Toga, A W

2017-01-01

With the fast advance of connectome imaging techniques, we have the opportunity of mapping the human brain pathways in vivo at unprecedented resolution. In this article we review the current developments of diffusion magnetic resonance imaging (MRI) for the reconstruction of anatomical pathways in connectome studies. We first introduce the background of diffusion MRI with an emphasis on the technical advances and challenges in state-of-the-art multi-shell acquisition schemes used in the Human Connectome Project. Characterization of the microstructural environment in the human brain is discussed from the tensor model to the general fiber orientation distribution (FOD) models that can resolve crossing fibers in each voxel of the image. Using FOD-based tractography, we describe novel methods for fiber bundle reconstruction and graph-based connectivity analysis. Building upon these novel developments, there have already been successful applications of connectome imaging techniques in reconstructing challenging brain pathways. Examples including retinofugal and brainstem pathways will be reviewed. Finally, we discuss future directions in connectome imaging and its interaction with other aspects of brain imaging research. PMID:28461700

A genome-scale map of expression for a mouse brain section obtained using voxelation

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

Chin, Mark H.; Geng, Alex B.; Khan, Arshad H.

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed 2- dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genesmore » with unexpected patterns were identified and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.« less

Brain CT image similarity retrieval method based on uncertain location graph.

PubMed

Pan, Haiwei; Li, Pengyuan; Li, Qing; Han, Qilong; Feng, Xiaoning; Gao, Linlin

2014-03-01

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish

PubMed Central

Yang, Zhe; Mei, Li; Xia, Fei; Luo, Qingming; Fu, Ling; Gong, Hui

2015-01-01

In vivo functional imaging at single-neuron resolution is an important approach to visualize biological processes in neuroscience. Light sheet microscopy (LSM) is a cutting edge in vivo imaging technique that provides micron-scale spatial resolution at high frame rate. Due to the scattering and absorption of tissue, however, conventional LSM is inadequate to resolve cells because of the attenuated signal to noise ratio (SNR). Using dual-beam illumination and confocal dual-slit detection, here a dual-slit confocal LSM is demonstrated to obtain the SNR enhanced images with frame rate twice as high as line confocal LSM method. Through theoretical calculations and experiments, the correlation between the slit’s width and SNR was determined to optimize the image quality. In vivo whole brain structural imaging stacks and the functional imaging sequences of single slice were obtained for analysis of calcium activities at single-cell resolution. A two-fold increase in imaging speed of conventional confocal LSM makes it possible to capture the sequence of the neurons’ activities and help reveal the potential functional connections in the whole zebrafish’s brain. PMID:26137381

EBG Based Microstrip Patch Antenna for Brain Tumor Detection via Scattering Parameters in Microwave Imaging System.

PubMed

Inum, Reefat; Rana, Md Masud; Shushama, Kamrun Nahar; Quader, Md Anwarul

2018-01-01

A microwave brain imaging system model is envisaged to detect and visualize tumor inside the human brain. A compact and efficient microstrip patch antenna is used in the imaging technique to transmit equivalent signal and receive backscattering signal from the stratified human head model. Electromagnetic band gap (EBG) structure is incorporated on the antenna ground plane to enhance the performance. Rectangular and circular EBG structures are proposed to investigate the antenna performance. Incorporation of circular EBG on the antenna ground plane provides an improvement of 22.77% in return loss, 5.84% in impedance bandwidth, and 16.53% in antenna gain with respect to the patch antenna with rectangular EBG. The simulation results obtained from CST are compared to those obtained from HFSS to validate the design. Specific absorption rate (SAR) of the modeled head tissue for the proposed antenna is determined. Different SAR values are compared with the established standard SAR limit to provide a safety regulation of the imaging system. A monostatic radar-based confocal microwave imaging algorithm is applied to generate the image of tumor inside a six-layer human head phantom model. S -parameter signals obtained from circular EBG loaded patch antenna in different scanning modes are utilized in the imaging algorithm to effectively produce a high-resolution image which reliably indicates the presence of tumor inside human brain.

EBG Based Microstrip Patch Antenna for Brain Tumor Detection via Scattering Parameters in Microwave Imaging System

PubMed Central

Rana, Md. Masud; Shushama, Kamrun Nahar; Quader, Md. Anwarul

2018-01-01

A microwave brain imaging system model is envisaged to detect and visualize tumor inside the human brain. A compact and efficient microstrip patch antenna is used in the imaging technique to transmit equivalent signal and receive backscattering signal from the stratified human head model. Electromagnetic band gap (EBG) structure is incorporated on the antenna ground plane to enhance the performance. Rectangular and circular EBG structures are proposed to investigate the antenna performance. Incorporation of circular EBG on the antenna ground plane provides an improvement of 22.77% in return loss, 5.84% in impedance bandwidth, and 16.53% in antenna gain with respect to the patch antenna with rectangular EBG. The simulation results obtained from CST are compared to those obtained from HFSS to validate the design. Specific absorption rate (SAR) of the modeled head tissue for the proposed antenna is determined. Different SAR values are compared with the established standard SAR limit to provide a safety regulation of the imaging system. A monostatic radar-based confocal microwave imaging algorithm is applied to generate the image of tumor inside a six-layer human head phantom model. S-parameter signals obtained from circular EBG loaded patch antenna in different scanning modes are utilized in the imaging algorithm to effectively produce a high-resolution image which reliably indicates the presence of tumor inside human brain. PMID:29623087

Brain's tumor image processing using shearlet transform

NASA Astrophysics Data System (ADS)

Cadena, Luis; Espinosa, Nikolai; Cadena, Franklin; Korneeva, Anna; Kruglyakov, Alexey; Legalov, Alexander; Romanenko, Alexey; Zotin, Alexander

2017-09-01

Brain tumor detection is well known research area for medical and computer scientists. In last decades there has been much research done on tumor detection, segmentation, and classification. Medical imaging plays a central role in the diagnosis of brain tumors and nowadays uses methods non-invasive, high-resolution techniques, especially magnetic resonance imaging and computed tomography scans. Edge detection is a fundamental tool in image processing, particularly in the areas of feature detection and feature extraction, which aim at identifying points in a digital image at which the image has discontinuities. Shearlets is the most successful frameworks for the efficient representation of multidimensional data, capturing edges and other anisotropic features which frequently dominate multidimensional phenomena. The paper proposes an improved brain tumor detection method by automatically detecting tumor location in MR images, its features are extracted by new shearlet transform.

Mechanism of Chronic Pain in Rodent Brain Imaging

NASA Astrophysics Data System (ADS)

Chang, Pei-Ching

Chronic pain is a significant health problem that greatly impacts the quality of life of individuals and imparts high costs to society. Despite intense research effort in understanding of the mechanism of pain, chronic pain remains a clinical problem that has few effective therapies. The advent of human brain imaging research in recent years has changed the way that chronic pain is viewed. To further extend the use of human brain imaging techniques for better therapies, the adoption of imaging technique onto the animal pain models is essential, in which underlying brain mechanisms can be systematically studied using various combination of imaging and invasive techniques. The general goal of this thesis is to addresses how brain develops and maintains chronic pain in an animal model using fMRI. We demonstrate that nucleus accumbens, the central component of mesolimbic circuitry, is essential in development of chronic pain. To advance our imaging technique, we develop an innovative methodology to carry out fMRI in awake, conscious rat. Using this cutting-edge technique, we show that allodynia is assoicated with shift brain response toward neural circuits associated nucleus accumbens and prefrontal cortex that regulate affective and cognitive component of pain. Taken together, this thesis provides a deeper understanding of how brain mediates pain. It builds on the existing body of knowledge through maximizing the depth of insight into brain imaging of chronic pain.

Brain MR image segmentation using NAMS in pseudo-color.

PubMed

Li, Hua; Chen, Chuanbo; Fang, Shaohong; Zhao, Shengrong

2017-12-01

Image segmentation plays a crucial role in various biomedical applications. In general, the segmentation of brain Magnetic Resonance (MR) images is mainly used to represent the image with several homogeneous regions instead of pixels for surgical analyzing and planning. This paper proposes a new approach for segmenting MR brain images by using pseudo-color based segmentation with Non-symmetry and Anti-packing Model with Squares (NAMS). First of all, the NAMS model is presented. The model can represent the image with sub-patterns to keep the image content and largely reduce the data redundancy. Second, the key idea is proposed that convert the original gray-scale brain MR image into a pseudo-colored image and then segment the pseudo-colored image with NAMS model. The pseudo-colored image can enhance the color contrast in different tissues in brain MR images, which can improve the precision of segmentation as well as directly visual perceptional distinction. Experimental results indicate that compared with other brain MR image segmentation methods, the proposed NAMS based pseudo-color segmentation method performs more excellent in not only segmenting precisely but also saving storage.

Development of a multi-exposure speckle imaging for mice brain imaging

NASA Astrophysics Data System (ADS)

Soleimanzad, Haleh; Gurden, Hirac; Pain, Frédéric

2017-02-01

In the last decade, Laser Speckle Contrast Imaging (LSCI) has been proposed and validated for imaging cerebral blood flow at the rodent brain surface in vivo. The technique relies on the calculation of the spatial speckle contrast, which is related to the velocity of scatterers (red blood cells). The implementation of the technique requires a partial craniotomy so that the brain tissues of interest can be illuminated with a laser diode. However, the studies of changes in the microcirculation during disease progression or treatment require longitudinal studies (i.e. imaging is done repeatedly over weeks or even months). Practically, the less invasive way to obtain such data is to image through the thinned skull without a craniotomy. However the presence of static scatterers (skull) will affect the speckle calculation and produce a bias in the estimation of the microcirculation changes. An extension to LSCI, termed Multi-Exposure Speckle Imaging (MESI) was proposed and validated a few years ago that address these limitations. It relies on a model of the speckle contrast as a function of the exposure time and the proportion of static scatterers. Here, we used MESI with the aim of repeatedly imaging the olfactory bulb of mice models of obesity. First, we have developed a MESI set up which was characterized on microfluidic flow phantoms with different flow-rates and channel diameters to simulate blood flow in animal model characteristics. Second, we show that MESI can discriminate flows in the presence of static scatterers and it can measure flow changes consistently. Finally we provide an in vivo validation of the technique in mice with and without a craniotomy.

Magnetic resonance imaging of the pediatric brain

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

Salamon, G.; Raynaud, C.; Regis, J.

1990-01-01

The atlas presents sequences of MRI sections parallel to the orbito-meatal plane in children from birth through the age of sixteen years. Each child was studied horizontally and sagitally and three-dimensional brain images were reconstructed to facilitate accurate identification of sulci and gyri. The images show crucial aspects of brain development such as the constancy of the brain stem and primitive brain from birth onward; the development of the telencephalon, characterized by deepening of sulci and growth of the cerebral cortex surface; and the different stages of white matter myelinization.

Beyond a bigger brain: Multivariable structural brain imaging and intelligence

PubMed Central

Ritchie, Stuart J.; Booth, Tom; Valdés Hernández, Maria del C.; Corley, Janie; Maniega, Susana Muñoz; Gow, Alan J.; Royle, Natalie A.; Pattie, Alison; Karama, Sherif; Starr, John M.; Bastin, Mark E.; Wardlaw, Joanna M.; Deary, Ian J.

2015-01-01

People with larger brains tend to score higher on tests of general intelligence (g). It is unclear, however, how much variance in intelligence other brain measurements would account for if included together with brain volume in a multivariable model. We examined a large sample of individuals in their seventies (n = 672) who were administered a comprehensive cognitive test battery. Using structural equation modelling, we related six common magnetic resonance imaging-derived brain variables that represent normal and abnormal features—brain volume, cortical thickness, white matter structure, white matter hyperintensity load, iron deposits, and microbleeds—to g and to fluid intelligence. As expected, brain volume accounted for the largest portion of variance (~ 12%, depending on modelling choices). Adding the additional variables, especially cortical thickness (+~ 5%) and white matter hyperintensity load (+~ 2%), increased the predictive value of the model. Depending on modelling choices, all neuroimaging variables together accounted for 18–21% of the variance in intelligence. These results reveal which structural brain imaging measures relate to g over and above the largest contributor, total brain volume. They raise questions regarding which other neuroimaging measures might account for even more of the variance in intelligence. PMID:26240470

Imaging of rat brain using short graded-index multimode fiber

NASA Astrophysics Data System (ADS)

Sato, Manabu; Kanno, Takahiro; Ishihara, Syoutarou; Suto, Hiroshi; Takahashi, Toshihiro; Kurotani, Reiko; Abe, Hiroyuki; Nishidate, Izumi

2014-03-01

Clinically it is important to image structures of brain at deeper areas with low invasions, for example, the pathological information is not obtained enough from the white matter. Preliminarily we have measured transmission images of rat brain using the short graded-index multimode fiber (SMMF) with the diameter of 140μm and length of 5mm. SMMF (core diameter, 100μm) was cut using a fiber cleaver and was fixed in a jig. Fiber lengths inside and outside jig were 3mm and 2mm, respectively. The jig was attached at the 20x objective lens. The conventional optical microscope was used to measure images. In basic characteristics, it was confirmed that the imaging conditions almost corresponded to calculations with the ray-transfer matrix and the spatial resolution was evaluated at about 4.4μm by measuring the test pattern. After euthanasia the rat parietal brain was excised with thickness around 1.5mm and was set on the slide glass. The tissue was illuminated through the slide glass by the bundle fiber with Halogen lamp. The tip of SMMF was inserted into the tissue by lifting the sample stage. The transmission image at each depth from 0.1mm to 1.53mm was measured. Around the depth of 1.45mm, granular structures with sizes of 4-5μm were recognized and corresponded to images by HE stained tissue. Total measurement time was within 2 hours. The feasibilities to image the depth of 5 mm with SMMF have been shown.

Novel Nanotechnologies for Brain Cancer Therapeutics and Imaging.

PubMed

Ferroni, Letizia; Gardin, Chiara; Della Puppa, Alessandro; Sivolella, Stefano; Brunello, Giulia; Scienza, Renato; Bressan, Eriberto; D'Avella, Domenico; Zavan, Barbara

2015-11-01

Despite progress in surgery, radiotherapy, and in chemotherapy, an effective curative treatment of brain cancer, specifically malignant gliomas, does not yet exist. The efficacy of current anti-cancer strategies in brain tumors is limited by the lack of specific therapies against malignant cells. Besides, the delivery of the drugs to brain tumors is limited by the presence of the blood-brain barrier. Nanotechnology today offers a unique opportunity to develop more effective brain cancer imaging and therapeutics. In particular, the development of nanocarriers that can be conjugated with several functional molecules including tumor-specific ligands, anticancer drugs, and imaging probes, can provide new devices which are able to overcome the difficulties of the classical strategies. Nanotechnology-based approaches hold great promise for revolutionizing brain cancer medical treatments, imaging, and diagnosis.

DCS-SVM: a novel semi-automated method for human brain MR image segmentation.

PubMed

Ahmadvand, Ali; Daliri, Mohammad Reza; Hajiali, Mohammadtaghi

2017-11-27

In this paper, a novel method is proposed which appropriately segments magnetic resonance (MR) brain images into three main tissues. This paper proposes an extension of our previous work in which we suggested a combination of multiple classifiers (CMC)-based methods named dynamic classifier selection-dynamic local training local Tanimoto index (DCS-DLTLTI) for MR brain image segmentation into three main cerebral tissues. This idea is used here and a novel method is developed that tries to use more complex and accurate classifiers like support vector machine (SVM) in the ensemble. This work is challenging because the CMC-based methods are time consuming, especially on huge datasets like three-dimensional (3D) brain MR images. Moreover, SVM is a powerful method that is used for modeling datasets with complex feature space, but it also has huge computational cost for big datasets, especially those with strong interclass variability problems and with more than two classes such as 3D brain images; therefore, we cannot use SVM in DCS-DLTLTI. Therefore, we propose a novel approach named "DCS-SVM" to use SVM in DCS-DLTLTI to improve the accuracy of segmentation results. The proposed method is applied on well-known datasets of the Internet Brain Segmentation Repository (IBSR) and promising results are obtained.

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.

Diagnostic imaging of traumatic brain injury.

PubMed

Furlow, Bryant

2006-01-01

In this Directed Reading, the history and epidemiology of traumatic brain injury (TBI) will be briefly introduced, the physical and physiological nature of TBI reviewed and the role of imaging in the assessment of TBI patients described. New imaging techniques and recent findings about the neurological correlates of TBI symptoms and outcomes from studies using different imaging modalities and techniques will also be discussed. This directed reading will focus on closed-head TBI; penetrating missile brain injuries, such as those caused by bullet wounds, will not be reviewed.

Groupwise registration of MR brain images with tumors.

PubMed

Tang, Zhenyu; Wu, Yihong; Fan, Yong

2017-08-04

A novel groupwise image registration framework is developed for registering MR brain images with tumors. Our method iteratively estimates a normal-appearance counterpart for each tumor image to be registered and constructs a directed graph (digraph) of normal-appearance images to guide the groupwise image registration. Particularly, our method maps each tumor image to its normal appearance counterpart by identifying and inpainting brain tumor regions with intensity information estimated using a low-rank plus sparse matrix decomposition based image representation technique. The estimated normal-appearance images are groupwisely registered to a group center image guided by a digraph of images so that the total length of 'image registration paths' to be the minimum, and then the original tumor images are warped to the group center image using the resulting deformation fields. We have evaluated our method based on both simulated and real MR brain tumor images. The registration results were evaluated with overlap measures of corresponding brain regions and average entropy of image intensity information, and Wilcoxon signed rank tests were adopted to compare different methods with respect to their regional overlap measures. Compared with a groupwise image registration method that is applied to normal-appearance images estimated using the traditional low-rank plus sparse matrix decomposition based image inpainting, our method achieved higher image registration accuracy with statistical significance (p  =  7.02  ×  10 -9 ).

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics.

PubMed

He, Bin; Sohrabpour, Abbas; Brown, Emery; Liu, Zhongming

2018-06-04

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Björk-Shiley convexoconcave valves: susceptibility artifacts at brain MR imaging and mechanical valve fractures.

PubMed

van Gorp, Maarten J; van der Graaf, Yolanda; de Mol, Bas A J M; Bakker, Chris J G; Witkamp, Theo D; Ramos, Lino M P; Mali, Willem P T M

2004-03-01

To assess the relationship between heart valve history and susceptibility artifacts at magnetic resonance (MR) imaging of the brain in patients with Björk-Shiley convexoconcave (BSCC) valves. MR images of the brain were obtained in 58 patients with prosthetic heart valves: 20 patients had BSCC valve replacements, and 38 had other types of heart valves. Two experienced neuroradiologists determined the presence or absence of susceptibility artifacts in a consensus reading. Artifacts were defined as characteristic black spots that were visible on T2*-weighted gradient-echo MR images. The statuses of the 20 explanted BSCC valves-specifically, whether they were intact or had an outlet strut fracture (OSF) or a single-leg fracture (SLF)-had been determined earlier. Number of artifacts seen at brain MR imaging was correlated with explanted valve status, and differences were analyzed with nonparametric statistical tests. Significantly more patients with BSCC valves (17 [85%] of 20 patients) than patients with other types of prosthetic valves (18 [47%] of 38 patients) had susceptibility artifacts at MR imaging (P =.005). BSCC valve OSFs were associated with a significantly higher number of artifacts than were intact BSCC valves (P =.01). No significant relationship between SLF and number of artifacts was observed. Susceptibility artifacts at brain MR imaging are not restricted to patients with BSCC valves. These artifacts can be seen on images obtained in patients with various other types of fractured and intact prosthetic heart valves. Copyright RSNA, 2004

MRI brain imaging.

PubMed

Skinner, Sarah

2013-11-01

General practitioners (GPs) are expected to be allowed to request MRI scans for adults for selected clinically appropriate indications from November 2013 as part of the expansion of Medicare-funded MRI services announced by the Federal Government in 2011. This article aims to give a brief overview of MRI brain imaging relevant to GPs, which will facilitate explanation of scan findings and management planning with their patients. Basic imaging techniques, common findings and terminology are presented using some illustrative case examples.

[Brain imaging in autism spectrum disorders. A review].

PubMed

Dziobek, I; Köhne, S

2011-05-01

In the past two decades, an increasing number of functional and structural brain imaging studies has provided insights into the neurobiological basis of autism spectrum disorders (ASD). This article summarizes pertinent functional brain imaging studies addressing the neuronal underpinnings of ASD symptomatology (impairments in social interaction and communication, repetitive and restrictive behavior) and associated neuropsychological deficits (theory of mind, executive functions, central coherence), complemented by relevant structural imaging findings. The results of these studies show that although cognitive functions in ASD are generally mediated by the same brain regions as in typically developed individuals, the degree and especially the patterns of brain activation often differ. Therefore, a hypothesis of aberrant network connectivity has increasingly been favored over one of focal brain dysfunction.

Contrast enhancement in EIT imaging of the brain.

PubMed

Nissinen, A; Kaipio, J P; Vauhkonen, M; Kolehmainen, V

2016-01-01

We consider electrical impedance tomography (EIT) imaging of the brain. The brain is surrounded by the poorly conducting skull which has low conductivity compared to the brain. The skull layer causes a partial shielding effect which leads to weak sensitivity for the imaging of the brain tissue. In this paper we propose an approach based on the Bayesian approximation error approach, to enhance the contrast in brain imaging. With this approach, both the (uninteresting) geometry and the conductivity of the skull are embedded in the approximation error statistics, which leads to a computationally efficient algorithm that is able to detect features such as internal haemorrhage with significantly increased sensitivity and specificity. We evaluate the approach with simulations and phantom data.

Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

NASA Astrophysics Data System (ADS)

Moeskops, Pim; Viergever, Max A.; Benders, Manon J. N. L.; Išgum, Ivana

2015-03-01

Automatic brain tissue segmentation is of clinical relevance in images acquired at all ages. The literature presents a clear distinction between methods developed for MR images of infants, and methods developed for images of adults. The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images. The evaluated method employs supervised voxel classification in subsequent stages, exploiting spatial and intensity information. Evaluation was performed using images available within the MRBrainS13 challenge. The obtained average Dice coefficients were 85.77% for grey matter, 88.66% for white matter, 81.08% for cerebrospinal fluid, 95.65% for cerebrum, and 96.92% for intracranial cavity, currently resulting in the best overall ranking. The possibility of applying the same method to neonatal as well as adult images can be of great value in cross-sectional studies that include a wide age range.

Look again: effects of brain images and mind-brain dualism on lay evaluations of research.

PubMed

Hook, Cayce J; Farah, Martha J

2013-09-01

Brain scans have frequently been credited with uniquely seductive and persuasive qualities, leading to claims that fMRI research receives a disproportionate share of public attention and funding. It has been suggested that functional brain images are fascinating because they contradict dualist beliefs regarding the relationship between the body and the mind. Although previous research has indicated that brain images can increase judgments of an article's scientific reasoning, the hypotheses that brain scans make research appear more interesting, surprising, or worthy of funding have not been tested. Neither has the relation between the allure of brain imaging and dualism. In the following three studies, laypersons rated both fictional research descriptions and real science news articles accompanied by brain scans, bar charts, or photographs. Across 988 participants, we found little evidence of neuroimaging's seductive allure or of its relation to self-professed dualistic beliefs. These results, taken together with other recent null findings, suggest that brain images are less powerful than has been argued.

Proton magnetic resonance spectroscopy imaging in the study of human brain cancer.

PubMed

Martínez-Bisbal, M C; Celda, B

2009-12-01

Magnetic resonance spectroscopic imaging (MRSI) is a non-invasive imaging technique that provides metabolic information on brain tumor. This biochemical information can be processed and presented as density maps of several metabolites, among them N-acetylaspartate (marker of neuronal viability), choline (marker of membrane turnover), creatine (related to the energy state of the cells), myo-Inositol (exclusively found in astrocytes), lipids and lactate (observed in necrosis and other pathological processes) which mean relevant information in the context of brain tumors. Thus, this technique is a multiparametrical molecular imaging method that can complete the magnetic resonance imaging (MRI) study enabling the detection of biochemical patterns of different features and aspects of brain tumors. In this article, the role of MRSI as a molecular imaging technique to provide biochemical information on human brain tumors is reviewed. The most frequent questions and situations in the study of human brain tumors in clinical settings will be considered, as well as the distinction of neoplastic lesions from non neoplastic, the tumor type identification, the study of heterogeneity and infiltration of normal appearing white matter and the therapy following with detection of side effects. The great amount of data in MRSI acquisition compared to the single voxel techniques requires the use of automated methods of quantification, but the possibility to obtain self-reference in the non-affected areas allows different strategies for data handling and interpretation, as presented in the literature. The combination of MRSI with other physiological MRI techniques and positron emission tomography is also included in this review.

[Functional magnetic resonance imaging of brain of college students with internet addiction].

PubMed

DU, Wanping; Liu, Jun; Gao, Xunping; Li, Lingjiang; Li, Weihui; Li, Xin; Zhang, Yan; Zhou, Shunke

2011-08-01

To explore the functional locations of brain regions related to internet addiction (IA)with task-functional magnetic resonance imaging (fMRI). Nineteen college students who had internet game addition and 19 controls accepted the stimuli of videos via computer. The 3.0 Tesla MRI was used to record the Results of echo plannar imaging. The block design method was used. Intragroup and intergroup analysis Results in the 2 groups were obtained. The differences between the 2 groups were analyzed. The internet game videos markedly activated the brain regions of the college students who had or had no internet game addiction. Compared with the control group, the IA group showed increased activation in the right superior parietal lobule, right insular lobe, right precuneus, right cingulated gyrus, and right superior temporal gyrus. Internet game tasks can activate the vision, space, attention and execution center which are composed of temporal occipital gyrus and frontal parietal gyrus. Abnormal brain function and lateral activation of the right brain may exist in IA.

Phase imaging in brain using SWIFT

NASA Astrophysics Data System (ADS)

Lehto, Lauri Juhani; Garwood, Michael; Gröhn, Olli; Corum, Curtis Andrew

2015-03-01

The majority of MRI phase imaging is based on gradient recalled echo (GRE) sequences. This work studies phase contrast behavior due to small off-resonance frequency offsets in brain using SWIFT, a FID-based sequence with nearly zero acquisition delay. 1D simulations and a phantom study were conducted to describe the behavior of phase accumulation in SWIFT. Imaging experiments of known brain phase contrast properties were conducted in a perfused rat brain comparing GRE and SWIFT. Additionally, a human brain sample was imaged. It is demonstrated how SWIFT phase is orientation dependent and correlates well with GRE, linking SWIFT phase to similar off-resonance sources as GRE. The acquisition time is shown to be analogous to TE for phase accumulation time. Using experiments with and without a magnetization transfer preparation, the likely effect of myelin water pool contribution is seen as a phase increase for all acquisition times. Due to the phase accumulation during acquisition, SWIFT phase contrast can be sensitized to small frequency differences between white and gray matter using low acquisition bandwidths.

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.

Three-dimensional imaging of the brain cavities in human embryos.

PubMed

Blaas, H G; Eik-Nes, S H; Kiserud, T; Berg, S; Angelsen, B; Olstad, B

1995-04-01

A system for high-resolution three-dimensional imaging of small structures has been developed, based on the Vingmed CFM-800 annular array sector scanner with a 7.5-MHz transducer attached to a PC-based TomTec Echo-Scan unit. A stepper motor rotates the transducer 180 degrees and the complete three-dimensional scan consists of 132 two-dimensional images, video-grabbed and scan-converted into a regular volumetric data set by the TomTec unit. Three normal pregnancies with embryos of gestational age 7, 9 and 10 weeks received a transvaginal examination with special attention to the embryonic/fetal brain. In all three cases, it was possible to obtain high-resolution images of the brain cavities. At 7 weeks, both hemispheres and their connection to the third ventricle were delineated. The isthmus rhombencephali could be visualized. At 9 weeks, the continuous development of the brain cavities could be followed and at 11 weeks the dominating size of the hemispheres could be depicted. It is concluded that present ultrasound technology has reached a stage where structures of only a few millimeters can be imaged in vivo in three-dimensions with a quality that resembles the plaster figures used in embryonic laboratories. The method can become an important tool in future embryological research and also in the detection of early developmental disorders of the embryo.

Brain imaging in methamphetamine-treated mice using a nitroxide contrast agent for EPR imaging of the redox status and a gadolinium contrast agent for MRI observation of blood-brain barrier function.

PubMed

Emoto, M C; Yamato, M; Sato-Akaba, H; Yamada, K; Matsuoka, Y; Fujii, H G

2015-01-01

Methamphetamine (METH)-induced neurotoxicity is associated with mitochondrial dysfunction and enhanced oxidative stress. The aims of the present study conducted in the mouse brain repetitively treated with METH were to (1) examine the redox status using the redox-sensitive imaging probe 3-methoxycarbonyl-2,2,5,5-tetramethylpiperidine-1-oxyl (MCP) and (2) non-invasively visualize the brain redox status with electron paramagnetic resonance (EPR) imaging. The rate of reduction of MCP was measured from a series of temporal EPR images of mouse heads, and this rate was used to construct a two-dimensional map of rate constants called a "redox map." The obtained redox map clearly illustrated the change in redox balance in the METH-treated mouse brain that is a known result of oxidative damage. Biochemical assays also showed that the level of thiobarbituric acid-reactive substance, an index of lipid peroxidation, was increased in mouse brains by METH. The enhanced reduction in MCP observed in mouse brains was remarkably suppressed by treatment with the dopamine synthase inhibitor, α-methyl-p-tyrosine, suggesting that enhancement of the reduction reaction of MCP resulted from enzymatic reduction in the mitochondrial respiratory chain. Furthermore, magnetic resonance imaging (MRI) of METH-treated mice using a blood-brain barrier (BBB)-impermeable paramagnetic contrast agent revealed BBB dysfunction after treatment with METH for 7 days. MRI also indicated that the impaired BBB recovered after withdrawal of METH. EPR imaging and MRI are useful tools not only for following changes in the redox status and BBB dysfunction in mouse brains repeatedly administered METH, but also for tracing the drug effect after withdrawal of METH.

Development of a practical image-based scatter correction method for brain perfusion SPECT: comparison with the TEW method.

PubMed

Shidahara, Miho; Watabe, Hiroshi; Kim, Kyeong Min; Kato, Takashi; Kawatsu, Shoji; Kato, Rikio; Yoshimura, Kumiko; Iida, Hidehiro; Ito, Kengo

2005-10-01

An image-based scatter correction (IBSC) method was developed to convert scatter-uncorrected into scatter-corrected SPECT images. The purpose of this study was to validate this method by means of phantom simulations and human studies with 99mTc-labeled tracers, based on comparison with the conventional triple energy window (TEW) method. The IBSC method corrects scatter on the reconstructed image I(mub)AC with Chang's attenuation correction factor. The scatter component image is estimated by convolving I(mub)AC with a scatter function followed by multiplication with an image-based scatter fraction function. The IBSC method was evaluated with Monte Carlo simulations and 99mTc-ethyl cysteinate dimer SPECT human brain perfusion studies obtained from five volunteers. The image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were compared. Using data obtained from the simulations, the image counts and contrast of the scatter-corrected images obtained by the IBSC and TEW methods were found to be nearly identical for both gray and white matter. In human brain images, no significant differences in image contrast were observed between the IBSC and TEW methods. The IBSC method is a simple scatter correction technique feasible for use in clinical routine.

Towards real-time diffuse optical tomography for imaging brain functions cooperated with Kalman estimator

NASA Astrophysics Data System (ADS)

Wang, Bingyuan; Zhang, Yao; Liu, Dongyuan; Ding, Xuemei; Dan, Mai; Pan, Tiantian; Wang, Yihan; Li, Jiao; Zhou, Zhongxing; Zhang, Limin; Zhao, Huijuan; Gao, Feng

2018-02-01

Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method to monitor the cerebral hemodynamic through the optical changes measured at the scalp surface. It has played a more and more important role in psychology and medical imaging communities. Real-time imaging of brain function using NIRS makes it possible to explore some sophisticated human brain functions unexplored before. Kalman estimator has been frequently used in combination with modified Beer-Lamber Law (MBLL) based optical topology (OT), for real-time brain function imaging. However, the spatial resolution of the OT is low, hampering the application of OT in exploring some complicated brain functions. In this paper, we develop a real-time imaging method combining diffuse optical tomography (DOT) and Kalman estimator, much improving the spatial resolution. Instead of only presenting one spatially distributed image indicating the changes of the absorption coefficients at each time point during the recording process, one real-time updated image using the Kalman estimator is provided. Its each voxel represents the amplitude of the hemodynamic response function (HRF) associated with this voxel. We evaluate this method using some simulation experiments, demonstrating that this method can obtain more reliable spatial resolution images. Furthermore, a statistical analysis is also conducted to help to decide whether a voxel in the field of view is activated or not.

Simulation of brain tumors in MR images for evaluation of segmentation efficacy.

PubMed

Prastawa, Marcel; Bullitt, Elizabeth; Gerig, Guido

2009-04-01

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST criteria (response evaluation criteria in solid tumors).

Brain redox imaging in the pentylenetetrazole (PTZ)-induced kindling model of epilepsy by using in vivo electron paramagnetic resonance and a nitroxide imaging probe.

PubMed

Emoto, Miho C; Yamato, Mayumi; Sato-Akaba, Hideo; Yamada, Ken-ichi; Fujii, Hirotada G

2015-11-03

Much evidence supports the idea that oxidative stress is involved in the pathogenesis of epilepsy, and therapeutic interventions with antioxidants are expected as adjunct antiepileptic therapy. The aims of this study were to non-invasively obtain spatially resolved redox data from control and pentylenetetrazole (PTZ)-induced kindled mouse brains by electron paramagnetic resonance (EPR) imaging and to visualize the brain regions that are sensitive to oxidative damage. After infusion of the redox-sensitive imaging probe 3-methoxycarbonyl-2,2,5,5-tetramethyl-piperidine-1-oxyl (MCP), a series of EPR images of PTZ-induced mouse heads were measured. Based on the pharmacokinetics of the reduction reaction of MCP in the mouse heads, the pixel-based rate constant of its reduction reaction was calculated as an index of redox status in vivo and mapped as a redox map. The obtained redox map showed heterogeneity in the redox status in PTZ-induced mouse brains compared with control. The co-registered image of the redox map and magnetic resonance imaging (MRI) for both control and PTZ-induced mice showed a clear change in the redox status around the hippocampus after PTZ. To examine the role of antioxidants on the brain redox status, the levels of antioxidants were measured in brain tissues of control and PTZ-induced mice. Significantly lower concentrations of glutathione in the hippocampus of PTZ-kindled mice were detected compared with control. From the results of both EPR imaging and the biochemical assay, the hippocampus was found to be susceptible to oxidative damage in the PTZ-induced animal model of epilepsy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Diffuse Optical Tomography for Brain Imaging: Continuous Wave Instrumentation and Linear Analysis Methods

NASA Astrophysics Data System (ADS)

Giacometti, Paolo; Diamond, Solomon G.

Diffuse optical tomography (DOT) is a functional brain imaging technique that measures cerebral blood oxygenation and blood volume changes. This technique is particularly useful in human neuroimaging measurements because of the coupling between neural and hemodynamic activity in the brain. DOT is a multichannel imaging extension of near-infrared spectroscopy (NIRS). NIRS uses laser sources and light detectors on the scalp to obtain noninvasive hemodynamic measurements from spectroscopic analysis of the remitted light. This review explains how NIRS data analysis is performed using a combination of the modified Beer-Lambert law (MBLL) and the diffusion approximation to the radiative transport equation (RTE). Laser diodes, photodiode detectors, and optical terminals that contact the scalp are the main components in most NIRS systems. Placing multiple sources and detectors over the surface of the scalp allows for tomographic reconstructions that extend the individual measurements of NIRS into DOT. Mathematically arranging the DOT measurements into a linear system of equations that can be inverted provides a way to obtain tomographic reconstructions of hemodynamics in the brain.

