Functional Magnetic Resonance Imaging and Spectroscopic Imaging of the Brain: Application of fMRI and fMRS to Reading Disabilities and Education.

ERIC Educational Resources Information Center

Richards, Todd L.

2001-01-01

This tutorial/review covers functional brain-imaging methods and results used to study language and reading disabilities. Although the emphasis is on magnetic resonance imaging and functional magnetic resonance spectroscopy, other imaging techniques are also discussed including positron emission tomography, electroencephalography,…

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

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.

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.

Use Your Head: Neuroscience Research and Teaching

ERIC Educational Resources Information Center

Hunter, William J.

2011-01-01

Brain science is a new and complex field. It has emerged with the application of new technologies for brain imaging like Magnetic Resonance Images (MRIs) and Computer Axial Tomography (CAT) scans. Since the brain is the site for learning, educators stand to benefit from this knowledge when it is applied to improving methods of teaching or…

Structural Brain Atlases: Design, Rationale, and Applications in Normal and Pathological Cohorts

PubMed Central

Mandal, Pravat K.; Mahajan, Rashima; Dinov, Ivo D.

2015-01-01

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer’s disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities. PMID:22647262

GRAPE: a graphical pipeline environment for image analysis in adaptive magnetic resonance imaging.

PubMed

Gabr, Refaat E; Tefera, Getaneh B; Allen, William J; Pednekar, Amol S; Narayana, Ponnada A

2017-03-01

We present a platform, GRAphical Pipeline Environment (GRAPE), to facilitate the development of patient-adaptive magnetic resonance imaging (MRI) protocols. GRAPE is an open-source project implemented in the Qt C++ framework to enable graphical creation, execution, and debugging of real-time image analysis algorithms integrated with the MRI scanner. The platform provides the tools and infrastructure to design new algorithms, and build and execute an array of image analysis routines, and provides a mechanism to include existing analysis libraries, all within a graphical environment. The application of GRAPE is demonstrated in multiple MRI applications, and the software is described in detail for both the user and the developer. GRAPE was successfully used to implement and execute three applications in MRI of the brain, performed on a 3.0-T MRI scanner: (i) a multi-parametric pipeline for segmenting the brain tissue and detecting lesions in multiple sclerosis (MS), (ii) patient-specific optimization of the 3D fluid-attenuated inversion recovery MRI scan parameters to enhance the contrast of brain lesions in MS, and (iii) an algebraic image method for combining two MR images for improved lesion contrast. GRAPE allows graphical development and execution of image analysis algorithms for inline, real-time, and adaptive MRI applications.

Advances in neuroimaging of traumatic brain injury and posttraumatic stress disorder

PubMed Central

Van Boven, Robert W.; Harrington, Greg S.; Hackney, David B.; Ebel, Andreas; Gauger, Grant; Bremner, J. Douglas; D’Esposito, Mark; Detre, John A.; Haacke, E. Mark; Jack, Clifford R.; Jagust, William J.; Le Bihan, Denis; Mathis, Chester A.; Mueller, Susanne; Mukherjee, Pratik; Schuff, Norbert; Chen, Anthony; Weiner, Michael W.

2011-01-01

Improved diagnosis and treatment of traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are needed for our military and veterans, their families, and society at large. Advances in brain imaging offer important biomarkers of structural, functional, and metabolic information concerning the brain. This article reviews the application of various imaging techniques to the clinical problems of TBI and PTSD. For TBI, we focus on findings and advances in neuroimaging that hold promise for better detection, characterization, and monitoring of objective brain changes in symptomatic patients with combat-related, closed-head brain injuries not readily apparent by standard computed tomography or conventional magnetic resonance imaging techniques. PMID:20104401

Potential application of a handheld confocal endomicroscope imaging system using a variety of fluorophores in experimental gliomas and normal brain.

PubMed

Martirosyan, Nikolay L; Georges, Joseph; Eschbacher, Jennifer M; Cavalcanti, Daniel D; Elhadi, Ali M; Abdelwahab, Mohammed G; Scheck, Adrienne C; Nakaji, Peter; Spetzler, Robert F; Preul, Mark C

2014-02-01

The authors sought to assess the feasibility of a handheld visible-wavelength confocal endomicroscope imaging system (Optiscan 5.1, Optiscan Pty., Ltd.) using a variety of rapid-acting fluorophores to provide histological information on gliomas, tumor margins, and normal brain in animal models. Mice (n = 25) implanted with GL261 cells were used to image fluorescein sodium (FNa), 5-aminolevulinic acid (5-ALA), acridine orange (AO), acriflavine (AF), and cresyl violet (CV). A U251 glioma xenograft model in rats (n = 5) was used to image sulforhodamine 101 (SR101). A swine (n = 3) model with AO was used to identify confocal features of normal brain. Images of normal brain, obvious tumor, and peritumoral zones were collected using the handheld confocal endomicroscope. Histological samples were acquired through biopsies from matched imaging areas. Samples were visualized with a benchtop confocal microscope. Histopathological features in corresponding confocal images and photomicrographs of H & E-stained tissues were reviewed. Fluorescence induced by FNa, 5-ALA, AO, AF, CV, and SR101 and detected with the confocal endomicroscope allowed interpretation of histological features. Confocal endomicroscopy revealed satellite tumor cells within peritumoral tissue, a definitive tumor border, and striking fluorescent cellular and subcellular structures. Fluorescence in various tumor regions correlated with standard histology and known tissue architecture. Characteristic features of different areas of normal brain were identified as well. Confocal endomicroscopy provided rapid histological information precisely related to the site of microscopic imaging with imaging characteristics of cells related to the unique labeling features of the fluorophores. Although experimental with further clinical trial validation required, these data suggest that intraoperative confocal imaging can help to distinguish normal brain from tumor and tumor margin and may have application in improving intraoperative decisions during resection of brain tumors.

Study on the application of MRF and the D-S theory to image segmentation of the human brain and quantitative analysis of the brain tissue

NASA Astrophysics Data System (ADS)

Guan, Yihong; Luo, Yatao; Yang, Tao; Qiu, Lei; Li, Junchang

2012-01-01

The features of the spatial information of Markov random field image was used in image segmentation. It can effectively remove the noise, and get a more accurate segmentation results. Based on the fuzziness and clustering of pixel grayscale information, we find clustering center of the medical image different organizations and background through Fuzzy cmeans clustering method. Then we find each threshold point of multi-threshold segmentation through two dimensional histogram method, and segment it. The features of fusing multivariate information based on the Dempster-Shafer evidence theory, getting image fusion and segmentation. This paper will adopt the above three theories to propose a new human brain image segmentation method. Experimental result shows that the segmentation result is more in line with human vision, and is of vital significance to accurate analysis and application of tissues.

Image guided constitutive modeling of the silicone brain phantom

NASA Astrophysics Data System (ADS)

Puzrin, Alexander; Skrinjar, Oskar; Ozan, Cem; Kim, Sihyun; Mukundan, Srinivasan

2005-04-01

The goal of this work is to develop reliable constitutive models of the mechanical behavior of the in-vivo human brain tissue for applications in neurosurgery. We propose to define the mechanical properties of the brain tissue in-vivo, by taking the global MR or CT images of a brain response to ventriculostomy - the relief of the elevated intracranial pressure. 3D image analysis translates these images into displacement fields, which by using inverse analysis allow for the constitutive models of the brain tissue to be developed. We term this approach Image Guided Constitutive Modeling (IGCM). The presented paper demonstrates performance of the IGCM in the controlled environment: on the silicone brain phantoms closely simulating the in-vivo brain geometry, mechanical properties and boundary conditions. The phantom of the left hemisphere of human brain was cast using silicon gel. An inflatable rubber membrane was placed inside the phantom to model the lateral ventricle. The experiments were carried out in a specially designed setup in a CT scanner with submillimeter isotropic voxels. The non-communicative hydrocephalus and ventriculostomy were simulated by consequently inflating and deflating the internal rubber membrane. The obtained images were analyzed to derive displacement fields, meshed, and incorporated into ABAQUS. The subsequent Inverse Finite Element Analysis (based on Levenberg-Marquardt algorithm) allowed for optimization of the parameters of the Mooney-Rivlin non-linear elastic model for the phantom material. The calculated mechanical properties were consistent with those obtained from the element tests, providing justification for the future application of the IGCM to in-vivo brain tissue.

New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury

DTIC Science & Technology

2016-04-01

the need for high power radio - frequency (RF) to saturate the electron spins. Addition- ally, as EPR frequencies are two orders of magnitude higher...Crozier S. Electromechanical design and construction of a rotating radio - frequency coil system for applications in magnetic resonance. IEEE Trans Biomed...1 Award Number: W81XWH- 11 -2-0076 TITLE: New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury PRINCIPAL

SAR and scan-time optimized 3D whole-brain double inversion recovery imaging at 7T.

PubMed

Pracht, Eberhard D; Feiweier, Thorsten; Ehses, Philipp; Brenner, Daniel; Roebroeck, Alard; Weber, Bernd; Stöcker, Tony

2018-05-01

The aim of this project was to implement an ultra-high field (UHF) optimized double inversion recovery (DIR) sequence for gray matter (GM) imaging, enabling whole brain coverage in short acquisition times ( ≈5 min, image resolution 1 mm 3 ). A 3D variable flip angle DIR turbo spin echo (TSE) sequence was optimized for UHF application. We implemented an improved, fast, and specific absorption rate (SAR) efficient TSE imaging module, utilizing improved reordering. The DIR preparation was tailored to UHF application. Additionally, fat artifacts were minimized by employing water excitation instead of fat saturation. GM images, covering the whole brain, were acquired in 7 min scan time at 1 mm isotropic resolution. SAR issues were overcome by using a dedicated flip angle calculation considering SAR and SNR efficiency. Furthermore, UHF related artifacts were minimized. The suggested sequence is suitable to generate GM images with whole-brain coverage at UHF. Due to the short total acquisition times and overall robustness, this approach can potentially enable DIR application in a routine setting and enhance lesion detection in neurological diseases. Magn Reson Med 79:2620-2628, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

In vivo multiphoton tomography and fluorescence lifetime imaging of human brain tumor tissue.

PubMed

Kantelhardt, Sven R; Kalasauskas, Darius; König, Karsten; Kim, Ella; Weinigel, Martin; Uchugonova, Aisada; Giese, Alf

2016-05-01

High resolution multiphoton tomography and fluorescence lifetime imaging differentiates glioma from adjacent brain in native tissue samples ex vivo. Presently, multiphoton tomography is applied in clinical dermatology and experimentally. We here present the first application of multiphoton and fluorescence lifetime imaging for in vivo imaging on humans during a neurosurgical procedure. We used a MPTflex™ Multiphoton Laser Tomograph (JenLab, Germany). We examined cultured glioma cells in an orthotopic mouse tumor model and native human tissue samples. Finally the multiphoton tomograph was applied to provide optical biopsies during resection of a clinical case of glioblastoma. All tissues imaged by multiphoton tomography were sampled and processed for conventional histopathology. The multiphoton tomograph allowed fluorescence intensity- and fluorescence lifetime imaging with submicron spatial resolution and 200 picosecond temporal resolution. Morphological fluorescence intensity imaging and fluorescence lifetime imaging of tumor-bearing mouse brains and native human tissue samples clearly differentiated tumor and adjacent brain tissue. Intraoperative imaging was found to be technically feasible. Intraoperative image quality was comparable to ex vivo examinations. To our knowledge we here present the first intraoperative application of high resolution multiphoton tomography and fluorescence lifetime imaging of human brain tumors in situ. It allowed in vivo identification and determination of cell density of tumor tissue on a cellular and subcellular level within seconds. The technology shows the potential of rapid intraoperative identification of native glioma tissue without need for tissue processing or staining.

Registration of 3D fetal neurosonography and MRI☆

PubMed Central

Kuklisova-Murgasova, Maria; Cifor, Amalia; Napolitano, Raffaele; Papageorghiou, Aris; Quaghebeur, Gerardine; Rutherford, Mary A.; Hajnal, Joseph V.; Noble, J. Alison; Schnabel, Julia A.

2013-01-01

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image. PMID:23969169

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 resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (Dice coefficient: 95.97%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.

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

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group

PubMed Central

Shenkin, Susan D.; Pernet, Cyril; Nichols, Thomas E.; Poline, Jean-Baptiste; Matthews, Paul M.; van der Lugt, Aad; Mackay, Clare; Lanyon, Linda; Mazoyer, Bernard; Boardman, James P.; Thompson, Paul M.; Fox, Nick; Marcus, Daniel S.; Sheikh, Aziz; Cox, Simon R.; Anblagan, Devasuda; Job, Dominic E.; Dickie, David Alexander; Rodriguez, David; Wardlaw, Joanna M.

2017-01-01

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining ‘normality’); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function. PMID:28232121

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group.

PubMed

Shenkin, Susan D; Pernet, Cyril; Nichols, Thomas E; Poline, Jean-Baptiste; Matthews, Paul M; van der Lugt, Aad; Mackay, Clare; Lanyon, Linda; Mazoyer, Bernard; Boardman, James P; Thompson, Paul M; Fox, Nick; Marcus, Daniel S; Sheikh, Aziz; Cox, Simon R; Anblagan, Devasuda; Job, Dominic E; Dickie, David Alexander; Rodriguez, David; Wardlaw, Joanna M

2017-06-01

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining 'normality'); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function. Copyright © 2017 Elsevier Inc. All rights reserved.

Perylene-diimide-based nanoparticles as highly efficient photoacoustic agents for deep brain tumor imaging in living mice

DOE PAGES

Fan, Quli; Cheng, Kai; Yang, Zhen; ...

2014-11-06

In order to promote preclinical and clinical applications of photoacoustic imaging, novel photoacoustic contrast agents are highly desired for molecular imaging of diseases, especially for deep tumor imaging. In this paper, perylene-3,4,9,10-tetracarboxylic diiimide-based near-infrared-absorptive organic nanoparticles are reported as an efficient agent for photoacoustic imaging of deep brain tumors in living mice with enhanced permeability and retention effect

Computational and mathematical methods in brain atlasing.

PubMed

Nowinski, Wieslaw L

2017-12-01

Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.

Automatic brain tissue segmentation based on graph filter.

PubMed

Kong, Youyong; Chen, Xiaopeng; Wu, Jiasong; Zhang, Pinzheng; Chen, Yang; Shu, Huazhong

2018-05-09

Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects. To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter. Firstly, an effective supervoxel method is employed to generate effective supervoxels for the 3D MRI image. Secondly, the supervoxels are classified into different types of tissues based on filtering of graph signals. The performance is evaluated on the BrainWeb 18 dataset and the Internet Brain Segmentation Repository (IBSR) 18 dataset. The proposed method achieves mean dice similarity coefficient (DSC) of 0.94, 0.92 and 0.90 for the segmentation of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) for BrainWeb 18 dataset, and mean DSC of 0.85, 0.87 and 0.57 for the segmentation of WM, GM and CSF for IBSR18 dataset. The proposed approach can well discriminate different types of brain tissues from the brain MRI image, which has high potential to be applied for clinical applications.

Traumatic Brain Injury Diffusion Magnetic Resonance Imaging Research Roadmap Development Project

DTIC Science & Technology

2010-10-01

Susceptibility- weighted MR imaging: a review of clinical applications in children . AJNR Am J Neuroradiol. 2008 Jan;29(1):9-17. Hou DJ, Tong KA, Ashwal S ...2005;33:184-194. Holshouser BA, Tong KA, Ashwal S . “Proton MR spectroscopic imaging depicts diffuse axonal injury in children with traumatic brain injury...Proton spectroscopy detected myoinositol in children with traumatic brain injury.” Pediatr Res 2004;56:630-638. Ashwal S , Holshouser B, Tong K, Serna T

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images.