Experimental evaluation of electrical conductivity imaging of anisotropic brain tissues using a combination of diffusion tensor imaging and magnetic resonance electrical impedance tomography

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

Sajib, Saurav Z. K.; Jeong, Woo Chul; Oh, Tong In

Anisotropy of biological tissues is a low-frequency phenomenon that is associated with the function and structure of cell membranes. Imaging of anisotropic conductivity has potential for the analysis of interactions between electromagnetic fields and biological systems, such as the prediction of current pathways in electrical stimulation therapy. To improve application to the clinical environment, precise approaches are required to understand the exact responses inside the human body subjected to the stimulated currents. In this study, we experimentally evaluate the anisotropic conductivity tensor distribution of canine brain tissues, using a recently developed diffusion tensor-magnetic resonance electrical impedance tomography method. At lowmore » frequency, electrical conductivity of the biological tissues can be expressed as a product of the mobility and concentration of ions in the extracellular space. From diffusion tensor images of the brain, we can obtain directional information on diffusive movements of water molecules, which correspond to the mobility of ions. The position dependent scale factor, which provides information on ion concentration, was successfully calculated from the magnetic flux density, to obtain the equivalent conductivity tensor. By combining the information from both techniques, we can finally reconstruct the anisotropic conductivity tensor images of brain tissues. The reconstructed conductivity images better demonstrate the enhanced signal intensity in strongly anisotropic brain regions, compared with those resulting from previous methods using a global scale factor.« less

Brain MRI Tumor Detection using Active Contour Model and Local Image Fitting Energy

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel

2014-03-01

Automatic abnormality detection in Magnetic Resonance Imaging (MRI) is an important issue in many diagnostic and therapeutic applications. Here an automatic brain tumor detection method is introduced that uses T1-weighted images and K. Zhang et. al.'s active contour model driven by local image fitting (LIF) energy. Local image fitting energy obtains the local image information, which enables the algorithm to segment images with intensity inhomogeneities. Advantage of this method is that the LIF energy functional has less computational complexity than the local binary fitting (LBF) energy functional; moreover, it maintains the sub-pixel accuracy and boundary regularization properties. In Zhang's algorithm, a new level set method based on Gaussian filtering is used to implement the variational formulation, which is not only vigorous to prevent the energy functional from being trapped into local minimum, but also effective in keeping the level set function regular. Experiments show that the proposed method achieves high accuracy brain tumor segmentation results.

Retractor-induced brain shift compensation in image-guided neurosurgery

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Ji, Songbai; Hartov, Alex; Roberts, David; Paulsen, Keith

2013-03-01

In image-guided neurosurgery, intraoperative brain shift significantly degrades the accuracy of neuronavigation that is solely based on preoperative magnetic resonance images (pMR). To compensate for brain deformation and to maintain the accuracy in image guidance achieved at the start of surgery, biomechanical models have been developed to simulate brain deformation and to produce model-updated MR images (uMR) to compensate for brain shift. To-date, most studies have focused on shift compensation at early stages of surgery (i.e., updated images are only produced after craniotomy and durotomy). Simulating surgical events at later stages such as retraction and tissue resection are, perhaps, clinically more relevant because of the typically much larger magnitudes of brain deformation. However, these surgical events are substantially more complex in nature, thereby posing significant challenges in model-based brain shift compensation strategies. In this study, we present results from an initial investigation to simulate retractor-induced brain deformation through a biomechanical finite element (FE) model where whole-brain deformation assimilated from intraoperative data was used produce uMR for improved accuracy in image guidance. Specifically, intensity-encoded 3D surface profiles at the exposed cortical area were reconstructed from intraoperative stereovision (iSV) images before and after tissue retraction. Retractor-induced surface displacements were then derived by coregistering the surfaces and served as sparse displacement data to drive the FE model. With one patient case, we show that our technique is able to produce uMR that agrees well with the reconstructed iSV surface after retraction. The computational cost to simulate retractor-induced brain deformation was approximately 10 min. In addition, our approach introduces minimal interruption to the surgical workflow, suggesting the potential for its clinical application.

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution

PubMed Central

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-01-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images. PMID:29062159

Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution.

PubMed

Zhang, Jinpeng; Zhang, Lichi; Xiang, Lei; Shao, Yeqin; Wu, Guorong; Zhou, Xiaodong; Shen, Dinggang; Wang, Qian

2017-03-01

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.

Fuzzy object models for newborn brain MR image segmentation

NASA Astrophysics Data System (ADS)

Kobashi, Syoji; Udupa, Jayaram K.

2013-03-01

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

The Potential of Using Brain Images for Authentication

PubMed Central

Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition. PMID:25126604

The potential of using brain images for authentication.

PubMed

Chen, Fanglin; Zhou, Zongtan; Shen, Hui; Hu, Dewen

2014-01-01

Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition.

Measuring Brain Connectivity: Diffusion Tensor Imaging Validates Resting State Temporal Correlations

PubMed Central

Skudlarski, Pawel; Jagannathan, Kanchana; Calhoun, Vince D.; Hampson, Michelle; Skudlarska, Beata A.; Pearlson, Godfrey

2015-01-01

Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions. PMID:18771736

Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations.

PubMed

Skudlarski, Pawel; Jagannathan, Kanchana; Calhoun, Vince D; Hampson, Michelle; Skudlarska, Beata A; Pearlson, Godfrey

2008-11-15

Diffusion tensor imaging (DTI) and resting state temporal correlations (RSTC) are two leading techniques for investigating the connectivity of the human brain. They have been widely used to investigate the strength of anatomical and functional connections between distant brain regions in healthy subjects, and in clinical populations. Though they are both based on magnetic resonance imaging (MRI) they have not yet been compared directly. In this work both techniques were employed to create global connectivity matrices covering the whole brain gray matter. This allowed for direct comparisons between functional connectivity measured by RSTC with anatomical connectivity quantified using DTI tractography. We found that connectivity matrices obtained using both techniques showed significant agreement. Connectivity maps created for a priori defined anatomical regions showed significant correlation, and furthermore agreement was especially high in regions showing strong overall connectivity, such as those belonging to the default mode network. Direct comparison between functional RSTC and anatomical DTI connectivity, presented here for the first time, links two powerful approaches for investigating brain connectivity and shows their strong agreement. It provides a crucial multi-modal validation for resting state correlations as representing neuronal connectivity. The combination of both techniques presented here allows for further combining them to provide richer representation of brain connectivity both in the healthy brain and in clinical conditions.

Brain lesions in septic shock: a magnetic resonance imaging study.

PubMed

Sharshar, Tarek; Carlier, Robert; Bernard, Francis; Guidoux, Céline; Brouland, Jean-Philippe; Nardi, Olivier; de la Grandmaison, Geoffroy Lorin; Aboab, Jérôme; Gray, Françoise; Menon, David; Annane, Djillali

2007-05-01

Understanding of sepsis-induced brain dysfunction remains poor, and relies mainly on data from animals or post-mortem studies in patients. The current study provided findings from magnetic resonance imaging of the brain in septic shock. Nine patients with septic shock and brain dysfunction [7 women, median age 63 years (interquartile range 61-79 years), SAPS II: 48 (44-56), SOFA: 8 (6-10)] underwent brain magnetic resonance imaging including gradient echo T1-weighted, fluid-attenuated inversion recovery (FLAIR), T2-weighted and diffusion isotropic images, and mapping of apparent diffusion coefficient. Brain imaging was normal in two patients, showed multiple ischaemic strokes in two patients, and in the remaining patients showed white matter lesions at the level of the centrum semiovale, predominating around Virchow-Robin spaces, ranging from small multiple areas to diffuse lesions, and characterised by hyperintensity on FLAIR images. The main lesions were also characterised by reduced signal on diffusion isotropic images and increased apparent diffusion coefficient. The lesions of the white matter worsened with increasing duration of shock and were correlated with Glasgow Outcome Score. This preliminary study showed that sepsis-induced brain lesions can be documented by magnetic resonance imaging. These lesions predominated in the white matter, suggesting increased blood-brain barrier permeability, and were associated with poor outcome.

Optical Brain Imaging: A Powerful Tool for Neuroscience.

PubMed

Zhu, Xinpei; Xia, Yanfang; Wang, Xuecen; Si, Ke; Gong, Wei

2017-02-01

As the control center of organisms, the brain remains little understood due to its complexity. Taking advantage of imaging methods, scientists have found an accessible approach to unraveling the mystery of neuroscience. Among these methods, optical imaging techniques are widely used due to their high molecular specificity and single-molecule sensitivity. Here, we overview several optical imaging techniques in neuroscience of recent years, including brain clearing, the micro-optical sectioning tomography system, and deep tissue imaging.

Development of integrated semiconductor optical sensors for functional brain imaging

NASA Astrophysics Data System (ADS)

Lee, Thomas T.

Optical imaging of neural activity is a widely accepted technique for imaging brain function in the field of neuroscience research, and has been used to study the cerebral cortex in vivo for over two decades. Maps of brain activity are obtained by monitoring intensity changes in back-scattered light, called Intrinsic Optical Signals (IOS), that correspond to fluctuations in blood oxygenation and volume associated with neural activity. Current imaging systems typically employ bench-top equipment including lamps and CCD cameras to study animals using visible light. Such systems require the use of anesthetized or immobilized subjects with craniotomies, which imposes limitations on the behavioral range and duration of studies. The ultimate goal of this work is to overcome these limitations by developing a single-chip semiconductor sensor using arrays of sources and detectors operating at near-infrared (NIR) wavelengths. A single-chip implementation, combined with wireless telemetry, will eliminate the need for immobilization or anesthesia of subjects and allow in vivo studies of free behavior. NIR light offers additional advantages because it experiences less absorption in animal tissue than visible light, which allows for imaging through superficial tissues. This, in turn, reduces or eliminates the need for traumatic surgery and enables long-term brain-mapping studies in freely-behaving animals. This dissertation concentrates on key engineering challenges of implementing the sensor. This work shows the feasibility of using a GaAs-based array of vertical-cavity surface emitting lasers (VCSELs) and PIN photodiodes for IOS imaging. I begin with in-vivo studies of IOS imaging through the skull in mice, and use these results along with computer simulations to establish minimum performance requirements for light sources and detectors. I also evaluate the performance of a current commercial VCSEL for IOS imaging, and conclude with a proposed prototype sensor.

Automated Computational Processing of 3-D MR Images of Mouse Brain for Phenotyping of Living Animals.

PubMed

Medina, Christopher S; Manifold-Wheeler, Brett; Gonzales, Aaron; Bearer, Elaine L

2017-07-05

Magnetic resonance (MR) imaging provides a method to obtain anatomical information from the brain in vivo that is not typically available by optical imaging because of this organ's opacity. MR is nondestructive and obtains deep tissue contrast with 100-µm 3 voxel resolution or better. Manganese-enhanced MRI (MEMRI) may be used to observe axonal transport and localized neural activity in the living rodent and avian brain. Such enhancement enables researchers to investigate differences in functional circuitry or neuronal activity in images of brains of different animals. Moreover, once MR images of a number of animals are aligned into a single matrix, statistical analysis can be done comparing MR intensities between different multi-animal cohorts comprising individuals from different mouse strains or different transgenic animals, or at different time points after an experimental manipulation. Although preprocessing steps for such comparisons (including skull stripping and alignment) are automated for human imaging, no such automated processing has previously been readily available for mouse or other widely used experimental animals, and most investigators use in-house custom processing. This protocol describes a stepwise method to perform such preprocessing for mouse. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

Cortical Enhanced Tissue Segmentation of Neonatal Brain MR Images Acquired by a Dedicated Phased Array Coil

PubMed Central

Shi, Feng; Yap, Pew-Thian; Fan, Yong; Cheng, Jie-Zhi; Wald, Lawrence L.; Gerig, Guido; Lin, Weili; Shen, Dinggang

2010-01-01

The acquisition of high quality MR images of neonatal brains is largely hampered by their characteristically small head size and low tissue contrast. As a result, subsequent image processing and analysis, especially for brain tissue segmentation, are often hindered. To overcome this problem, a dedicated phased array neonatal head coil is utilized to improve MR image quality by effectively combing images obtained from 8 coil elements without lengthening data acquisition time. In addition, a subject-specific atlas based tissue segmentation algorithm is specifically developed for the delineation of fine structures in the acquired neonatal brain MR images. The proposed tissue segmentation method first enhances the sheet-like cortical gray matter (GM) structures in neonatal images with a Hessian filter for generation of cortical GM prior. Then, the prior is combined with our neonatal population atlas to form a cortical enhanced hybrid atlas, which we refer to as the subject-specific atlas. Various experiments are conducted to compare the proposed method with manual segmentation results, as well as with additional two population atlas based segmentation methods. Results show that the proposed method is capable of segmenting the neonatal brain with the highest accuracy, compared to other two methods. PMID:20862268

Brain MR image segmentation based on an improved active contour model

PubMed Central

Meng, Xiangrui; Gu, Wenya; Zhang, Jianwei

2017-01-01

It is often a difficult task to accurately segment brain magnetic resonance (MR) images with intensity in-homogeneity and noise. This paper introduces a novel level set method for simultaneous brain MR image segmentation and intensity inhomogeneity correction. To reduce the effect of noise, novel anisotropic spatial information, which can preserve more details of edges and corners, is proposed by incorporating the inner relationships among the neighbor pixels. Then the proposed energy function uses the multivariate Student's t-distribution to fit the distribution of the intensities of each tissue. Furthermore, the proposed model utilizes Hidden Markov random fields to model the spatial correlation between neigh-boring pixels/voxels. The means of the multivariate Student's t-distribution can be adaptively estimated by multiplying a bias field to reduce the effect of intensity inhomogeneity. In the end, we reconstructed the energy function to be convex and calculated it by using the Split Bregman method, which allows our framework for random initialization, thereby allowing fully automated applications. Our method can obtain the final result in less than 1 second for 2D image with size 256 × 256 and less than 300 seconds for 3D image with size 256 × 256 × 171. The proposed method was compared to other state-of-the-art segmentation methods using both synthetic and clinical brain MR images and increased the accuracies of the results more than 3%. PMID:28854235

An architecture for a brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.

2000-01-01

The widespread availability of methods for noninvasive assessment of brain structure has enabled researchers to investigate neuroimaging correlates of normal aging, cerebrovascular disease, and other processes; we designate such studies as image-based clinical trials (IBCTs). We propose an architecture for a brain-image database, which integrates image processing and statistical operators, and thus supports the implementation and analysis of IBCTs. The implementation of this architecture is described and results from the analysis of image and clinical data from two IBCTs are presented. We expect that systems such as this will play a central role in the management and analysis of complex research data sets.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising

A Unified Framework for Brain Segmentation in MR Images

PubMed Central

Yazdani, S.; Yusof, R.; Karimian, A.; Riazi, A. H.; Bennamoun, M.

2015-01-01

Brain MRI segmentation is an important issue for discovering the brain structure and diagnosis of subtle anatomical changes in different brain diseases. However, due to several artifacts brain tissue segmentation remains a challenging task. The aim of this paper is to improve the automatic segmentation of brain into gray matter, white matter, and cerebrospinal fluid in magnetic resonance images (MRI). We proposed an automatic hybrid image segmentation method that integrates the modified statistical expectation-maximization (EM) method and the spatial information combined with support vector machine (SVM). The combined method has more accurate results than what can be achieved with its individual techniques that is demonstrated through experiments on both real data and simulated images. Experiments are carried out on both synthetic and real MRI. The results of proposed technique are evaluated against manual segmentation results and other methods based on real T1-weighted scans from Internet Brain Segmentation Repository (IBSR) and simulated images from BrainWeb. The Kappa index is calculated to assess the performance of the proposed framework relative to the ground truth and expert segmentations. The results demonstrate that the proposed combined method has satisfactory results on both simulated MRI and real brain datasets. PMID:26089978

Large-scale imaging in small brains.

PubMed

Ahrens, Misha B; Engert, Florian

2015-06-01

The dense connectivity in the brain means that one neuron's activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates. Copyright © 2015 Elsevier Ltd. All rights reserved.

First in vivo traumatic brain injury imaging via magnetic particle imaging

NASA Astrophysics Data System (ADS)

Orendorff, Ryan; Peck, Austin J.; Zheng, Bo; Shirazi, Shawn N.; Ferguson, R. Matthew; Khandhar, Amit P.; Kemp, Scott J.; Goodwill, Patrick; Krishnan, Kannan M.; Brooks, George A.; Kaufer, Daniela; Conolly, Steven

2017-05-01

Emergency room visits due to traumatic brain injury (TBI) is common, but classifying the severity of the injury remains an open challenge. Some subjective methods such as the Glasgow Coma Scale attempt to classify traumatic brain injuries, as well as some imaging based modalities such as computed tomography and magnetic resonance imaging. However, to date it is still difficult to detect and monitor mild to moderate injuries. In this report, we demonstrate that the magnetic particle imaging (MPI) modality can be applied to imaging TBI events with excellent contrast. MPI can monitor injected iron nanoparticles over long time scales without signal loss, allowing researchers and clinicians to monitor the change in blood pools as the wound heals.

Neonatal brain resting-state functional connectivity imaging modalities.

PubMed

Mohammadi-Nejad, Ali-Reza; Mahmoudzadeh, Mahdi; Hassanpour, Mahlegha S; Wallois, Fabrice; Muzik, Otto; Papadelis, Christos; Hansen, Anne; Soltanian-Zadeh, Hamid; Gelovani, Juri; Nasiriavanaki, Mohammadreza

2018-06-01

Infancy is the most critical period in human brain development. Studies demonstrate that subtle brain abnormalities during this state of life may greatly affect the developmental processes of the newborn infants. One of the rapidly developing methods for early characterization of abnormal brain development is functional connectivity of the brain at rest. While the majority of resting-state studies have been conducted using magnetic resonance imaging (MRI), there is clear evidence that resting-state functional connectivity (rs-FC) can also be evaluated using other imaging modalities. The aim of this review is to compare the advantages and limitations of different modalities used for the mapping of infants' brain functional connectivity at rest. In addition, we introduce photoacoustic tomography, a novel functional neuroimaging modality, as a complementary modality for functional mapping of infants' brain.

High-Speed and Scalable Whole-Brain Imaging in Rodents and Primates.

PubMed

Seiriki, Kaoru; Kasai, Atsushi; Hashimoto, Takeshi; Schulze, Wiebke; Niu, Misaki; Yamaguchi, Shun; Nakazawa, Takanobu; Inoue, Ken-Ichi; Uezono, Shiori; Takada, Masahiko; Naka, Yuichiro; Igarashi, Hisato; Tanuma, Masato; Waschek, James A; Ago, Yukio; Tanaka, Kenji F; Hayata-Takano, Atsuko; Nagayasu, Kazuki; Shintani, Norihito; Hashimoto, Ryota; Kunii, Yasuto; Hino, Mizuki; Matsumoto, Junya; Yabe, Hirooki; Nagai, Takeharu; Fujita, Katsumasa; Matsuda, Toshio; Takuma, Kazuhiro; Baba, Akemichi; Hashimoto, Hitoshi

2017-06-21

Subcellular resolution imaging of the whole brain and subsequent image analysis are prerequisites for understanding anatomical and functional brain networks. Here, we have developed a very high-speed serial-sectioning imaging system named FAST (block-face serial microscopy tomography), which acquires high-resolution images of a whole mouse brain in a speed range comparable to that of light-sheet fluorescence microscopy. FAST enables complete visualization of the brain at a resolution sufficient to resolve all cells and their subcellular structures. FAST renders unbiased quantitative group comparisons of normal and disease model brain cells for the whole brain at a high spatial resolution. Furthermore, FAST is highly scalable to non-human primate brains and human postmortem brain tissues, and can visualize neuronal projections in a whole adult marmoset brain. Thus, FAST provides new opportunities for global approaches that will allow for a better understanding of brain systems in multiple animal models and in human diseases. Copyright © 2017 Elsevier Inc. All rights reserved.

Neuroanatomical phenotyping of the mouse brain with three-dimensional autofluorescence imaging

PubMed Central

Wong, Michael D.; Dazai, Jun; Altaf, Maliha; Mark Henkelman, R.; Lerch, Jason P.; Nieman, Brian J.

2012-01-01

The structural organization of the brain is important for normal brain function and is critical to understand in order to evaluate changes that occur during disease processes. Three-dimensional (3D) imaging of the mouse brain is necessary to appreciate the spatial context of structures within the brain. In addition, the small scale of many brain structures necessitates resolution at the ∼10 μm scale. 3D optical imaging techniques, such as optical projection tomography (OPT), have the ability to image intact large specimens (1 cm3) with ∼5 μm resolution. In this work we assessed the potential of autofluorescence optical imaging methods, and specifically OPT, for phenotyping the mouse brain. We found that both specimen size and fixation methods affected the quality of the OPT image. Based on these findings we developed a specimen preparation method to improve the images. Using this method we assessed the potential of optical imaging for phenotyping. Phenotypic differences between wild-type male and female mice were quantified using computer-automated methods. We found that optical imaging of the endogenous autofluorescence in the mouse brain allows for 3D characterization of neuroanatomy and detailed analysis of brain phenotypes. This will be a powerful tool for understanding mouse models of disease and development and is a technology that fits easily within the workflow of biology and neuroscience labs. PMID:22718750

Reproducibility study of whole-brain 1H spectroscopic imaging with automated quantification.

PubMed

Gu, Meng; Kim, Dong-Hyun; Mayer, Dirk; Sullivan, Edith V; Pfefferbaum, Adolf; Spielman, Daniel M

2008-09-01

A reproducibility study of proton MR spectroscopic imaging ((1)H-MRSI) of the human brain was conducted to evaluate the reliability of an automated 3D in vivo spectroscopic imaging acquisition and associated quantification algorithm. A PRESS-based pulse sequence was implemented using dualband spectral-spatial RF pulses designed to fully excite the singlet resonances of choline (Cho), creatine (Cre), and N-acetyl aspartate (NAA) while simultaneously suppressing water and lipids; 1% of the water signal was left to be used as a reference signal for robust data processing, and additional lipid suppression was obtained using adiabatic inversion recovery. Spiral k-space trajectories were used for fast spectral and spatial encoding yielding high-quality spectra from 1 cc voxels throughout the brain with a 13-min acquisition time. Data were acquired with an 8-channel phased-array coil and optimal signal-to-noise ratio (SNR) for the combined signals was achieved using a weighting based on the residual water signal. Automated quantification of the spectrum of each voxel was performed using LCModel. The complete study consisted of eight healthy adult subjects to assess intersubject variations and two subjects scanned six times each to assess intrasubject variations. The results demonstrate that reproducible whole-brain (1)H-MRSI data can be robustly obtained with the proposed methods.

Functional brain imaging: an evidence-based analysis.

PubMed

2006-01-01

The objective of this analysis is to review a spectrum of functional brain imaging technologies to identify whether there are any imaging modalities that are more effective than others for various brain pathology conditions. This evidence-based analysis reviews magnetoencephalography (MEG), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI) for the diagnosis or surgical management of the following conditions: Alzheimer's disease (AD), brain tumours, epilepsy, multiple sclerosis (MS), and Parkinson's disease (PD). TARGET POPULATION AND CONDITION Alzheimer's disease is a progressive, degenerative, neurologic condition characterized by cognitive impairment and memory loss. The Canadian Study on Health and Aging estimated that there will be 97,000 incident cases (about 60,000 women) of dementia (including AD) in Canada in 2006. In Ontario, there will be an estimated 950 new cases and 580 deaths due to brain cancer in 2006. Treatments for brain tumours include surgery and radiation therapy. However, one of the limitations of radiation therapy is that it damages tissue though necrosis and scarring. Computed tomography (CT) and magnetic resonance imaging (MRI) may not distinguish between radiation effects and resistant tissue, creating a potential role for functional brain imaging. Epilepsy is a chronic disorder that provokes repetitive seizures. In Ontario, the rate of epilepsy is estimated to be 5 cases per 1,000 people. Most people with epilepsy are effectively managed with drug therapy; but about 50% do not respond to drug therapy. Surgical resection of the seizure foci may be considered in these patients, and functional brain imaging may play a role in localizing the seizure foci. Multiple sclerosis is a progressive, inflammatory, demyelinating disease of the central nervous system (CNS). The cause of MS is unknown; however, it is thought to be due to a combination of etiologies, including

Optical Coherence Tomography for Brain Imaging and Developmental Biology

PubMed Central

Men, Jing; Huang, Yongyang; Solanki, Jitendra; Zeng, Xianxu; Alex, Aneesh; Jerwick, Jason; Zhang, Zhan; Tanzi, Rudolph E.; Li, Airong; Zhou, Chao

2016-01-01

Optical coherence tomography (OCT) is a promising research tool for brain imaging and developmental biology. Serving as a three-dimensional optical biopsy technique, OCT provides volumetric reconstruction of brain tissues and embryonic structures with micrometer resolution and video rate imaging speed. Functional OCT enables label-free monitoring of hemodynamic and metabolic changes in the brain in vitro and in vivo in animal models. Due to its non-invasiveness nature, OCT enables longitudinal imaging of developing specimens in vivo without potential damage from surgical operation, tissue fixation and processing, and staining with exogenous contrast agents. In this paper, various OCT applications in brain imaging and developmental biology are reviewed, with a particular focus on imaging heart development. In addition, we report findings on the effects of a circadian gene (Clock) and high-fat-diet on heart development in Drosophila melanogaster. These findings contribute to our understanding of the fundamental mechanisms connecting circadian genes and obesity to heart development and cardiac diseases. PMID:27721647

Scalable Joint Segmentation and Registration Framework for Infant Brain Images.

PubMed

Dong, Pei; Wang, Li; Lin, Weili; Shen, Dinggang; Wu, Guorong

2017-03-15

The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap

Phase-difference and spectroscopic imaging for monitoring of human brain temperature during cooling.

PubMed

Weis, Jan; Covaciu, Lucian; Rubertsson, Sten; Allers, Mats; Lunderquist, Anders; Ortiz-Nieto, Francisco; Ahlström, Håkan

2012-12-01

Decrease of the human brain temperature was induced by intranasal cooling. The main purpose of this study was to compare the two magnetic resonance methods for monitoring brain temperature changes during cooling: phase-difference and magnetic resonance spectroscopic imaging (MRSI) with high spatial resolution. Ten healthy volunteers were measured. Selective brain cooling was performed through nasal cavities using saline-cooled balloon catheters. MRSI was based on a radiofrequency spoiled gradient echo sequence. The spectral information was encoded by incrementing the echo time of the subsequent eight image records. Reconstructed voxel size was 1×1×5 mm(3). Relative brain temperature was computed from the positions of water spectral lines. Phase maps were obtained from the first image record of the MRSI sequence. Mild hypothermia was achieved in 15-20 min. Mean brain temperature reduction varied in the interval <-3.0; -0.6>°C and <-2.7; -0.7>°C as measured by the MRSI and phase-difference methods, respectively. Very good correlation was found in all locations between the temperatures measured by both techniques except in the frontal lobe. Measurements in the transversal slices were more robust to the movement artifacts than those in the sagittal planes. Good agreement was found between the MRSI and phase-difference techniques. Copyright © 2012 Elsevier Inc. All rights reserved.

A dedicated neonatal brain imaging system

PubMed Central

Winchman, Tobias; Padormo, Francesco; Teixeira, Rui; Wurie, Julia; Sharma, Maryanne; Fox, Matthew; Hutter, Jana; Cordero‐Grande, Lucilio; Price, Anthony N.; Allsop, Joanna; Bueno‐Conde, Jose; Tusor, Nora; Arichi, Tomoki; Edwards, A. D.; Rutherford, Mary A.; Counsell, Serena J.; Hajnal, Joseph V.

2016-01-01

Purpose The goal of the Developing Human Connectome Project is to acquire MRI in 1000 neonates to create a dynamic map of human brain connectivity during early development. High‐quality imaging in this cohort without sedation presents a number of technical and practical challenges. Methods We designed a neonatal brain imaging system (NBIS) consisting of a dedicated 32‐channel receive array coil and a positioning device that allows placement of the infant's head deep into the coil for maximum signal‐to‐noise ratio (SNR). Disturbance to the infant was minimized by using an MRI‐compatible trolley to prepare and transport the infant and by employing a slow ramp‐up and continuation of gradient noise during scanning. Scan repeats were minimized by using a restart capability for diffusion MRI and retrospective motion correction. We measured the 1) SNR gain, 2) number of infants with a completed scan protocol, and 3) number of anatomical images with no motion artifact using NBIS compared with using an adult 32‐channel head coil. Results The NBIS has 2.4 times the SNR of the adult coil and 90% protocol completion rate. Conclusion The NBIS allows advanced neonatal brain imaging techniques to be employed in neonatal brain imaging with high protocol completion rates. Magn Reson Med 78:794–804, 2017. © 2016 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‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. PMID:27643791

Large-scale imaging in small brains

PubMed Central

Ahrens, Misha B.; Engert, Florian

2016-01-01

The dense connectivity in the brain and arrangements of cells into circuits means that one neuron’s activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates. PMID:25636154

Threshold selection for classification of MR brain images by clustering method

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

Moldovanu, Simona; Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi; Obreja, Cristian

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzedmore » images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.« less

Threshold selection for classification of MR brain images by clustering method

NASA Astrophysics Data System (ADS)

Moldovanu, Simona; Obreja, Cristian; Moraru, Luminita

2015-12-01

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

Assessment of the usefulness of magnetic resonance brain imaging in patients presenting with acute seizures.

PubMed

Olszewska, D A; Costello, D J

2014-12-01

Magnetic Resonance Imaging (MRI) is increasingly available as a tool for assessment of patients presenting to acute services with seizures. We set out to prospectively determine the usefulness of early MRI brain in a cohort of patients presenting with acute seizures. We examined the MR imaging studies performed in patients admitted solely because of acute seizures to Cork University Hospital over a 12-month period. The main aim of the study was to determine if the MRI established the proximate cause for the patient's recent seizure. We identified 91 patients who underwent MRI brain within 48 h of admission for seizures. Of the 91 studies, 51 were normal (56 %). The remaining 40 studies were abnormal as follows: microvascular disease (usually moderate/severe) (n = 19), post-traumatic gliosis (n = 7), remote symptomatic lesion (n = 6), primary brain tumour (n = 5), venous sinus thrombosis (n = 3), developmental lesion (n = 3), post-surgical gliosis (n = 3) and single cases of demyelination, unilateral hippocampal sclerosis, lobar haemorrhage and metastatic malignant melanoma. Abnormalities in diffusion-weighted sequences that were attributable to prolonged ictal activity were seen in nine patients, all of who had significant ongoing clinical deficits, most commonly delirium. Of the 40 patients with abnormal MRI studies, seven patients had unremarkable CT brain. MR brain imaging revealed the underlying cause for acute seizures in 44 % of patients. CT brain imaging failed to detect the cause of the acute seizures in 19 % of patients in whom subsequent MRI established the cause. This study emphasises the importance of obtaining optimal imaging in people admitted with acute seizures.

Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain

PubMed Central

Osechinskiy, Sergey; Kruggel, Frithjof

2011-01-01

Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function. PMID:22567290

BRAIN IMAGING IN THE STUDY OF ALZHEIMER'S DISEASE

PubMed Central

Reiman, Eric M.; Jagust, William J.

2012-01-01

Over the last 20 years, there has been extraordinary progress in brain imaging research and its application to the study of Alzheimer's disease (AD). Brain imaging researchers have contributed to the scientific understanding, early detection and tracking of AD. They have set the stage for imaging techniques to play growing roles in the clinical setting, the evaluation of disease-modifying treatments, and the identification of demonstrably effective prevention therapies. They have developed ground-breaking methods, including positron emission tomography (PET) ligands to measure fibrillar amyloid-β (Aβ) deposition, new magnetic resonance imaging (MRI) pulse sequences, and powerful image analysis techniques, to help in these endeavors. Additional work is needed to develop even more powerful imaging methods, to further clarify the relationship and time course of Aβ and other disease processes in the predisposition to AD, to establish the role of brain imaging methods in the clinical setting, and to provide the scientific means and regulatory approval pathway needed to evaluate the range of promising disease-modifying and prevention therapies as quickly as possible. Twenty years from now, AD may not yet be a distant memory, but the best is yet to come. PMID:22173295

Introduction of a novel ultrahigh sensitivity collimator for brain SPECT imaging

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

Park, Mi-Ae, E-mail: miaepark@bwh.harvard.edu; Kij

Purpose: Noise levels of brain SPECT images are highest in central regions, due to preferential attenuation of photons emitted from deep structures. To address this problem, the authors have designed a novel collimator for brain SPECT imaging that yields greatly increased sensitivity near the center of the brain without loss of resolution. This hybrid collimator consisted of ultrashort cone-beam holes in the central regions and slant-holes in the periphery (USCB). We evaluated this collimator for quantitative brain imaging tasks. Methods: Owing to the uniqueness of the USCB collimation, the hole pattern required substantial variations in collimator parameters. To utilize themore » lead-casting technique, the authors designed two supporting plates to position about 37 000 hexagonal, slightly tapered pins. The holes in the supporting plates were modeled to yield the desired focal length, hole length, and septal thickness. To determine the properties of the manufactured collimator and to compute the system matrix, the authors prepared an array of point sources that covered the entire detector area. Each point source contained 32 μCi of Tc-99m at the first scan time. The array was imaged for 5 min at each of the 64 shifted locations to yield a 2-mm sampling distance, and hole parameters were calculated. The sensitivity was also measured using a point source placed along the central ray at several distances from the collimator face. High-count projection data from a five-compartment brain phantom were acquired with the three collimators on a dual-head SPECT/CT system. The authors calculated Cramer-Rao bounds on the precision of estimates of striatal and background activity concentration. In order to assess the new collimation system to detect changes in striatal activity, the authors evaluated the precision of measuring a 5% decrease in right putamen activity. The authors also reconstructed images of projection data obtained by summing data from the individual

Pulse Coupled Neural Networks for the Segmentation of Magnetic Resonance Brain Images.

DTIC Science & Technology

1996-12-01

PULSE COUPLED NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGES THESIS Shane Lee Abrahamson First Lieutenant, USAF AFIT/GCS/ENG...COUPLED NEURAL NETWORKS FOR THE SEGMENTATION OF MAGNETIC RESONANCE BRAIN IMAGES THESIS Shane Lee Abrahamson First Lieutenant, USAF AFIT/GCS/ENG/96D-01...research develops an automated method for segmenting Magnetic Resonance (MR) brain images based on Pulse Coupled Neural Networks (PCNN). MR brain image

Photoacoustic imaging for transvascular drug delivery to the rat brain

NASA Astrophysics Data System (ADS)

Watanabe, Ryota; Sato, Shunichi; Tsunoi, Yasuyuki; Kawauchi, Satoko; Takemura, Toshiya; Terakawa, Mitsuhiro

2015-03-01

Transvascular drug delivery to the brain is difficult due to the blood-brain barrier (BBB). Thus, various methods for safely opening the BBB have been investigated, for which real-time imaging methods are desired both for the blood vessels and distribution of a drug. Photoacoustic (PA) imaging, which enables depth-resolved visualization of chromophores in tissue, would be useful for this purpose. In this study, we performed in vivo PA imaging of the blood vessels and distribution of a drug in the rat brain by using an originally developed compact PA imaging system with fiber-based illumination. As a test drug, Evans blue (EB) was injected to the tail vein, and a photomechanical wave was applied to the targeted brain tissue to increase the permeability of the blood vessel walls. For PA imaging of blood vessels and EB distribution, nanosecond pulses at 532 nm and 670 nm were used, respectively. We clearly visualized blood vessels with diameters larger than 50 μm and the distribution of EB in the brain, showing spatiotemporal characteristics of EB that was transvascularly delivered to the target tissue in the brain.

Computer-aided diagnosis of cavernous malformations in brain MR images.