PubMed

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S; Lin, Weili; Shen, Dinggang

2015-09-01

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Prediction of standard-dose brain PET image by using MRI and low-dose brain [18F]FDG PET images

PubMed Central

Kang, Jiayin; Gao, Yaozong; Shi, Feng; Lalush, David S.; Lin, Weili; Shen, Dinggang

2015-01-01

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [18F]FDG PET image by using a low-dose brain [18F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [18F]FDG PET image by low-dose brain [18F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [18F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [18F]FDG PET image and substantially enhanced image quality of low-dose brain [18F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [18F]FDG PET image using low-dose brain [18F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [18F]FDG PET can be well-predicted using MRI and low-dose brain [18F]FDG PET. PMID:26328979

Prediction of standard-dose brain PET image by using MRI and low-dose brain [{sup 18}F]FDG PET images

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

Kang, Jiayin; Gao, Yaozong; Shi, Feng

Purpose: Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient’s exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. Asmore » yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [{sup 18}F]FDG PET image by using a low-dose brain [{sup 18}F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. Methods: The authors employ a regression forest for predicting the standard-dose brain [{sup 18}F]FDG PET image by low-dose brain [{sup 18}F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [{sup 18}F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. Results: The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [{sup 18}F]FDG PET image and substantially enhanced image quality of low-dose brain [{sup 18}F]FDG PET image. Conclusions: In this paper, the authors propose a framework to generate standard-dose brain [{sup 18}F]FDG PET image using low-dose brain [{sup 18}F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [{sup 18}F]FDG PET can be well-predicted using MRI and low-dose brain [{sup 18}F]FDG PET.« less

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.

Rat brain digital stereotaxic white matter atlas with fine tract delineation in Paxinos space and its automated applications in DTI data analysis.

PubMed

Liang, Shengxiang; Wu, Shang; Huang, Qi; Duan, Shaofeng; Liu, Hua; Li, Yuxiao; Zhao, Shujun; Nie, Binbin; Shan, Baoci

2017-11-01

To automatically analyze diffusion tensor images of the rat brain via both voxel-based and ROI-based approaches, we constructed a new white matter atlas of the rat brain with fine tracts delineation in the Paxinos and Watson space. Unlike in previous studies, we constructed a digital atlas image from the latest edition of the Paxinos and Watson. This atlas contains 111 carefully delineated white matter fibers. A white matter network of rat brain based on anatomy was constructed by locating the intersection of all these tracts and recording the nuclei on the pathway of each white matter tract. Moreover, a compatible rat brain template from DTI images was created and standardized into the atlas space. To evaluate the automated application of the atlas in DTI data analysis, a group of rats with right-side middle cerebral artery occlusion (MCAO) and those without were enrolled in this study. The voxel-based analysis result shows that the brain region showing significant declines in signal in the MCAO rats was consistent with the occlusion position. We constructed a stereotaxic white matter atlas of the rat brain with fine tract delineation and a compatible template for the data analysis of DTI images of the rat brain. Copyright © 2017 Elsevier Inc. All rights reserved.

Magnetic Resonance Imaging with laser polarized 129Xe

NASA Astrophysics Data System (ADS)

Swanson, Scott D.; Rosen, Matthew S.; Agranoff, Bernard W.; Coulter, Kevin P.; Welsh, Robert C.; Chupp, Timothy E.

1998-01-01

Magnetic Resonance Imaging with laser-polarized 129Xe can be utilized to trace blood flow and perfusion in tissue for a variety of biomedical applications. Polarized xenon gas introduced in to the lungs dissolves in the blood and is transported to organs such as the brain where it accumulates in the tissue. Spectroscopic studies combined with imaging have been used to produce brain images of 129Xe in the rat head. This work establishes that nuclear polarization produced in the gas phases survives transport to the brain where it may be imaged. Increases in polarization and delivered volume of 129Xe will allow clinical measurements of regional blood flow.

The smartphone brain scanner: a portable real-time neuroimaging system.

PubMed

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system--Smartphone Brain Scanner--combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings.

Implantable self-reset CMOS image sensor and its application to hemodynamic response detection in living mouse brain

NASA Astrophysics Data System (ADS)

Yamaguchi, Takahiro; Takehara, Hiroaki; Sunaga, Yoshinori; Haruta, Makito; Motoyama, Mayumi; Ohta, Yasumi; Noda, Toshihiko; Sasagawa, Kiyotaka; Tokuda, Takashi; Ohta, Jun

2016-04-01

A self-reset pixel of 15 × 15 µm2 with high signal-to-noise ratio (effective peak SNR ≃64 dB) for an implantable image sensor has been developed for intrinsic signal detection arising from hemodynamic responses in a living mouse brain. For detecting local conversion between oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in brain tissues, an implantable imaging device was fabricated with our newly designed self-reset image sensor and orange light-emitting diodes (LEDs; λ = 605 nm). We demonstrated imaging of hemodynamic responses in the sensory cortical area accompanied by forelimb stimulation of a living mouse. The implantable imaging device for intrinsic signal detection is expected to be a powerful tool to measure brain activities in living animals used in behavioral analysis.

Fiber optic in vivo imaging in the mammalian nervous system

PubMed Central

Mehta, Amit D; Jung, Juergen C; Flusberg, Benjamin A; Schnitzer, Mark J

2010-01-01

The compact size, mechanical flexibility, and growing functionality of optical fiber and fiber optic devices are enabling several new modalities for imaging the mammalian nervous system in vivo. Fluorescence microendoscopy is a minimally invasive fiber modality that provides cellular resolution in deep brain areas. Diffuse optical tomography is a non-invasive modality that uses assemblies of fiber optic emitters and detectors on the cranium for volumetric imaging of brain activation. Optical coherence tomography is a sensitive interferometric imaging technique that can be implemented in a variety of fiber based formats and that might allow intrinsic optical detection of brain activity at a high resolution. Miniaturized fiber optic microscopy permits cellular level imaging in the brains of behaving animals. Together, these modalities will enable new uses of imaging in the intact nervous system for both research and clinical applications. PMID:15464896

Simulation study of a high performance brain PET system with dodecahedral geometry.

PubMed

Tao, Weijie; Chen, Gaoyu; Weng, Fenghua; Zan, Yunlong; Zhao, Zhixiang; Peng, Qiyu; Xu, Jianfeng; Huang, Qiu

2018-05-25

In brain imaging, the spherical PET system achieves the highest sensitivity when the solid angle is concerned. However it is not practical. In this work we designed an alternative sphere-like scanner, the dodecahedral scanner, which has a high sensitivity in imaging and a high feasibility to manufacture. We simulated this system and compared the performance with a few other dedicated brain PET systems. Monte Carlo simulations were conducted to generate data of the dedicated brain PET system with the dodecahedral geometry (11 regular pentagon detectors). The data were then reconstructed using the in-house developed software with the fully three-dimensional maximum-likelihood expectation maximization (3D-MLEM) algorithm. Results show that the proposed system has a high sensitivity distribution for the whole field of view (FOV). With a depth-of-interaction (DOI) resolution around 6.67 mm, the proposed system achieves the spatial resolution of 1.98 mm. Our simulation study also shows that the proposed system improves the image contrast and reduces noise compared with a few other dedicated brain PET systems. Finally, simulations with the Hoffman phantom show the potential application of the proposed system in clinical applications. In conclusion, the proposed dodecahedral PET system is potential for widespread applications in high-sensitivity, high-resolution PET imaging, to lower the injected dose. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

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.