PubMed

Wang, Huiquan; Ahmed, S Nizam; Mandal, Mrinal

2018-06-01

Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders. Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. In this paper, we propose a computer-aided diagnosis (CAD) system for cavernomas based on T2-weighted axial plane MRI image analysis. The proposed technique first extracts the brain area based on atlas registration and active contour model, and then performs template matching to obtain candidate cavernoma regions. Texture, the histogram of oriented gradients and local binary pattern features of each candidate region are calculated, and principal component analysis is applied to reduce the feature dimensionality. Support vector machines (SVMs) are finally used to classify each region into cavernoma or non-cavernoma so that most of the false positives (obtained by template matching) are eliminated. The performance of the proposed CAD system is evaluated and experimental results show that it provides superior performance in cavernoma detection compared to existing techniques. Copyright © 2018 Elsevier Ltd. All rights reserved.

S-values calculated from a tomographic head/brain model for brain imaging

NASA Astrophysics Data System (ADS)

Chao, Tsi-chian; Xu, X. George

2004-11-01

A tomographic head/brain model was developed from the Visible Human images and used to calculate S-values for brain imaging procedures. This model contains 15 segmented sub-regions including caudate nucleus, cerebellum, cerebral cortex, cerebral white matter, corpus callosum, eyes, lateral ventricles, lenses, lentiform nucleus, optic chiasma, optic nerve, pons and middle cerebellar peduncle, skull CSF, thalamus and thyroid. S-values for C-11, O-15, F-18, Tc-99m and I-123 have been calculated using this model and a Monte Carlo code, EGS4. Comparison of the calculated S-values with those calculated from the MIRD (1999) stylized head/brain model shows significant differences. In many cases, the stylized head/brain model resulted in smaller S-values (as much as 88%), suggesting that the doses to a specific patient similar to the Visible Man could have been underestimated using the existing clinical dosimetry.

Tissue Probability Map Constrained 4-D Clustering Algorithm for Increased Accuracy and Robustness in Serial MR Brain Image Segmentation

PubMed Central

Xue, Zhong; Shen, Dinggang; Li, Hai; Wong, Stephen

2010-01-01

The traditional fuzzy clustering algorithm and its extensions have been successfully applied in medical image segmentation. However, because of the variability of tissues and anatomical structures, the clustering results might be biased by the tissue population and intensity differences. For example, clustering-based algorithms tend to over-segment white matter tissues of MR brain images. To solve this problem, we introduce a tissue probability map constrained clustering algorithm and apply it to serial MR brain image segmentation, i.e., a series of 3-D MR brain images of the same subject at different time points. Using the new serial image segmentation algorithm in the framework of the CLASSIC framework, which iteratively segments the images and estimates the longitudinal deformations, we improved both accuracy and robustness for serial image computing, and at the mean time produced longitudinally consistent segmentation and stable measures. In the algorithm, the tissue probability maps consist of both the population-based and subject-specific segmentation priors. Experimental study using both simulated longitudinal MR brain data and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data confirmed that using both priors more accurate and robust segmentation results can be obtained. The proposed algorithm can be applied in longitudinal follow up studies of MR brain imaging with subtle morphological changes for neurological disorders. PMID:26566399

Validated Automatic Brain Extraction of Head CT Images

PubMed Central

Muschelli, John; Ullman, Natalie L.; Mould, W. Andrew; Vespa, Paul; Hanley, Daniel F.; Crainiceanu, Ciprian M.

2015-01-01

Background X-ray Computed Tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction – the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated. Aim To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported. Methods All images were thresholded using a 0 – 100 Hounsfield units (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ = 1mm3) and re-thresholded to 0 – 100 HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial

Simultaneous MRI and PET imaging of a rat brain

NASA Astrophysics Data System (ADS)

Raylman, Raymond R.; Majewski, Stan; Lemieux, Susan K.; Sendhil Velan, S.; Kross, Brian; Popov, Vladimir; Smith, Mark F.; Weisenberger, Andrew G.; Zorn, Carl; Marano, Gary D.

2006-12-01

Multi-modality imaging is rapidly becoming a valuable tool in the diagnosis of disease and in the development of new drugs. Functional images produced with PET fused with anatomical structure images created by MRI will allow the correlation of form with function. Our group is developing a system to acquire MRI and PET images contemporaneously. The prototype device consists of two opposed detector heads, operating in coincidence mode. Each MRI-PET detector module consists of an array of LSO detector elements coupled through a long fibre optic light guide to a single Hamamatsu flat panel position-sensitive photomultiplier tube (PSPMT). The use of light guides allows the PSPMTs to be positioned outside the bore of a 3T MRI scanner where the magnetic field is relatively small. To test the device, simultaneous MRI and PET images of the brain of a male Sprague Dawley rat injected with FDG were successfully obtained. The images revealed no noticeable artefacts in either image set. Future work includes the construction of a full ring PET scanner, improved light guides and construction of a specialized MRI coil to permit higher quality MRI imaging.

Clinical applications of modern imaging technology: stereo image formation and location of brain cancer

NASA Astrophysics Data System (ADS)

Wang, Dezong; Wang, Jinxiang

1994-05-01

It is very important to locate the tumor for a patient, who has cancer in his brain. If he only gets X-CT or MRI pictures, the doctor does not know the size, shape location of the tumor and the relation between the tumor and other organs. This paper presents the formation of stereo images of cancer. On the basis of color code and color 3D reconstruction. The stereo images of tumor, brain and encephalic truncus are formed. The stereo image of cancer can be round on X, Y, Z-coordinates to show the shape from different directions. In order to show the location of tumor, stereo image of tumor and encephalic truncus are provided on different angles. The cross section pictures are also offered to indicate the relation of brain, tumor and encephalic truncus on cross sections. In this paper the calculating of areas, volume and the space between cancer and the side of the brain are also described.

A review of anisotropic conductivity models of brain white matter based on diffusion tensor imaging.

PubMed

Wu, Zhanxiong; Liu, Yang; Hong, Ming; Yu, Xiaohui

2018-06-01

The conductivity of brain tissues is not only essential for electromagnetic source estimation (ESI), but also a key reflector of the brain functional changes. Different from the other brain tissues, the conductivity of whiter matter (WM) is highly anisotropic and a tensor is needed to describe it. The traditional electrical property imaging methods, such as electrical impedance tomography (EIT) and magnetic resonance electrical impedance tomography (MREIT), usually fail to image the anisotropic conductivity tensor of WM with high spatial resolution. The diffusion tensor imaging (DTI) is a newly developed technique that can fulfill this purpose. This paper reviews the existing anisotropic conductivity models of WM based on the DTI and discusses their advantages and disadvantages, as well as identifies opportunities for future research on this subject. It is crucial to obtain the linear conversion coefficient between the eigenvalues of anisotropic conductivity tensor and diffusion tensor, since they share the same eigenvectors. We conclude that the electrochemical model is suitable for ESI analysis because the conversion coefficient can be directly obtained from the concentration of ions in extracellular liquid and that the volume fraction model is appropriate to study the influence of WM structural changes on electrical conductivity. Graphical abstract ᅟ.

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping.

PubMed

Studholme, Colin

2011-08-15

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Multicolor Fluorescence Imaging of Traumatic Brain Injury in a Cryolesion Mouse Model

PubMed Central

2012-01-01

Traumatic brain injury is characterized by initial tissue damage, which then can lead to secondary processes such as cell death and blood-brain-barrier disruption. Clinical and preclinical studies of traumatic brain injury typically employ anatomical imaging techniques and there is a need for new molecular imaging methods that provide complementary biochemical information. Here, we assess the ability of a targeted, near-infrared fluorescent probe, named PSS-794, to detect cell death in a brain cryolesion mouse model that replicates certain features of traumatic brain injury. In short, the model involves brief contact of a cold rod to the head of a living, anesthetized mouse. Using noninvasive whole-body fluorescence imaging, PSS-794 permitted visualization of the cryolesion in the living animal. Ex vivo imaging and histological analysis confirmed PSS-794 localization to site of brain cell death. The nontargeted, deep-red Tracer-653 was validated as a tracer dye for monitoring blood-brain-barrier disruption, and a binary mixture of PSS-794 and Tracer-653 was employed for multicolor imaging of cell death and blood-brain-barrier permeability in a single animal. The imaging data indicates that at 3 days after brain cryoinjury the amount of cell death had decreased significantly, but the integrity of the blood-brain-barrier was still impaired; at 7 days, the blood-brain-barrier was still three times more permeable than before cryoinjury. PMID:22860222

Intraoperative brain hemodynamic response assessment with real-time hyperspectral optical imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Laurence, Audrey; Pichette, Julien; Angulo-Rodríguez, Leticia M.; Saint Pierre, Catherine; Lesage, Frédéric; Bouthillier, Alain; Nguyen, Dang Khoa; Leblond, Frédéric

2016-03-01

Following normal neuronal activity, there is an increase in cerebral blood flow and cerebral blood volume to provide oxygenated hemoglobin to active neurons. For abnormal activity such as epileptiform discharges, this hemodynamic response may be inadequate to meet the high metabolic demands. To verify this hypothesis, we developed a novel hyperspectral imaging system able to monitor real-time cortical hemodynamic changes during brain surgery. The imaging system is directly integrated into a surgical microscope, using the white-light source for illumination. A snapshot hyperspectral camera is used for detection (4x4 mosaic filter array detecting 16 wavelengths simultaneously). We present calibration experiments where phantoms made of intralipid and food dyes were imaged. Relative concentrations of three dyes were recovered at a video rate of 30 frames per second. We also present hyperspectral recordings during brain surgery of epileptic patients with concurrent electrocorticography recordings. Relative concentration maps of oxygenated and deoxygenated hemoglobin were extracted from the data, allowing real-time studies of hemodynamic changes with a good spatial resolution. Finally, we present preliminary results on phantoms obtained with an integrated spatial frequency domain imaging system to recover tissue optical properties. This additional module, used together with the hyperspectral imaging system, will allow quantification of hemoglobin concentrations maps. Our hyperspectral imaging system offers a new tool to analyze hemodynamic changes, especially in the case of epileptiform discharges. It also offers an opportunity to study brain connectivity by analyzing correlations between hemodynamic responses of different tissue regions.

Dorsal brain stem syndrome: MR imaging location of brain stem tegmental lesions in neonates with oral motor dysfunction.

PubMed

Quattrocchi, C C; Longo, D; Delfino, L N; Cilio, M R; Piersigilli, F; Capua, M D; Seganti, G; Danhaive, O; Fariello, G

2010-09-01

The anatomic extent of brain stem damage may provide information about clinical outcome and prognosis in children with hypoxic-ischemic encephalopathy and oral motor dysfunction. The aim of this study was to retrospectively characterize the location and extent of brain stem lesions in children with oral motor dysfunction. From January 2005 to August 2009, 43 infants hospitalized at our institution were included in the study because of a history of hypoxic-ischemic events. Of this group, 14 patients showed oral motor dysfunction and brain stem tegmental lesions detected at MR imaging. MR imaging showed hypoxic-ischemic lesions in supra- and infratentorial areas. Six of 14 patients revealed only infratentorial lesions. Focal symmetric lesions of the tegmental brain stem were always present. The lesions appeared hyperintense on T2-weighted images and hypointense on IR images. We found a strong association (P < .0001) between oral motor dysfunction and infratentorial lesions on MR imaging. Oral motor dysfunction was associated with brain stem tegmental lesions in posthypoxic-ischemic infants. The MR imaging examination should be directed to the brain stem, especially when a condition of prolonged gavage feeding is necessary in infants.

Brain imaging in the study of Alzheimer's disease.

PubMed

Reiman, Eric M; Jagust, William J

2012-06-01

Over the last 20 years, there has been extraordinary progress in brain imaging research and its application to the study of Alzheimer's disease (AD). Brain imaging researchers have contributed to the scientific understanding, early detection and tracking of AD. They have set the stage for imaging techniques to play growing roles in the clinical setting, the evaluation of disease-modifying treatments, and the identification of demonstrably effective prevention therapies. They have developed ground-breaking methods, including positron emission tomography (PET) ligands to measure fibrillar amyloid-β (Aβ) deposition, new magnetic resonance imaging (MRI) pulse sequences, and powerful image analysis techniques, to help in these endeavors. Additional work is needed to develop even more powerful imaging methods, to further clarify the relationship and time course of Aβ and other disease processes in the predisposition to AD, to establish the role of brain imaging methods in the clinical setting, and to provide the scientific means and regulatory approval pathway needed to evaluate the range of promising disease-modifying and prevention therapies as quickly as possible. Twenty years from now, AD may not yet be a distant memory, but the best is yet to come. Copyright © 2011 Elsevier Inc. All rights reserved.

Missouri University Multi-Plane Imager (MUMPI): A high sensitivity rapid dynamic ECT brain imager

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

Logan, K.W.; Holmes, R.A.

1984-01-01

The authors have designed a unique ECT imaging device that can record rapid dynamic images of brain perfusion. The Missouri University Multi-Plane Imager (MUMPI) uses a single crystal detector that produces four orthogonal two-dimensional images simultaneously. Multiple slice images are reconstructed from counts recorded from stepwise or continuous collimator rotation. Four simultaneous 2-d image fields may also be recorded and reviewed. The cylindrical sodium iodide crystal and the rotating collimator concentrically surround the source volume being imaged with the collimator the only moving part. The design and function parameters of MUMPI have been compared to other competitive tomographic head imagingmore » devices. MUMPI's principal advantages are: 1) simultaneous direct acquisition of four two-dimensional images; 2) extremely rapid project set acquisition for ECT reconstruction; and 3) instrument practicality and economy due to single detector design and the absence of heavy mechanical moving components (only collimator rotation is required). MUMPI should be ideal for imaging neutral lipophilic chelates such as Tc-99m-PnAO which passively diffuses across the intact blood-brain-barrier and rapidly clears from brain tissue.« less

MEG source imaging method using fast L1 minimum-norm and its applications to signals with brain noise and human resting-state source amplitude images.

PubMed

Huang, Ming-Xiong; Huang, Charles W; Robb, Ashley; Angeles, AnneMarie; Nichols, Sharon L; Baker, Dewleen G; Song, Tao; Harrington, Deborah L; Theilmann, Rebecca J; Srinivasan, Ramesh; Heister, David; Diwakar, Mithun; Canive, Jose M; Edgar, J Christopher; Chen, Yu-Han; Ji, Zhengwei; Shen, Max; El-Gabalawy, Fady; Levy, Michael; McLay, Robert; Webb-Murphy, Jennifer; Liu, Thomas T; Drake, Angela; Lee, Roland R

2014-01-01

The present study developed a fast MEG source imaging technique based on Fast Vector-based Spatio-Temporal Analysis using a L1-minimum-norm (Fast-VESTAL) and then used the method to obtain the source amplitude images of resting-state magnetoencephalography (MEG) signals for different frequency bands. The Fast-VESTAL technique consists of two steps. First, L1-minimum-norm MEG source images were obtained for the dominant spatial modes of sensor-waveform covariance matrix. Next, accurate source time-courses with millisecond temporal resolution were obtained using an inverse operator constructed from the spatial source images of Step 1. Using simulations, Fast-VESTAL's performance was assessed for its 1) ability to localize multiple correlated sources; 2) ability to faithfully recover source time-courses; 3) robustness to different SNR conditions including SNR with negative dB levels; 4) capability to handle correlated brain noise; and 5) statistical maps of MEG source images. An objective pre-whitening method was also developed and integrated with Fast-VESTAL to remove correlated brain noise. Fast-VESTAL's performance was then examined in the analysis of human median-nerve MEG responses. The results demonstrated that this method easily distinguished sources in the entire somatosensory network. Next, Fast-VESTAL was applied to obtain the first whole-head MEG source-amplitude images from resting-state signals in 41 healthy control subjects, for all standard frequency bands. Comparisons between resting-state MEG sources images and known neurophysiology were provided. Additionally, in simulations and cases with MEG human responses, the results obtained from using conventional beamformer technique were compared with those from Fast-VESTAL, which highlighted the beamformer's problems of signal leaking and distorted source time-courses. © 2013.

Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

NASA Astrophysics Data System (ADS)

Deng, He; Deng, Wankai; Sun, Xianping; Ye, Chaohui; Zhou, Xin

2016-10-01

Image enhancement techniques are able to improve the contrast and visual quality of magnetic resonance (MR) images. However, conventional methods cannot make up some deficiencies encountered by respective brain tumor MR imaging modes. In this paper, we propose an adaptive intuitionistic fuzzy sets-based scheme, called as AIFE, which takes information provided from different MR acquisitions and tries to enhance the normal and abnormal structural regions of the brain while displaying the enhanced results as a single image. The AIFE scheme firstly separates an input image into several sub images, then divides each sub image into object and background areas. After that, different novel fuzzification, hyperbolization and defuzzification operations are implemented on each object/background area, and finally an enhanced result is achieved via nonlinear fusion operators. The fuzzy implementations can be processed in parallel. Real data experiments demonstrate that the AIFE scheme is not only effectively useful to have information from images acquired with different MR sequences fused in a single image, but also has better enhancement performance when compared to conventional baseline algorithms. This indicates that the proposed AIFE scheme has potential for improving the detection and diagnosis of brain tumors.

Image and emotion: from outcomes to brain behavior.

PubMed

Nanda, Upali; Zhu, Xi; Jansen, Ben H

2012-01-01

A systematic review of neuroscience articles on the emotional states of fear, anxiety, and pain to understand how emotional response is linked to the visual characteristics of an image at the level of brain behavior. A number of outcome studies link exposure to visual images (with nature content) to improvements in stress, anxiety, and pain perception. However, an understanding of the underlying perceptual mechanisms has been lacking. In this article, neuroscience studies that use visual images to induce fear, anxiety, or pain are reviewed to gain an understanding of how the brain processes visual images in this context and to explore whether this processing can be linked to specific visual characteristics. The amygdala was identified as one of the key regions of the brain involved in the processing of fear, anxiety, and pain (induced by visual images). Other key areas included the thalamus, insula, and hippocampus. Characteristics of visual images such as the emotional dimension (valence/arousal), subject matter (familiarity, ambiguity, novelty, realism, and facial expressions), and form (sharp and curved contours) were identified as key factors influencing emotional processing. The broad structural properties of an image and overall content were found to have a more pivotal role in the emotional response than the specific details of an image. Insights on specific visual properties were translated to recommendations for what should be incorporated-and avoided-in healthcare environments.

Accurate and robust brain image alignment using boundary-based registration.

PubMed

Greve, Douglas N; Fischl, Bruce

2009-10-15

The fine spatial scales of the structures in the human brain represent an enormous challenge to the successful integration of information from different images for both within- and between-subject analysis. While many algorithms to register image pairs from the same subject exist, visual inspection shows that their accuracy and robustness to be suspect, particularly when there are strong intensity gradients and/or only part of the brain is imaged. This paper introduces a new algorithm called Boundary-Based Registration, or BBR. The novelty of BBR is that it treats the two images very differently. The reference image must be of sufficient resolution and quality to extract surfaces that separate tissue types. The input image is then aligned to the reference by maximizing the intensity gradient across tissue boundaries. Several lower quality images can be aligned through their alignment with the reference. Visual inspection and fMRI results show that BBR is more accurate than correlation ratio or normalized mutual information and is considerably more robust to even strong intensity inhomogeneities. BBR also excels at aligning partial-brain images to whole-brain images, a domain in which existing registration algorithms frequently fail. Even in the limit of registering a single slice, we show the BBR results to be robust and accurate.

Geometry Processing of Conventionally Produced Mouse Brain Slice Images.

PubMed

Agarwal, Nitin; Xu, Xiangmin; Gopi, M

2018-04-21

Brain mapping research in most neuroanatomical laboratories relies on conventional processing techniques, which often introduce histological artifacts such as tissue tears and tissue loss. In this paper we present techniques and algorithms for automatic registration and 3D reconstruction of conventionally produced mouse brain slices in a standardized atlas space. This is achieved first by constructing a virtual 3D mouse brain model from annotated slices of Allen Reference Atlas (ARA). Virtual re-slicing of the reconstructed model generates ARA-based slice images corresponding to the microscopic images of histological brain sections. These image pairs are aligned using a geometric approach through contour images. Histological artifacts in the microscopic images are detected and removed using Constrained Delaunay Triangulation before performing global alignment. Finally, non-linear registration is performed by solving Laplace's equation with Dirichlet boundary conditions. Our methods provide significant improvements over previously reported registration techniques for the tested slices in 3D space, especially on slices with significant histological artifacts. Further, as one of the application we count the number of neurons in various anatomical regions using a dataset of 51 microscopic slices from a single mouse brain. To the best of our knowledge the presented work is the first that automatically registers both clean as well as highly damaged high-resolutions histological slices of mouse brain to a 3D annotated reference atlas space. This work represents a significant contribution to this subfield of neuroscience as it provides tools to neuroanatomist for analyzing and processing histological data. Copyright © 2018 Elsevier B.V. All rights reserved.

Monitoring fractional anisotropy in developing rabbit brain using MR diffusion tensor imaging at 3T

NASA Astrophysics Data System (ADS)

Jao, Jo-Chi; Yang, Yu-Ting; Hsiao, Chia-Chi; Chen, Po-Chou

2016-03-01

The aim of this study was to investigate the factional anisotropy (FA) in various regions of developing rabbit brain using magnetic resonance diffusion tensor imaging (MR DTI) at 3 T. A whole-body clinical MR imaging (MRI) scanner with a 15-channel high resolution knee coil was used. An echo-planar-imaging (EPI)-DTI pulse sequence was performed. Five 5 week-old New Zealand white (NZW) rabbits underwent MRI once per week for 24 weeks. After scanning, FA maps were obtained. ROIs (regions of interests) in the frontal lobe, parietal & temporal lobe, and occipital lobe were measured. FA changes with time were evaluated with a linear regression analysis. The results show that the FA values in all lobes of the brain increased linearly with age. The ranking of FA values was FA(frontal lobe) < FA(parietal & temporal lobe) > FA(occipital lobe). There was significant difference (p < 0.05) among these lobes. FA values are associated with the nerve development and brain functions. The FA change rate could be a biomarker to monitor the brain development. Understanding the FA values of various lobes during development could provide helpful information to diagnosis the abnormal syndrome earlier and have a better treatment and prognosis. This study established a brain MR-DTI protocol for rabbits to investigate the brain anatomy during development using clinical MRI. This technique can be further applied to the pre-clinical diagnosis, treatment, prognosis and follow-up of brain lesions.

Phosphatidylserine-Targeted Nanotheranostics for Brain Tumor Imaging and Therapeutic Potential

PubMed Central

Wang, Lulu; Habib, Amyn A.; Mintz, Akiva; Li, King C.; Zhao, Dawen

2017-01-01

Phosphatidylserine (PS), the most abundant anionic phospholipid in cell membrane, is strictly confined to the inner leaflet in normal cells. However, this PS asymmetry is found disruptive in many tumor vascular endothelial cells. We discuss the underlying mechanisms for PS asymmetry maintenance in normal cells and its loss in tumor cells. The specificity of PS exposure in tumor vasculature but not normal blood vessels may establish it a useful biomarker for cancer molecular imaging. Indeed, utilizing PS-targeting antibodies, multiple imaging probes have been developed and multimodal imaging data have shown their high tumor-selective targeting in various cancers. There is a critical need for improved diagnosis and therapy for brain tumors. We have recently established PS-targeted nanoplatforms, aiming to enhance delivery of imaging contrast agents across the blood–brain barrier to facilitate imaging of brain tumors. Advantages of using the nanodelivery system, in particular, lipid-based nanocarriers, are discussed here. We also describe our recent research interest in developing PS-targeted nanotheranostics for potential image-guided drug delivery to treat brain tumors. PMID:28654387

Phosphatidylserine-Targeted Nanotheranostics for Brain Tumor Imaging and Therapeutic Potential.

PubMed

Wang, Lulu; Habib, Amyn A; Mintz, Akiva; Li, King C; Zhao, Dawen

2017-01-01

Phosphatidylserine (PS), the most abundant anionic phospholipid in cell membrane, is strictly confined to the inner leaflet in normal cells. However, this PS asymmetry is found disruptive in many tumor vascular endothelial cells. We discuss the underlying mechanisms for PS asymmetry maintenance in normal cells and its loss in tumor cells. The specificity of PS exposure in tumor vasculature but not normal blood vessels may establish it a useful biomarker for cancer molecular imaging. Indeed, utilizing PS-targeting antibodies, multiple imaging probes have been developed and multimodal imaging data have shown their high tumor-selective targeting in various cancers. There is a critical need for improved diagnosis and therapy for brain tumors. We have recently established PS-targeted nanoplatforms, aiming to enhance delivery of imaging contrast agents across the blood-brain barrier to facilitate imaging of brain tumors. Advantages of using the nanodelivery system, in particular, lipid-based nanocarriers, are discussed here. We also describe our recent research interest in developing PS-targeted nanotheranostics for potential image-guided drug delivery to treat brain tumors.

Transmission in near-infrared optical windows for deep brain imaging.

PubMed

Shi, Lingyan; Sordillo, Laura A; Rodríguez-Contreras, Adrián; Alfano, Robert

2016-01-01

Near-infrared (NIR) radiation has been employed using one- and two-photon excitation of fluorescence imaging at wavelengths 650-950 nm (optical window I) for deep brain imaging; however, longer wavelengths in NIR have been overlooked due to a lack of suitable NIR-low band gap semiconductor imaging detectors and/or femtosecond laser sources. This research introduces three new optical windows in NIR and demonstrates their potential for deep brain tissue imaging. The transmittances are measured in rat brain tissue in the second (II, 1,100-1,350 nm), third (III, 1,600-1,870 nm), and fourth (IV, centered at 2,200 nm) NIR optical tissue windows. The relationship between transmission and tissue thickness is measured and compared with the theory. Due to a reduction in scattering and minimal absorption, window III is shown to be the best for deep brain imaging, and windows II and IV show similar but better potential for deep imaging than window I. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chapter 18: the origins of functional brain imaging in humans.

PubMed

Raichle, Marcus E

2010-01-01

Functional brain imaging in humans as we presently know it began when the experimental strategies of cognitive psychology were combined with modern brain imaging techniques, first positron emission tomography (PET) and then functional magnetic resonance imaging (fMRI), to examine how brain function supports mental activities. This marriage of disciplines and techniques galvanized the field of cognitive neuroscience, which has rapidly expanded to include a broad range of the social sciences as well as basic scientists interested in the neurophysiology, cell biology and genetics of the imaging signals. While much of this work has transpired over the past couple of decades, its roots can be traced back more than a century.

Microstructural effects of Ramadan fasting on the brain: a diffusion tensor imaging study.

PubMed

Bakan, Ayse Ahsen; Yıldız, Seyma; Alkan, Alpay; Yetis, Huseyin; Kurtcan, Serpil; Ilhan, Mahmut Muzaffer

2015-01-01

We aimed to examine whether the brain displays any microstructural changes after a three-week Ramadan fasting period using diffusion tenson imaging. This study included a study and a control group of 25 volunteers each. In the study group, we examined and compared apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values of the participants during (phase 1) and after (phase 2) a period of fasting. The control group included individuals who did not fast. ADC and FA values obtained in phase 1 and phase 2 were compared between the study and control groups. In the study group, ADC values of hypothalamus and, to a lesser extent, of insula were lower in phase 1 compared with phase 2 and the control group. The FA values of amygdala, middle temporal cortex, thalamus and, to a lesser extent, of medial prefrontal cortex were lower in phase 1 compared with phase 2 and the control group. Phase 2 ADC and FA values of the study group were not significantly different compared with the control group at any brain location. A three-week Ramadan fasting period can cause microstructural changes in the brain, and diffusion tensor imaging enables the visualization of these changes. The identification of brain locations where changes occurred in ADC and FA values during fasting can be helpful in diagnostic imaging and understanding the pathophysiology of eating disorders.

High-sensitivity terahertz imaging of traumatic brain injury in a rat model

NASA Astrophysics Data System (ADS)

Zhao, Hengli; Wang, Yuye; Chen, Linyu; Shi, Jia; Ma, Kang; Tang, Longhuang; Xu, Degang; Yao, Jianquan; Feng, Hua; Chen, Tunan

2018-03-01

We demonstrated that different degrees of experimental traumatic brain injury (TBI) can be differentiated clearly in fresh slices of rat brain tissues using transmission-type terahertz (THz) imaging system. The high absorption region in THz images corresponded well with the injured area in visible images and magnetic resonance imaging results. The THz image and absorption characteristics of dehydrated paraffin-embedded brain slices and the hematoxylin and eosin (H&E)-stained microscopic images were investigated to account for the intrinsic differences in the THz images for the brain tissues suffered from different degrees of TBI and normal tissue aside from water. The THz absorption coefficients of rat brain tissues showed an increase in the aggravation of brain damage, particularly in the high-frequency range, whereas the cell density decreased as the order of mild, moderate, and severe TBI tissues compared with the normal tissue. Our results indicated that the different degrees of TBI were distinguishable owing to the different water contents and probable hematoma components distribution rather than intrinsic cell intensity. These promising results suggest that THz imaging has great potential as an alternative method for the fast diagnosis of TBI.

Clinical brain MR imaging prescriptions in Talairach space: technologist- and computer-driven methods.

PubMed

Weiss, Kenneth L; Pan, Hai; Storrs, Judd; Strub, William; Weiss, Jane L; Jia, Li; Eldevik, O Petter

2003-05-01

Variability in patient head positioning may yield substantial interstudy image variance in the clinical setting. We describe and test three-step technologist and computer-automated algorithms designed to image the brain in a standard reference system and reduce variance. Triple oblique axial images obtained parallel to the Talairach anterior commissure (AC)-posterior commissure (PC) plane were reviewed in a prospective analysis of 126 consecutive patients. Requisite roll, yaw, and pitch correction, as three authors determined independently and subsequently by consensus, were compared with the technologists' actual graphical prescriptions and those generated by a novel computer automated three-step (CATS) program. Automated pitch determinations generated with Statistical Parametric Mapping '99 (SPM'99) were also compared. Requisite pitch correction (15.2 degrees +/- 10.2 degrees ) far exceeded that for roll (-0.6 degrees +/- 3.7 degrees ) and yaw (-0.9 degrees +/- 4.7 degrees ) in terms of magnitude and variance (P <.001). Technologist and computer-generated prescriptions substantially reduced interpatient image variance with regard to roll (3.4 degrees and 3.9 degrees vs 13.5 degrees ), yaw (0.6 degrees and 2.5 degrees vs 22.3 degrees ), and pitch (28.6 degrees, 18.5 degrees with CATS, and 59.3 degrees with SPM'99 vs 104 degrees ). CATS performed worse than the technologists in yaw prescription, and it was equivalent in roll and pitch prescriptions. Talairach prescriptions better approximated standard CT canthomeatal angulations (9 degrees vs 24 degrees ) and provided more efficient brain coverage than that of routine axial imaging. Brain MR prescriptions corrected for direct roll, yaw, and Talairach AC-PC pitch can be readily achieved by trained technologists or automated computer algorithms. This ability will substantially reduce interpatient variance, allow better approximation of standard CT angulation, and yield more efficient brain coverage than that of

Relationship between position of brain activity and change in optical density for NIR imaging

NASA Astrophysics Data System (ADS)

Kashio, Yoshihiko; Ono, Muneo; Firbank, Michael; Schweiger, Martin; Arridge, Simon R.; Okada, Eiji

2000-11-01

Multi-channel NIR system can obtain the topographic image of brain activity. Since the image is reconstructed from the change in optical density measured with the source-detector pairs, it is important to reveal the volume of tissue sampled by each source-detector pair. In this study, the light propagation in three-dimensional adult head model is calculated by hybrid radiosity-diffusion method. The model is a layered slab which mimics the extra cerebral tissue (skin, skull), CSF and brain. The change in optical density caused by the absorption change in a small cylindrical region of 10 mm in diameter at various positions in the brain is calculated. The greatest change in optical density can be observed when the absorber is located in the middle of the source and detector. When the absorber is located just below the source or detector, the change in optical density is almost half of that caused by the same absorber in the midpoint. The light propagation in the brain is strongly affected by the presence of non-scattering layer and consequently sensitive region is broadly distributed on the brain surface.

Human brain activity with functional NIR optical imager

NASA Astrophysics Data System (ADS)

Luo, Qingming

2001-08-01

In this paper we reviewed the applications of functional near infrared optical imager in human brain activity. Optical imaging results of brain activity, including memory for new association, emotional thinking, mental arithmetic, pattern recognition ' where's Waldo?, occipital cortex in visual stimulation, and motor cortex in finger tapping, are demonstrated. It is shown that the NIR optical method opens up new fields of study of the human population, in adults under conditions of simulated or real stress that may have important effects upon functional performance. It makes practical and affordable for large populations the complex technology of measuring brain function. It is portable and low cost. In cognitive tasks subjects could report orally. The temporal resolution could be millisecond or less in theory. NIR method will have good prospects in exploring human brain secret.

PCA based clustering for brain tumor segmentation of T1w MRI images.

PubMed

Kaya, Irem Ersöz; Pehlivanlı, Ayça Çakmak; Sekizkardeş, Emine Gezmez; Ibrikci, Turgay

2017-03-01

Medical images are huge collections of information that are difficult to store and process consuming extensive computing time. Therefore, the reduction techniques are commonly used as a data pre-processing step to make the image data less complex so that a high-dimensional data can be identified by an appropriate low-dimensional representation. PCA is one of the most popular multivariate methods for data reduction. This paper is focused on T1-weighted MRI images clustering for brain tumor segmentation with dimension reduction by different common Principle Component Analysis (PCA) algorithms. Our primary aim is to present a comparison between different variations of PCA algorithms on MRIs for two cluster methods. Five most common PCA algorithms; namely the conventional PCA, Probabilistic Principal Component Analysis (PPCA), Expectation Maximization Based Principal Component Analysis (EM-PCA), Generalize Hebbian Algorithm (GHA), and Adaptive Principal Component Extraction (APEX) were applied to reduce dimensionality in advance of two clustering algorithms, K-Means and Fuzzy C-Means. In the study, the T1-weighted MRI images of the human brain with brain tumor were used for clustering. In addition to the original size of 512 lines and 512 pixels per line, three more different sizes, 256 × 256, 128 × 128 and 64 × 64, were included in the study to examine their effect on the methods. The obtained results were compared in terms of both the reconstruction errors and the Euclidean distance errors among the clustered images containing the same number of principle components. According to the findings, the PPCA obtained the best results among all others. Furthermore, the EM-PCA and the PPCA assisted K-Means algorithm to accomplish the best clustering performance in the majority as well as achieving significant results with both clustering algorithms for all size of T1w MRI images. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Two-photon imaging in living brain slices.

PubMed

Mainen, Z F; Maletic-Savatic, M; Shi, S H; Hayashi, Y; Malinow, R; Svoboda, K

1999-06-01

Two-photon excitation laser scanning microscopy (TPLSM) has become the tool of choice for high-resolution fluorescence imaging in intact neural tissues. Compared with other optical techniques, TPLSM allows high-resolution imaging and efficient detection of fluorescence signal with minimal photobleaching and phototoxicity. The advantages of TPLSM are especially pronounced in highly scattering environments such as the brain slice. Here we describe our approaches to imaging various aspects of synaptic function in living brain slices. To combine several imaging modes together with patch-clamp electrophysiological recordings we found it advantageous to custom-build an upright microscope. Our design goals were primarily experimental convenience and efficient collection of fluorescence. We describe our TPLSM imaging system and its performance in detail. We present dynamic measurements of neuronal morphology of neurons expressing green fluorescent protein (GFP) and GFP fusion proteins as well as functional imaging of calcium dynamics in individual dendritic spines. Although our microscope is a custom instrument, its key advantages can be easily implemented as a modification of commercial laser scanning microscopes. Copyright 1999 Academic Press.

Tomographic brain imaging with nucleolar detail and automatic cell counting

NASA Astrophysics Data System (ADS)

Hieber, Simone E.; Bikis, Christos; Khimchenko, Anna; Schweighauser, Gabriel; Hench, Jürgen; Chicherova, Natalia; Schulz, Georg; Müller, Bert

2016-09-01

Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm3 of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm2 on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner.

Mapping whole-brain activity with cellular resolution by light-sheet microscopy and high-throughput image analysis (Conference Presentation)

NASA Astrophysics Data System (ADS)

Silvestri, Ludovico; Rudinskiy, Nikita; Paciscopi, Marco; Müllenbroich, Marie Caroline; Costantini, Irene; Sacconi, Leonardo; Frasconi, Paolo; Hyman, Bradley T.; Pavone, Francesco S.

2016-03-01

Mapping neuronal activity patterns across the whole brain with cellular resolution is a challenging task for state-of-the-art imaging methods. Indeed, despite a number of technological efforts, quantitative cellular-resolution activation maps of the whole brain have not yet been obtained. Many techniques are limited by coarse resolution or by a narrow field of view. High-throughput imaging methods, such as light sheet microscopy, can be used to image large specimens with high resolution and in reasonable times. However, the bottleneck is then moved from image acquisition to image analysis, since many TeraBytes of data have to be processed to extract meaningful information. Here, we present a full experimental pipeline to quantify neuronal activity in the entire mouse brain with cellular resolution, based on a combination of genetics, optics and computer science. We used a transgenic mouse strain (Arc-dVenus mouse) in which neurons which have been active in the last hours before brain fixation are fluorescently labelled. Samples were cleared with CLARITY and imaged with a custom-made confocal light sheet microscope. To perform an automatic localization of fluorescent cells on the large images produced, we used a novel computational approach called semantic deconvolution. The combined approach presented here allows quantifying the amount of Arc-expressing neurons throughout the whole mouse brain. When applied to cohorts of mice subject to different stimuli and/or environmental conditions, this method helps finding correlations in activity between different neuronal populations, opening the possibility to infer a sort of brain-wide 'functional connectivity' with cellular resolution.

A versatile clearing agent for multi-modal brain imaging

PubMed Central

Costantini, Irene; Ghobril, Jean-Pierre; Di Giovanna, Antonino Paolo; Mascaro, Anna Letizia Allegra; Silvestri, Ludovico; Müllenbroich, Marie Caroline; Onofri, Leonardo; Conti, Valerio; Vanzi, Francesco; Sacconi, Leonardo; Guerrini, Renzo; Markram, Henry; Iannello, Giulio; Pavone, Francesco Saverio

2015-01-01

Extensive mapping of neuronal connections in the central nervous system requires high-throughput µm-scale imaging of large volumes. In recent years, different approaches have been developed to overcome the limitations due to tissue light scattering. These methods are generally developed to improve the performance of a specific imaging modality, thus limiting comprehensive neuroanatomical exploration by multi-modal optical techniques. Here, we introduce a versatile brain clearing agent (2,2′-thiodiethanol; TDE) suitable for various applications and imaging techniques. TDE is cost-efficient, water-soluble and low-viscous and, more importantly, it preserves fluorescence, is compatible with immunostaining and does not cause deformations at sub-cellular level. We demonstrate the effectiveness of this method in different applications: in fixed samples by imaging a whole mouse hippocampus with serial two-photon tomography; in combination with CLARITY by reconstructing an entire mouse brain with light sheet microscopy and in translational research by imaging immunostained human dysplastic brain tissue. PMID:25950610

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

PubMed

Gorgolewski, Krzysztof J; Auer, Tibor; Calhoun, Vince D; Craddock, R Cameron; Das, Samir; Duff, Eugene P; Flandin, Guillaume; Ghosh, Satrajit S; Glatard, Tristan; Halchenko, Yaroslav O; Handwerker, Daniel A; Hanke, Michael; Keator, David; Li, Xiangrui; Michael, Zachary; Maumet, Camille; Nichols, B Nolan; Nichols, Thomas E; Pellman, John; Poline, Jean-Baptiste; Rokem, Ariel; Schaefer, Gunnar; Sochat, Vanessa; Triplett, William; Turner, Jessica A; Varoquaux, Gaël; Poldrack, Russell A

2016-06-21

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

Brain imaging in the context of food perception and eating.

PubMed

Hollmann, Maurice; Pleger, Burkhard; Villringer, Arno; Horstmann, Annette

2013-02-01

Eating behavior depends heavily on brain function. In recent years, brain imaging has proved to be a powerful tool to elucidate brain function and brain structure in the context of eating. In this review, we summarize recent findings in the fast growing body of literature in the field and provide an overview of technical aspects as well as the basic brain mechanisms identified with imaging. Furthermore, we highlight findings linking neural processing of eating-related stimuli with obesity. The consumption of food is based on a complex interplay between homeostatic and hedonic mechanisms. Several hormones influence brain activity to regulate food intake and interact with the brain's reward circuitry, which is partly mediated by dopamine signaling. Additionally, it was shown that food stimuli trigger cognitive control mechanisms that incorporate internal goals into food choice. The brain mechanisms observed in this context are strongly influenced by genetic factors, sex and personality traits. Overall, a complex picture arises from brain-imaging findings, because a multitude of factors influence human food choice. Although several key mechanisms have been identified, there is no comprehensive model that is able to explain the behavioral observations to date. Especially a careful characterization of patients according to genotypes and phenotypes could help to better understand the current and future findings in neuroimaging studies.

Automatic Brain Tumor Detection in T2-weighted Magnetic Resonance Images

NASA Astrophysics Data System (ADS)

Dvořák, P.; Kropatsch, W. G.; Bartušek, K.

2013-10-01

This work focuses on fully automatic detection of brain tumors. The first aim is to determine, whether the image contains a brain with a tumor, and if it does, localize it. The goal of this work is not the exact segmentation of tumors, but the localization of their approximate position. The test database contains 203 T2-weighted images of which 131 are images of healthy brain and the remaining 72 images contain brain with pathological area. The estimation, whether the image shows an afflicted brain and where a pathological area is, is done by multi resolution symmetry analysis. The first goal was tested by five-fold cross-validation technique with 100 repetitions to avoid the result dependency on sample order. This part of the proposed method reaches the true positive rate of 87.52% and the true negative rate of 93.14% for an afflicted brain detection. The evaluation of the second part of the algorithm was carried out by comparing the estimated location to the true tumor location. The detection of the tumor location reaches the rate of 95.83% of correct anomaly detection and the rate 87.5% of correct tumor location.

Exploring sex differences in the adult zebra finch brain: In vivo diffusion tensor imaging and ex vivo super-resolution track density imaging.

PubMed

Hamaide, Julie; De Groof, Geert; Van Steenkiste, Gwendolyn; Jeurissen, Ben; Van Audekerke, Johan; Naeyaert, Maarten; Van Ruijssevelt, Lisbeth; Cornil, Charlotte; Sijbers, Jan; Verhoye, Marleen; Van der Linden, Annemie

2017-02-01

Zebra finches are an excellent model to study the process of vocal learning, a complex socially-learned tool of communication that forms the basis of spoken human language. So far, structural investigation of the zebra finch brain has been performed ex vivo using invasive methods such as histology. These methods are highly specific, however, they strongly interfere with performing whole-brain analyses and exclude longitudinal studies aimed at establishing causal correlations between neuroplastic events and specific behavioral performances. Therefore, the aim of the current study was to implement an in vivo Diffusion Tensor Imaging (DTI) protocol sensitive enough to detect structural sex differences in the adult zebra finch brain. Voxel-wise comparison of male and female DTI parameter maps shows clear differences in several components of the song control system (i.e. Area X surroundings, the high vocal center (HVC) and the lateral magnocellular nucleus of the anterior nidopallium (LMAN)), which corroborate previous findings and are in line with the clear behavioral difference as only males sing. Furthermore, to obtain additional insights into the 3-dimensional organization of the zebra finch brain and clarify findings obtained by the in vivo study, ex vivo DTI data of the male and female brain were acquired as well, using a recently established super-resolution reconstruction (SRR) imaging strategy. Interestingly, the SRR-DTI approach led to a marked reduction in acquisition time without interfering with the (spatial and angular) resolution and SNR which enabled to acquire a data set characterized by a 78μm isotropic resolution including 90 diffusion gradient directions within 44h of scanning time. Based on the reconstructed SRR-DTI maps, whole brain probabilistic Track Density Imaging (TDI) was performed for the purpose of super resolved track density imaging, further pushing the resolution up to 40μm isotropic. The DTI and TDI maps realized atlas

Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis.

PubMed

Popescu, V; Battaglini, M; Hoogstrate, W S; Verfaillie, S C J; Sluimer, I C; van Schijndel, R A; van Dijk, B W; Cover, K S; Knol, D L; Jenkinson, M; Barkhof, F; de Stefano, N; Vrenken, H

2012-07-16

Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability. To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard. Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5 T or 3T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the "f" parameter) and with either one of the main BET options ("B" - bias field correction and neck cleanup, "R" - robust brain center estimation, or "S" - eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test. The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f=0.5, g=0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was

MEG Source Imaging Method using Fast L1 Minimum-norm and its Applications to Signals with Brain Noise and Human Resting-state Source Amplitude Images

PubMed Central

Huang, Ming-Xiong; Huang, Charles W.; Robb, Ashley; Angeles, AnneMarie; Nichols, Sharon L.; Baker, Dewleen G.; Song, Tao; Harrington, Deborah L.; Theilmann, Rebecca J.; Srinivasan, Ramesh; Heister, David; Diwakar, Mithun; Canive, Jose M.; Edgar, J. Christopher; Chen, Yu-Han; Ji, Zhengwei; Shen, Max; El-Gabalawy, Fady; Levy, Michael; McLay, Robert; Webb-Murphy, Jennifer; Liu, Thomas T.; Drake, Angela; Lee, Roland R.

2014-01-01

The present study developed a fast MEG source imaging technique based on Fast Vector-based Spatio-Temporal Analysis using a L1-minimum-norm (Fast-VESTAL) and then used the method to obtain the source amplitude images of resting-state magnetoencephalography (MEG) signals for different frequency bands. The Fast-VESTAL technique consists of two steps. First, L1-minimum-norm MEG source images were obtained for the dominant spatial modes of sensor-waveform covariance matrix. Next, accurate source time-courses with millisecond temporal resolution were obtained using an inverse operator constructed from the spatial source images of Step 1. Using simulations, Fast-VESTAL’s performance of was assessed for its 1) ability to localize multiple correlated sources; 2) ability to faithfully recover source time-courses; 3) robustness to different SNR conditions including SNR with negative dB levels; 4) capability to handle correlated brain noise; and 5) statistical maps of MEG source images. An objective pre-whitening method was also developed and integrated with Fast-VESTAL to remove correlated brain noise. Fast-VESTAL’s performance was then examined in the analysis of human mediannerve MEG responses. The results demonstrated that this method easily distinguished sources in the entire somatosensory network. Next, Fast-VESTAL was applied to obtain the first whole-head MEG source-amplitude images from resting-state signals in 41 healthy control subjects, for all standard frequency bands. Comparisons between resting-state MEG sources images and known neurophysiology were provided. Additionally, in simulations and cases with MEG human responses, the results obtained from using conventional beamformer technique were compared with those from Fast-VESTAL, which highlighted the beamformer’s problems of signal leaking and distorted source time-courses. PMID:24055704

The study on increasing the equivalent SNR in the certain DOI by adjusting the SD separation in near-infrared brain imaging application

NASA Astrophysics Data System (ADS)

Wang, Jinhai; Liu, Dongyuan; Sun, Jinggong; Zhang, Yanjun; Sun, Qiuming; Ma, Jun; Zheng, Yu; Wang, Huiquan

2016-10-01

Near-infrared (NIR) brain imaging is one of the most promising techniques for brain research in recent years. As a significant supplement to the clinical imaging technique, such as CT and MRI, the NIR technique can achieve a fast, non-invasive, and low cost imaging of the brain, which is widely used for the brain functional imaging and hematoma detection. NIR imaging can achieve an imaging depth up to only several centimeters due to the reduced optical attenuation. The structure of the human brain is so particularly complex, from the perspective of optical detection, the measurement light needs go through the skin, skull, cerebrospinal fluid (CSF), grey matter, and white matter, and then reverses the order reflected by the detector. The more photons from the Depth of Interest (DOI) in brain the detector capture, the better detection accuracy and stability can be obtained. In this study, the Equivalent Signal to Noise Ratio (ESNR) was defined as the proportion of the photons from the DOI to the total photons the detector evaluated the best Source and Detector (SD) separation. The Monte-Carlo (MC) simulation was used to establish a multi brain layer model to analyze the distribution of the ESNR along the radial direction for different DOIs and several basic brain optical and structure parameters. A map between the best detection SD separation, in which distance the ESNR was the highest, and the brain parameters was established for choosing the best detection point in the NIR brain imaging application. The results showed that the ESNR was very sensitivity to the SD separation. So choosing the best SD separation based on the ESNR is very significant for NIR brain imaging application. It provides an important reference and new thinking for the brain imaging in the near infrared.

The iconographic brain. A critical philosophical inquiry into (the resistance of) the image

PubMed Central

De Vos, Jan

2014-01-01

The brain image plays a central role in contemporary image culture and, in turn, (co)constructs contemporary forms of subjectivity. The central aim of this paper is to probe the unmistakably potent interpellative power of brain images by delving into the power of imaging and the power of the image itself. This is not without relevance for the neurosciences, inasmuch as these do not take place in a vacuum; hence the importance of inquiring into the status of the image within scientific culture and science itself. I will mount a critical philosophical investigation of the brain qua image, focusing on the issue of mapping the mental onto the brain and how, in turn, the brain image plays a pivotal role in processes of subjectivation. Hereto, I draw upon Science & Technology Studies, juxtaposed with culture and ideology critique and theories of image culture. The first section sets out from Althusser's concept of interpellation, linking ideology to subjectivity. Doing so allows to spell out the central question of the paper: what could serve as the basis for a critical approach, or, where can a locus of resistance be found? In the second section, drawing predominantly on Baudrillard, I delve into the dimension of virtuality as this is opened up by brain image culture. This leads to the question of whether the digital brain must be opposed to old analog psychology: is it the psyche which resists? This issue is taken up in the third section which, ultimately, concludes that the psychological is not the requisite locus of resistance. The fourth section proceeds to delineate how the brain image is constructed from what I call the data-gaze (the claim that brain data are always already visual). In the final section, I discuss how an engagement with theories of iconology affords a critical understanding of the interpellative force of the brain image, which culminates in the somewhat unexpected claim that the sought after resistance lies in the very status of the image itself

Normal feline brain: clinical anatomy using magnetic resonance imaging.

PubMed

Mogicato, G; Conchou, F; Layssol-Lamour, C; Raharison, F; Sautet, J

2012-04-01

The purpose of this study was to provide a clinical anatomy atlas of the feline brain using magnetic resonance imaging (MRI). Brains of twelve normal cats were imaged using a 1.5 T magnetic resonance unit and an inversion/recovery sequence (T1). Fourteen relevant MRI sections were chosen in transverse, dorsal, median and sagittal planes. Anatomic structures were identified and labelled using anatomical texts and Nomina Anatomica Veterinaria, sectioned specimen heads, and previously published articles. The MRI sections were stained according to the major embryological and anatomical subdivisions of the brain. The relevant anatomical structures seen on MRI will assist clinicians to better understand MR images and to relate this neuro-anatomy to clinical signs. © 2011 Blackwell Verlag GmbH.

In vivo rat deep brain imaging using photoacoustic computed tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lin, Li; Li, Lei; Zhu, Liren; Hu, Peng; Wang, Lihong V.

2017-03-01

The brain has been likened to a great stretch of unknown territory consisting of a number of unexplored continents. Small animal brain imaging plays an important role charting that territory. By using 1064 nm illumination from the side, we imaged the full coronal depth of rat brains in vivo. The experiment was performed using a real-time full-ring-array photoacoustic computed tomography (PACT) imaging system, which achieved an imaging depth of 11 mm and a 100 μm radial resolution. Because of the fast imaging speed of the full-ring-array PACT system, no animal motion artifact was induced. The frame rate of the system was limited by the laser repetition rate (50 Hz). In addition to anatomical imaging of the blood vessels in the brain, we continuously monitored correlations between the two brain hemispheres in one of the coronal planes. The resting states in the coronal plane were measured before and after stroke ligation surgery at a neck artery.

Electroencephalographic imaging of higher brain function

NASA Technical Reports Server (NTRS)

Gevins, A.; Smith, M. E.; McEvoy, L. K.; Leong, H.; Le, J.

1999-01-01

High temporal resolution is necessary to resolve the rapidly changing patterns of brain activity that underlie mental function. Electroencephalography (EEG) provides temporal resolution in the millisecond range. However, traditional EEG technology and practice provide insufficient spatial detail to identify relationships between brain electrical events and structures and functions visualized by magnetic resonance imaging or positron emission tomography. Recent advances help to overcome this problem by recording EEGs from more electrodes, by registering EEG data with anatomical images, and by correcting the distortion caused by volume conduction of EEG signals through the skull and scalp. In addition, statistical measurements of sub-second interdependences between EEG time-series recorded from different locations can help to generate hypotheses about the instantaneous functional networks that form between different cortical regions during perception, thought and action. Example applications are presented from studies of language, attention and working memory. Along with its unique ability to monitor brain function as people perform everyday activities in the real world, these advances make modern EEG an invaluable complement to other functional neuroimaging modalities.

COBRA: A prospective multimodal imaging study of dopamine, brain structure and function, and cognition.

PubMed

Nevalainen, N; Riklund, K; Andersson, M; Axelsson, J; Ögren, M; Lövdén, M; Lindenberger, U; Bäckman, L; Nyberg, L

2015-07-01

Cognitive decline is a characteristic feature of normal human aging. Previous work has demonstrated marked interindividual variability in onset and rate of decline. Such variability has been linked to factors such as maintenance of functional and structural brain integrity, genetics, and lifestyle. Still, few, if any, studies have combined a longitudinal design with repeated multimodal imaging and a comprehensive assessment of cognition as well as genetic and lifestyle factors. The present paper introduces the Cognition, Brain, and Aging (COBRA) study, in which cognitive performance and brain structure and function are measured in a cohort of 181 older adults aged 64 to 68 years at baseline. Participants will be followed longitudinally over a 10-year period, resulting in a total of three equally spaced measurement occasions. The measurement protocol at each occasion comprises a comprehensive set of behavioral and imaging measures. Cognitive performance is evaluated via computerized testing of working memory, episodic memory, perceptual speed, motor speed, implicit sequence learning, and vocabulary. Brain imaging is performed using positron emission tomography with [(11)C]-raclopride to assess dopamine D2/D3 receptor availability. Structural magnetic resonance imaging (MRI) is used for assessment of white and gray-matter integrity and cerebrovascular perfusion, and functional MRI maps brain activation during rest and active task conditions. Lifestyle descriptives are collected, and blood samples are obtained and stored for future evaluation. Here, we present selected results from the baseline assessment along with a discussion of sample characteristics and methodological considerations that determined the design of the study. This article is part of a Special Issue entitled SI: Memory & Aging. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Diffusion tensor imaging using multiple coils for mouse brain connectomics.

PubMed

Nouls, John C; Badea, Alexandra; Anderson, Robert B J; Cofer, Gary P; Allan Johnson, G

2018-06-01

The correlation between brain connectivity and psychiatric or neurological diseases has intensified efforts to develop brain connectivity mapping techniques on mouse models of human disease. The neural architecture of mouse brain specimens can be shown non-destructively and three-dimensionally by diffusion tensor imaging, which enables tractography, the establishment of a connectivity matrix and connectomics. However, experiments on cohorts of animals can be prohibitively long. To improve throughput in a 7-T preclinical scanner, we present a novel two-coil system in which each coil is shielded, placed off-isocenter along the axis of the magnet and connected to a receiver circuit of the scanner. Preservation of the quality factor of each coil is essential to signal-to-noise ratio (SNR) performance and throughput, because mouse brain specimen imaging at 7 T takes place in the coil-dominated noise regime. In that regime, we show a shielding configuration causing no SNR degradation in the two-coil system. To acquire data from several coils simultaneously, the coils are placed in the magnet bore, around the isocenter, in which gradient field distortions can bias diffusion tensor imaging metrics, affect tractography and contaminate measurements of the connectivity matrix. We quantified the experimental alterations in fractional anisotropy and eigenvector direction occurring in each coil. We showed that, when the coils were placed 12 mm away from the isocenter, measurements of the brain connectivity matrix appeared to be minimally altered by gradient field distortions. Simultaneous measurements on two mouse brain specimens demonstrated a full doubling of the diffusion tensor imaging throughput in practice. Each coil produced images devoid of shading or artifact. To further improve the throughput of mouse brain connectomics, we suggested a future expansion of the system to four coils. To better understand acceptable trade-offs between imaging throughput and connectivity

Is the spatial distribution of brain lesions associated with closed-head injury predictive of subsequent development of attention-deficit/hyperactivity disorder? Analysis with brain-image database

NASA Technical Reports Server (NTRS)

Herskovits, E. H.; Megalooikonomou, V.; Davatzikos, C.; Chen, A.; Bryan, R. N.; Gerring, J. P.

1999-01-01

PURPOSE: To determine whether there is an association between the spatial distribution of lesions detected at magnetic resonance (MR) imaging of the brain in children after closed-head injury and the development of secondary attention-deficit/hyperactivity disorder (ADHD). MATERIALS AND METHODS: Data obtained from 76 children without prior history of ADHD were analyzed. MR images were obtained 3 months after closed-head injury. After manual delineation of lesions, images were registered to the Talairach coordinate system. For each subject, registered images and secondary ADHD status were integrated into a brain-image database, which contains depiction (visualization) and statistical analysis software. Using this database, we assessed visually the spatial distributions of lesions and performed statistical analysis of image and clinical variables. RESULTS: Of the 76 children, 15 developed secondary ADHD. Depiction of the data suggested that children who developed secondary ADHD had more lesions in the right putamen than children who did not develop secondary ADHD; this impression was confirmed statistically. After Bonferroni correction, we could not demonstrate significant differences between secondary ADHD status and lesion burdens for the right caudate nucleus or the right globus pallidus. CONCLUSION: Closed-head injury-induced lesions in the right putamen in children are associated with subsequent development of secondary ADHD. Depiction software is useful in guiding statistical analysis of image data.

Use of High Resolution 3D Diffusion Tensor Imaging to Study Brain White Matter Development in Live Neonatal Rats

PubMed Central

Cai, Yu; McMurray, Matthew S.; Oguz, Ipek; Yuan, Hong; Styner, Martin A.; Lin, Weili; Johns, Josephine M.; An, Hongyu

2011-01-01

High resolution diffusion tensor imaging (DTI) can provide important information on brain development, yet it is challenging in live neonatal rats due to the small size of neonatal brain and motion-sensitive nature of DTI. Imaging in live neonatal rats has clear advantages over fixed brain scans, as longitudinal and functional studies would be feasible to understand neuro-developmental abnormalities. In this study, we developed imaging strategies that can be used to obtain high resolution 3D DTI images in live neonatal rats at postnatal day 5 (PND5) and PND14, using only 3 h of imaging acquisition time. An optimized 3D DTI pulse sequence and appropriate animal setup to minimize physiological motion artifacts are the keys to successful high resolution 3D DTI imaging. Thus, a 3D rapid acquisition relaxation enhancement DTI sequence with twin navigator echoes was implemented to accelerate imaging acquisition time and minimize motion artifacts. It has been suggested that neonatal mammals possess a unique ability to tolerate mild-to-moderate hypothermia and hypoxia without long term impact. Thus, we additionally utilized this ability to minimize motion artifacts in magnetic resonance images by carefully suppressing the respiratory rate to around 15/min for PND5 and 30/min for PND14 using mild-to-moderate hypothermia. These imaging strategies have been successfully implemented to study how the effect of cocaine exposure in dams might affect brain development in their rat pups. Image quality resulting from this in vivo DTI study was comparable to ex vivo scans. fractional anisotropy values were also similar between the live and fixed brain scans. The capability of acquiring high quality in vivo DTI imaging offers a valuable opportunity to study many neurological disorders in brain development in an authentic living environment. PMID:22013426

Probing the extracellular diffusion of antibodies in brain using in vivo integrative optical imaging and ex vivo fluorescence imaging.

PubMed

Wolak, Daniel J; Pizzo, Michelle E; Thorne, Robert G

2015-01-10

Antibody-based therapeutics exhibit great promise in the treatment of central nervous system (CNS) disorders given their unique customizable properties. Although several clinical trials have evaluated therapeutic antibodies for treatment of CNS disorders, success to date has likely been limited in part due to complex issues associated with antibody delivery to the brain and antibody distribution within the CNS compartment. Major obstacles to effective CNS delivery of full length immunoglobulin G (IgG) antibodies include transport across the blood-brain and blood-cerebrospinal fluid barriers. IgG diffusion within brain extracellular space (ECS) may also play a role in limiting central antibody distribution; however, IgG transport in brain ECS has not yet been explored using established in vivo methods. Here, we used real-time integrative optical imaging to measure the diffusion properties of fluorescently labeled, non-targeted IgG after pressure injection in both free solution and in adult rat neocortex in vivo, revealing IgG diffusion in free medium is ~10-fold greater than in brain ECS. The pronounced hindered diffusion of IgG in brain ECS is likely due to a number of general factors associated with the brain microenvironment (e.g. ECS volume fraction and geometry/width) but also molecule-specific factors such as IgG size, shape, charge and specific binding interactions with ECS components. Co-injection of labeled IgG with an excess of unlabeled Fc fragment yielded a small yet significant increase in the IgG effective diffusion coefficient in brain, suggesting that binding between the IgG Fc domain and endogenous Fc-specific receptors may contribute to the hindered mobility of IgG in brain ECS. Importantly, local IgG diffusion coefficients from integrative optical imaging were similar to those obtained from ex vivo fluorescence imaging of transport gradients across the pial brain surface following controlled intracisternal infusions in anesthetized animals. Taken

Probing the extracellular diffusion of antibodies in brain using in vivo integrative optical imaging and ex vivo fluorescence imaging

PubMed Central

Wolak, Daniel J.; Pizzo, Michelle E.; Thorne, Robert G.

2014-01-01

Antibody-based therapeutics exhibit great promise in the treatment of central nervous system (CNS) disorders given their unique customizable properties. Although several clinical trials have evaluated therapeutic antibodies for treatment of CNS disorders, success to date has likely been limited in part due to complex issues associated with antibody delivery to the brain and antibody distribution within the CNS compartment. Major obstacles to effective CNS delivery of full length immunoglobulin G (IgG) antibodies include transport across the blood-brain and blood-cerebrospinal fluid barriers. IgG diffusion within brain extracellular space (ECS) may also play a role in limiting central antibody distribution; however, IgG transport in brain ECS has not yet been explored using established in vivo methods. Here, we used real-time integrative optical imaging to measure the diffusion properties of fluorescently labeled, non-targeted IgG after pressure injection in both free solution and in adult rat neocortex in vivo, revealing IgG diffusion in free medium is ~10-fold greater than in brain ECS. The pronounced hindered diffusion of IgG in brain ECS is likely due to a number of general factors associated with the brain microenvironment (e.g. ECS volume fraction and geometry/width) but also molecule-specific factors such as IgG size, shape, charge and specific binding interactions with ECS components. Co-injection of labeled IgG with an excess of unlabeled Fc fragment yielded a small yet significant increase in the IgG effective diffusion coefficient in brain, suggesting that binding between the IgG Fc domain and endogenous Fc-specific receptors may contribute to the hindered mobility of IgG in brain ECS. Importantly, local IgG diffusion coefficients from integrative optical imaging were similar to those obtained from ex vivo fluorescence imaging of transport gradients across the pial brain surface following controlled intracisternal infusions in anesthetized animals. Taken

[In vivo measurement of rabbits brain impedance frequency response and the elementary imaging of EIT].

PubMed

Wu, Xiaoming; Dong, Xiuzhen; Qin, Mingxin; Fu, Feng; Wang, Yuemin; You, Fusheng; Xiang, Haiyan; Liu, Ruigang; Shi, Xuetao

2003-03-01

The in vivo measurements of rabbit brain tissue impedance were taken under both normal and ischemic conditions by using two-electrode measurement method in the frequency range from 0.1 Hz to 1 MHz. The dynamic images about the resistivity of cerebral ischemia were reconstructed based on a 16-electrode system. The results of in vivo measurement showed that the ratio of impedance increased can be as high as 75% at frequencies lower than 10 Hz. In the range from 1 KHz to 1 MHz, the ratio showed a constant value of 15%. The electrical impedance tomography (EIT) images obtained suggested that the regions of impedance changes highly correspond to the position of ischemia. It is confirmed that the brain function changes caused by local deficiency of blood can be detected and imaged by EIT method.

Imaging evidence and recommendations for traumatic brain injury: advanced neuro- and neurovascular imaging techniques.

PubMed

Wintermark, M; Sanelli, P C; Anzai, Y; Tsiouris, A J; Whitlow, C T

2015-02-01

Neuroimaging plays a critical role in the evaluation of patients with traumatic brain injury, with NCCT as the first-line of imaging for patients with traumatic brain injury and MR imaging being recommended in specific settings. Advanced neuroimaging techniques, including MR imaging DTI, blood oxygen level-dependent fMRI, MR spectroscopy, perfusion imaging, PET/SPECT, and magnetoencephalography, are of particular interest in identifying further injury in patients with traumatic brain injury when conventional NCCT and MR imaging findings are normal, as well as for prognostication in patients with persistent symptoms. These advanced neuroimaging techniques are currently under investigation in an attempt to optimize them and substantiate their clinical relevance in individual patients. However, the data currently available confine their use to the research arena for group comparisons, and there remains insufficient evidence at the time of this writing to conclude that these advanced techniques can be used for routine clinical use at the individual patient level. TBI imaging is a rapidly evolving field, and a number of the recommendations presented will be updated in the future to reflect the advances in medical knowledge. © 2015 by American Journal of Neuroradiology.

Change detection and classification in brain MR images using change vector analysis.

PubMed

Simões, Rita; Slump, Cornelis

2011-01-01

The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.

Image updating for brain deformation compensation in tumor resection

NASA Astrophysics Data System (ADS)

Fan, Xiaoyao; Ji, Songbai; Olson, Jonathan D.; Roberts, David W.; Hartov, Alex; Paulsen, Keith D.

2016-03-01

Preoperative magnetic resonance images (pMR) are typically used for intraoperative guidance in image-guided neurosurgery, the accuracy of which can be significantly compromised by brain deformation. Biomechanical finite element models (FEM) have been developed to estimate whole-brain deformation and produce model-updated MR (uMR) that compensates for brain deformation at different surgical stages. Early stages of surgery, such as after craniotomy and after dural opening, have been well studied, whereas later stages after tumor resection begins remain challenging. In this paper, we present a method to simulate tumor resection by incorporating data from intraoperative stereovision (iSV). The amount of tissue resection was estimated from iSV using a "trial-and-error" approach, and the cortical shift was measured from iSV through a surface registration method using projected images and an optical flow (OF) motion tracking algorithm. The measured displacements were employed to drive the biomechanical brain deformation model, and the estimated whole-brain deformation was subsequently used to deform pMR and produce uMR. We illustrate the method using one patient example. The results show that the uMR aligned well with iSV and the overall misfit between model estimates and measured displacements was 1.46 mm. The overall computational time was ~5 min, including iSV image acquisition after resection, surface registration, modeling, and image warping, with minimal interruption to the surgical flow. Furthermore, we compare uMR against intraoperative MR (iMR) that was acquired following iSV acquisition.

Microstructural effects of Ramadan fasting on the brain: a diffusion tensor imaging study

PubMed Central

Bakan, Ayse Ahsen; Yıldız, Seyma; Alkan, Alpay; Yetis, Huseyin; Kurtcan, Serpil; Ilhan, Mahmut Muzaffer

2015-01-01

PURPOSE We aimed to examine whether the brain displays any microstructural changes after a three-week Ramadan fasting period using diffusion tenson imaging. METHODS This study included a study and a control group of 25 volunteers each. In the study group, we examined and compared apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values of the participants during (phase 1) and after (phase 2) a period of fasting. The control group included individuals who did not fast. ADC and FA values obtained in phase 1 and phase 2 were compared between the study and control groups. RESULTS In the study group, ADC values of hypothalamus and, to a lesser extent, of insula were lower in phase 1 compared with phase 2 and the control group. The FA values of amygdala, middle temporal cortex, thalamus and, to a lesser extent, of medial prefrontal cortex were lower in phase 1 compared with phase 2 and the control group. Phase 2 ADC and FA values of the study group were not significantly different compared with the control group at any brain location. CONCLUSION A three-week Ramadan fasting period can cause microstructural changes in the brain, and diffusion tensor imaging enables the visualization of these changes. The identification of brain locations where changes occurred in ADC and FA values during fasting can be helpful in diagnostic imaging and understanding the pathophysiology of eating disorders. PMID:25835077

The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments

PubMed Central

Gorgolewski, Krzysztof J.; Auer, Tibor; Calhoun, Vince D.; Craddock, R. Cameron; Das, Samir; Duff, Eugene P.; Flandin, Guillaume; Ghosh, Satrajit S.; Glatard, Tristan; Halchenko, Yaroslav O.; Handwerker, Daniel A.; Hanke, Michael; Keator, David; Li, Xiangrui; Michael, Zachary; Maumet, Camille; Nichols, B. Nolan; Nichols, Thomas E.; Pellman, John; Poline, Jean-Baptiste; Rokem, Ariel; Schaefer, Gunnar; Sochat, Vanessa; Triplett, William; Turner, Jessica A.; Varoquaux, Gaël; Poldrack, Russell A.

2016-01-01

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations. PMID:27326542

Low cost light-sheet microscopy for whole brain imaging

NASA Astrophysics Data System (ADS)

Kumar, Manish; Nasenbeny, Jordan; Kozorovitskiy, Yevgenia

2018-02-01

Light-sheet microscopy has evolved as an indispensable tool in imaging biological samples. It can image 3D samples at fast speed, with high-resolution optical sectioning, and with reduced photobleaching effects. These properties make light-sheet microscopy ideal for imaging fluorophores in a variety of biological samples and organisms, e.g. zebrafish, drosophila, cleared mouse brains, etc. While most commercial turnkey light-sheet systems are expensive, the existing lower cost implementations, e.g. OpenSPIM, are focused on achieving high-resolution imaging of small samples or organisms like zebrafish. In this work, we substantially reduce the cost of light-sheet microscope system while targeting to image much larger samples, i.e. cleared mouse brains, at single-cell resolution. The expensive components of a lightsheet system - excitation laser, water-immersion objectives, and translation stage - are replaced with an incoherent laser diode, dry objectives, and a custom-built Arduino-controlled translation stage. A low-cost CUBIC protocol is used to clear fixed mouse brain samples. The open-source platforms of μManager and Fiji support image acquisition, processing, and visualization. Our system can easily be extended to multi-color light-sheet microscopy.

Deep-tissue two-photon imaging in brain and peripheral nerve with a compact high-pulse energy ytterbium fiber laser

NASA Astrophysics Data System (ADS)

Fontaine, Arjun K.; Kirchner, Matthew S.; Caldwell, John H.; Weir, Richard F.; Gibson, Emily A.

2018-02-01

Two-photon microscopy is a powerful tool of current scientific research, allowing optical visualization of structures below the surface of tissues. This is of particular value in neuroscience, where optically accessing regions within the brain is critical for the continued advancement in understanding of neural circuits. However, two-photon imaging at significant depths have typically used Ti:Sapphire based amplifiers that are prohibitively expensive and bulky. In this study, we demonstrate deep tissue two-photon imaging using a compact, inexpensive, turnkey operated Ytterbium fiber laser (Y-Fi, KM Labs). The laser is based on all-normal dispersion (ANDi) that provides short pulse durations and high pulse energies. Depth measurements obtained in ex vivo mouse cortex exceed those obtainable with standard two-photon microscopes using Ti:Sapphire lasers. In addition to demonstrating the capability of deep-tissue imaging in the brain, we investigated imaging depth in highly-scattering white matter with measurements in sciatic nerve showing limited optical penetration of heavily myelinated nerve tissue relative to grey matter.