Pulsed laser diode photoacoustic tomography (PLD-PAT) system for fast in vivo imaging of small animal brain

NASA Astrophysics Data System (ADS)

Upputuri, Paul Kumar; Kalva, Sandeep Kumar; Moothanchery, Mohesh; Pramanik, Manojit

2017-03-01

In recent years, high-repetition rate pulsed laser diode (PLD) was used as an alternative to the Nd:YAG lasers for photoacoustic tomography (PAT). The use of PLD makes the overall PAT system, a low-cost, portable, and high frame rate imaging tool for preclinical applications. In this work, we will present a portable in vivo pulsed laser diode based photoacoustic tomography (PLD-PAT) system. The PLD is integrated inside a circular scanning geometry. The PLD can provide near-infrared ( 803 nm) pulses with pulse duration 136 ns, and pulse energy 1.4 mJ / pulse at 7 kHz repetition rate. The system will be demonstrated for in vivo fast imaging of small animal brain. To enhance the contrast of brain imaging, experiments will be carried out using contrast agents which have strong absorption around laser excitation wavelength. This low-cost, portable small animal brain imaging system could be very useful for brain tumor imaging and therapy.

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

A fast image registration approach of neural activities in light-sheet fluorescence microscopy images

NASA Astrophysics Data System (ADS)

Meng, Hui; Hui, Hui; Hu, Chaoen; Yang, Xin; Tian, Jie

2017-03-01

The ability of fast and single-neuron resolution imaging of neural activities enables light-sheet fluorescence microscopy (LSFM) as a powerful imaging technique in functional neural connection applications. The state-of-art LSFM imaging system can record the neuronal activities of entire brain for small animal, such as zebrafish or C. elegans at single-neuron resolution. However, the stimulated and spontaneous movements in animal brain result in inconsistent neuron positions during recording process. It is time consuming to register the acquired large-scale images with conventional method. In this work, we address the problem of fast registration of neural positions in stacks of LSFM images. This is necessary to register brain structures and activities. To achieve fast registration of neural activities, we present a rigid registration architecture by implementation of Graphics Processing Unit (GPU). In this approach, the image stacks were preprocessed on GPU by mean stretching to reduce the computation effort. The present image was registered to the previous image stack that considered as reference. A fast Fourier transform (FFT) algorithm was used for calculating the shift of the image stack. The calculations for image registration were performed in different threads while the preparation functionality was refactored and called only once by the master thread. We implemented our registration algorithm on NVIDIA Quadro K4200 GPU under Compute Unified Device Architecture (CUDA) programming environment. The experimental results showed that the registration computation can speed-up to 550ms for a full high-resolution brain image. Our approach also has potential to be used for other dynamic image registrations in biomedical applications.

Interactive brain shift compensation using GPU based programming

NASA Astrophysics Data System (ADS)

van der Steen, Sander; Noordmans, Herke Jan; Verdaasdonk, Rudolf

2009-02-01

Processing large images files or real-time video streams requires intense computational power. Driven by the gaming industry, the processing power of graphic process units (GPUs) has increased significantly. With the pixel shader model 4.0 the GPU can be used for image processing 10x faster than the CPU. Dedicated software was developed to deform 3D MR and CT image sets for real-time brain shift correction during navigated neurosurgery using landmarks or cortical surface traces defined by the navigation pointer. Feedback was given using orthogonal slices and an interactively raytraced 3D brain image. GPU based programming enables real-time processing of high definition image datasets and various applications can be developed in medicine, optics and image sciences.

If the skull fits: magnetic resonance imaging and microcomputed tomography for combined analysis of brain and skull phenotypes in the mouse

PubMed Central

Blank, Marissa C.; Roman, Brian B.; Henkelman, R. Mark; Millen, Kathleen J.

2012-01-01

The mammalian brain and skull develop concurrently in a coordinated manner, consistently producing a brain and skull that fit tightly together. It is common that abnormalities in one are associated with related abnormalities in the other. However, this is not always the case. A complete characterization of the relationship between brain and skull phenotypes is necessary to understand the mechanisms that cause them to be coordinated or divergent and to provide perspective on the potential diagnostic or prognostic significance of brain and skull phenotypes. We demonstrate the combined use of magnetic resonance imaging and microcomputed tomography for analysis of brain and skull phenotypes in the mouse. Co-registration of brain and skull images allows comparison of the relationship between phenotypes in the brain and those in the skull. We observe a close fit between the brain and skull of two genetic mouse models that both show abnormal brain and skull phenotypes. Application of these three-dimensional image analyses in a broader range of mouse mutants will provide a map of the relationships between brain and skull phenotypes generally and allow characterization of patterns of similarities and differences. PMID:22947655

Advanced neuroimaging techniques for the term newborn with encephalopathy.

PubMed

Chau, Vann; Poskitt, Kenneth John; Miller, Steven Paul

2009-03-01

Neonatal encephalopathy is associated with a high risk of morbidity and mortality in the neonatal period and of long-term neurodevelopmental disability in survivors. Advanced magnetic resonance techniques now play a major role in the clinical care of newborns with encephalopathy and in research addressing this important condition. From conventional magnetic resonance imaging, typical patterns of injury have been defined in neonatal encephalopathy. When applied in contemporary cohorts of newborns with encephalopathy, the patterns of brain injury on magnetic resonance imaging distinguish risk factors, clinical presentation, and risk of abnormal outcome. Advanced magnetic resonance techniques such as magnetic resonance spectroscopy, diffusion-weighted imaging, and diffusion tensor imaging provide novel perspectives on neonatal brain metabolism, microstructure, and connectivity. With the application of these imaging tools, it is increasingly apparent that brain injury commonly occurs at or near the time of birth and evolves over the first weeks of life. These observations have complemented findings from trials of emerging strategies of brain protection, such as hypothermia. Application of these advanced magnetic resonance techniques may enable the earliest possible identification of newborns at risk of neurodevelopmental impairment, thereby ensuring appropriate follow-up with rehabilitation and psychoeducational resources.

Toward a workbench for rodent brain image data: systems architecture and design.

PubMed

Moene, Ivar A; Subramaniam, Shankar; Darin, Dmitri; Leergaard, Trygve B; Bjaalie, Jan G

2007-01-01

We present a novel system for storing and manipulating microscopic images from sections through the brain and higher-level data extracted from such images. The system is designed and built on a three-tier paradigm and provides the research community with a web-based interface for facile use in neuroscience research. The Oracle relational database management system provides the ability to store a variety of objects relevant to the images and provides the framework for complex querying of data stored in the system. Further, the suite of applications intimately tied into the infrastructure in the application layer provide the user the ability not only to query and visualize the data, but also to perform analysis operations based on the tools embedded into the system. The presentation layer uses extant protocols of the modern web browser and this provides ease of use of the system. The present release, named Functional Anatomy of the Cerebro-Cerebellar System (FACCS), available through The Rodent Brain Workbench (http:// rbwb.org/), is targeted at the functional anatomy of the cerebro-cerebellar system in rats, and holds axonal tracing data from these projections. The system is extensible to other circuits and projections and to other categories of image data and provides a unique environment for analysis of rodent brain maps in the context of anatomical data. The FACCS application assumes standard animal brain atlas models and can be extended to future models. The system is available both for interactive use from a remote web-browser client as well as for download to a local server machine.

Bedside functional brain imaging in critically-ill children using high-density EEG source modeling and multi-modal sensory stimulation.

PubMed

Eytan, Danny; Pang, Elizabeth W; Doesburg, Sam M; Nenadovic, Vera; Gavrilovic, Bojan; Laussen, Peter; Guerguerian, Anne-Marie

2016-01-01

Acute brain injury is a common cause of death and critical illness in children and young adults. Fundamental management focuses on early characterization of the extent of injury and optimizing recovery by preventing secondary damage during the days following the primary injury. Currently, bedside technology for measuring neurological function is mainly limited to using electroencephalography (EEG) for detection of seizures and encephalopathic features, and evoked potentials. We present a proof of concept study in patients with acute brain injury in the intensive care setting, featuring a bedside functional imaging set-up designed to map cortical brain activation patterns by combining high density EEG recordings, multi-modal sensory stimulation (auditory, visual, and somatosensory), and EEG source modeling. Use of source-modeling allows for examination of spatiotemporal activation patterns at the cortical region level as opposed to the traditional scalp potential maps. The application of this system in both healthy and brain-injured participants is demonstrated with modality-specific source-reconstructed cortical activation patterns. By combining stimulation obtained with different modalities, most of the cortical surface can be monitored for changes in functional activation without having to physically transport the subject to an imaging suite. The results in patients in an intensive care setting with anatomically well-defined brain lesions suggest a topographic association between their injuries and activation patterns. Moreover, we report the reproducible application of a protocol examining a higher-level cortical processing with an auditory oddball paradigm involving presentation of the patient's own name. This study reports the first successful application of a bedside functional brain mapping tool in the intensive care setting. This application has the potential to provide clinicians with an additional dimension of information to manage critically-ill children and adults, and potentially patients not suited for magnetic resonance imaging technologies.