A Primer on Brain Imaging in Developmental Psychopathology: What Is It Good For?

ERIC Educational Resources Information Center

Pine, Daniel S.

2006-01-01

This primer introduces a Special Section on brain imaging, which includes a commentary and 10 data papers presenting applications of brain imaging to questions on developmental psychopathology. This primer serves two purposes. First, the article summarizes the strength and weaknesses of various brain-imaging techniques typically employed in…

Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury

PubMed Central

Huang, Ming-Xiong; Nichols, Sharon; Baker, Dewleen G.; Robb, Ashley; Angeles, Annemarie; Yurgil, Kate A.; Drake, Angela; Levy, Michael; Song, Tao; McLay, Robert; Theilmann, Rebecca J.; Diwakar, Mithun; Risbrough, Victoria B.; Ji, Zhengwei; Huang, Charles W.; Chang, Douglas G.; Harrington, Deborah L.; Muzzatti, Laura; Canive, Jose M.; Christopher Edgar, J.; Chen, Yu-Han; Lee, Roland R.

2014-01-01

Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1–4 Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1–4 Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI. PMID:25009772

Super-resolution Imaging of Chemical Synapses in the Brain

PubMed Central

Dani, Adish; Huang, Bo; Bergan, Joseph; Dulac, Catherine; Zhuang, Xiaowei

2010-01-01

Determination of the molecular architecture of synapses requires nanoscopic image resolution and specific molecular recognition, a task that has so far defied many conventional imaging approaches. Here we present a super-resolution fluorescence imaging method to visualize the molecular architecture of synapses in the brain. Using multicolor, three-dimensional stochastic optical reconstruction microscopy, the distributions of synaptic proteins can be measured with nanometer precision. Furthermore, the wide-field, volumetric imaging method enables high-throughput, quantitative analysis of a large number of synapses from different brain regions. To demonstrate the capabilities of this approach, we have determined the organization of ten protein components of the presynaptic active zone and the postsynaptic density. Variations in synapse morphology, neurotransmitter receptor composition, and receptor distribution were observed both among synapses and across different brain regions. Combination with optogenetics further allowed molecular events associated with synaptic plasticity to be resolved at the single-synapse level. PMID:21144999

Intermittent behavior in the brain neuronal network in the perception of ambiguous images

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Kurovskaya, Maria K.; Runnova, Anastasiya E.; Zhuravlev, Maxim O.; Grubov, Vadim V.; Koronovskii, Alexey A.; Pavlov, Alexey N.; Pisarchik, Alexander N.

2017-03-01

Characteristics of intermittency during the perception of ambiguous images have been studied in the case the Necker cube image has been used as a bistable object for demonstration in the experiments, with EEG being simultaneously measured. Distributions of time interval lengths corresponding to the left-oriented and right-oriented Necker cube perception have been obtain. EEG data have been analyzed using continuous wavelet transform which was shown that the destruction of alpha rhythm with accompanying generation of high frequency oscillations can serve as a electroencephalographical marker of Necker cube recognition process in human brain.

Imaging fast electrical activity in the brain with electrical impedance tomography

PubMed Central

Aristovich, Kirill Y.; Packham, Brett C.; Koo, Hwan; Santos, Gustavo Sato dos; McEvoy, Andy; Holder, David S.

2016-01-01

Imaging of neuronal depolarization in the brain is a major goal in neuroscience, but no technique currently exists that could image neural activity over milliseconds throughout the whole brain. Electrical impedance tomography (EIT) is an emerging medical imaging technique which can produce tomographic images of impedance changes with non-invasive surface electrodes. We report EIT imaging of impedance changes in rat somatosensory cerebral cortex with a resolution of 2 ms and < 200 μm during evoked potentials using epicortical arrays with 30 electrodes. Images were validated with local field potential recordings and current source-sink density analysis. Our results demonstrate that EIT can image neural activity in a volume 7 × 5 × 2 mm in somatosensory cerebral cortex with reduced invasiveness, greater resolution and imaging volume than other methods. Modeling indicates similar resolutions are feasible throughout the entire brain so this technique, uniquely, has the potential to image functional connectivity of cortical and subcortical structures. PMID:26348559

A Proposal of New Reference System for the Standard Axial, Sagittal, Coronal Planes of Brain Based on the Serially-Sectioned Images

PubMed Central

Park, Jin Seo; Park, Hyo Seok; Shin, Dong Sun; Har, Dong-Hwan; Cho, Zang-Hee; Kim, Young-Bo; Han, Jae-Yong; Chi, Je-Geun

2010-01-01

Sectional anatomy of human brain is useful to examine the diseased brain as well as normal brain. However, intracerebral reference points for the axial, sagittal, and coronal planes of brain have not been standardized in anatomical sections or radiological images. We made 2,343 serially-sectioned images of a cadaver head with 0.1 mm intervals, 0.1 mm pixel size, and 48 bit color and obtained axial, sagittal, and coronal images based on the proposed reference system. This reference system consists of one principal reference point and two ancillary reference points. The two ancillary reference points are the anterior commissure and the posterior commissure. And the principal reference point is the midpoint of two ancillary reference points. It resides in the center of whole brain. From the principal reference point, Cartesian coordinate of x, y, z could be made to be the standard axial, sagittal, and coronal planes. PMID:20052359

MR image denoising method for brain surface 3D modeling

NASA Astrophysics Data System (ADS)

Zhao, De-xin; Liu, Peng-jie; Zhang, De-gan

2014-11-01

Three-dimensional (3D) modeling of medical images is a critical part of surgical simulation. In this paper, we focus on the magnetic resonance (MR) images denoising for brain modeling reconstruction, and exploit a practical solution. We attempt to remove the noise existing in the MR imaging signal and preserve the image characteristics. A wavelet-based adaptive curve shrinkage function is presented in spherical coordinates system. The comparative experiments show that the denoising method can preserve better image details and enhance the coefficients of contours. Using these denoised images, the brain 3D visualization is given through surface triangle mesh model, which demonstrates the effectiveness of the proposed method.

Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics.

PubMed

Kawaguchi, Atsushi; Yamashita, Fumio

2017-10-01

This article proposes a procedure for describing the relationship between high-dimensional data sets, such as multimodal brain images and genetic data. We propose a supervised technique to incorporate the clinical outcome to determine a score, which is a linear combination of variables with hieratical structures to multimodalities. This approach is expected to obtain interpretable and predictive scores. The proposed method was applied to a study of Alzheimer's disease (AD). We propose a diagnostic method for AD that involves using whole-brain magnetic resonance imaging (MRI) and positron emission tomography (PET), and we select effective brain regions for the diagnostic probability and investigate the genome-wide association with the regions using single nucleotide polymorphisms (SNPs). The two-step dimension reduction method, which we previously introduced, was considered applicable to such a study and allows us to partially incorporate the proposed method. We show that the proposed method offers classification functions with feasibility and reasonable prediction accuracy based on the receiver operating characteristic (ROC) analysis and reasonable regions of the brain and genomes. Our simulation study based on the synthetic structured data set showed that the proposed method outperformed the original method and provided the characteristic for the supervised feature. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

3.0-T functional brain imaging: a 5-year experience.

PubMed

Scarabino, T; Giannatempo, G M; Popolizio, T; Tosetti, M; d'Alesio, V; Esposito, F; Di Salle, F; Di Costanzo, A; Bertolino, A; Maggialetti, A; Salvolini, U

2007-02-01

reliability of the localisation of brain functions, making it possible to map additional areas, even in the millimetre and submillimetre scale. The data presented and results obtained to date show that 3.0-T morphofunctional imaging can become the standard for high-resolution investigation of brain disease.

Imaging of subacute blood-brain barrier disruption after methadone overdose.

PubMed

Huisa, Branko N; Gasparovic, Charles; Taheri, Saeid; Prestopnik, Jillian L; Rosenberg, Gary A

2013-07-01

Methadone intoxication can cause respiratory depression, leading to hypoxia with subsequent coma and death. Delayed postanoxic leukoencephalopathy (DAL) has been reported with intoxication by carbon monoxide, narcotics, and other toxins. To investigate the metabolic derangement of the white matter (WM) and blood-brain barrier (BBB) after DAL caused by methadone overdose. Case report of 2 patients with DAL after a single dose of "diverted" methadone used for pain control. In both cases brain magnetic resonance imaging (MRI) revealed initial extensive bilateral restricted diffusion lesions within the WM. Follow-up MRI using proton magnetic resonance spectroscopic imaging ((1) H-MRSI) showed markedly lower N-acetylaspartate and higher choline within the WM. BBB permeability, calculated by Patlak graphical analysis of MRI T1 data obtained after contrast agent injection, showed disruption of the BBB within the WM lesions, which persisted longer than a year in 1 patient. Neuropsychological evaluation showed executive dysfunction in both patients. After 1 year, one patient recovered whereas the second remained impaired. Methadone overdose can cause DAL with profound disturbances of neural metabolism and the BBB. The time course of these disturbances can be monitored with MR methods. Copyright © 2011 by the American Society of Neuroimaging.

Brain Abnormalities in Congenital Fibrosis of the Extraocular Muscles Type 1: A Multimodal MRI Imaging Study.

PubMed

Miao, Wen; Man, Fengyuan; Wu, Shaoqin; Lv, Bin; Wang, Zhenchang; Xian, Junfang; Sabel, Bernhard A; He, Huiguang; Jiao, Yonghong

2015-01-01

To explore the possible brain structural and functional alterations in congenital fibrosis of extraocular muscles type 1 (CFEOM1) patients using multimodal MRI imaging. T1-weighted, diffusion tensor images and functional MRI data were obtained from 9 KIF21A positive patients and 19 age- and gender-matched healthy controls. Voxel based morphometry and tract based spatial statistics were applied to the T1-weighted and diffusion tensor images, respectively. Amplitude of low frequency fluctuations and regional homogeneity were used to process the functional MRI data. We then compared these multimodal characteristics between CFEOM1 patients and healthy controls. Compared with healthy controls, CFEOM1 patients demonstrated increased grey matter volume in bilateral frontal orbital cortex and in the right temporal pole. No diffusion indices changes were detected, indicating unaffected white matter microstructure. In addition, from resting state functional MRI data, trend of amplitude of low-frequency fluctuations increases were noted in the right inferior parietal lobe and in the right frontal cortex, and a trend of ReHo increase (p<0.001 uncorrected) in the left precentral gyrus, left orbital frontal cortex, temporal pole and cingulate gyrus. CFEOM1 patients had structural and functional changes in grey matter, but the white matter was unaffected. These alterations in the brain may be due to the abnormality of extraocular muscles and their innervating nerves. Future studies should consider the possible correlations between brain morphological/functional findings and clinical data, especially pertaining to eye movements, to obtain more precise answers about the role of brain area changes and their functional consequence in CFEOM1.

Prominent hypointense veins on susceptibility weighted image in the cat brain with acute infarction: DWI, SWI, and PWI.

PubMed

Kim, Yong-Woo; Kim, Hak Jin; Choi, Seon Hee; Kim, Dong Chan

2014-10-01

The multiple prominent hypointense veins on susceptibility-weighted imaging (SWI) have been found in the ischemic territory of patients with acute ischemic stroke. Venous side is the unknown area in the hemodynamics of brain infarction. To evaluate the venous aspect in acute brain infarction through an animal study. The acute infarction in cat brains was induced with a bolus infusion of 0.25 mL of triolein through one side of the common carotid artery. The magnetic resonance (MR) images, including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) map, SW, and perfusion-weighted (PWI) images, were obtained serially at 2 h (n = 17), 1 day (n = 11), and 4 days (n = 4) after triolein infusion. The obtained MR images were evaluated qualitatively and quantitatively. For qualitative assessment, the signal intensity of the serial MR images was evaluated. The presence or absence and the location with serial changes of infarction were identified on DWI and ADC map images. The presence or absence of prominent hypointense veins and the serial changes of cortical veins were also evaluated on SWI. Quantitative assessment was performed by comparing the relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit times (MTT) of the lesions with those of the contralateral normal side calculated on PWI. The serial changes of rCBV, rCBF, and MTT ratio were also evaluated. Acute infarction in the first and second medial gyrus of lesion hemisphere was found by qualitative evaluation of DWI and ADC map images. On the serial evaluation of SWI, the cortical veins of cat brain with infarction were obscured at 2 h and then re-appeared at 1 day. The hemorrhage transformation and prominent hypointense veins were seen at 4 days on SWI. The quantitative evaluation revealed increased MTT ratios and decreased rCBV and rCBF ratios on PWIs in the acute infarction of cat brain. The prominent hypointense veins on SWI were seen in the half of the acute

Imaging Evaluation of Acute Traumatic Brain Injury

PubMed Central

Mutch, Christopher A.; Talbott, Jason F.; Gean, Alisa

2016-01-01

SYNOPSIS Traumatic brain injury (TBI) is a major cause of morbidity and mortality worldwide. Imaging plays an important role in the evaluation, diagnosis, and triage of patients with TBI. Recent studies suggest that it will also help predict patient outcomes. TBI consists of multiple pathoanatomical entities. Here we review the current state of TBI imaging including its indications, benefits and limitations of the modalities, imaging protocols, and imaging findings for each these pathoanatomic entities. We also briefly survey advanced imaging techniques, which include a number of promising areas of TBI research. PMID:27637393

Brain tumor imaging of rat fresh tissue using terahertz spectroscopy

NASA Astrophysics Data System (ADS)

Yamaguchi, Sayuri; Fukushi, Yasuko; Kubota, Oichi; Itsuji, Takeaki; Ouchi, Toshihiko; Yamamoto, Seiji

2016-07-01

Tumor imaging by terahertz spectroscopy of fresh tissue without dye is demonstrated using samples from a rat glioma model. The complex refractive index spectrum obtained by a reflection terahertz time-domain spectroscopy system can discriminate between normal and tumor tissues. Both the refractive index and absorption coefficient of tumor tissues are higher than those of normal tissues and can be attributed to the higher cell density and water content of the tumor region. The results of this study indicate that terahertz technology is useful for detecting brain tumor tissue.

Development of a High Angular Resolution Diffusion Imaging Human Brain Template

PubMed Central

Varentsova, Anna; Zhang, Shengwei; Arfanakis, Konstantinos

2014-01-01

Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy. PMID:24440528

Development of a high angular resolution diffusion imaging human brain template.

PubMed

Varentsova, Anna; Zhang, Shengwei; Arfanakis, Konstantinos

2014-05-01

Brain diffusion templates contain rich information about the microstructure of the brain, and are used as references in spatial normalization or in the development of brain atlases. The accuracy of diffusion templates constructed based on the diffusion tensor (DT) model is limited in regions with complex neuronal micro-architecture. High angular resolution diffusion imaging (HARDI) overcomes limitations of the DT model and is capable of resolving intravoxel heterogeneity. However, when HARDI is combined with multiple-shot sequences to minimize image artifacts, the scan time becomes inappropriate for human brain imaging. In this work, an artifact-free HARDI template of the human brain was developed from low angular resolution multiple-shot diffusion data. The resulting HARDI template was produced in ICBM-152 space based on Turboprop diffusion data, was shown to resolve complex neuronal micro-architecture in regions with intravoxel heterogeneity, and contained fiber orientation information consistent with known human brain anatomy. Copyright © 2014 Elsevier Inc. All rights reserved.

Chemical imaging analysis of the brain with X-ray methods

NASA Astrophysics Data System (ADS)

Collingwood, Joanna F.; Adams, Freddy

2017-04-01

Cells employ various metal and metalloid ions to augment the structure and the function of proteins and to assist with vital biological processes. In the brain they mediate biochemical processes, and disrupted metabolism of metals may be a contributing factor in neurodegenerative disorders. In this tutorial review we will discuss the particular role of X-ray methods for elemental imaging analysis of accumulated metal species and metal-containing compounds in biological materials, in the context of post-mortem brain tissue. X-rays have the advantage that they have a short wavelength and can penetrate through a thick biological sample. Many of the X-ray microscopy techniques that provide the greatest sensitivity and specificity for trace metal concentrations in biological materials are emerging at synchrotron X-ray facilities. Here, the extremely high flux available across a wide range of soft and hard X-rays, combined with state-of-the-art focusing techniques and ultra-sensitive detectors, makes it viable to undertake direct imaging of a number of elements in brain tissue. The different methods for synchrotron imaging of metals in brain tissues at regional, cellular, and sub-cellular spatial resolution are discussed. Methods covered include X-ray fluorescence for elemental imaging, X-ray absorption spectrometry for speciation imaging, X-ray diffraction for structural imaging, phase contrast for enhanced contrast imaging and scanning transmission X-ray microscopy for spectromicroscopy. Two- and three-dimensional (confocal and tomographic) imaging methods are considered as well as the correlation of X-ray microscopy with other imaging tools.

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…

Spatial Mapping of Structural and Connectional Imaging Data for the Developing Human Brain with Diffusion Tensor Imaging

PubMed Central

Ouyang, Austin; Jeon, Tina; Sunkin, Susan M.; Pletikos, Mihovil; Sedmak, Goran; Sestan, Nenad; Lein, Ed S.; Huang, Hao

2014-01-01

During human brain development from fetal stage to adulthood, the white matter (WM) tracts undergo dramatic changes. Diffusion tensor imaging (DTI), a widely used magnetic resonance imaging (MRI) modality, offers insight into the dynamic changes of WM fibers as these fibers can be noninvasively traced and three-dimensionally (3D) reconstructed with DTI tractography. The DTI and conventional T1 weighted MRI images also provide sufficient cortical anatomical details for mapping the cortical regions of interests (ROIs). In this paper, we described basic concepts and methods of DTI techniques that can be used to trace major WM tracts noninvasively from fetal brain of 14 postconceptional weeks (pcw) to adult brain. We applied these techniques to acquire DTI data and trace, reconstruct and visualize major WM tracts during development. After categorizing major WM fiber bundles into five unique functional tract groups, namely limbic, brain stem, projection, commissural and association tracts, we revealed formation and maturation of these 3D reconstructed WM tracts of the developing human brain. The structural and connectional imaging data offered by DTI provides the anatomical backbone of transcriptional atlas of the developing human brain. PMID:25448302

Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.

PubMed

Sun, Xiaofei; Shi, Lin; Luo, Yishan; Yang, Wei; Li, Hongpeng; Liang, Peipeng; Li, Kuncheng; Mok, Vincent C T; Chu, Winnie C W; Wang, Defeng

2015-07-28

Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN),where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. We have proposed

[Brain imaging of first-episode psychosis].

PubMed

Jardri, R

2013-09-01

In the last decades, schizophrenia has intensively been studied using various brain imaging techniques. However, several potential confounding factors limited their interpretation power (e.g. chronicity, the impact of antipsychotic medication). By considering psychosis as a continuum of changes starting from mild cognitive impairments to serious psychotic symptoms, it became possible to provide deeper insight in the neurobiological mechanisms underlying the onset of psychosis by focusing on at-risk individuals and first-episodes. Recent brain imaging meta-analyses of the first episode psychosis (FEP), noteworthy reported conjoint bilateral structural and functional differences at the level of the insula, the superior temporal gyrus and the medial frontal gyrus, encompassing the anterior cingulate cortex. In the present review, we thus provide an update of brain imaging studies of FEP with a particular emphasis on more recent anatomical, functional and molecular explorations. Specifically, we provide 1) a review of the common features observed in individuals with high risk for psychosis and changes characterizing the transition to psychosis, 2) a description of the environmental and drug factors influencing these abnormalities, 3) how these findings in FEP may differ from those observed in chronic individuals with schizophrenia, and 4) a short overview of new classification algorithms able to use MRI findings as valuable biomarkers to guide early detection in the prodromal phase of psychosis. Copyright © 2013 L’Encéphale. Published by Elsevier Masson SAS.. All rights reserved.

Imaging whole mouse brains with a dual resolution serial swept-source optical coherence tomography scanner

NASA Astrophysics Data System (ADS)

Lefebvre, Joël.; Castonguay, Alexandre; Lesage, Frédéric

2018-02-01

High resolution imaging of whole rodent brains using serial OCT scanners is a promising method to investigate microstructural changes in tissue related to the evolution of neuropathologies. Although micron to sub-micron sampling resolution can be obtained by using high numerical aperture objectives and dynamic focusing, such an imaging system is not adapted to whole brain imaging. This is due to the large amount of data it generates and the significant computational resources required for reconstructing such volumes. To address this limitation, a dual resolution serial OCT scanner was developed. The optical setup consists in a swept-source OCT made of two sample and reference arms, each arm being coupled with different microscope objectives (3X / 40X). Motorized flip mirrors were used to switch between each OCT arm, thus allowing low and high resolution acquisitions within the same sample. The low resolution OCT volumes acquired with the 3X arm were stitched together, providing a 3D map of the whole mouse brain. This brain can be registered to an OCT brain template to enable neurological structures localization. The high resolution volumes acquired with the 40X arm were also stitched together to create local high resolution 3D maps of the tissue microstructure. The 40X data can be acquired at any arbitrary location in the sample, thus limiting storage-heavy high resolution data to application restricted to specific regions of interest. By providing dual-resolution OCT data, this setup can be used to validate diffusion MRI with tissue microstructure derived metrics measured at any location in ex vivo brains.

MR images of mouse brain using clinical 3T MR scanner and 4CH-Mouse coil

NASA Astrophysics Data System (ADS)

Lim, Soo Mee; Park, Eun Mi; Lyoo, In Kyoon; Lee, Junghyun; Han, Bo Mi; Lee, Jeong Kyong; Lee, Su Bin

2015-07-01

Objectives: Although small-bore high-field magnets are useful for research in small rodent models,this technology, however, has not been easily accessible to most researchers. This current study, thus,tried to evaluate the usability of 4CH-Mouse coil (Philips Healthcare, Best, the Netherlands) forpreclinical investigations in clinical 3T MR scan environment. We evaluated the effects of ischemicpreconditioning (IP) in the mouse stroke model with clinical 3T MR scanner and 4CH-Mouse coil. Materials and Methods: Experiments were performed on male C57BL/6 mice that either received the IP or sham operation (control). Three different MR sequences including diffusion weighted images (DWI), T2-weighted images (T2WI), and fluid attenuated inversion recovery (FLAIR) were performed on the mouse brains following 24, 72 hours of middle cerebral artery occlusion (MCAO) and analyzed for infarct lesions. Results: The images showed that the IP-treated mouse brains had significantly smaller infarct volumes compared to the control group. Of the MR sequences employed, the T2WI showed the highest level of correlations with postmortem infarct volume measurements. Conclusions: The clinical 3T MR scanner turned out to have a solid potential as a practical tool for imaging small animal brains. MR sequences including DWI, T2WI, FLAIR were obtained with acceptable resolution and in a reasonable time constraint in evaluating a mouse stroke model brain.

Magnetic resonance imaging spectrum of perinatal hypoxic-ischemic brain injury

PubMed Central

Varghese, Binoj; Xavier, Rose; Manoj, V C; Aneesh, M K; Priya, P S; Kumar, Ashok; Sreenivasan, V K

2016-01-01

Perinatal hypoxic–ischemic brain injury results in neonatal hypoxic–ischemic encephalopathy and serious long-term neurodevelopmental sequelae. Magnetic resonance imaging (MRI) of the brain is an ideal and safe imaging modality for suspected hypoxic–ischemic injury. The pattern of injury depends on brain maturity at the time of insult, severity of hypotension, and duration of insult. Time of imaging after the insult influences the imaging findings. Mild to moderate hypoperfusion results in germinal matrix hemorrhages and periventricular leukomalacia in preterm neonates and parasagittal watershed territory infarcts in full-term neonates. Severe insult preferentially damages the deep gray matter in both term and preterm infants. However, associated frequent perirolandic injury is seen in term neonates. MRI is useful in establishing the clinical diagnosis, assessing the severity of injury, and thereby prognosticating the outcome. Familiarity with imaging spectrum and insight into factors affecting the injury will enlighten the radiologist to provide an appropriate diagnosis. PMID:27857456

Gold nanoparticle imaging and radiotherapy of brain tumors in mice

PubMed Central

Hainfeld, James F; Smilowitz, Henry M; O'Connor, Michael J; Dilmanian, Farrokh Avraham; Slatkin, Daniel N

2013-01-01

Aim To test intravenously injected gold nanoparticles for x-ray imaging and radiotherapy enhancement of large, imminently lethal, intracerebral malignant gliomas. Materials & methods Gold nanoparticles approximately 11 nm in size were injected intravenously and brains imaged using microcomputed tomography. A total of 15 h after an intravenous dose of 4 g Au/kg was administered, brains were irradiated with 30 Gy 100 kVp x-rays. Results Gold uptake gave a 19:1 tumor-to-normal brain ratio with 1.5% w/w gold in tumor, calculated to increase local radiation dose by approximately 300%. Mice receiving gold and radiation (30 Gy) demonstrated 50% long term (>1 year) tumor-free survival, whereas all mice receiving radiation only died. Conclusion Intravenously injected gold nanoparticles cross the blood–tumor barrier, but are largely blocked by the normal blood–brain barrier, enabling high-resolution computed tomography tumor imaging. Gold radiation enhancement significantly improved long-term survival compared with radiotherapy alone. This approach holds promise to improve therapy of human brain tumors and other cancers. PMID:23265347

Segmentation of Brain Lesions in MRI and CT Scan Images: A Hybrid Approach Using k-Means Clustering and Image Morphology

NASA Astrophysics Data System (ADS)

Agrawal, Ritu; Sharma, Manisha; Singh, Bikesh Kumar

2018-04-01

Manual segmentation and analysis of lesions in medical images is time consuming and subjected to human errors. Automated segmentation has thus gained significant attention in recent years. This article presents a hybrid approach for brain lesion segmentation in different imaging modalities by combining median filter, k means clustering, Sobel edge detection and morphological operations. Median filter is an essential pre-processing step and is used to remove impulsive noise from the acquired brain images followed by k-means segmentation, Sobel edge detection and morphological processing. The performance of proposed automated system is tested on standard datasets using performance measures such as segmentation accuracy and execution time. The proposed method achieves a high accuracy of 94% when compared with manual delineation performed by an expert radiologist. Furthermore, the statistical significance test between lesion segmented using automated approach and that by expert delineation using ANOVA and correlation coefficient achieved high significance values of 0.986 and 1 respectively. The experimental results obtained are discussed in lieu of some recently reported studies.

Natural image classification driven by human brain activity

NASA Astrophysics Data System (ADS)

Zhang, Dai; Peng, Hanyang; Wang, Jinqiao; Tang, Ming; Xue, Rong; Zuo, Zhentao

2016-03-01

Natural image classification has been a hot topic in computer vision and pattern recognition research field. Since the performance of an image classification system can be improved by feature selection, many image feature selection methods have been developed. However, the existing supervised feature selection methods are typically driven by the class label information that are identical for different samples from the same class, ignoring with-in class image variability and therefore degrading the feature selection performance. In this study, we propose a novel feature selection method, driven by human brain activity signals collected using fMRI technique when human subjects were viewing natural images of different categories. The fMRI signals associated with subjects viewing different images encode the human perception of natural images, and therefore may capture image variability within- and cross- categories. We then select image features with the guidance of fMRI signals from brain regions with active response to image viewing. Particularly, bag of words features based on GIST descriptor are extracted from natural images for classification, and a sparse regression base feature selection method is adapted to select image features that can best predict fMRI signals. Finally, a classification model is built on the select image features to classify images without fMRI signals. The validation experiments for classifying images from 4 categories of two subjects have demonstrated that our method could achieve much better classification performance than the classifiers built on image feature selected by traditional feature selection methods.

Label-free imaging of brain and brain tumor specimens with combined two-photon excited fluorescence and second harmonic generation microscopy

NASA Astrophysics Data System (ADS)

Jiang, Liwei; Wang, Xingfu; Wu, Zanyi; Du, Huiping; Wang, Shu; Li, Lianhuang; Fang, Na; Lin, Peihua; Chen, Jianxin; Kang, Dezhi; Zhuo, Shuangmu

2017-10-01

Label-free imaging techniques are gaining acceptance within the medical imaging field, including brain imaging, because they have the potential to be applied to intraoperative in situ identifications of pathological conditions. In this paper, we describe the use of two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopy in combination for the label-free detection of brain and brain tumor specimens; gliomas. Two independently detecting channels were chosen to subsequently collect TPEF/SHG signals from the specimen to increase TPEF/SHG image contrasts. Our results indicate that the combined TPEF/SHG microscopic techniques can provide similar rat brain structural information and produce a similar resolution like conventional H&E staining in neuropathology; including meninges, cerebral cortex, white-matter structure corpus callosum, choroid plexus, hippocampus, striatum, and cerebellar cortex. It can simultaneously detect infiltrating human brain tumor cells, the extracellular matrix collagen fiber of connective stroma within brain vessels and collagen depostion in tumor microenvironments. The nuclear-to-cytoplasmic ratio and collagen content can be extracted as quantitative indicators for differentiating brain gliomas from healthy brain tissues. With the development of two-photon fiberscopes and microendoscope probes and their clinical applications, the combined TPEF and SHG microcopy may become an important multimodal, nonlinear optical imaging approach for real-time intraoperative histological diagnostics of residual brain tumors. These occur in various brain regions during ongoing surgeries through the method of simultaneously identifying tumor cells, and the change of tumor microenvironments, without the need for the removal biopsies and without the need for tissue labelling or fluorescent markers.

Deformation Invariant Attribute Vector for Deformable Registration of Longitudinal Brain MR Images

PubMed Central

Li, Gang; Guo, Lei; Liu, Tianming

2009-01-01

This paper presents a novel approach to define deformation invariant attribute vector (DIAV) for each voxel in 3D brain image for the purpose of anatomic correspondence detection. The DIAV method is validated by using synthesized deformation in 3D brain MRI images. Both theoretic analysis and experimental studies demonstrate that the proposed DIAV is invariant to general nonlinear deformation. Moreover, our experimental results show that the DIAV is able to capture rich anatomic information around the voxels and exhibit strong discriminative ability. The DIAV has been integrated into a deformable registration algorithm for longitudinal brain MR images, and the results on both simulated and real brain images are provided to demonstrate the good performance of the proposed registration algorithm based on matching of DIAVs. PMID:19369031

Brain and cord myelin water imaging: a progressive multiple sclerosis biomarker

PubMed Central

Kolind, Shannon; Seddigh, Arshia; Combes, Anna; Russell-Schulz, Bretta; Tam, Roger; Yogendrakumar, Vignan; Deoni, Sean; Sibtain, Naomi A.; Traboulsee, Anthony; Williams, Steven C.R.; Barker, Gareth J.; Brex, Peter A.

2015-01-01

Objectives Conventional magnetic resonance imaging (MRI) is used to diagnose and monitor inflammatory disease in relapsing remitting (RR) multiple sclerosis (MS). In the less common primary progressive (PP) form of MS, in which focal inflammation is less evident, biomarkers are still needed to enable evaluation of novel therapies in clinical trials. Our objective was to characterize the association — across the brain and cervical spinal cord — between clinical disability measures in PPMS and two potential biomarkers (one for myelin, and one for atrophy, both resulting from the same imaging technique). Methods Multi-component driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) MRI of the brain and cervical spinal cord were obtained for 15 PPMS patients and 11 matched controls. Data were analysed to estimate the signal related to myelin water (VFM), as well as volume measurements. MS disability was assessed using the Multiple Sclerosis Functional Composite score, which includes measures of cognitive processing (Paced Auditory Serial Addition Test), manual dexterity (9-Hole Peg Test) and ambulatory function (Timed 25-Foot Walk); and the Expanded Disability Status Scale. Results Brain and spinal cord volumes were different in PPMS compared to controls, particularly ventricular (+ 46%, p = 0.0006) and cervical spinal cord volume (− 16%, p = 0.0001). Brain and spinal cord myelin (VFM) were also reduced in PPMS (brain: − 11%, p = 0.01; spine: − 19%, p = 0.000004). Cognitive processing correlated with brain ventricular volume (p = 0.009). Manual dexterity correlated with brain ventricular volume (p = 0.007), and both brain and spinal cord VFM (p = 0.01 and 0.06, respectively). Ambulation correlated with spinal cord volume (p = 0.04) and spinal cord VFM (p = 0.04). Interpretation In this study we demonstrated that mcDESPOT can be used to measure myelin and atrophy in the brain and spinal cord. Results correlate well with

Automated brain tumor segmentation in magnetic resonance imaging based on sliding-window technique and symmetry analysis.

PubMed

Lian, Yanyun; Song, Zhijian

2014-01-01

Brain tumor segmentation from magnetic resonance imaging (MRI) is an important step toward surgical planning, treatment planning, monitoring of therapy. However, manual tumor segmentation commonly used in clinic is time-consuming and challenging, and none of the existed automated methods are highly robust, reliable and efficient in clinic application. An accurate and automated tumor segmentation method has been developed for brain tumor segmentation that will provide reproducible and objective results close to manual segmentation results. Based on the symmetry of human brain, we employed sliding-window technique and correlation coefficient to locate the tumor position. At first, the image to be segmented was normalized, rotated, denoised, and bisected. Subsequently, through vertical and horizontal sliding-windows technique in turn, that is, two windows in the left and the right part of brain image moving simultaneously pixel by pixel in two parts of brain image, along with calculating of correlation coefficient of two windows, two windows with minimal correlation coefficient were obtained, and the window with bigger average gray value is the location of tumor and the pixel with biggest gray value is the locating point of tumor. At last, the segmentation threshold was decided by the average gray value of the pixels in the square with center at the locating point and 10 pixels of side length, and threshold segmentation and morphological operations were used to acquire the final tumor region. The method was evaluated on 3D FSPGR brain MR images of 10 patients. As a result, the average ratio of correct location was 93.4% for 575 slices containing tumor, the average Dice similarity coefficient was 0.77 for one scan, and the average time spent on one scan was 40 seconds. An fully automated, simple and efficient segmentation method for brain tumor is proposed and promising for future clinic use. Correlation coefficient is a new and effective feature for tumor location.

Preoperative magnetic resonance and intraoperative ultrasound fusion imaging for real-time neuronavigation in brain tumor surgery.

PubMed

Prada, F; Del Bene, M; Mattei, L; Lodigiani, L; DeBeni, S; Kolev, V; Vetrano, I; Solbiati, L; Sakas, G; DiMeco, F

2015-04-01

Brain shift and tissue deformation during surgery for intracranial lesions are the main actual limitations of neuro-navigation (NN), which currently relies mainly on preoperative imaging. Ultrasound (US), being a real-time imaging modality, is becoming progressively more widespread during neurosurgical procedures, but most neurosurgeons, trained on axial computed tomography (CT) and magnetic resonance imaging (MRI) slices, lack specific US training and have difficulties recognizing anatomic structures with the same confidence as in preoperative imaging. Therefore real-time intraoperative fusion imaging (FI) between preoperative imaging and intraoperative ultrasound (ioUS) for virtual navigation (VN) is highly desirable. We describe our procedure for real-time navigation during surgery for different cerebral lesions. We performed fusion imaging with virtual navigation for patients undergoing surgery for brain lesion removal using an ultrasound-based real-time neuro-navigation system that fuses intraoperative cerebral ultrasound with preoperative MRI and simultaneously displays an MRI slice coplanar to an ioUS image. 58 patients underwent surgery at our institution for intracranial lesion removal with image guidance using a US system equipped with fusion imaging for neuro-navigation. In all cases the initial (external) registration error obtained by the corresponding anatomical landmark procedure was below 2 mm and the craniotomy was correctly placed. The transdural window gave satisfactory US image quality and the lesion was always detectable and measurable on both axes. Brain shift/deformation correction has been successfully employed in 42 cases to restore the co-registration during surgery. The accuracy of ioUS/MRI fusion/overlapping was confirmed intraoperatively under direct visualization of anatomic landmarks and the error was < 3 mm in all cases (100 %). Neuro-navigation using intraoperative US integrated with preoperative MRI is reliable, accurate

FROM SELECTIVE VULNERABILITY TO CONNECTIVITY: INSIGHTS FROM NEWBORN BRAIN IMAGING

PubMed Central

Miller, Steven P.; Ferriero, Donna M

2009-01-01

The ability to image the newborn brain during development has provided new information regarding the effects of injury on brain development at different vulnerable time periods. Studies in animal models of brain injury correlate beautifully with what is now observed in the human newborn. We now know that injury at term results in a predilection for gray matter injury while injury in the premature brain results in a white matter predominant pattern although recent evidence suggests a blurring of this distinction. These injuries affect how the brain matures subsequently and again, imaging has led to new insights that allow us to match function and structure. This review will focus on these patterns of injury that are so critically determined by age at insult. In addition, this review will highlight how the brain responds to these insults with changes in connectivity that have profound functional consequences. PMID:19712981

Brain Imaging in Alzheimer Disease

PubMed Central

Johnson, Keith A.; Fox, Nick C.; Sperling, Reisa A.; Klunk, William E.