Interactive 3D visualization of structural changes in the brain of a person with corticobasal syndrome

PubMed Central

Hänel, Claudia; Pieperhoff, Peter; Hentschel, Bernd; Amunts, Katrin; Kuhlen, Torsten

2014-01-01

The visualization of the progression of brain tissue loss in neurodegenerative diseases like corticobasal syndrome (CBS) can provide not only information about the localization and distribution of the volume loss, but also helps to understand the course and the causes of this neurodegenerative disorder. The visualization of such medical imaging data is often based on 2D sections, because they show both internal and external structures in one image. Spatial information, however, is lost. 3D visualization of imaging data is capable to solve this problem, but it faces the difficulty that more internally located structures may be occluded by structures near the surface. Here, we present an application with two designs for the 3D visualization of the human brain to address these challenges. In the first design, brain anatomy is displayed semi-transparently; it is supplemented by an anatomical section and cortical areas for spatial orientation, and the volumetric data of volume loss. The second design is guided by the principle of importance-driven volume rendering: A direct line-of-sight to the relevant structures in the deeper parts of the brain is provided by cutting out a frustum-like piece of brain tissue. The application was developed to run in both, standard desktop environments and in immersive virtual reality environments with stereoscopic viewing for improving the depth perception. We conclude, that the presented application facilitates the perception of the extent of brain degeneration with respect to its localization and affected regions. PMID:24847243

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

The Smartphone Brain Scanner: A Portable Real-Time Neuroimaging System

PubMed Central

Stopczynski, Arkadiusz; Stahlhut, Carsten; Larsen, Jakob Eg; Petersen, Michael Kai; Hansen, Lars Kai

2014-01-01

Combining low-cost wireless EEG sensors with smartphones offers novel opportunities for mobile brain imaging in an everyday context. Here we present the technical details and validation of a framework for building multi-platform, portable EEG applications with real-time 3D source reconstruction. The system – Smartphone Brain Scanner – combines an off-the-shelf neuroheadset or EEG cap with a smartphone or tablet, and as such represents the first fully portable system for real-time 3D EEG imaging. We discuss the benefits and challenges, including technical limitations as well as details of real-time reconstruction of 3D images of brain activity. We present examples of brain activity captured in a simple experiment involving imagined finger tapping, which shows that the acquired signal in a relevant brain region is similar to that obtained with standard EEG lab equipment. Although the quality of the signal in a mobile solution using an off-the-shelf consumer neuroheadset is lower than the signal obtained using high-density standard EEG equipment, we propose mobile application development may offset the disadvantages and provide completely new opportunities for neuroimaging in natural settings. PMID:24505263

Segmentation of human brain using structural MRI.

PubMed

Helms, Gunther

2016-04-01

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

Swept-source optical coherence tomography powered by a 1.3-μm vertical cavity surface emitting laser enables 2.3-mm-deep brain imaging in mice in vivo

NASA Astrophysics Data System (ADS)

Choi, Woo June; Wang, Ruikang K.

2015-10-01

We report noninvasive, in vivo optical imaging deep within a mouse brain by swept-source optical coherence tomography (SS-OCT), enabled by a 1.3-μm vertical cavity surface emitting laser (VCSEL). VCSEL SS-OCT offers a constant signal sensitivity of 105 dB throughout an entire depth of 4.25 mm in air, ensuring an extended usable imaging depth range of more than 2 mm in turbid biological tissue. Using this approach, we show deep brain imaging in mice with an open-skull cranial window preparation, revealing intact mouse brain anatomy from the superficial cerebral cortex to the deep hippocampus. VCSEL SS-OCT would be applicable to small animal studies for the investigation of deep tissue compartments in living brains where diseases such as dementia and tumor can take their toll.

A high-definition fiber tracking report for patients with traumatic brain injury and their doctors.

PubMed

Chmura, Jon; Presson, Nora; Benso, Steven; Puccio, Ava M; Fissel, Katherine; Hachey, Rebecca; Braun, Emily; Okonkwo, David O; Schneider, Walter

2015-03-01

We have developed a tablet-based application, the High-Definition Fiber Tracking Report App, to enable clinicians and patients in research studies to see and understand damage from Traumatic Brain Injury (TBI) by viewing 2-dimensional and 3-dimensional images of their brain, with a focus on white matter tracts with quantitative metrics. The goal is to visualize white matter fiber tract injury like bone fractures; that is, to make the "invisible wounds of TBI" understandable for patients. Using mobile computing technology (iPad), imaging data for individual patients can be downloaded remotely within hours of a magnetic resonance imaging brain scan. Clinicians and patients can view the data in the form of images of each tract, rotating animations of the tracts, 3-dimensional models, and graphics. A growing number of tracts can be examined for asymmetry, gaps in streamline coverage, reduced arborization (branching), streamline volume, and standard quantitative metrics (e.g., Fractional Anisotropy (FA)). Novice users can learn to effectively navigate and interact with the application (explain the figures and graphs representing normal and injured brain tracts) within 15 minutes of simple orientation with high accuracy (96%). The architecture supports extensive graphics, configurable reports, provides an easy-to-use, attractive interface with a smooth user experience, and allows for securely serving cases from a database. Patients and clinicians have described the application as providing dramatic benefits in understanding their TBI and improving their lives. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Reproducibility of Ultrasound-Guided High Intensity Focused Ultrasound (HIFU) Thermal Lesions in Minimally-Invasive Brain Surgery

NASA Astrophysics Data System (ADS)

Zahedi, Sulmaz

This study aims to prove the feasibility of using Ultrasound-Guided High Intensity Focused Ultrasound (USg-HIFU) to create thermal lesions in neurosurgical applications, allowing for precise ablation of brain tissue, while simultaneously providing real time imaging. To test the feasibility of the system, an optically transparent HIFU compatible tissue-mimicking phantom model was produced. USg-HIFU was then used for ablation of the phantom, with and without targets. Finally, ex vivo lamb brain tissue was imaged and ablated using the USg-HIFU system. Real-time ultrasound images and videos obtained throughout the ablation process showing clear lesion formation at the focal point of the HIFU transducer. Post-ablation gross and histopathology examinations were conducted to verify thermal and mechanical damage in the ex vivo lamb brain tissue. Finally, thermocouple readings were obtained, and HIFU field computer simulations were conducted to verify findings. Results of the study concluded reproducibility of USg-HIFU thermal lesions for neurosurgical applications.

Applications of Ultrasound in the Resection of Brain Tumors

PubMed Central

Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve; Frisken, Sarah; Kapur, Tina; Wells, William; Golby, Alexandra J.

2016-01-01

Neurosurgery makes use of pre-operative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of pre-operative imaging for neuronavigation, however, is diminished by the well characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography have dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies. PMID:27541694

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.

The Owner's Manual for the Brain: Everyday Applications from Mind-Brain Research. Second Edition.

ERIC Educational Resources Information Center

Howard, Pierce J.

This book discusses what is known about the brain and memory storage and how people can improve their recall of information. There are 10 parts with 37 chapters. Part 1, "Forming a Foundation: The Context for Using Your Owner's Manual," includes topics like brain basics and brain imaging. Part 2, "Wellness: Getting the Most Out of…

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.