2012-01-01

Imaging has played a variety of roles in the study of Alzheimer disease (AD) over the past four decades. Initially, computed tomography (CT) and then magnetic resonance imaging (MRI) were used diagnostically to rule out other causes of dementia. More recently, a variety of imaging modalities including structural and functional MRI and positron emission tomography (PET) studies of cerebral metabolism with fluoro-deoxy-d-glucose (FDG) and amyloid tracers such as Pittsburgh Compound-B (PiB) have shown characteristic changes in the brains of patients with AD, and in prodromal and even presymptomatic states that can help rule-in the AD pathophysiological process. No one imaging modality can serve all purposes as each have unique strengths and weaknesses. These modalities and their particular utilities are discussed in this article. The challenge for the future will be to combine imaging biomarkers to most efficiently facilitate diagnosis, disease staging, and, most importantly, development of effective disease-modifying therapies. PMID:22474610

Ethanol fixed brain imaging by phase-contrast X-ray technique

NASA Astrophysics Data System (ADS)

Takeda, Tohoru; Thet-Thet-Lwin; Kunii, Takuya; Sirai, Ryota; Ohizumi, Takahito; Maruyama, Hiroko; Hyodo, Kazuyuki; Yoneyama, Akio; Ueda, Kazuhiro

2013-03-01

The two-crystal phase-contrast X-ray imaging technique using an X-ray crystal interferometer can depict the fine structures of rat's brain such as cerebral cortex, white matter, and basal ganglia. Image quality and contrast by ethanol fixed brain showed significantly better than those by usually used formalin fixation at 35 keV X-ray energy. Image contrast of cortex by ethanol fixation was more than 3-times higher than that by formalin fixation. Thus, the technique of ethanol fixation might be better suited to image cerebral structural detail at 35 keV X-ray energy.

Application of an enhanced fuzzy algorithm for MR brain tumor image segmentation

NASA Astrophysics Data System (ADS)

Hemanth, D. Jude; Vijila, C. Kezi Selva; Anitha, J.

2010-02-01

Image segmentation is one of the significant digital image processing techniques commonly used in the medical field. One of the specific applications is tumor detection in abnormal Magnetic Resonance (MR) brain images. Fuzzy approaches are widely preferred for tumor segmentation which generally yields superior results in terms of accuracy. But most of the fuzzy algorithms suffer from the drawback of slow convergence rate which makes the system practically non-feasible. In this work, the application of modified Fuzzy C-means (FCM) algorithm to tackle the convergence problem is explored in the context of brain image segmentation. This modified FCM algorithm employs the concept of quantization to improve the convergence rate besides yielding excellent segmentation efficiency. This algorithm is experimented on real time abnormal MR brain images collected from the radiologists. A comprehensive feature vector is extracted from these images and used for the segmentation technique. An extensive feature selection process is performed which reduces the convergence time period and improve the segmentation efficiency. After segmentation, the tumor portion is extracted from the segmented image. Comparative analysis in terms of segmentation efficiency and convergence rate is performed between the conventional FCM and the modified FCM. Experimental results show superior results for the modified FCM algorithm in terms of the performance measures. Thus, this work highlights the application of the modified algorithm for brain tumor detection in abnormal MR brain images.

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images.

PubMed

Gong, Kuang; Yang, Jaewon; Kim, Kyungsang; El Fakhri, Georges; Seo, Youngho; Li, Quanzheng

2018-05-23

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure. © 2018 Institute of Physics and Engineering in Medicine.

Brain Imaging, Forward Inference, and Theories of Reasoning

PubMed Central

Heit, Evan

2015-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities. PMID:25620926

Brain imaging, forward inference, and theories of reasoning.

PubMed

Heit, Evan

2014-01-01

This review focuses on the issue of how neuroimaging studies address theoretical accounts of reasoning, through the lens of the method of forward inference (Henson, 2005, 2006). After theories of deductive and inductive reasoning are briefly presented, the method of forward inference for distinguishing between psychological theories based on brain imaging evidence is critically reviewed. Brain imaging studies of reasoning, comparing deductive and inductive arguments, comparing meaningful versus non-meaningful material, investigating hemispheric localization, and comparing conditional and relational arguments, are assessed in light of the method of forward inference. Finally, conclusions are drawn with regard to future research opportunities.

Dynamic time warping-based averaging framework for functional near-infrared spectroscopy brain imaging studies

NASA Astrophysics Data System (ADS)

Zhu, Li; Najafizadeh, Laleh

2017-06-01

We investigate the problem related to the averaging procedure in functional near-infrared spectroscopy (fNIRS) brain imaging studies. Typically, to reduce noise and to empower the signal strength associated with task-induced activities, recorded signals (e.g., in response to repeated stimuli or from a group of individuals) are averaged through a point-by-point conventional averaging technique. However, due to the existence of variable latencies in recorded activities, the use of the conventional averaging technique can lead to inaccuracies and loss of information in the averaged signal, which may result in inaccurate conclusions about the functionality of the brain. To improve the averaging accuracy in the presence of variable latencies, we present an averaging framework that employs dynamic time warping (DTW) to account for the temporal variation in the alignment of fNIRS signals to be averaged. As a proof of concept, we focus on the problem of localizing task-induced active brain regions. The framework is extensively tested on experimental data (obtained from both block design and event-related design experiments) as well as on simulated data. In all cases, it is shown that the DTW-based averaging technique outperforms the conventional-based averaging technique in estimating the location of task-induced active regions in the brain, suggesting that such advanced averaging methods should be employed in fNIRS brain imaging studies.

Image enhancement by spatial frequency post-processing of images obtained with pupil filters

NASA Astrophysics Data System (ADS)

Estévez, Irene; Escalera, Juan C.; Stefano, Quimey Pears; Iemmi, Claudio; Ledesma, Silvia; Yzuel, María J.; Campos, Juan

2016-12-01

The use of apodizing or superresolving filters improves the performance of an optical system in different frequency bands. This improvement can be seen as an increase in the OTF value compared to the OTF for the clear aperture. In this paper we propose a method to enhance the contrast of an image in both its low and its high frequencies. The method is based on the generation of a synthetic Optical Transfer Function, by multiplexing the OTFs given by the use of different non-uniform transmission filters on the pupil. We propose to capture three images, one obtained with a clear pupil, one obtained with an apodizing filter that enhances the low frequencies and another one taken with a superresolving filter that improves the high frequencies. In the Fourier domain the three spectra are combined by using smoothed passband filters, and then the inverse transform is performed. We show that we can create an enhanced image better than the image obtained with the clear aperture. To evaluate the performance of the method, bar tests (sinusoidal tests) with different frequency content are used. The results show that a contrast improvement in the high and low frequencies is obtained.

Brain Volume Estimation Enhancement by Morphological Image Processing Tools.

PubMed

Zeinali, R; Keshtkar, A; Zamani, A; Gharehaghaji, N

2017-12-01

Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution. In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth. The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method. Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.

In vivo imaging of endogenous neural stem cells in the adult brain

PubMed Central

Rueger, Maria Adele; Schroeter, Michael

2015-01-01

The discovery of endogenous neural stem cells (eNSCs) in the adult mammalian brain with their ability to self-renew and differentiate into functional neurons, astrocytes and oligodendrocytes has raised the hope for novel therapies of neurological diseases. Experimentally, those eNSCs can be mobilized in vivo, enhancing regeneration and accelerating functional recovery after, e.g., focal cerebral ischemia, thus constituting a most promising approach in stem cell research. In order to translate those current experimental approaches into a clinical setting in the future, non-invasive imaging methods are required to monitor eNSC activation in a longitudinal and intra-individual manner. As yet, imaging protocols to assess eNSC mobilization non-invasively in the live brain remain scarce, but considerable progress has been made in this field in recent years. This review summarizes and discusses the current imaging modalities suitable to monitor eNSCs in individual experimental animals over time, including optical imaging, magnetic resonance tomography and-spectroscopy, as well as positron emission tomography (PET). Special emphasis is put on the potential of each imaging method for a possible clinical translation, and on the specificity of the signal obtained. PET-imaging with the radiotracer 3’-deoxy-3’-[18F]fluoro-L-thymidine in particular constitutes a modality with excellent potential for clinical translation but low specificity; however, concomitant imaging of neuroinflammation is feasible and increases its specificity. The non-invasive imaging strategies presented here allow for the exploitation of novel treatment strategies based upon the regenerative potential of eNSCs, and will help to facilitate a translation into the clinical setting. PMID:25621107

Brain Abnormalities in Congenital Fibrosis of the Extraocular Muscles Type 1: A Multimodal MRI Imaging Study

PubMed Central

Wu, Shaoqin; Lv, Bin; Wang, Zhenchang; Xian, Junfang; Sabel, Bernhard A.; He, Huiguang; Jiao, Yonghong

2015-01-01

Purpose To explore the possible brain structural and functional alterations in congenital fibrosis of extraocular muscles type 1 (CFEOM1) patients using multimodal MRI imaging. Methods T1-weighted, diffusion tensor images and functional MRI data were obtained from 9 KIF21A positive patients and 19 age- and gender- matched healthy controls. Voxel based morphometry and tract based spatial statistics were applied to the T1-weighted and diffusion tensor images, respectively. Amplitude of low frequency fluctuations and regional homogeneity were used to process the functional MRI data. We then compared these multimodal characteristics between CFEOM1 patients and healthy controls. Results Compared with healthy controls, CFEOM1 patients demonstrated increased grey matter volume in bilateral frontal orbital cortex and in the right temporal pole. No diffusion indices changes were detected, indicating unaffected white matter microstructure. In addition, from resting state functional MRI data, trend of amplitude of low-frequency fluctuations increases were noted in the right inferior parietal lobe and in the right frontal cortex, and a trend of ReHo increase (p<0.001 uncorrected) in the left precentral gyrus, left orbital frontal cortex, temporal pole and cingulate gyrus. Conclusions CFEOM1 patients had structural and functional changes in grey matter, but the white matter was unaffected. These alterations in the brain may be due to the abnormality of extraocular muscles and their innervating nerves. Future studies should consider the possible correlations between brain morphological/functional findings and clinical data, especially pertaining to eye movements, to obtain more precise answers about the role of brain area changes and their functional consequence in CFEOM1. PMID:26186732

Detection of Normal Aging Effects on Human Brain Metabolite Concentrations and Microstructure with Whole-Brain MR Spectroscopic Imaging and Quantitative MR Imaging.

PubMed

Eylers, V V; Maudsley, A A; Bronzlik, P; Dellani, P R; Lanfermann, H; Ding, X-Q

2016-03-01

Knowledge of age-related physiological changes in the human brain is a prerequisite to identify neurodegenerative diseases. Therefore, in this study whole-brain (1)H-MRS was used in combination with quantitative MR imaging to study the effects of normal aging on healthy human brain metabolites and microstructure. Sixty healthy volunteers, 21-70 years of age, were studied. Brain maps of the metabolites NAA, creatine and phosphocreatine, and Cho and the tissue irreversible and reversible transverse relaxation times T2 and T2' were derived from the datasets. The relative metabolite concentrations and the values of relaxation times were measured with ROIs placed within the frontal and parietal WM, centrum semiovale, splenium of the corpus callosum, hand motor area, occipital GM, putamen, thalamus, pons ventral/dorsal, and cerebellar white matter and posterior lobe. Linear regression analysis and Pearson correlation tests were used to analyze the data. Aging resulted in decreased NAA concentrations in the occipital GM, putamen, splenium of the corpus callosum, and pons ventral and decreased creatine and phosphocreatine concentrations in the pons dorsal and putamen. Cho concentrations did not change significantly in selected brain regions. T2 increased in the cerebellar white matter and decreased in the splenium of the corpus callosum with aging, while the T2' decreased in the occipital GM, hand motor area, and putamen, and increased in the splenium of the corpus callosum. Correlations were found between NAA concentrations and T2' in the occipital GM and putamen and between creatine and phosphocreatine concentrations and T2' in the putamen. The effects of normal aging on brain metabolites and microstructure are region-dependent. Correlations between both processes are evident in the gray matter. The obtained data could be used as references for future studies on patients. © 2016 by American Journal of Neuroradiology.

Towards Ultra-High Resolution Fibre Tract Mapping of the Human Brain – Registration of Polarised Light Images and Reorientation of Fibre Vectors

PubMed Central

Palm, Christoph; Axer, Markus; Gräßel, David; Dammers, Jürgen; Lindemeyer, Johannes; Zilles, Karl; Pietrzyk, Uwe; Amunts, Katrin

2009-01-01

Polarised light imaging (PLI) utilises the birefringence of the myelin sheaths in order to visualise the orientation of nerve fibres in microtome sections of adult human post-mortem brains at ultra-high spatial resolution. The preparation of post-mortem brains for PLI involves fixation, freezing and cutting into 100-μm-thick sections. Hence, geometrical distortions of histological sections are inevitable and have to be removed for 3D reconstruction and subsequent fibre tracking. We here present a processing pipeline for 3D reconstruction of these sections using PLI derived multimodal images of post-mortem brains. Blockface images of the brains were obtained during cutting; they serve as reference data for alignment and elimination of distortion artefacts. In addition to the spatial image transformation, fibre orientation vectors were reoriented using the transformation fields, which consider both affine and subsequent non-linear registration. The application of this registration and reorientation approach results in a smooth fibre vector field, which reflects brain morphology. PLI combined with 3D reconstruction and fibre tracking is a powerful tool for human brain mapping. It can also serve as an independent method for evaluating in vivo fibre tractography. PMID:20461231

Age-dependent association of thyroid function with brain morphology and microstructural organization: evidence from brain imaging.

PubMed

Chaker, Layal; Cremers, Lotte G M; Korevaar, Tim I M; de Groot, Marius; Dehghan, Abbas; Franco, Oscar H; Niessen, Wiro J; Ikram, M Arfan; Peeters, Robin P; Vernooij, Meike W

2018-01-01

Thyroid hormone (TH) is crucial during neurodevelopment, but high levels of TH have been linked to neurodegenerative disorders. No data on the association of thyroid function with brain imaging in the general population are available. We therefore investigated the association of thyroid-stimulating hormone and free thyroxine (FT4) with magnetic resonance imaging (MRI)-derived total intracranial volume, brain tissue volumes, and diffusion tensor imaging measures of white matter microstructure in 4683 dementia- and stroke-free participants (mean age 60.2, range 45.6-89.9 years). Higher FT4 levels were associated with larger total intracranial volumes (β = 6.73 mL, 95% confidence interval = 2.94-9.80). Higher FT4 levels were also associated with larger total brain and white matter volumes in younger individuals, but with smaller total brain and white matter volume in older individuals (p-interaction 0.02). There was a similar interaction by age for the association of FT4 with mean diffusivity on diffusion tensor imaging (p-interaction 0.026). These results are in line with differential effects of TH during neurodevelopmental and neurodegenerative processes and can improve the understanding of the role of thyroid function in neurodegenerative disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging.

PubMed

Fu, J C; Chen, C C; Chai, J W; Wong, S T C; Li, I C

2010-06-01

We propose an automatic hybrid image segmentation model that integrates the statistical expectation maximization (EM) model and the spatial pulse coupled neural network (PCNN) for brain magnetic resonance imaging (MRI) segmentation. In addition, an adaptive mechanism is developed to fine tune the PCNN parameters. The EM model serves two functions: evaluation of the PCNN image segmentation and adaptive adjustment of the PCNN parameters for optimal segmentation. To evaluate the performance of the adaptive EM-PCNN, we use it to segment MR brain image into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The performance of the adaptive EM-PCNN is compared with that of the non-adaptive EM-PCNN, EM, and Bias Corrected Fuzzy C-Means (BCFCM) algorithms. The result is four sets of boundaries for the GM and the brain parenchyma (GM+WM), the two regions of most interest in medical research and clinical applications. Each set of boundaries is compared with the golden standard to evaluate the segmentation performance. The adaptive EM-PCNN significantly outperforms the non-adaptive EM-PCNN, EM, and BCFCM algorithms in gray mater segmentation. In brain parenchyma segmentation, the adaptive EM-PCNN significantly outperforms the BCFCM only. However, the adaptive EM-PCNN is better than the non-adaptive EM-PCNN and EM on average. We conclude that of the three approaches, the adaptive EM-PCNN yields the best results for gray matter and brain parenchyma segmentation. Copyright 2009 Elsevier Ltd. All rights reserved.

Comparative assessments of the effects of alcohol exposure on fetal brain development using optical coherence tomography and ultrasound imaging

NASA Astrophysics Data System (ADS)

Sudheendran, Narendran; Bake, Shameena; Miranda, Rajesh C.; Larin, Kirill V.

2013-02-01

The developing fetal brain is vulnerable to a variety of environmental agents including maternal ethanol consumption. Preclinical studies on the development and amelioration of fetal teratology would be significantly facilitated by the application of high resolution imaging technologies like optical coherence tomography (OCT) and high-frequency ultrasound (US). This study investigates the ability of these imaging technologies to measure the effects of maternal ethanol exposure on brain development, ex vivo, in fetal mice. Pregnant mice at gestational day 12.5 were administered ethanol (3 g/Kg b.wt.) or water by intragastric gavage, twice daily for three consecutive days. On gestational day 14.5, fetuses were collected and imaged. Three-dimensional images of the mice fetus brains were obtained by OCT and high-resolution US, and the volumes of the left and right ventricles of the brain were measured. Ethanol-exposed fetuses exhibited a statistically significant, 2-fold increase in average left and right ventricular volumes compared with the ventricular volume of control fetuses, with OCT-derived measures of 0.38 and 0.18 mm3, respectively, whereas the boundaries of the fetal mouse lateral ventricles were not clearly definable with US imaging. Our results indicate that OCT is a useful technology for assessing ventriculomegaly accompanying alcohol-induced developmental delay. This study clearly demonstrated advantages of using OCT for quantitative assessment of embryonic development compared with US imaging.

The impact of verbal framing on brain activity evoked by emotional images.

PubMed

Kisley, Michael A; Campbell, Alana M; Larson, Jenna M; Naftz, Andrea E; Regnier, Jesse T; Davalos, Deana B

2011-12-01

Emotional stimuli generally command more brain processing resources than non-emotional stimuli, but the magnitude of this effect is subject to voluntary control. Cognitive reappraisal represents one type of emotion regulation that can be voluntarily employed to modulate responses to emotional stimuli. Here, the late positive potential (LPP), a specific event-related brain potential (ERP) component, was measured in response to neutral, positive and negative images while participants performed an evaluative categorization task. One experimental group adopted a "negative frame" in which images were categorized as negative or not. The other adopted a "positive frame" in which the exact same images were categorized as positive or not. Behavioral performance confirmed compliance with random group assignment, and peak LPP amplitude to negative images was affected by group membership: brain responses to negative images were significantly reduced in the "positive frame" group. This suggests that adopting a more positive appraisal frame can modulate brain activity elicited by negative stimuli in the environment.

Imaging the delivery of brain-penetrating PLGA nanoparticles in the brain using magnetic resonance.

PubMed

Strohbehn, Garth; Coman, Daniel; Han, Liang; Ragheb, Ragy R T; Fahmy, Tarek M; Huttner, Anita J; Hyder, Fahmeed; Piepmeier, Joseph M; Saltzman, W Mark; Zhou, Jiangbing

2015-02-01

Current therapy for glioblastoma multiforme (GBM) is largely ineffective, with nearly universal tumor recurrence. The failure of current therapy is primarily due to the lack of approaches for the efficient delivery of therapeutics to diffuse tumors in the brain. In our prior study, we developed brain-penetrating nanoparticles that are capable of penetrating brain tissue and distribute over clinically relevant volumes when administered via convection-enhanced delivery (CED). We demonstrated that these particles are capable of efficient delivery of chemotherapeutics to diffuse tumors in the brain, indicating that they may serve as a groundbreaking approach for the treatment of GBM. In the original study, nanoparticles in the brain were imaged using positron emission tomography (PET). However, clinical translation of this delivery platform can be enabled by engineering a non-invasive detection modality using magnetic resonance imaging (MRI). For this purpose, we developed chemistry to incorporate superparamagnetic iron oxide (SPIO) into the brain-penetrating nanoparticles. We demonstrated that SPIO-loaded nanoparticles, which retain the same morphology as nanoparticles without SPIO, have an excellent transverse (T(2)) relaxivity. After CED, the distribution of nanoparticles in the brain (i.e., in the vicinity of injection site) can be detected using MRI and the long-lasting signal attenuation of SPIO-loaded brain-penetrating nanoparticles lasted over a one-month timecourse. Development of these nanoparticles is significant as, in future clinical applications, co-administration of SPIO-loaded nanoparticles will allow for intraoperative monitoring of particle distribution in the brain to ensure drug-loaded nanoparticles reach tumors as well as for monitoring the therapeutic benefit with time and to evaluate tumor relapse patterns.

Hidden Markov random field model and Broyden-Fletcher-Goldfarb-Shanno algorithm for brain image segmentation

NASA Astrophysics Data System (ADS)

Guerrout, EL-Hachemi; Ait-Aoudia, Samy; Michelucci, Dominique; Mahiou, Ramdane

2018-05-01

Many routine medical examinations produce images of patients suffering from various pathologies. With the huge number of medical images, the manual analysis and interpretation became a tedious task. Thus, automatic image segmentation became essential for diagnosis assistance. Segmentation consists in dividing the image into homogeneous and significant regions. We focus on hidden Markov random fields referred to as HMRF to model the problem of segmentation. This modelisation leads to a classical function minimisation problem. Broyden-Fletcher-Goldfarb-Shanno algorithm referred to as BFGS is one of the most powerful methods to solve unconstrained optimisation problem. In this paper, we investigate the combination of HMRF and BFGS algorithm to perform the segmentation operation. The proposed method shows very good segmentation results comparing with well-known approaches. The tests are conducted on brain magnetic resonance image databases (BrainWeb and IBSR) largely used to objectively confront the results obtained. The well-known Dice coefficient (DC) was used as similarity metric. The experimental results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice Coefficient above .9. Moreover, it generally outperforms other methods in the tests conducted.

Biomarker-guided translation of brain imaging into disease pathway models

PubMed Central

Younesi, Erfan; Hofmann-Apitius, Martin

2013-01-01

The advent of state-of-the-art brain imaging technologies in recent years and the ability of such technologies to provide high-resolution information at both structural and functional levels has spawned large efforts to introduce novel non-invasive imaging biomarkers for early prediction and diagnosis of brain disorders; however, their utility in both clinic and drug development at their best resolution remains limited to visualizing and monitoring disease progression. Given the fact that efficient translation of valuable information embedded in brain scans into clinical application is of paramount scientific and public health importance, a strategy is needed to bridge the current gap between imaging and molecular biology, particularly in neurodegenerative diseases. As an attempt to address this issue, we present a novel computational method to link readouts of imaging biomarkers to their underlying molecular pathways with the aim of guiding clinical diagnosis, prognosis and even target identification in drug discovery for Alzheimer's disease. PMID:24287435

Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping in the Brain

PubMed Central

Liu, Chunlei; Li, Wei; Tong, Karen A.; Yeom, Kristen W.; Kuzminski, Samuel

2015-01-01

Susceptibility-weighted imaging (SWI) is a magnetic resonance imaging (MRI) technique that enhances image contrast by using the susceptibility differences between tissues. It is created by combining both magnitude and phase in the gradient echo data. SWI is sensitive to both paramagnetic and diamagnetic substances which generate different phase shift in MRI data. SWI images can be displayed as a minimum intensity projection that provides high resolution delineation of the cerebral venous architecture, a feature that is not available in other MRI techniques. As such, SWI has been widely applied to diagnose various venous abnormalities. SWI is especially sensitive to deoxygenated blood and intracranial mineral deposition and, for that reason, has been applied to image various pathologies including intracranial hemorrhage, traumatic brain injury, stroke, neoplasm, and multiple sclerosis. SWI, however, does not provide quantitative measures of magnetic susceptibility. This limitation is currently being addressed with the development of quantitative susceptibility mapping (QSM) and susceptibility tensor imaging (STI). While QSM treats susceptibility as isotropic, STI treats susceptibility as generally anisotropic characterized by a tensor quantity. This article reviews the basic principles of SWI, its clinical and research applications, the mechanisms governing brain susceptibility properties, and its practical implementation, with a focus on brain imaging. PMID:25270052

Functional photoacoustic tomography for neonatal brain imaging: developments and challenges

NASA Astrophysics Data System (ADS)

Hariri, Ali; Tavakoli, Emytis; Adabi, Saba; Gelovani, Juri; Avanaki, Mohammad R. N.

2017-03-01

Transfontanelle ultrasound imaging (TFUSI) is a routine diagnostic brain imaging method in infants who are born prematurely, whose skull bones have not completely fused together and have openings between them, so-called fontanelles. Open fontanelles in neonates provide acoustic windows, allowing the ultrasound beam to freely pass through. TFUSI is used to rule out neurological complications of premature birth including subarachnoid hemorrhage (SAH), intraventricular (IVH), subependimal (SEPH), subdural (SDH) or intracerebral (ICH) hemorrhages, as well as hypoxic brain injuries. TFUSI is widely used in the clinic owing to its low cost, safety, accessibility, and noninvasive nature. Nevertheless, the accuracy of TFUSI is limited. To address several limitations of current clinical imaging modalities, we develop a novel transfontanelle photoacoustic imaging (TFPAI) probe, which, for the first time, should allow for non-invasive structural and functional imaging of the infant brain. In this study, we test the feasibility of TFPAI for detection of experimentally-induced intra ventricular and Intraparenchymal hemorrhage phantoms in a sheep model with a surgically-induced cranial window which will serve as a model of neonatal fontanelle. This study is towards using the probe we develop for bedside monitoring of neonates with various disease conditions and complications affecting brain perfusion and oxygenation, including apnea, asphyxia, as well as for detection of various types of intracranial hemorrhages (SAH, IVH, SEPH, SDH, ICH).

A survey of MRI-based medical image analysis for brain tumor studies

NASA Astrophysics Data System (ADS)

Bauer, Stefan; Wiest, Roland; Nolte, Lutz-P.; Reyes, Mauricio

2013-07-01

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.

Characterization of the Distance Relationship Between Localized Serotonin Receptors and Glia Cells on Fluorescence Microscopy Images of Brain Tissue.

PubMed

Jacak, Jaroslaw; Schaller, Susanne; Borgmann, Daniela; Winkler, Stephan M

2015-08-01

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

Imaging of Subacute Blood–Brain Barrier Disruption After Methadone Overdose

PubMed Central

Huisa, Branko N.; Gasparovic, Charles; Taheri, Saeid; Prestopnik, Jillian L.; Rosenberg, Gary A.

2014-01-01

BACKGROUND Methadone intoxication can cause respiratory depression, leading to hypoxia with subsequent coma and death. Delayed postanoxic leukoencephalopathy (DAL) has been reported with intoxication by carbon monoxide, narcotics, and other toxins. OBJECTIVE To investigate the metabolic derangement of the white matter (WM) and blood–brain barrier (BBB) after DAL caused by methadone overdose. DESIGN, SETTING, AND PATIENTS Case report of 2 patients with DAL after a single dose of “diverted” methadone used for pain control. RESULTS In both cases brain magnetic resonance imaging (MRI) revealed initial extensive bilateral restricted diffusion lesions within the WM. Follow-up MRI using proton magnetic resonance spectroscopic imaging (1H-MRSI) showed markedly lower N-acetylaspartate and higher choline within the WM. BBB permeability, calculated by Patlak graphical analysis of MRI T1 data obtained after contrast agent injection, showed disruption of the BBB within the WM lesions, which persisted longer than a year in 1 patient. Neuropsychological evaluation showed executive dysfunction in both patients. After 1 year, one patient recovered whereas the second remained impaired. CONCLUSIONS Methadone overdose can cause DAL with profound disturbances of neural metabolism and the BBB. The time course of these disturbances can be monitored with MR methods. PMID:22211853

Deep Convolutional Neural Networks for Multi-Modality Isointense Infant Brain Image Segmentation

PubMed Central

Zhang, Wenlu; Li, Rongjian; Deng, Houtao; Wang, Li; Lin, Weili; Ji, Shuiwang; Shen, Dinggang

2015-01-01

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. In the isointense stage (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, making the tissue segmentation very challenging. Only a small number of existing methods have been designed for tissue segmentation in this isointense stage; however, they only used a single T1 or T2 images, or the combination of T1 and T2 images. In this paper, we propose to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images. CNNs are a type of deep models in which trainable filters and local neighborhood pooling operations are applied alternatingly on the raw input images, resulting in a hierarchy of increasingly complex features. Specifically, we used multimodality information from T1, T2, and fractional anisotropy (FA) images as inputs and then generated the segmentation maps as outputs. The multiple intermediate layers applied convolution, pooling, normalization, and other operations to capture the highly nonlinear mappings between inputs and outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense stage brain images. Results showed that our proposed model significantly outperformed prior methods on infant brain tissue segmentation. In addition, our results indicated that integration of multi-modality images led to significant performance improvement. PMID:25562829

Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses.

PubMed

Bansal, Ravi; Staib, Lawrence H; Laine, Andrew F; Hao, Xuejun; Xu, Dongrong; Liu, Jun; Weissman, Myrna; Peterson, Bradley S

2012-01-01

Diagnoses using imaging-based measures alone offer the hope of improving the accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect treatments. Previous attempts to use brain imaging for diagnosis, however, have had only limited success in diagnosing patients who are independent of the samples used to derive the diagnostic algorithms. We aimed to develop a classification algorithm that can accurately diagnose chronic, well-characterized neuropsychiatric illness in single individuals, given the availability of sufficiently precise delineations of brain regions across several neural systems in anatomical MR images of the brain. We have developed an automated method to diagnose individuals as having one of various neuropsychiatric illnesses using only anatomical MRI scans. The method employs a semi-supervised learning algorithm that discovers natural groupings of brains based on the spatial patterns of variation in the morphology of the cerebral cortex and other brain regions. We used split-half and leave-one-out cross-validation analyses in large MRI datasets to assess the reproducibility and diagnostic accuracy of those groupings. In MRI datasets from persons with Attention-Deficit/Hyperactivity Disorder, Schizophrenia, Tourette Syndrome, Bipolar Disorder, or persons at high or low familial risk for Major Depressive Disorder, our method discriminated with high specificity and nearly perfect sensitivity the brains of persons who had one specific neuropsychiatric disorder from the brains of healthy participants and the brains of persons who had a different neuropsychiatric disorder. Although the classification algorithm presupposes the availability of precisely delineated brain regions, our findings suggest that patterns of morphological variation across brain surfaces, extracted from MRI scans alone, can successfully diagnose the presence of chronic neuropsychiatric disorders. Extensions of these methods are likely to provide biomarkers

Brain tissue analysis using texture features based on optical coherence tomography images

NASA Astrophysics Data System (ADS)

Lenz, Marcel; Krug, Robin; Dillmann, Christopher; Gerhardt, Nils C.; Welp, Hubert; Schmieder, Kirsten; Hofmann, Martin R.

2018-02-01

Brain tissue differentiation is highly demanded in neurosurgeries, i.e. tumor resection. Exact navigation during the surgery is essential in order to guarantee best life quality afterwards. So far, no suitable method has been found that perfectly covers this demands. With optical coherence tomography (OCT), fast three dimensional images can be obtained in vivo and contactless with a resolution of 1-15 μm. With these specifications OCT is a promising tool to support neurosurgeries. Here, we investigate ex vivo samples of meningioma, healthy white and healthy gray matter in a preliminary study towards in vivo brain tumor removal assistance. Raw OCT images already display structural variations for different tissue types, especially meningioma. But, in order to achieve neurosurgical guidance directly during resection, an automated differentiation approach is desired. For this reason, we employ different texture feature based algorithms, perform a Principal Component Analysis afterwards and then train a Support Vector Machine classifier. In the future we will try different combinations of texture features and perform in vivo measurements in order to validate our findings.

Some Problems for Representations of Brain Organization Based on Activation in Functional Imaging

ERIC Educational Resources Information Center

Sidtis, John J.

2007-01-01

Functional brain imaging has overshadowed traditional lesion studies in becoming the dominant approach to the study of brain-behavior relationships. The proponents of functional imaging studies frequently argue that this approach provides an advantage over lesion studies by observing normal brain activity in vivo without the disruptive effects of…

Evaluation of five diffeomorphic image registration algorithms for mouse brain magnetic resonance microscopy.

PubMed

Fu, Zhenrong; Lin, Lan; Tian, Miao; Wang, Jingxuan; Zhang, Baiwen; Chu, Pingping; Li, Shaowu; Pathan, Muhammad Mohsin; Deng, Yulin; Wu, Shuicai

2017-11-01

The development of genetically engineered mouse models for neuronal diseases and behavioural disorders have generated a growing need for small animal imaging. High-resolution magnetic resonance microscopy (MRM) provides powerful capabilities for noninvasive studies of mouse brains, while avoiding some limits associated with the histological procedures. Quantitative comparison of structural images is a critical step in brain imaging analysis, which highly relies on the performance of image registration techniques. Nowadays, there is a mushrooming growth of human brain registration algorithms, while fine-tuning of those algorithms for mouse brain MRMs is rarely addressed. Because of their topology preservation property and outstanding performance in human studies, diffeomorphic transformations have become popular in computational anatomy. In this study, we specially tuned five diffeomorphic image registration algorithms [DARTEL, geodesic shooting, diffeo-demons, SyN (Greedy-SyN and geodesic-SyN)] for mouse brain MRMs and evaluated their performance using three measures [volume overlap percentage (VOP), residual intensity error (RIE) and surface concordance ratio (SCR)]. Geodesic-SyN performed significantly better than the other methods according to all three different measures. These findings are important for the studies on structural brain changes that may occur in wild-type and transgenic mouse brains. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

Imaging brain microstructure with diffusion MRI: practicality and applications.

PubMed

Alexander, Daniel C; Dyrby, Tim B; Nilsson, Markus; Zhang, Hui

2017-11-29

This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term. Copyright © 2017 John Wiley & Sons, Ltd.

Brain imaging before primary lung cancer resection: a controversial topic.