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

Adaptive optical microscope for brain imaging in vivo

NASA Astrophysics Data System (ADS)

Wang, Kai

2017-04-01

The optical heterogeneity of biological tissue imposes a major limitation to acquire detailed structural and functional information deep in the biological specimens using conventional microscopes. To restore optimal imaging performance, we developed an adaptive optical microscope based on direct wavefront sensing technique. This microscope can reliably measure and correct biological samples induced aberration. We demonstrated its performance and application in structural and functional brain imaging in various animal models, including fruit fly, zebrafish and mouse.

A simple rapid process for semi-automated brain extraction from magnetic resonance images of the whole mouse head.

PubMed

Delora, Adam; Gonzales, Aaron; Medina, Christopher S; Mitchell, Adam; Mohed, Abdul Faheem; Jacobs, Russell E; Bearer, Elaine L

2016-01-15

Magnetic resonance imaging (MRI) is a well-developed technique in neuroscience. Limitations in applying MRI to rodent models of neuropsychiatric disorders include the large number of animals required to achieve statistical significance, and the paucity of automation tools for the critical early step in processing, brain extraction, which prepares brain images for alignment and voxel-wise statistics. This novel timesaving automation of template-based brain extraction ("skull-stripping") is capable of quickly and reliably extracting the brain from large numbers of whole head images in a single step. The method is simple to install and requires minimal user interaction. This method is equally applicable to different types of MR images. Results were evaluated with Dice and Jacquard similarity indices and compared in 3D surface projections with other stripping approaches. Statistical comparisons demonstrate that individual variation of brain volumes are preserved. A downloadable software package not otherwise available for extraction of brains from whole head images is included here. This software tool increases speed, can be used with an atlas or a template from within the dataset, and produces masks that need little further refinement. Our new automation can be applied to any MR dataset, since the starting point is a template mask generated specifically for that dataset. The method reliably and rapidly extracts brain images from whole head images, rendering them useable for subsequent analytical processing. This software tool will accelerate the exploitation of mouse models for the investigation of human brain disorders by MRI. Copyright © 2015 Elsevier B.V. All rights reserved.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

PubMed Central

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

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.

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

PubMed

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

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.

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.

A framework for correcting brain retraction based on an eXtended Finite Element Method using a laser range scanner.

PubMed

Li, Ping; Wang, Weiwei; Song, Zhijian; An, Yong; Zhang, Chenxi

2014-07-01

Brain retraction causes great distortion that limits the accuracy of an image-guided neurosurgery system that uses preoperative images. Therefore, brain retraction correction is an important intraoperative clinical application. We used a linear elastic biomechanical model, which deforms based on the eXtended Finite Element Method (XFEM) within a framework for brain retraction correction. In particular, a laser range scanner was introduced to obtain a surface point cloud of the exposed surgical field including retractors inserted into the brain. A brain retraction surface tracking algorithm converted these point clouds into boundary conditions applied to XFEM modeling that drive brain deformation. To test the framework, we performed a brain phantom experiment involving the retraction of tissue. Pairs of the modified Hausdorff distance between Canny edges extracted from model-updated images, pre-retraction, and post-retraction CT images were compared to evaluate the morphological alignment of our framework. Furthermore, the measured displacements of beads embedded in the brain phantom and the predicted ones were compared to evaluate numerical performance. The modified Hausdorff distance of 19 pairs of images decreased from 1.10 to 0.76 mm. The forecast error of 23 stainless steel beads in the phantom was between 0 and 1.73 mm (mean 1.19 mm). The correction accuracy varied between 52.8 and 100 % (mean 81.4 %). The results demonstrate that the brain retraction compensation can be incorporated intraoperatively into the model-updating process in image-guided neurosurgery systems.

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

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.

Classification of Non-Time-Locked Rapid Serial Visual Presentation Events for Brain-Computer Interaction Using Deep Learning

DTIC Science & Technology

2014-07-08

internction ( BCI ) system allows h uman subjects to communicate with or control an extemal device with their brain signals [1], or to use those brain...signals to interact with computers, environments, or even other humans [2]. One application of BCI is to use brnin signals to distinguish target...images within a large collection of non-target images [2]. Such BCI -based systems can drastically increase the speed of target identification in

Real-time functional magnetic imaging-brain-computer interface and virtual reality promising tools for the treatment of pedophilia.

PubMed

Renaud, Patrice; Joyal, Christian; Stoleru, Serge; Goyette, Mathieu; Weiskopf, Nikolaus; Birbaumer, Niels

2011-01-01

This chapter proposes a prospective view on using a real-time functional magnetic imaging (rt-fMRI) brain-computer interface (BCI) application as a new treatment for pedophilia. Neurofeedback mediated by interactive virtual stimuli is presented as the key process in this new BCI application. Results on the diagnostic discriminant power of virtual characters depicting sexual stimuli relevant to pedophilia are given. Finally, practical and ethical implications are briefly addressed. Copyright © 2011 Elsevier B.V. All rights reserved.

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.

Semiautomatic Segmentation of Glioma on Mobile Devices.

PubMed

Wu, Ya-Ping; Lin, Yu-Song; Wu, Wei-Guo; Yang, Cong; Gu, Jian-Qin; Bai, Yan; Wang, Mei-Yun

2017-01-01

Brain tumor segmentation is the first and the most critical step in clinical applications of radiomics. However, segmenting brain images by radiologists is labor intense and prone to inter- and intraobserver variability. Stable and reproducible brain image segmentation algorithms are thus important for successful tumor detection in radiomics. In this paper, we propose a supervised brain image segmentation method, especially for magnetic resonance (MR) brain images with glioma. This paper uses hard edge multiplicative intrinsic component optimization to preprocess glioma medical image on the server side, and then, the doctors could supervise the segmentation process on mobile devices in their convenient time. Since the preprocessed images have the same brightness for the same tissue voxels, they have small data size (typically 1/10 of the original image size) and simple structure of 4 types of intensity value. This observation thus allows follow-up steps to be processed on mobile devices with low bandwidth and limited computing performance. Experiments conducted on 1935 brain slices from 129 patients show that more than 30% of the sample can reach 90% similarity; over 60% of the samples can reach 85% similarity, and more than 80% of the sample could reach 75% similarity. The comparisons with other segmentation methods also demonstrate both efficiency and stability of the proposed approach.

Applications of wavelets in morphometric analysis of medical images

NASA Astrophysics Data System (ADS)

Davatzikos, Christos; Tao, Xiaodong; Shen, Dinggang

2003-11-01

Morphometric analysis of medical images is playing an increasingly important role in understanding brain structure and function, as well as in understanding the way in which these change during development, aging and pathology. This paper presents three wavelet-based methods with related applications in morphometric analysis of magnetic resonance (MR) brain images. The first method handles cases where very limited datasets are available for the training of statistical shape models in the deformable segmentation. The method is capable of capturing a larger range of shape variability than the standard active shape models (ASMs) can, by using the elegant spatial-frequency decomposition of the shape contours provided by wavelet transforms. The second method addresses the difficulty of finding correspondences in anatomical images, which is a key step in shape analysis and deformable registration. The detection of anatomical correspondences is completed by using wavelet-based attribute vectors as morphological signatures of voxels. The third method uses wavelets to characterize the morphological measurements obtained from all voxels in a brain image, and the entire set of wavelet coefficients is further used to build a brain classifier. Since the classification scheme operates in a very-high-dimensional space, it can determine subtle population differences with complex spatial patterns. Experimental results are provided to demonstrate the performance of the proposed methods.

Tracking short-term biodistribution and long-term clearance of SPIO tracers in magnetic particle imaging

NASA Astrophysics Data System (ADS)

Keselman, Paul; Yu, Elaine Y.; Zhou, Xinyi Y.; Goodwill, Patrick W.; Chandrasekharan, Prashant; Ferguson, R. Matthew; Khandhar, Amit P.; Kemp, Scott J.; Krishnan, Kannan M.; Zheng, Bo; Conolly, Steven M.

2017-05-01

Magnetic particle imaging (MPI) is an emerging tracer-based medical imaging modality that images non-radioactive, kidney-safe superparamagnetic iron oxide (SPIO) tracers. MPI offers quantitative, high-contrast and high-SNR images, so MPI has exceptional promise for applications such as cell tracking, angiography, brain perfusion, cancer detection, traumatic brain injury and pulmonary imaging. In assessing MPI’s utility for applications mentioned above, it is important to be able to assess tracer short-term biodistribution as well as long-term clearance from the body. Here, we describe the biodistribution and clearance for two commonly used tracers in MPI: Ferucarbotran (Meito Sangyo Co., Japan) and LS-oo8 (LodeSpin Labs, Seattle, WA). We successfully demonstrate that 3D MPI is able to quantitatively assess short-term biodistribution, as well as long-term tracking and clearance of these tracers in vivo.

The Implications of Social Neuroscience for Social Disability

ERIC Educational Resources Information Center

McPartland, James C.; Pelphrey, Kevin A.

2012-01-01

Social disability represents a unifying feature in the diverse group of individuals with autism spectrum disorder (ASD). Social neuroscience is the study of brain mechanisms supporting interpersonal interaction. In this paper, we review brain imaging studies of the social brain and highlight practical applications of these scientific insights.…

Medical diagnosis imaging systems: image and signal processing applications aided by fuzzy logic

NASA Astrophysics Data System (ADS)

Hata, Yutaka

2010-04-01

First, we describe an automated procedure for segmenting an MR image of a human brain based on fuzzy logic for diagnosing Alzheimer's disease. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram. After these thresholds are evaluated in a region growing, the whole brain can be identified. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Our method then identified the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. Secondly, we describe a transskull sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and some brain diseases. We employ fuzzy signal processing to determine the skull and brain surface. The phantom model, the animal model with soft tissue, the animal model with brain tissue, and a human subjects' forehead is applied in our system. The all shapes of the skin surface, skull surface, skull bottom, and brain tissue surface are successfully determined.

Planar implantable sensor for in vivo measurement of cellular oxygen metabolism in brain tissue.

PubMed

Tsytsarev, Vassiliy; Akkentli, Fatih; Pumbo, Elena; Tang, Qinggong; Chen, Yu; Erzurumlu, Reha S; Papkovsky, Dmitri B

2017-04-01

Brain imaging methods are continually improving. Imaging of the cerebral cortex is widely used in both animal experiments and charting human brain function in health and disease. Among the animal models, the rodent cerebral cortex has been widely used because of patterned neural representation of the whiskers on the snout and relative ease of activating cortical tissue with whisker stimulation. We tested a new planar solid-state oxygen sensor comprising a polymeric film with a phosphorescent oxygen-sensitive coating on the working side, to monitor dynamics of oxygen metabolism in the cerebral cortex following sensory stimulation. Sensory stimulation led to changes in oxygenation and deoxygenation processes of activated areas in the barrel cortex. We demonstrate the possibility of dynamic mapping of relative changes in oxygenation in live mouse brain tissue with such a sensor. Oxygenation-based functional magnetic resonance imaging (fMRI) is very effective method for functional brain mapping but have high costs and limited spatial resolution. Optical imaging of intrinsic signal (IOS) does not provide the required sensitivity, and voltage-sensitive dye optical imaging (VSDi) has limited applicability due to significant toxicity of the voltage-sensitive dye. Our planar solid-state oxygen sensor imaging approach circumvents these limitations, providing a simple optical contrast agent with low toxicity and rapid application. The planar solid-state oxygen sensor described here can be used as a tool in visualization and real-time analysis of sensory-evoked neural activity in vivo. Further, this approach allows visualization of local neural activity with high temporal and spatial resolution. Copyright © 2017 Elsevier B.V. All rights reserved.

A simple water-immersion condenser for imaging living brain slices on an inverted microscope.

PubMed

Prusky, G T

1997-09-05

Due to some physical limitations of conventional condensers, inverted compound microscopes are not optimally suited for imaging living brain slices with transmitted light. Herein is described a simple device that converts an inverted microscope into an effective tool for this application by utilizing an objective as a condenser. The device is mounted on a microscope in place of the condenser, is threaded to accept a water immersion objective, and has a slot for a differential interference contrast (DIC) slider. When combined with infrared video techniques, this device allows an inverted microscope to effectively image living cells within thick brain slices in an open perfusion chamber.

Diffusion-Weighted Imaging Outside the Brain: Consensus Statement From an ISMRM-Sponsored Workshop

PubMed Central

Taouli, Bachir; Beer, Ambros J.; Chenevert, Thomas; Collins, David; Lehman, Constance; Matos, Celso; Padhani, Anwar R.; Rosenkrantz, Andrew B.; Shukla-Dave, Amita; Sigmund, Eric; Tanenbaum, Lawrence; Thoeny, Harriet; Thomassin-Naggara, Isabelle; Barbieri, Sebastiano; Corcuera-Solano, Idoia; Orton, Matthew; Partridge, Savannah C.; Koh, Dow-Mu

2016-01-01

The significant advances in magnetic resonance imaging (MRI) hardware and software, sequence design, and postprocessing methods have made diffusion-weighted imaging (DWI) an important part of body MRI protocols and have fueled extensive research on quantitative diffusion outside the brain, particularly in the oncologic setting. In this review, we summarize the most up-to-date information on DWI acquisition and clinical applications outside the brain, as discussed in an ISMRM-sponsored symposium held in April 2015. We first introduce recent advances in acquisition, processing, and quality control; then review scientific evidence in major organ systems; and finally describe future directions. PMID:26892827

Strong surfaces, surface skeletons, and image superimposition

NASA Astrophysics Data System (ADS)

Malgouyres, Remy; Fourey, Sebastien

1998-10-01

After having recalled the definition and some local properties of strong surfaces, we present a related new thinning algorithm with surface skeleton and a specific application of these notions to superimposition of images of brains obtained by Magnetic Resonance Image.

Identifying Lesions on Structural Brain Images-Validation of the Method and Application to Neuropsychological Patients

ERIC Educational Resources Information Center

Stamatakis, E.A.; Tyler, L.K.

2005-01-01

The study of neuropsychological disorders has been greatly facilitated by the localization of brain lesions on MRI scans. Current popular approaches for the assessment of MRI brain scans mostly depend on the successful segmentation of the brain into grey and white matter. These methods cannot be used effectively with large lesions because lesions…

New design concept of monopole antenna array for UHF 7T MRI.

PubMed

Hong, Suk-Min; Park, Joshua Haekyun; Woo, Myung-Kyun; Kim, Young-Bo; Cho, Zang-Hee

2014-05-01

We have developed and evaluated a monopole antenna array that can increase sensitivity at the center of the brain for 7T MRI applications. We have developed a monopole antenna array that has half the length of a conventional dipole antenna with eight channels for brain imaging with a 7T MRI. The eight-channel monopole antenna array and conventional eight-channel transceiver surface coil array were evaluated and compared in terms of transmit properties, specific absorption ratio (SAR), and sensitivity. The sensitivity maps were generated by dividing the SNR map by the flip angle distribution. A single surface coil provides asymmetric sensitivity resulting in reduced sensitivity at the center of the brain. In contrast, a single monopole antenna provides higher sensitivity at the center of the brain. Moreover, the monopole antenna array provides uniform sensitivity over the entire brain, and the sensitivity gain was 1.5 times higher at the center of the brain compared with the surface coil array. The monopole antenna array is a promising candidate for MRI applications, especially for brain imaging in a 7T MRI because it provides increased sensitivity at the center of the brain. Copyright © 2013 Wiley Periodicals, Inc.

Dual energy computed tomography for the head.

PubMed

Naruto, Norihito; Itoh, Toshihide; Noguchi, Kyo

2018-02-01

Dual energy CT (DECT) is a promising technology that provides better diagnostic accuracy in several brain diseases. DECT can generate various types of CT images from a single acquisition data set at high kV and low kV based on material decomposition algorithms. The two-material decomposition algorithm can separate bone/calcification from iodine accurately. The three-material decomposition algorithm can generate a virtual non-contrast image, which helps to identify conditions such as brain hemorrhage. A virtual monochromatic image has the potential to eliminate metal artifacts by reducing beam-hardening effects. DECT also enables exploration of advanced imaging to make diagnosis easier. One such novel application of DECT is the X-Map, which helps to visualize ischemic stroke in the brain without using iodine contrast medium.