PubMed

Hudson, Zoe; Internullo, Eveline; Edey, Anthony; Laurence, Isabel; Bianchi, Davide; Addeo, Alfredo

2017-01-01

International and national recommendations for brain imaging in patients planned to undergo potentially curative resection of non-small-cell lung cancer (NSCLC) are variably implemented throughout the United Kingdom [Hudson BJ, Crawford MB, and Curtin J et al (2015) Brain imaging in lung cancer patients without symptoms of brain metastases: a national survey of current practice in England Clin Radiol https://doi.org/10.1016/j.crad.2015.02.007]. However, the recommendations are not based on high-quality evidence and do not take into account cost implications and local resources. Our aim was to determine local practice based on historic outcomes in this patient cohort. This retrospective study took place in a regional thoracic surgical centre in the United Kingdom. Pathology records for all patients who had undergone lung resection with curative intent during the time period January 2012-December 2014 were analysed in October 2015. Electronic pathology and radiology reports were accessed for each patient and data collected about their histological findings, TNM stage, resection margins, and the presence of brain metastases on either pre-operative or post-operative imaging. From the dates given on imaging, we calculated the number of days post-resection that the brain metastases were detected. 585 patients were identified who had undergone resection of their lung cancer. Of these, 471 had accessible electronic radiology records to assess for the radiological evidence of brain metastases. When their electronic records were evaluated, 25/471 (5.3%) patients had radiological evidence of brain metastasis. Of these, five patients had been diagnosed with a brain metastasis at initial presentation and had undergone primary resection of the brain metastasis followed by resection of the lung primary. One patient had been diagnosed with both a primary lung and a primary bowel adenocarcinoma; on review of the case, it was felt that the brain metastasis was more likely to have

Diagnosing Autism Spectrum Disorder through Brain Functional Magnetic Resonance Imaging

DTIC Science & Technology

2016-03-01

Diagnosing Autism Spectrum Disorder through Brain Functional Magnetic Resonance Imaging THESIS MARCH 2016 Kyle A. Palko, Second Lieutenant, USAF AFIT...declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT-ENC-MS-16-M-123 DIAGNOSING AUTISM SPECTRUM...PUBLIC RELEASE; DISTRIBUTION UNLIMITED. AFIT-ENC-MS-16-M-123 DIAGNOSING AUTISM SPECTRUM DISORDER THROUGH BRAIN FUNCTIONAL MAGNETIC RESONANCE IMAGING Kyle

Determination of regional brain temperature using proton magnetic resonance spectroscopy to assess brain-body temperature differences in healthy human subjects.

PubMed

Childs, Charmaine; Hiltunen, Yrjö; Vidyasagar, Rishma; Kauppinen, Risto A

2007-01-01

Proton magnetic resonance spectroscopy ((1)H MRS) was used to determine brain temperature in healthy volunteers. Partially water-suppressed (1)H MRS data sets were acquired at 3T from four different gray matter (GM)/white matter (WM) volumes. Brain temperatures were determined from the chemical-shift difference between the CH(3) of N-acetyl aspartate (NAA) at 2.01 ppm and water. Brain temperatures in (1)H MRS voxels of 2 x 2 x 2 cm(3) showed no substantial heterogeneity. The volume-averaged temperature from single-voxel spectroscopy was compared with body temperatures obtained from the oral cavity, tympanum, and temporal artery regions. The mean brain parenchyma temperature was 0.5 degrees C cooler than readings obtained from three extra-brain sites (P < 0.01). (1)H MRS imaging (MRSI) data were acquired from a slice encompassing the single-voxel volumes to assess the ability of spectroscopic imaging to determine regional brain temperature within the imaging slice. Brain temperature away from the center of the brain determined by MRSI differed from that obtained by single-voxel MRS in the same brain region, possibly due to a poor line width (LW) in MRSI. The data are discussed in the light of proposed brain-body temperature gradients and the use of (1)H MRSI to monitor brain temperature in pathologies, such as brain trauma.

Information system to manage anatomical knowledge and image data about brain

NASA Astrophysics Data System (ADS)

Barillot, Christian; Gibaud, Bernard; Montabord, E.; Garlatti, S.; Gauthier, N.; Kanellos, I.

1994-09-01

This paper reports about first results obtained in a project aiming at developing a computerized system to manage knowledge about brain anatomy. The emphasis is put on the design of a knowledge base which includes a symbolic model of cerebral anatomical structures (grey nuclei, cortical structures such as gyri and sulci, verntricles, vessels, etc.) and of hypermedia facilities allowing to retrieve and display information associated with the objects (texts, drawings, images). Atlas plates digitized from a stereotactic atlas are also used to provide natural and effective communication means between the user and the system.

Advanced Pediatric Brain Imaging Research and Training Program

DTIC Science & Technology

2014-10-01

death and disability in children. Recent advances in pediatric magnetic resonance imaging ( MRI ) techniques are revolutionizing our understanding of... MRI , brain injury. 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a...principles of pediatric brain injury and recovery following injury, as well as the clinical application of sophisticated MRI techniques that are

High-contrast differentiation resolution 3D imaging of rodent brain by X-ray computed microtomography

NASA Astrophysics Data System (ADS)

Zikmund, T.; Novotná, M.; Kavková, M.; Tesařová, M.; Kaucká, M.; Szarowská, B.; Adameyko, I.; Hrubá, E.; Buchtová, M.; Dražanová, E.; Starčuk, Z.; Kaiser, J.

2018-02-01

The biomedically focused brain research is largely performed on laboratory mice considering a high homology between the human and mouse genomes. A brain has an intricate and highly complex geometrical structure that is hard to display and analyse using only 2D methods. Applying some fast and efficient methods of brain visualization in 3D will be crucial for the neurobiology in the future. A post-mortem analysis of experimental animals' brains usually involves techniques such as magnetic resonance and computed tomography. These techniques are employed to visualize abnormalities in the brains' morphology or reparation processes. The X-ray computed microtomography (micro CT) plays an important role in the 3D imaging of internal structures of a large variety of soft and hard tissues. This non-destructive technique is applied in biological studies because the lab-based CT devices enable to obtain a several-micrometer resolution. However, this technique is always used along with some visualization methods, which are based on the tissue staining and thus differentiate soft tissues in biological samples. Here, a modified chemical contrasting protocol of tissues for a micro CT usage is introduced as the best tool for ex vivo 3D imaging of a post-mortem mouse brain. This way, the micro CT provides a high spatial resolution of the brain microscopic anatomy together with a high tissue differentiation contrast enabling to identify more anatomical details in the brain. As the micro CT allows a consequent reconstruction of the brain structures into a coherent 3D model, some small morphological changes can be given into context of their mutual spatial relationships.

Fluorescence laminar optical tomography for brain imaging: system implementation and performance evaluation.

PubMed

Azimipour, Mehdi; Sheikhzadeh, Mahya; Baumgartner, Ryan; Cullen, Patrick K; Helmstetter, Fred J; Chang, Woo-Jin; Pashaie, Ramin

2017-01-01

We present our effort in implementing a fluorescence laminar optical tomography scanner which is specifically designed for noninvasive three-dimensional imaging of fluorescence proteins in the brains of small rodents. A laser beam, after passing through a cylindrical lens, scans the brain tissue from the surface while the emission signal is captured by the epi-fluorescence optics and is recorded using an electron multiplication CCD sensor. Image reconstruction algorithms are developed based on Monte Carlo simulation to model light–tissue interaction and generate the sensitivity matrices. To solve the inverse problem, we used the iterative simultaneous algebraic reconstruction technique. The performance of the developed system was evaluated by imaging microfabricated silicon microchannels embedded inside a substrate with optical properties close to the brain as a tissue phantom and ultimately by scanning brain tissue in vivo. Details of the hardware design and reconstruction algorithms are discussed and several experimental results are presented. The developed system can specifically facilitate neuroscience experiments where fluorescence imaging and molecular genetic methods are used to study the dynamics of the brain circuitries.

Image-guided removal of supratentorial cavernomas in critical brain areas: application of neuronavigation and intraoperative magnetic resonance imaging.

PubMed

Gralla, J; Ganslandt, O; Kober, H; Buchfelder, M; Fahlbusch, R; Nimsky, C

2003-04-01

In a retrospective study the postoperative results of 26 patients operated on for supratentorial cavernous hemangiomas either deep-seated or near eloquent brain areas are summarized. An exact surgical approach to these lesions is essential to prevent neurological deterioration. Three different navigation systems were used and compared according to their clinical applicability. Complete removal of the lesion was obtained in all patients of this series. In six cases (23 %) functional data from magnetoencephalography or functional magnetic resonance imaging were integrated into the navigational setup. In 14 cases (54 %) intraoperative magnetic resonance imaging was performed. The follow-up time was 3 - 26 months (mean: 10 months). In the postoperative course one patient (3.8 %) developed a hemiparesis, another one developed quadrantopia. Nineteen patients presented with preoperative seizure history, 16 of these (84 %) had no further or rare seizures after surgery. The better results in seizure control were achieved in those patients with shorter duration of seizure history before surgery. The study indicates that the application of neuronavigation allows surgery on supratentorial cavernous hemangiomas in critical brain areas with low morbidity. The intraoperative visualization of eloquent cortex areas by integration of functional data allows a fast identification and exemption of eloquent brain areas, preventing neurological deterioration. Furthermore, the intraoperative MR resection control ensures a complete resection and illustrates the minimal invasive approach.

Dye-enhanced multimodal confocal imaging as a novel approach to intraoperative diagnosis of brain tumors.

PubMed

Snuderl, Matija; Wirth, Dennis; Sheth, Sameer A; Bourne, Sarah K; Kwon, Churl-Su; Ancukiewicz, Marek; Curry, William T; Frosch, Matthew P; Yaroslavsky, Anna N

2013-01-01

Intraoperative diagnosis plays an important role in accurate sampling of brain tumors, limiting the number of biopsies required and improving the distinction between brain and tumor. The goal of this study was to evaluate dye-enhanced multimodal confocal imaging for discriminating gliomas from nonglial brain tumors and from normal brain tissue for diagnostic use. We investigated a total of 37 samples including glioma (13), meningioma (7), metastatic tumors (9) and normal brain removed for nontumoral indications (8). Tissue was stained in 0.05 mg/mL aqueous solution of methylene blue (MB) for 2-5 minutes and multimodal confocal images were acquired using a custom-built microscope. After imaging, tissue was formalin fixed and paraffin embedded for standard neuropathologic evaluation. Thirteen pathologists provided diagnoses based on the multimodal confocal images. The investigated tumor types exhibited distinctive and complimentary characteristics in both the reflectance and fluorescence responses. Images showed distinct morphological features similar to standard histology. Pathologists were able to distinguish gliomas from normal brain tissue and nonglial brain tumors, and to render diagnoses from the images in a manner comparable to haematoxylin and eosin (H&E) slides. These results confirm the feasibility of multimodal confocal imaging for intravital intraoperative diagnosis. © 2012 The Authors; Brain Pathology © 2012 International Society of Neuropathology.

A synthetic luciferin improves in vivo bioluminescence imaging of gene expression in cardiovascular brain regions.

PubMed

Simonyan, Hayk; Hurr, Chansol; Young, Colin N

2016-10-01

Bioluminescence imaging is an effective tool for in vivo investigation of molecular processes. We have demonstrated the applicability of bioluminescence imaging to spatiotemporally monitor gene expression in cardioregulatory brain nuclei during the development of cardiovascular disease, via incorporation of firefly luciferase into living animals, combined with exogenous d-luciferin substrate administration. Nevertheless, d-luciferin uptake into the brain tissue is low, which decreases the sensitivity of bioluminescence detection, particularly when considering small changes in gene expression in tiny central areas. Here, we tested the hypothesis that a synthetic luciferin, cyclic alkylaminoluciferin (CycLuc1), would be superior to d-luciferin for in vivo bioluminescence imaging in cardiovascular brain regions. Male C57B1/6 mice underwent targeted delivery of an adenovirus encoding the luciferase gene downstream of the CMV promoter to the subfornical organ (SFO) or paraventricular nucleus of hypothalamus (PVN), two crucial cardioregulatory neural regions. While bioluminescent signals could be obtained following d-luciferin injection (150 mg/kg), CycLuc1 administration resulted in a three- to fourfold greater bioluminescent emission from the SFO and PVN, at 10- to 20-fold lower substrate concentrations (7.5-15 mg/kg). This CycLuc1-mediated enhancement in bioluminescent emission was evident early following substrate administration (i.e., 6-10 min) and persisted for up to 1 h. When the exposure time was reduced from 60 s to 1,500 ms, minimal signal in the PVN was detectable with d-luciferin, whereas bioluminescent images could be reliably captured with CycLuc1. These findings demonstrate that bioluminescent imaging with the synthetic luciferin CycLuc1 provides an improved physiological genomics tool to investigate molecular events in discrete cardioregulatory brain nuclei. Copyright © 2016 the American Physiological Society.

Diffusion Tensor Imaging: Application to the Study of the Developing Brain

ERIC Educational Resources Information Center

Cascio, Carissa J.; Gerig, Guido; Piven, Joseph

2007-01-01

Objective: To provide an overview of diffusion tensor imaging (DTI) and its application to the study of white matter in the developing brain in both healthy and clinical samples. Method: The development of DTI and its application to brain imaging of white matter tracts is discussed. Forty-eight studies using DTI to examine diffusion properties of…

Susceptibility Tensor Imaging (STI) of the Brain

PubMed Central

Li, Wei; Liu, Chunlei; Duong, Timothy Q.; van Zijl, Peter C.M.; Li, Xu

2016-01-01

Susceptibility tensor imaging (STI) is a recently developed MRI technique that allows quantitative determination of orientation-independent magnetic susceptibility parameters from the dependence of gradient echo signal phase on the orientation of biological tissues with respect to the main magnetic field. By modeling the magnetic susceptibility of each voxel as a symmetric rank-2 tensor, individual magnetic susceptibility tensor elements as well as the mean magnetic susceptibility (MMS) and magnetic susceptibility anisotropy (MSA) can be determined for brain tissues that would still show orientation dependence after conventional scalar-based quantitative susceptibility mapping (QSM) to remove such dependence. Similar to diffusion tensor imaging (DTI), STI allows mapping of brain white matter fiber orientations and reconstruction of 3D white matter pathways using the principal eigenvectors of the susceptibility tensor. In contrast to diffusion anisotropy, the main determinant factor of susceptibility anisotropy in brain white matter is myelin. Another unique feature of susceptibility anisotropy of white matter is its sensitivity to gadolinium-based contrast agents. Mechanistically, MRI-observed susceptibility anisotropy is mainly attributed to the highly ordered lipid molecules in myelin sheath. STI provides a consistent interpretation of the dependence of phase and susceptibility on orientation at multiple scales. This article reviews the key experimental findings and physical theories that led to the development of STI, its practical implementations, and its applications for brain research. PMID:27120169

Sequential variation in brain functional magnetic resonance imaging after peripheral nerve injury: A rat study.

PubMed

Onishi, Okihiro; Ikoma, Kazuya; Oda, Ryo; Yamazaki, Tetsuro; Fujiwara, Hiroyoshi; Yamada, Shunji; Tanaka, Masaki; Kubo, Toshikazu

2018-04-23

Although treatment protocols are available, patients experience both acute neuropathic pain and chronic neuropathic pain, hyperalgesia, and allodynia after peripheral nerve injury. The purpose of this study was to identify the brain regions activated after peripheral nerve injury using functional magnetic resonance imaging (fMRI) sequentially and assess the relevance of the imaging results using histological findings. To model peripheral nerve injury in male Sprague-Dawley rats, the right sciatic nerve was crushed using an aneurysm clip, under general anesthesia. We used a 7.04T MRI system. T 2 * weighted image, coronal slice, repetition time, 7 ms; echo time, 3.3 ms; field of view, 30 mm × 30 mm; pixel matrix, 64 × 64 by zero-filling; slice thickness, 2 mm; numbers of slices, 9; numbers of average, 2; and flip angle, 8°. fMR images were acquired during electrical stimulation to the rat's foot sole; after 90 min, c-Fos immunohistochemical staining of the brain was performed in rats with induced peripheral nerve injury for 3, 6, and 9 weeks. Data were pre-processed by realignment in the Statistical Parametric Mapping 8 software. A General Linear Model first level analysis was used to obtain T-values. One week after the injury, significant changes were detected in the cingulate cortex, insular cortex, amygdala, and basal ganglia; at 6 weeks, the brain regions with significant changes in signal density were contracted; at 9 weeks, the amygdala and hippocampus showed activation. Histological findings of the rat brain supported the fMRI findings. We detected sequential activation in the rat brain using fMRI after sciatic nerve injury. Many brain regions were activated during the acute stage of peripheral nerve injury. Conversely, during the chronic stage, activation of the amygdala and hippocampus may be related to chronic-stage hyperalgesia, allodynia, and chronic neuropathic pain. Copyright © 2018 Elsevier B.V. All rights reserved.

[An experimental study on the Chinese lung adenocarcinoma cell clone CPA-Yang1-BR with brain metastasis potency in nude mice and in vivo imaging research].

PubMed

Lei, Bei; Cao, Jie; Shen, Jie; Zhao, Lanxiang; Liang, Sheng; Meng, Qinggang; Xie, Wenhui; Yang, Shunfang

2013-08-20

Lung cancer is the leading cause of cancer-related death in men and women. It is also the most common cause of brain metastases. A brain metastasis model is difficult to be established because of the presence of the blood-brain barrier (BBB) and the lack of optimal methods for detecting brain metastasis in nude mice. Thus, the establishment of a Chinese lung adenocarcinoma cell line and its animal model with brain metastasis potency and in vivo research is of great significance. CPA-Yang1 cells were obtained from a patient with human lung adenocarcinoma by lentiviral vector-mediated transfection of green fluorescence protein. Intracardiac inoculation of the cells was performed in nude mice, and brain metastatic lesions were detected using micro ¹⁸F FDG-PET/CT scanners, small animal in vivo imaging system for fluorescence, radionuclide and X ray fused imaging, magnetic resonance imaging (MRI) with sense body detection, and resection. The samples were divided into two parts for cell culture and histological diagnosis. The process was repeated in vivo and in vitro for four cycles to obtain a novel cell clone, CPA-Yang1-BR. A novel cell clone, CPA-Yang1-BR, was obtained with a brain metastatic rate of 50%. The use of MRI for the detection of brain metastases has obvious advantages. An experimental Chinese lung adenocarcinoma cell clone (CPA-Yang1-BR) and its animal model with brain metastasis potency in nude mice were established. MRI with sense body or micro MRI may be used as a sensitive, accurate, and noninvasive method to detect experimental brain metastases in intact live immunodeficient mice. The results of this study may serve as a technical platform for brain metastases from lung adenocarcinoma.

Towards hyperpolarized 13C-succinate imaging of brain cancer

PubMed Central

Bhattacharya, Pratip; Chekmenev, Eduard Y.; Perman, William H.; Harris, Kent C.; Lin, Alexander P.; Norton, Valerie A.; Tan, Chou T.; Ross, Brian D.; Weitekamp, Daniel P.

2009-01-01

We describe a novel 13C enriched precursor molecule, sodium 1-13C acetylenedicarboxylate, which after hydrogenation by PASADE-NA (Parahydrogen and Synthesis Allows Dramatically Enhanced Nuclear Alignment) under controlled experimental conditions, becomes hyperpolarized 13C sodium succinate. Fast in vivo 3D FIESTA MR imaging demonstrated that, following carotid arterial injection, the hyperpolarized 13C-succinate appeared in the head and cerebral circulation of normal and tumor-bearing rats. At this time, no in vivo hyperpolarized signal has been localized to normal brain or brain tumor. On the other hand, ex vivo samples of brain harvested from rats bearing a 9L brain tumor, 1 h or more following in vivo carotid injection of hyperpolarized 13C sodium succinate, contained significant concentrations of the injected substrate, 13C sodium succinate, together with 13C maleate and succinate metabolites 1-13C-glutamate, 5-13C-glutamate, 1-13C-glutamine and 5-13C-glutamine. The 13C substrates and products were below the limits of NMR detection in ex vivo samples of normal brain consistent with an intact blood–brain barrier. These ex vivo results indicate that hyperpolarized 13C sodium succinate may become a useful tool for rapid in vivo identification of brain tumors, providing novel biomarkers in 13C MR spectral-spatial images. PMID:17303454

Towards hyperpolarized 13C-succinate imaging of brain cancer

NASA Astrophysics Data System (ADS)

Bhattacharya, Pratip; Chekmenev, Eduard Y.; Perman, William H.; Harris, Kent C.; Lin, Alexander P.; Norton, Valerie A.; Tan, Chou T.; Ross, Brian D.; Weitekamp, Daniel P.

2007-05-01

We describe a novel 13C enriched precursor molecule, sodium 1- 13C acetylenedicarboxylate, which after hydrogenation by PASADENA (Parahydrogen and Synthesis Allows Dramatically Enhanced Nuclear Alignment) under controlled experimental conditions, becomes hyperpolarized 13C sodium succinate. Fast in vivo 3D FIESTA MR imaging demonstrated that, following carotid arterial injection, the hyperpolarized 13C-succinate appeared in the head and cerebral circulation of normal and tumor-bearing rats. At this time, no in vivo hyperpolarized signal has been localized to normal brain or brain tumor. On the other hand, ex vivo samples of brain harvested from rats bearing a 9L brain tumor, 1 h or more following in vivo carotid injection of hyperpolarized 13C sodium succinate, contained significant concentrations of the injected substrate, 13C sodium succinate, together with 13C maleate and succinate metabolites 1- 13C-glutamate, 5- 13C-glutamate, 1- 13C-glutamine and 5- 13C-glutamine. The 13C substrates and products were below the limits of NMR detection in ex vivo samples of normal brain consistent with an intact blood-brain barrier. These ex vivo results indicate that hyperpolarized 13C sodium succinate may become a useful tool for rapid in vivo identification of brain tumors, providing novel biomarkers in 13C MR spectral-spatial images.

Forthergillian Lecture. Imaging human brain function.

PubMed

Frackowiak, R S

The non-invasive brain scanning techniques introduced a quarter of a century ago have become crucial for diagnosis in clinical neurology. They have also been used to investigate brain function and have provided information about normal activity and pathogenesis. They have been used to investigate functional specialization in the brain and how specialized areas communicate to generate complex integrated functions such as speech, memory, the emotions and so on. The phenomenon of brain plasticity is poorly understood and yet clinical neurologists are aware, from everyday observations, that spontaneous recovery from brain lesions is common. An improved understanding of the mechanisms of recovery may generate new therapeutic strategies and indicate ways of modulating mechanisms that promote plastic compensation for loss of function. The main methods used to investigate these issues are positron emission tomography and magnetic resonance imaging (M.R.I.). M.R.I. is also used to map brain structure. The techniques of functional brain mapping and computational morphometrics depend on high performance scanners and a validated set of analytic statistical procedures that generate reproducible data and meaningful inferences from brain scanning data. The motor system presents a good paradigm to illustrate advances made by scanning towards an understanding of plasticity at the level of brain areas. The normal motor system is organized in a nested hierarchy. Recovery from paralysis caused by internal capsule strokes involves functional reorganization manifesting itself as changed patterns of activity in the component brain areas of the normal motor system. The pattern of plastic modification depends in part on patterns of residual or disturbed connectivity after brain injury. Therapeutic manipulations in patients with Parkinson's disease using deep brain stimulation, dopaminergic agents or fetal mesencephalic transplantation provide a means to examine mechanisms underpinning

Brain stem/brain stem occipital bone ratio and the four-line view in nuchal translucency images of fetuses with open spina bifida.

PubMed

Iuculano, Ambra; Zoppi, Maria Angelica; Piras, Alessandra; Arras, Maurizio; Monni, Giovanni

2014-09-10

Abstract Objective: Brain stem depth/brain stem occipital bone distance (BS/BSOB ratio) and the four-line view, in images obtained for nuchal translucency (NT) screening in fetuses with open spina bifida (OSB). Methods: Single center, retrospective study based on the assessment of NT screening images of fetuses with OSB. A ratio between the BS depth and the BSOB distance was calculated (BS/BSOB ratio) and the four-line view observed, and the sensitivity for a BS/BSOB ratio superior/equal to 1, and for the lack of detection of the four-line view were calculated. Results: There were 17 cases of prenatal diagnosis OSB. In six cases, the suspicion on OSB was raised during NT screening, in six cases, the diagnosis was made before 20 weeks and in five cases during anomaly scan. The BS/BSOB ratio was superior/equal to 1 in all 17 cases, and three lines, were visualized in 15/17 images of the OSB cases, being the sensitivity 100% (95% CI, 81 to 100%) and 88% (95% CI, 65 to 96%). Conclusion: Assessment of BS/BSOB ratio and four-line view in NT images is feasible detecting affected by OSB with high sensitivity. The presence of associated anomalies or of an enlarged NT enhances the early detection.

Brain Development in Fetuses of Mothers with Diabetes: A Case-Control MR Imaging Study.

PubMed

Denison, F C; Macnaught, G; Semple, S I K; Terris, G; Walker, J; Anblagan, D; Serag, A; Reynolds, R M; Boardman, J P

2017-05-01

Offspring exposed to maternal diabetes are at increased risk of neurocognitive impairment, but its origins are unknown. With MR imaging, we investigated the feasibility of comprehensive assessment of brain metabolism ( 1 H-MRS), microstructure (DWI), and macrostructure (structural MRI) in third-trimester fetuses in women with diabetes and determined normal ranges for the MR imaging parameters measured. Women with singleton pregnancies with diabetes ( n = 26) and healthy controls ( n = 26) were recruited prospectively for MR imaging studies between 34 and 38 weeks' gestation. Data suitable for postprocessing were obtained from 79%, 71%, and 46% of women for 1 H-MRS, DWI, and structural MRI, respectively. There was no difference in the NAA/Cho and NAA/Cr ratios (mean [SD]) in the fetal brain in women with diabetes compared with controls (1.74 [0.79] versus 1.79 [0.64], P = .81; and 0.78 [0.28] versus 0.94 [0.36], P = .12, respectively), but the Cho/Cr ratio was marginally lower (0.46 [0.11] versus 0.53 [0.10], P = .04). There was no difference in mean [SD] anterior white, posterior white, and deep gray matter ADC between patients and controls (1.16 [0.12] versus 1.16 [0.08], P = .96; 1.54 [0.16] versus 1.59 [0.20], P = .56; and 1.49 [0.23] versus 1.52 [0.23], P = .89, respectively) or volume of the cerebrum (243.0 mL [22.7 mL] versus 253.8 mL [31.6 mL], P = .38). Acquiring multimodal MR imaging of the fetal brain at 3T from pregnant women with diabetes is feasible. Further study of fetal brain metabolism in maternal diabetes is warranted. © 2017 by American Journal of Neuroradiology.

Quantifying structural alterations in Alzheimer's disease brains using quantitative phase imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lee, Moosung; Lee, Eeksung; Jung, JaeHwang; Yu, Hyeonseung; Kim, Kyoohyun; Yoon, Jonghee; Lee, Shinhwa; Jeong, Yong; Park, YongKeun

2017-02-01

Imaging brain tissues is an essential part of neuroscience because understanding brain structure provides relevant information about brain functions and alterations associated with diseases. Magnetic resonance imaging and positron emission tomography exemplify conventional brain imaging tools, but these techniques suffer from low spatial resolution around 100 μm. As a complementary method, histopathology has been utilized with the development of optical microscopy. The traditional method provides the structural information about biological tissues to cellular scales, but relies on labor-intensive staining procedures. With the advances of illumination sources, label-free imaging techniques based on nonlinear interactions, such as multiphoton excitations and Raman scattering, have been applied to molecule-specific histopathology. Nevertheless, these techniques provide limited qualitative information and require a pulsed laser, which is difficult to use for pathologists with no laser training. Here, we present a label-free optical imaging of mouse brain tissues for addressing structural alteration in Alzheimer's disease. To achieve the mesoscopic, unlabeled tissue images with high contrast and sub-micrometer lateral resolution, we employed holographic microscopy and an automated scanning platform. From the acquired hologram of the brain tissues, we could retrieve scattering coefficients and anisotropies according to the modified scattering-phase theorem. This label-free imaging technique enabled direct access to structural information throughout the tissues with a sub-micrometer lateral resolution and presented a unique means to investigate the structural changes in the optical properties of biological tissues.

Classification of tumor based on magnetic resonance (MR) brain images using wavelet energy feature and neuro-fuzzy model

NASA Astrophysics Data System (ADS)

Damayanti, A.; Werdiningsih, I.

2018-03-01

The brain is the organ that coordinates all the activities that occur in our bodies. Small abnormalities in the brain will affect body activity. Tumor of the brain is a mass formed a result of cell growth not normal and unbridled in the brain. MRI is a non-invasive medical test that is useful for doctors in diagnosing and treating medical conditions. The process of classification of brain tumor can provide the right decision and correct treatment and right on the process of treatment of brain tumor. In this study, the classification process performed to determine the type of brain tumor disease, namely Alzheimer’s, Glioma, Carcinoma and normal, using energy coefficient and ANFIS. Process stages in the classification of images of MR brain are the extraction of a feature, reduction of a feature, and process of classification. The result of feature extraction is a vector approximation of each wavelet decomposition level. The feature reduction is a process of reducing the feature by using the energy coefficients of the vector approximation. The feature reduction result for energy coefficient of 100 per feature is 1 x 52 pixels. This vector will be the input on the classification using ANFIS with Fuzzy C-Means and FLVQ clustering process and LM back-propagation. Percentage of success rate of MR brain images recognition using ANFIS-FLVQ, ANFIS, and LM back-propagation was obtained at 100%.

Gold Nanoparticles for Brain Tumor Imaging: A Systematic Review.

PubMed

Meola, Antonio; Rao, Jianghong; Chaudhary, Navjot; Sharma, Mayur; Chang, Steven D

2018-01-01

Demarcation of malignant brain tumor boundaries is critical to achieve complete resection and to improve patient survival. Contrast-enhanced brain magnetic resonance imaging (MRI) is the gold standard for diagnosis and pre-surgical planning, despite limitations of gadolinium (Gd)-based contrast agents to depict tumor margins. Recently, solid metal-based nanoparticles (NPs) have shown potential as diagnostic probes for brain tumors. Gold nanoparticles (GNPs) emerged among those, because of their unique physical and chemical properties and biocompatibility. The aim of the present study is to review the application of GNPs for in vitro and in vivo brain tumor diagnosis. We performed a PubMed search of reports exploring the application of GNPs in the diagnosis of brain tumors in biological models including cells, animals, primates, and humans. The search words were "gold" AND "NP" AND "brain tumor." Two reviewers performed eligibility assessment independently in an unblinded standardized manner. The following data were extracted from each paper: first author, year of publication, animal/cellular model, GNP geometry, GNP size, GNP coating [i.e., polyethylene glycol (PEG) and Gd], blood-brain barrier (BBB) crossing aids, imaging modalities, and therapeutic agents conjugated to the GNPs. The PubMed search provided 100 items. A total of 16 studies, published between the 2011 and 2017, were included in our review. No studies on humans were found. Thirteen studies were conducted in vivo on rodent models. The most common shape was a nanosphere (12 studies). The size of GNPs ranged between 20 and 120 nm. In eight studies, the GNPs were covered in PEG. The BBB penetration was increased by surface molecules (nine studies) or by means of external energy sources (in two studies). The most commonly used imaging modalities were MRI (four studies), surface-enhanced Raman scattering (three studies), and fluorescent microscopy (three studies). In two studies, the GNPs were conjugated

Improved tumor identification using dual tracer molecular imaging in fluorescence guided brain surgery

NASA Astrophysics Data System (ADS)

Xu, Xiaochun; Torres, Veronica; Straus, David; Brey, Eric M.; Byrne, Richard W.; Tichauer, Kenneth M.

2015-03-01

Brain tumors represent a leading cause of cancer death for people under the age of 40 and the probability complete surgical resection of brain tumors remains low owing to the invasive nature of these tumors and the consequences of damaging healthy brain tissue. Molecular imaging is an emerging approach that has the potential to improve the ability for surgeons to correctly discriminate between healthy and cancerous tissue; however, conventional molecular imaging approaches in brain suffer from significant background signal in healthy tissue or an inability target more invasive sections of the tumor. This work presents initial studies investigating the ability of novel dual-tracer molecular imaging strategies to be used to overcome the major limitations of conventional "single-tracer" molecular imaging. The approach is evaluated in simulations and in an in vivo mice study with animals inoculated orthotopically using fluorescent human glioma cells. An epidermal growth factor receptor (EGFR) targeted Affibody-fluorescent marker was employed as a targeted imaging agent, and the suitability of various FDA approved untargeted fluorescent tracers (e.g. fluorescein & indocyanine green) were evaluated in terms of their ability to account for nonspecific uptake and retention of the targeted imaging agent. Signal-to-background ratio was used to measure and compare the amount of reporter in the tissue between targeted and untargeted tracer. The initial findings suggest that FDA-approved fluorescent imaging agents are ill-suited to act as untargeted imaging agents for dual-tracer fluorescent guided brain surgery as they suffer from poor delivery to the healthy brain tissue and therefore cannot be used to identify nonspecific vs. specific uptake of the targeted imaging agent where current surgery is most limited.

5-HT Radioligands for Human Brain Imaging With PET and SPECT

PubMed Central

Paterson, Louise M.; Kornum, Birgitte R.; Nutt, David J.; Pike, Victor W.; Knudsen, Gitte M.

2014-01-01

The serotonergic system plays a key modulatory role in the brain and is the target for many drug treatments for brain disorders either through reuptake blockade or via interactions at the 14 subtypes of 5-HT receptors. This review provides the history and current status of radioligands used for positron emission tomography (PET) and single photon emission computerized tomography (SPECT) imaging of human brain serotonin (5-HT) receptors, the 5-HT transporter (SERT), and 5-HT synthesis rate. Currently available radioligands for in vivo brain imaging of the 5-HT system in humans include antagonists for the 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT4 receptors, and for SERT. Here we describe the evolution of these radioligands, along with the attempts made to develop radioligands for additional serotonergic targets. We describe the properties needed for a radioligand to become successful and the main caveats. The success of a PET or SPECT radioligand can ultimately be assessed by its frequency of use, its utility in humans, and the number of research sites using it relative to its invention date, and so these aspects are also covered. In conclusion, the development of PET and SPECT radioligands to image serotonergic targets is of high interest, and successful evaluation in humans is leading to invaluable insight into normal and abnormal brain function, emphasizing the need for continued development of both SPECT and PET radioligands for human brain imaging. PMID:21674551

Brain imaging and cognition in young narcoleptic patients.

PubMed

Huang, Yu-Shu; Liu, Feng-Yuan; Lin, Chin-Yang; Hsiao, Ing-Tsung; Guilleminault, Christian

2016-08-01

The relationship between functional brain images and performances in narcoleptic patients and controls is a new field of investigation. We studied 71 young, type 1 narcoleptic patients and 20 sex- and age-matched control individuals using brain positron emission tomography (PET) images and neurocognitive testing. Clinical investigation was carried out using sleep-wake evaluation questionnaires; a sleep-wake study was conducted with actigraphy, polysomnography, multiple sleep latency test (MSLT), and blood tests (with human leukocyte antigen typing). The continuous performance test (CPT) and Wisconsin card sorting test (WCST) were administered on the same day as the PET study. PET data were analyzed using Statistical Parametric Mapping (version 8) software. Correlation of brain imaging and neurocognitive function was performed by Pearson's correlation. Statistical analyses (Student's t-test) were conducted with SPSS version-18. Seventy-one narcoleptic patients (mean age: 16.15 years, 41 boys (57.7%)) and 20 controls (mean age: 15.1 years, 12 boys (60%)) were studied. Results from the CPT and WCST showed significantly worse scores in narcoleptic patients than in controls (P < 0.05). Compared to controls, narcoleptic patients presented with hypometabolism in the right mid-frontal lobe and angular gyrus (P < 0.05) and significant hypermetabolism in the olfactory lobe, hippocampus, parahippocampus, amygdala, fusiform, left inferior parietal lobe, left superior temporal lobe, striatum, basal ganglia and thalamus, right hypothalamus, and pons (P < 0.05) in the PET study. Changes in brain metabolic activity in narcoleptic patients were positively correlated with results from the sleepiness scales and performance tests. Young, type 1 narcoleptic patients face a continuous cognitive handicap. Our imaging cognitive test protocol can be useful for investigating the effects of treatment trials in these patients. Copyright © 2016 Elsevier B.V. All rights reserved.

Mesoscale brain explorer, a flexible python-based image analysis and visualization tool.

PubMed

Haupt, Dirk; Vanni, Matthieu P; Bolanos, Federico; Mitelut, Catalin; LeDue, Jeffrey M; Murphy, Tim H

2017-07-01

Imaging of mesoscale brain activity is used to map interactions between brain regions. This work has benefited from the pioneering studies of Grinvald et al., who employed optical methods to image brain function by exploiting the properties of intrinsic optical signals and small molecule voltage-sensitive dyes. Mesoscale interareal brain imaging techniques have been advanced by cell targeted and selective recombinant indicators of neuronal activity. Spontaneous resting state activity is often collected during mesoscale imaging to provide the basis for mapping of connectivity relationships using correlation. However, the information content of mesoscale datasets is vast and is only superficially presented in manuscripts given the need to constrain measurements to a fixed set of frequencies, regions of interest, and other parameters. We describe a new open source tool written in python, termed mesoscale brain explorer (MBE), which provides an interface to process and explore these large datasets. The platform supports automated image processing pipelines with the ability to assess multiple trials and combine data from different animals. The tool provides functions for temporal filtering, averaging, and visualization of functional connectivity relations using time-dependent correlation. Here, we describe the tool and show applications, where previously published datasets were reanalyzed using MBE.

Photoacoustic imaging to detect rat brain activation after cocaine hydrochloride injection

NASA Astrophysics Data System (ADS)

Jo, Janggun; Yang, Xinmai

2011-03-01

Photoacoustic imaging (PAI) was employed to detect small animal brain activation after the administration of cocaine hydrochloride. Sprague Dawley rats were injected with different concentrations (2.5, 3.0, and 5.0 mg per kg body) of cocaine hydrochloride in saline solution through tail veins. The brain functional response to the injection was monitored by photoacoustic tomography (PAT) system with horizontal scanning of cerebral cortex of rat brain. Photoacoustic microscopy (PAM) was also used for coronal view images. The modified PAT system used multiple ultrasonic detectors to reduce the scanning time and maintain a good signal-to-noise ratio (SNR). The measured photoacoustic signal changes confirmed that cocaine hydrochloride injection excited high blood volume in brain. This result shows PAI can be used to monitor drug abuse-induced brain activation.

Brain Injury Lesion Imaging Using Preconditioned Quantitative Susceptibility Mapping without Skull Stripping.

PubMed

Soman, S; Liu, Z; Kim, G; Nemec, U; Holdsworth, S J; Main, K; Lee, B; Kolakowsky-Hayner, S; Selim, M; Furst, A J; Massaband, P; Yesavage, J; Adamson, M M; Spincemallie, P; Moseley, M; Wang, Y

2018-04-01

Identifying cerebral microhemorrhage burden can aid in the diagnosis and management of traumatic brain injury, stroke, hypertension, and cerebral amyloid angiopathy. MR imaging susceptibility-based methods are more sensitive than CT for detecting cerebral microhemorrhage, but methods other than quantitative susceptibility mapping provide results that vary with field strength and TE, require additional phase maps to distinguish blood from calcification, and depict cerebral microhemorrhages as bloom artifacts. Quantitative susceptibility mapping provides universal quantification of tissue magnetic property without these constraints but traditionally requires a mask generated by skull-stripping, which can pose challenges at tissue interphases. We evaluated the preconditioned quantitative susceptibility mapping MR imaging method, which does not require skull-stripping, for improved depiction of brain parenchyma and pathology. Fifty-six subjects underwent brain MR imaging with a 3D multiecho gradient recalled echo acquisition. Mask-based quantitative susceptibility mapping images were created using a commonly used mask-based quantitative susceptibility mapping method, and preconditioned quantitative susceptibility images were made using precondition-based total field inversion. All images were reviewed by a neuroradiologist and a radiology resident. Ten subjects (18%), all with traumatic brain injury, demonstrated blood products on 3D gradient recalled echo imaging. All lesions were visible on preconditioned quantitative susceptibility mapping, while 6 were not visible on mask-based quantitative susceptibility mapping. Thirty-one subjects (55%) demonstrated brain parenchyma and/or lesions that were visible on preconditioned quantitative susceptibility mapping but not on mask-based quantitative susceptibility mapping. Six subjects (11%) demonstrated pons artifacts on preconditioned quantitative susceptibility mapping and mask-based quantitative susceptibility mapping

A fast atlas-guided high density diffuse optical tomography system for brain imaging

NASA Astrophysics Data System (ADS)

Dai, Xianjin; Zhang, Tao; Yang, Hao; Jiang, Huabei

2017-02-01

Near infrared spectroscopy (NIRS) is an emerging functional brain imaging tool capable of assessing cerebral concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) during brain activation noninvasively. As an extension of NIRS, diffuse optical tomography (DOT) not only shares the merits of providing continuous readings of cerebral oxygenation, but also has the ability to provide spatial resolution in the millimeter scale. Based on the scattering and absorption properties of nonionizing near-infrared light in biological tissue, DOT has been successfully applied in the imaging of breast tumors, osteoarthritis and cortex activations. Here, we present a state-of-art fast high density DOT system suitable for brain imaging. It can achieve up to a 21 Hz sampling rate for a full set of two-wavelength data for 3-D DOT brain image reconstruction. The system was validated using tissue-mimicking brain-model phantom. Then, experiments on healthy subjects were conducted to demonstrate the capability of the system.

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation

PubMed Central

Ortega, Samuel; M. Callicó, Gustavo; Juárez, Eduardo; Bulters, Diederik; Szolna, Adam; Piñeiro, Juan F.; Sosa, Coralia; J. O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Morera, Jesús; Ravi, Daniele; Kiran, B. Ravi; Vega, Aurelio; Báez-Quevedo, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Sarmiento, Roberto

2018-01-01

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400–1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes. PMID:29389893

An Intraoperative Visualization System Using Hyperspectral Imaging to Aid in Brain Tumor Delineation.

PubMed

Fabelo, Himar; Ortega, Samuel; Lazcano, Raquel; Madroñal, Daniel; M Callicó, Gustavo; Juárez, Eduardo; Salvador, Rubén; Bulters, Diederik; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Sosa, Coralia; J O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Morera, Jesús; Ravi, Daniele; Kiran, B Ravi; Vega, Aurelio; Báez-Quevedo, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Sarmiento, Roberto

2018-02-01

Hyperspectral imaging (HSI) allows for the acquisition of large numbers of spectral bands throughout the electromagnetic spectrum (within and beyond the visual range) with respect to the surface of scenes captured by sensors. Using this information and a set of complex classification algorithms, it is possible to determine which material or substance is located in each pixel. The work presented in this paper aims to exploit the characteristics of HSI to develop a demonstrator capable of delineating tumor tissue from brain tissue during neurosurgical operations. Improved delineation of tumor boundaries is expected to improve the results of surgery. The developed demonstrator is composed of two hyperspectral cameras covering a spectral range of 400-1700 nm. Furthermore, a hardware accelerator connected to a control unit is used to speed up the hyperspectral brain cancer detection algorithm to achieve processing during the time of surgery. A labeled dataset comprised of more than 300,000 spectral signatures is used as the training dataset for the supervised stage of the classification algorithm. In this preliminary study, thematic maps obtained from a validation database of seven hyperspectral images of in vivo brain tissue captured and processed during neurosurgical operations demonstrate that the system is able to discriminate between normal and tumor tissue in the brain. The results can be provided during the surgical procedure (~1 min), making it a practical system for neurosurgeons to use in the near future to improve excision and potentially improve patient outcomes.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm.

PubMed

Yang, Zhang; Shufan, Ye; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method.

Segmentation of MRI Brain Images with an Improved Harmony Searching Algorithm

PubMed Central

Yang, Zhang; Li, Guo; Weifeng, Ding

2016-01-01

The harmony searching (HS) algorithm is a kind of optimization search algorithm currently applied in many practical problems. The HS algorithm constantly revises variables in the harmony database and the probability of different values that can be used to complete iteration convergence to achieve the optimal effect. Accordingly, this study proposed a modified algorithm to improve the efficiency of the algorithm. First, a rough set algorithm was employed to improve the convergence and accuracy of the HS algorithm. Then, the optimal value was obtained using the improved HS algorithm. The optimal value of convergence was employed as the initial value of the fuzzy clustering algorithm for segmenting magnetic resonance imaging (MRI) brain images. Experimental results showed that the improved HS algorithm attained better convergence and more accurate results than those of the original HS algorithm. In our study, the MRI image segmentation effect of the improved algorithm was superior to that of the original fuzzy clustering method. PMID:27403428

Automatic brain MR image denoising based on texture feature-based artificial neural networks.

PubMed

Chang, Yu-Ning; Chang, Herng-Hua

2015-01-01

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.

Inner-volume echo volumar imaging (IVEVI) for robust fetal brain imaging.

PubMed

Nunes, Rita G; Ferrazzi, Giulio; Price, Anthony N; Hutter, Jana; Gaspar, Andreia S; Rutherford, Mary A; Hajnal, Joseph V

2018-07-01

Fetal functional MRI studies using conventional 2-dimensional single-shot echo-planar imaging sequences may require discarding a large data fraction as a result of fetal and maternal motion. Increasing the temporal resolution using echo volumar imaging (EVI) could provide an effective alternative strategy. Echo volumar imaging was combined with inner volume (IV) imaging (IVEVI) to locally excite the fetal brain and acquire full 3-dimensional images, fast enough to freeze most fetal head motion. IVEVI was implemented by modifying a standard multi-echo echo-planar imaging sequence. A spin echo with orthogonal excitation and refocusing ensured localized excitation. To introduce T2* weighting and to save time, the k-space center was shifted relative to the spin echo. Both single and multi-shot variants were tested. Acoustic noise was controlled by adjusting the amplitude and switching frequency of the readout gradient. Image-based shimming was used to minimize B 0 inhomogeneities within the fetal brain. The sequence was first validated in an adult. Eight fetuses were scanned using single-shot IVEVI at a 3.5 × 3.5 × 5.0 mm 3 resolution with a readout duration of 383 ms. Multishot IVEVI showed reduced geometric distortions along the second phase-encode direction. Fetal EVI remains challenging. Although effective echo times comparable to the T2* values of fetal cortical gray matter at 3 T could be achieved, controlling acoustic noise required longer readouts, leading to substantial distortions in single-shot images. Although multishot variants enabled us to reduce susceptibility-induced geometric distortions, sensitivity to motion was increased. Future studies should therefore focus on improvements to multishot variants. Magn Reson Med 80:279-285, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

High-resolution brain SPECT imaging by combination of parallel and tilted detector heads.

PubMed

Suzuki, Atsuro; Takeuchi, Wataru; Ishitsu, Takafumi; Morimoto, Yuichi; Kobashi, Keiji; Ueno, Yuichiro

2015-10-01

To improve the spatial resolution of brain single-photon emission computed tomography (SPECT), we propose a new brain SPECT system in which the detector heads are tilted towards the rotation axis so that they are closer to the brain. In addition, parallel detector heads are used to obtain the complete projection data set. We evaluated this parallel and tilted detector head system (PT-SPECT) in simulations. In the simulation study, the tilt angle of the detector heads relative to the axis was 45°. The distance from the collimator surface of the parallel detector heads to the axis was 130 mm. The distance from the collimator surface of the tilted detector heads to the origin on the axis was 110 mm. A CdTe semiconductor panel with a 1.4 mm detector pitch and a parallel-hole collimator were employed in both types of detector head. A line source phantom, cold-rod brain-shaped phantom, and cerebral blood flow phantom were evaluated. The projection data were generated by forward-projection of the phantom images using physics models, and Poisson noise at clinical levels was applied to the projection data. The ordered-subsets expectation maximization algorithm with physics models was used. We also evaluated conventional SPECT using four parallel detector heads for the sake of comparison. The evaluation of the line source phantom showed that the transaxial FWHM in the central slice for conventional SPECT ranged from 6.1 to 8.5 mm, while that for PT-SPECT ranged from 5.3 to 6.9 mm. The cold-rod brain-shaped phantom image showed that conventional SPECT could visualize up to 8-mm-diameter rods. By contrast, PT-SPECT could visualize up to 6-mm-diameter rods in upper slices of a cerebrum. The cerebral blood flow phantom image showed that the PT-SPECT system provided higher resolution at the thalamus and caudate nucleus as well as at the longitudinal fissure of the cerebrum compared with conventional SPECT. PT-SPECT provides improved image resolution at not only upper but also at

Combined multi-kernel head computed tomography images optimized for depicting both brain parenchyma and bone.

PubMed

Takagi, Satoshi; Nagase, Hiroyuki; Hayashi, Tatsuya; Kita, Tamotsu; Hayashi, Katsumi; Sanada, Shigeru; Koike, Masayuki

2014-01-01

The hybrid convolution kernel technique for computed tomography (CT) is known to enable the depiction of an image set using different window settings. Our purpose was to decrease the number of artifacts in the hybrid convolution kernel technique for head CT and to determine whether our improved combined multi-kernel head CT images enabled diagnosis as a substitute for both brain (low-pass kernel-reconstructed) and bone (high-pass kernel-reconstructed) images. Forty-four patients with nondisplaced skull fractures were included. Our improved multi-kernel images were generated so that pixels of >100 Hounsfield unit in both brain and bone images were composed of CT values of bone images and other pixels were composed of CT values of brain images. Three radiologists compared the improved multi-kernel images with bone images. The improved multi-kernel images and brain images were identically displayed on the brain window settings. All three radiologists agreed that the improved multi-kernel images on the bone window settings were sufficient for diagnosing skull fractures in all patients. This improved multi-kernel technique has a simple algorithm and is practical for clinical use. Thus, simplified head CT examinations and fewer images that need to be stored can be expected.

Structural imaging of mild traumatic brain injury may not be enough: overview of functional and metabolic imaging of mild traumatic brain injury.

PubMed

Shin, Samuel S; Bales, James W; Edward Dixon, C; Hwang, Misun

2017-04-01

A majority of patients with traumatic brain injury (TBI) present as mild injury with no findings on conventional clinical imaging methods. Due to this difficulty of imaging assessment on mild TBI patients, there has been much emphasis on the development of diffusion imaging modalities such as diffusion tensor imaging (DTI). However, basic science research in TBI shows that many of the functional and metabolic abnormalities in TBI may be present even in the absence of structural damage. Moreover, structural damage may be present at a microscopic and molecular level that is not detectable by structural imaging modality. The use of functional and metabolic imaging modalities can provide information on pathological changes in mild TBI patients that may not be detected by structural imaging. Although there are various differences in protocols of positron emission tomography (PET), single photon emission computed tomography (SPECT), functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) methods, these may be important modalities to be used in conjunction with structural imaging in the future in order to detect and understand the pathophysiology of mild TBI. In this review, studies of mild TBI patients using these modalities that detect functional and metabolic state of the brain are discussed. Each modality's advantages and disadvantages are compared, and potential future applications of using combined modalities are explored.

Regional differences in brain glucose metabolism determined by imaging mass spectrometry.

PubMed

Kleinridders, André; Ferris, Heather A; Reyzer, Michelle L; Rath, Michaela; Soto, Marion; Manier, M Lisa; Spraggins, Jeffrey; Yang, Zhihong; Stanton, Robert C; Caprioli, Richard M; Kahn, C Ronald

2018-06-01

Glucose is the major energy substrate of the brain and crucial for normal brain function. In diabetes, the brain is subject to episodes of hypo- and hyperglycemia resulting in acute outcomes ranging from confusion to seizures, while chronic metabolic dysregulation puts patients at increased risk for depression and Alzheimer's disease. In the present study, we aimed to determine how glucose is metabolized in different regions of the brain using imaging mass spectrometry (IMS). To examine the relative abundance of glucose and other metabolites in the brain, mouse brain sections were subjected to imaging mass spectrometry at a resolution of 100 μm. This was correlated with immunohistochemistry, qPCR, western blotting and enzyme assays of dissected brain regions to determine the relative contributions of the glycolytic and pentose phosphate pathways to regional glucose metabolism. In brain, there are significant regional differences in glucose metabolism, with low levels of hexose bisphosphate (a glycolytic intermediate) and high levels of the pentose phosphate pathway (PPP) enzyme glucose-6-phosphate dehydrogenase (G6PD) and PPP metabolite hexose phosphate in thalamus compared to cortex. The ratio of ATP to ADP is significantly higher in white matter tracts, such as corpus callosum, compared to less myelinated areas. While the brain is able to maintain normal ratios of hexose phosphate, hexose bisphosphate, ATP, and ADP during fasting, fasting causes a large increase in cortical and hippocampal lactate. These data demonstrate the importance of direct measurement of metabolic intermediates to determine regional differences in brain glucose metabolism and illustrate the strength of imaging mass spectrometry for investigating the impact of changing metabolic states on brain function at a regional level with high resolution. Copyright © 2018 The Authors. Published by Elsevier GmbH.. All rights reserved.

Improving the convergence rate in affine registration of PET and SPECT brain images using histogram equalization.

PubMed

Salas-Gonzalez, D; Górriz, J M; Ramírez, J; Padilla, P; Illán, I A

2013-01-01

A procedure to improve the convergence rate for affine registration methods of medical brain images when the images differ greatly from the template is presented. The methodology is based on a histogram matching of the source images with respect to the reference brain template before proceeding with the affine registration. The preprocessed source brain images are spatially normalized to a template using a general affine model with 12 parameters. A sum of squared differences between the source images and the template is considered as objective function, and a Gauss-Newton optimization algorithm is used to find the minimum of the cost function. Using histogram equalization as a preprocessing step improves the convergence rate in the affine registration algorithm of brain images as we show in this work using SPECT and PET brain images.

Molecular brain imaging in the multimodality era

PubMed Central

Price, Julie C

2012-01-01

Multimodality molecular brain imaging encompasses in vivo visualization, evaluation, and measurement of cellular/molecular processes. Instrumentation and software developments over the past 30 years have fueled advancements in multimodality imaging platforms that enable acquisition of multiple complementary imaging outcomes by either combined sequential or simultaneous acquisition. This article provides a general overview of multimodality neuroimaging in the context of positron emission tomography as a molecular imaging tool and magnetic resonance imaging as a structural and functional imaging tool. Several image examples are provided and general challenges are discussed to exemplify complementary features of the modalities, as well as important strengths and weaknesses of combined assessments. Alzheimer's disease is highlighted, as this clinical area has been strongly impacted by multimodality neuroimaging findings that have improved understanding of the natural history of disease progression, early disease detection, and informed therapy evaluation. PMID:22434068

Thermal Image of Coffee-Seed Germ Obtained by Photoacoustic Microscopy

NASA Astrophysics Data System (ADS)

Domínguez-Pacheco, A.; Hernández Aguilar, C.; Cruz-Orea, Alfredo; Isaac Alemán, E.; Martínez Ortiz, E.

2013-09-01

Photoacoustic microscopy (PAM) has been shown to be a suitable technique to obtain thermal images of a wide variety of samples from semiconductors to biological material. In PAM, the incidence of a modulated laser beam on a sample within a photoacoustic (PA) cell, hermetically sealed, produces a PA signal which depends on the thermal and optical properties of the studied sample. By making a sweep of the modulated laser beam on the sample surface, it is possible to obtain the PA signal as a function of their x- y coordinates, and from this signal, it is possible to reconstruct thermal images of the sample. In this study, thermal images of a coffee-seed germ were obtained, with a difference of 12 h between them, by using the PAM technique. Thermal differences observed between images give information which reflects degradation due to the fact that germ cells undergo changes as a function of time. The thermal images obtained by the PAM technique could be applied to biological materials that have a complex constitution (not homogeneous) in their structures, and thermal differences can be observed. PAM is a non-destructive technique, which is an important feature for this type of study. Other applications of this technique can be performed in the agricultural and biotechnological areas.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Quantitative magnetic resonance imaging in traumatic brain injury.

PubMed

Bigler, E D

2001-04-01

Quantitative neuroimaging has now become a well-established method for analyzing magnetic resonance imaging in traumatic brain injury (TBI). A general review of studies that have examined quantitative changes following TBI is presented. The consensus of quantitative neuroimaging studies is that most brain structures demonstrate changes in volume or surface area after injury. The patterns of atrophy are consistent with the generalized nature of brain injury and diffuse axonal injury. Various clinical caveats are provided including how quantitative neuroimaging findings can be used clinically and in predicting rehabilitation outcome. The future of quantitative neuroimaging also is discussed.

High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

NASA Astrophysics Data System (ADS)

Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

2016-03-01

Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

Research on segmentation based on multi-atlas in brain MR image

NASA Astrophysics Data System (ADS)

Qian, Yuejing

2018-03-01

Accurate segmentation of specific tissues in brain MR image can be effectively achieved with the multi-atlas-based segmentation method, and the accuracy mainly depends on the image registration accuracy and fusion scheme. This paper proposes an automatic segmentation method based on the multi-atlas for brain MR image. Firstly, to improve the registration accuracy in the area to be segmented, we employ a target-oriented image registration method for the refinement. Then In the label fusion, we proposed a new algorithm to detect the abnormal sparse patch and simultaneously abandon the corresponding abnormal sparse coefficients, this method is made based on the remaining sparse coefficients combined with the multipoint label estimator strategy. The performance of the proposed method was compared with those of the nonlocal patch-based label fusion method (Nonlocal-PBM), the sparse patch-based label fusion method (Sparse-PBM) and majority voting method (MV). Based on our experimental results, the proposed method is efficient in the brain MR images segmentation compared with MV, Nonlocal-PBM, and Sparse-PBM methods.

In vivo High Angular Resolution Diffusion-Weighted Imaging of Mouse Brain at 16.4 Tesla

PubMed Central

Alomair, Othman I.; Brereton, Ian M.; Smith, Maree T.; Galloway, Graham J.; Kurniawan, Nyoman D.

2015-01-01

Magnetic Resonance Imaging (MRI) of the rodent brain at ultra-high magnetic fields (> 9.4 Tesla) offers a higher signal-to-noise ratio that can be exploited to reduce image acquisition time or provide higher spatial resolution. However, significant challenges are presented due to a combination of longer T 1 and shorter T 2/T2* relaxation times and increased sensitivity to magnetic susceptibility resulting in severe local-field inhomogeneity artefacts from air pockets and bone/brain interfaces. The Stejskal-Tanner spin echo diffusion-weighted imaging (DWI) sequence is often used in high-field rodent brain MRI due to its immunity to these artefacts. To accurately determine diffusion-tensor or fibre-orientation distribution, high angular resolution diffusion imaging (HARDI) with strong diffusion weighting (b >3000 s/mm2) and at least 30 diffusion-encoding directions are required. However, this results in long image acquisition times unsuitable for live animal imaging. In this study, we describe the optimization of HARDI acquisition parameters at 16.4T using a Stejskal-Tanner sequence with echo-planar imaging (EPI) readout. EPI segmentation and partial Fourier encoding acceleration were applied to reduce the echo time (TE), thereby minimizing signal decay and distortion artefacts while maintaining a reasonably short acquisition time. The final HARDI acquisition protocol was achieved with the following parameters: 4 shot EPI, b = 3000 s/mm2, 64 diffusion-encoding directions, 125×150 μm2 in-plane resolution, 0.6 mm slice thickness, and 2h acquisition time. This protocol was used to image a cohort of adult C57BL/6 male mice, whereby the quality of the acquired data was assessed and diffusion tensor imaging (DTI) derived parameters were measured. High-quality images with high spatial and angular resolution, low distortion and low variability in DTI-derived parameters were obtained, indicating that EPI-DWI is feasible at 16.4T to study animal models of white matter (WM

Reformatted images improve the detection rate of acute traumatic subdural hematomas on brain CT compared with axial images alone.

PubMed

Amrhein, Timothy J; Mostertz, William; Matheus, Maria Gisele; Maass-Bolles, Genevieve; Sharma, Komal; Collins, Heather R; Kranz, Peter G

2017-02-01

Subdural hematomas (SDHs) comprise a significant percentage of missed intracranial hemorrhage on axial brain CT. SDH detection rates could be improved with the addition of reformatted images. Though performed at some centers, the potential additional diagnostic sensitivity of reformatted images has not yet been investigated. The purpose of our study is to determine if the addition of coronal and sagittal reformatted images to an axial brain CT increases the sensitivity and specificity for detection of acute traumatic SDH. We retrospectively reviewed consecutive brain CTs acquired for acute trauma that contained new SDHs. An equivalent number of normal brain CTs served as control. Paired sets of images were created for each case: (1) axial images only ("axial only") and (2) axial, coronal, sagittal images ("reformat added"). Three readers interpreted both the axial only and companion reformat added for each case, separated by 1 month. Reading times and SDH detection rates were compared. One hundred SDH and 100 negative examinations were collected. Sensitivity and specificity for the axial-only scans were 75.7 and 94.3 %, respectively, compared with 88.3 and 98.3 % for reformat added. There was a 24.3 % false negative (missed SDH) rate with axial-only scans versus 11.7 % with reformat added (p = <0.001). Median reader interpretation times were longer with the addition of reformatted images (125 versus 89 s), but this difference was not significant (p = 0.23). The addition of coronal and sagittal images in trauma brain CT resulted in improved sensitivity and specificity as well as a reduction in SDH false negatives by greater than 50 %. Reformatted images substantially reduce the number of missed SDHs compared with axial images alone.

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

NASA Astrophysics Data System (ADS)

Jiji, G. Wiselin; Dehmeshki, Jamshid

2014-04-01

Tissue classification in computed tomography (CT) brain images is an important issue in the analysis of several brain dementias. A combination of different approaches for the segmentation of brain images is presented in this paper. A multi resolution algorithm is proposed along with scaled versions using Gaussian filter and wavelet analysis that extends expectation maximization (EM) algorithm. It is found that it is less sensitive to noise and got more accurate image segmentation than traditional EM. Moreover the algorithm has been applied on 20 sets of CT of the human brain and compared with other works. The segmentation results show the advantages of the proposed work have achieved more promising results and the results have been tested with Doctors.

Brain connectivity study of joint attention using frequency-domain optical imaging technique

NASA Astrophysics Data System (ADS)

Chaudhary, Ujwal; Zhu, Banghe; Godavarty, Anuradha

2010-02-01

Autism is a socio-communication brain development disorder. It is marked by degeneration in the ability to respond to joint attention skill task, from as early as 12 to 18 months of age. This trait is used to distinguish autistic from nonautistic populations. In this study, diffuse optical imaging is being used to study brain connectivity for the first time in response to joint attention experience in normal adults. The prefrontal region of the brain was non-invasively imaged using a frequency-domain based optical imager. The imaging studies were performed on 11 normal right-handed adults and optical measurements were acquired in response to joint-attention based video clips. While the intensity-based optical data provides information about the hemodynamic response of the underlying neural process, the time-dependent phase-based optical data has the potential to explicate the directional information on the activation of the brain. Thus brain connectivity studies are performed by computing covariance/correlations between spatial units using this frequency-domain based optical measurements. The preliminary results indicate that the extent of synchrony and directional variation in the pattern of activation varies in the left and right frontal cortex. The results have significant implication for research in neural pathways associated with autism that can be mapped using diffuse optical imaging tools in the future.

Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.

PubMed

Alfaro-Almagro, Fidel; Jenkinson, Mark; Bangerter, Neal K; Andersson, Jesper L R; Griffanti, Ludovica; Douaud, Gwenaëlle; Sotiropoulos, Stamatios N; Jbabdi, Saad; Hernandez-Fernandez, Moises; Vallee, Emmanuel; Vidaurre, Diego; Webster, Matthew; McCarthy, Paul; Rorden, Christopher; Daducci, Alessandro; Alexander, Daniel C; Zhang, Hui; Dragonu, Iulius; Matthews, Paul M; Miller, Karla L; Smith, Stephen M

2018-02-01

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation.

PubMed

al-Rifaie, Mohammad Majid; Aber, Ahmed; Hemanth, Duraiswamy Jude

2015-12-01

This study proposes an umbrella deployment of swarm intelligence algorithm, such as stochastic diffusion search for medical imaging applications. After summarising the results of some previous works which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of the aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this study is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed in the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to train the LVQ which eliminates the iteration-dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features

PubMed Central

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B.; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes. PMID:28731430

Neuroimaging Feature Terminology: A Controlled Terminology for the Annotation of Brain Imaging Features.

PubMed

Iyappan, Anandhi; Younesi, Erfan; Redolfi, Alberto; Vrooman, Henri; Khanna, Shashank; Frisoni, Giovanni B; Hofmann-Apitius, Martin

2017-01-01

Ontologies and terminologies are used for interoperability of knowledge and data in a standard manner among interdisciplinary research groups. Existing imaging ontologies capture general aspects of the imaging domain as a whole such as methodological concepts or calibrations of imaging instruments. However, none of the existing ontologies covers the diagnostic features measured by imaging technologies in the context of neurodegenerative diseases. Therefore, the Neuro-Imaging Feature Terminology (NIFT) was developed to organize the knowledge domain of measured brain features in association with neurodegenerative diseases by imaging technologies. The purpose is to identify quantitative imaging biomarkers that can be extracted from multi-modal brain imaging data. This terminology attempts to cover measured features and parameters in brain scans relevant to disease progression. In this paper, we demonstrate the systematic retrieval of measured indices from literature and how the extracted knowledge can be further used for disease modeling that integrates neuroimaging features with molecular processes.

[Exploring the dark continent: medical image and brain].

PubMed

Garcia-Molina, A; Ensenat, A

2017-04-01

Until the late 19th century, direct observation of the central nervous system was practically impossible. The discovery of X-rays in 1895 and their subsequent application in the field of medicine brought about a shift of paradigm that completely revolutionised the way in which neurology was practised. The possibility of viewing the inside of the brain had a pronounced impact on clinical practice, and enriched the diagnosis and treatment of brain pathologies in a manner that was unimaginable up until then. The aim of this study is to describe the birth and development of medical imaging of the brain, from the discovery of X-rays and the early days of radiography to the appearance of computerised tomography and magnetic resonance in the 60s, both of which are techniques that were to change the world of diagnostic imaging forever. This brief overview of the history of radiology also includes the origins of angiography and other techniques that are no longer in use, but which were ground-breaking innovations in their time, such as ventriculography or pneumoencephalography. The procedures and techniques described in this article made it possible to view the inside of the brain, thereby facilitating the diagnosis and treatment of a number of neurological processes.

3D surface rendered MR images of the brain and its vasculature.

PubMed

Cline, H E; Lorensen, W E; Souza, S P; Jolesz, F A; Kikinis, R; Gerig, G; Kennedy, T E

1991-01-01

Both time-of-flight and phase contrast magnetic resonance angiography images are combined with stationary tissue images to provide data depicting two contrast relationships yielding intrinsic discrimination of brain matter and flowing blood. A computer analysis is based on nearest neighbor segmentation and the connection between anatomical structures to partition the images into different tissue categories: from which, high resolution brain parenchymal and vascular surfaces are constructed and rendered in juxtaposition, aiding in surgical planning.

Automatic delineation of brain regions on MRI and PET images from the pig.

PubMed

Villadsen, Jonas; Hansen, Hanne D; Jørgensen, Louise M; Keller, Sune H; Andersen, Flemming L; Petersen, Ida N; Knudsen, Gitte M; Svarer, Claus

2018-01-15

The increasing use of the pig as a research model in neuroimaging requires standardized processing tools. For example, extraction of regional dynamic time series from brain PET images requires parcellation procedures that benefit from being automated. Manual inter-modality spatial normalization to a MRI atlas is operator-dependent, time-consuming, and can be inaccurate with lack of cortical radiotracer binding or skull uptake. A parcellated PET template that allows for automatic spatial normalization to PET images of any radiotracer. MRI and [ 11 C]Cimbi-36 PET scans obtained in sixteen pigs made the basis for the atlas. The high resolution MRI scans allowed for creation of an accurately averaged MRI template. By aligning the within-subject PET scans to their MRI counterparts, an averaged PET template was created in the same space. We developed an automatic procedure for spatial normalization of the averaged PET template to new PET images and hereby facilitated transfer of the atlas regional parcellation. Evaluation of the automatic spatial normalization procedure found the median voxel displacement to be 0.22±0.08mm using the MRI template with individual MRI images and 0.92±0.26mm using the PET template with individual [ 11 C]Cimbi-36 PET images. We tested the automatic procedure by assessing eleven PET radiotracers with different kinetics and spatial distributions by using perfusion-weighted images of early PET time frames. We here present an automatic procedure for accurate and reproducible spatial normalization and parcellation of pig PET images of any radiotracer with reasonable blood-brain barrier penetration. Copyright © 2017 Elsevier B.V. All rights reserved.

Brain tumor response to nimotuzumab treatment evaluated on magnetic resonance imaging.

PubMed

Dalmau, Evelio Rafael González; Cabal Mirabal, Carlos; Martínez, Giselle Saurez; Dávila, Agustín Lage; Suárez, José Carlos Ugarte; Cabanas Armada, Ricardo; Rodriguez Cruz, Gretel; Darias Zayas, Daniel; Castillo, Martha Ríos; Valle Garrido, Luis; Sotolongo, Luis Quevedo; Fernández, Mercedes Monzón

2014-02-01

Nimotuzumab, a humanized monoclonal antibody anti-epidermal growth factor receptor, has been shown to improve survival and quality of life in patients with pediatric malignant brain tumor. It is necessary, however, to increase the objective response criteria to define the optimal therapeutic schedule. The aim of this study was to obtain magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) quantitative information related to dimensions and morphology, molecular mobility and metabolic activity of the lesion and surroundings in order to evaluate any changes through time. Fourteen pediatric patients treated with nimotuzumab were evaluated on MRI and MRS for >2 years. Each patient was their own control. The MRI/MRS pulse sequence parameters were standardized to ensure experimental reproducibility. A total of 71.4% of patients had stable disease; 21.4% had objective response and 7.1% had progression of disease during the >2 year evaluation period. MRI/MRS data with clinical information provide a clearer picture of treatment response and confirm once again that nimotuzumab is effective in the treatment of pediatric brain tumor. These imaging procedures can be a useful tool for the clinical evaluation of study protocol in clinical practice. © 2013 The Authors. Pediatrics International © 2013 Japan Pediatric Society.

Added Value of Including Entire Brain on Body Imaging With FDG PET/MRI.

PubMed

Franceschi, Ana M; Matthews, Robert; Bangiyev, Lev; Relan, Nand; Chaudhry, Ammar; Franceschi, Dinko

2018-05-24

FDG PET/MRI examination of the body is routinely performed from the skull base to the mid thigh. Many types of brain abnormalities potentially could be detected on PET/MRI if the head was included. The objective of this study was therefore to identify and characterize brain findings incidentally detected on PET/MRI of the body with the head included. We retrospectively identified 269 patients with FDG PET/MRI whole-body scans that included the head. PET/MR images of the brain were reviewed by a nuclear medicine physician and neuroradiologist, first individually and then concurrently. Both PET and MRI findings were identified, including abnormal FDG uptake, standardized uptake value, lesion size, and MRI signal characteristics. For each patient, relevant medical history and prior imaging were reviewed. Of the 269 subjects, 173 were women and 96 were men (mean age, 57.4 years). Only the initial PET/MR image of each patient was reviewed. A total of 37 of the 269 patients (13.8%) had abnormal brain findings noted on the PET/MRI whole-body scan. Sixteen patients (5.9%) had vascular disease, nine patients (3.3%) had posttherapy changes, and two (0.7%) had benign cystic lesions in the brain. Twelve patients (4.5%) had serious nonvascular brain abnormalities, including cerebral metastasis in five patients and pituitary adenomas in two patients. Only nine subjects (3.3%) had a new neurologic or cognitive symptom suggestive of a brain abnormality. Routine body imaging with FDG PET/MRI of the area from the skull base to the mid thigh may miss important brain abnormalities when the head is not included. The additional brain abnormalities identified on whole-body imaging may provide added clinical value to the management of oncology patients.