[Study of dopamine transporter imaging on the brain of children with autism].

PubMed

Sun, Xiaomian; Yue, Jing; Zheng, Chongxun

2008-04-01

This study was conducted to evaluate the applicability of 99mTc-2beta-[ N, N'-bis (2-mercaptoethyl) ethylenediamino]methyl,3beta(4-chlorophenyl)tropane(TRODAT-1) dopamine transporter(DAT) SPECT imaging in children with autism, and thus to provide an academic basis for the etiology, mechanism and clinical therapy of autism. Ten autistic children and ten healthy controls were examined with 99mTc-TRODAT-1 DAT SPECT imaging. Striatal specific uptake of 99mTc-TRODAT-1 was calculated with region of interest analysis according to the ratics between striatum and cerebellum [(STR-BKG)/BKG]. There was no statistically significant difference in semiquantitative dopamine transporter between the bilateral striata of autistic children (P=0.562), and between those of normal controls (p=0.573); Dopamine transporter in the brain of patients with autism increased significantly as compared with that in the brain of normal controls (P=0.017). Dopaminergic nervous system is dysfunctioning in the brain of children with autism, and DAT 99mTc-TRODAT-1 SPECT imaging on the brain will help the imaging diagnosis of childhcod autism.

Iron deposition quantification: Applications in the brain and liver.

PubMed

Yan, Fuhua; He, Naying; Lin, Huimin; Li, Ruokun

2018-06-13

Iron has long been implicated in many neurological and other organ diseases. It is known that over and above the normal increases in iron with age, in certain diseases there is an excessive iron accumulation in the brain and liver. MRI is a noninvasive means by which to image the various structures in the brain in three dimensions and quantify iron over the volume of the object of interest. The quantification of iron can provide information about the severity of iron-related diseases as well as quantify changes in iron for patient follow-up and treatment monitoring. This article provides an overview of current MRI-based methods for iron quantification, specifically for the brain and liver, including: signal intensity ratio, R 2 , R2*, R2', phase, susceptibility weighted imaging and quantitative susceptibility mapping (QSM). Although there are numerous approaches to measuring iron, R 2 and R2* are currently preferred methods in imaging the liver and QSM has become the preferred approach for imaging iron in the brain. 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. © 2018 International Society for Magnetic Resonance in Medicine.

Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks.

PubMed

Smitha, K A; Akhil Raja, K; Arun, K M; Rajesh, P G; Thomas, Bejoy; Kapilamoorthy, T R; Kesavadas, Chandrasekharan

2017-08-01

The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.

Hyperpolarized 13C pyruvate mouse brain metabolism with absorptive-mode EPSI at 1 T

NASA Astrophysics Data System (ADS)

Miloushev, Vesselin Z.; Di Gialleonardo, Valentina; Salamanca-Cardona, Lucia; Correa, Fabian; Granlund, Kristin L.; Keshari, Kayvan R.

2017-02-01

The expected signal in echo-planar spectroscopic imaging experiments was explicitly modeled jointly in spatial and spectral dimensions. Using this as a basis, absorptive-mode type detection can be achieved by appropriate choice of spectral delays and post-processing techniques. We discuss the effects of gradient imperfections and demonstrate the implementation of this sequence at low field (1.05 T), with application to hyperpolarized [1-13C] pyruvate imaging of the mouse brain. The sequence achieves sufficient signal-to-noise to monitor the conversion of hyperpolarized [1-13C] pyruvate to lactate in the mouse brain. Hyperpolarized pyruvate imaging of mouse brain metabolism using an absorptive-mode EPSI sequence can be applied to more sophisticated murine disease and treatment models. The simple modifications presented in this work, which permit absorptive-mode detection, are directly translatable to human clinical imaging and generate improved absorptive-mode spectra without the need for refocusing pulses.

Automatic MRI 2D brain segmentation using graph searching technique.

PubMed

Pedoia, Valentina; Binaghi, Elisabetta

2013-09-01

Accurate and efficient segmentation of the whole brain in magnetic resonance (MR) images is a key task in many neuroscience and medical studies either because the whole brain is the final anatomical structure of interest or because the automatic extraction facilitates further analysis. The problem of segmenting brain MRI images has been extensively addressed by many researchers. Despite the relevant achievements obtained, automated segmentation of brain MRI imagery is still a challenging problem whose solution has to cope with critical aspects such as anatomical variability and pathological deformation. In the present paper, we describe and experimentally evaluate a method for segmenting brain from MRI images basing on two-dimensional graph searching principles for border detection. The segmentation of the whole brain over the entire volume is accomplished slice by slice, automatically detecting frames including eyes. The method is fully automatic and easily reproducible by computing the internal main parameters directly from the image data. The segmentation procedure is conceived as a tool of general applicability, although design requirements are especially commensurate with the accuracy required in clinical tasks such as surgical planning and post-surgical assessment. Several experiments were performed to assess the performance of the algorithm on a varied set of MRI images obtaining good results in terms of accuracy and stability. Copyright © 2012 John Wiley & Sons, Ltd.

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

The SRI24 multichannel brain atlas: construction and applications

NASA Astrophysics Data System (ADS)

Rohlfing, Torsten; Zahr, Natalie M.; Sullivan, Edith V.; Pfefferbaum, Adolf

2008-03-01

We present a new standard atlas of the human brain based on magnetic resonance images. The atlas was generated using unbiased population registration from high-resolution images obtained by multichannel-coil acquisition at 3T in a group of 24 normal subjects. The final atlas comprises three anatomical channels (T I-weighted, early and late spin echo), three diffusion-related channels (fractional anisotropy, mean diffusivity, diffusion-weighted image), and three tissue probability maps (CSF, gray matter, white matter). The atlas is dynamic in that it is implicitly represented by nonrigid transformations between the 24 subject images, as well as distortion-correction alignments between the image channels in each subject. The atlas can, therefore, be generated at essentially arbitrary image resolutions and orientations (e.g., AC/PC aligned), without compounding interpolation artifacts. We demonstrate in this paper two different applications of the atlas: (a) region definition by label propagation in a fiber tracking study is enabled by the increased sharpness of our atlas compared with other available atlases, and (b) spatial normalization is enabled by its average shape property. In summary, our atlas has unique features and will be made available to the scientific community as a resource and reference system for future imaging-based studies of the human brain.

Skull removal in MR images using a modified artificial bee colony optimization algorithm.

PubMed

Taherdangkoo, Mohammad

2014-01-01

Removal of the skull from brain Magnetic Resonance (MR) images is an important preprocessing step required for other image analysis techniques such as brain tissue segmentation. In this paper, we propose a new algorithm based on the Artificial Bee Colony (ABC) optimization algorithm to remove the skull region from brain MR images. We modify the ABC algorithm using a different strategy for initializing the coordinates of scout bees and their direction of search. Moreover, we impose an additional constraint to the ABC algorithm to avoid the creation of discontinuous regions. We found that our algorithm successfully removed all bony skull from a sample of de-identified MR brain images acquired from different model scanners. The obtained results of the proposed algorithm compared with those of previously introduced well known optimization algorithms such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) demonstrate the superior results and computational performance of our algorithm, suggesting its potential for clinical applications.

Android application for determining surgical variables in brain-tumor resection procedures

PubMed Central

Vijayan, Rohan C.; Thompson, Reid C.; Chambless, Lola B.; Morone, Peter J.; He, Le; Clements, Logan W.; Griesenauer, Rebekah H.; Kang, Hakmook; Miga, Michael I.

2017-01-01

Abstract. The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient’s preoperative image volume to more closely match the intraoperative state of the patient’s brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient’s neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables. PMID:28331887

Android application for determining surgical variables in brain-tumor resection procedures.

PubMed

Vijayan, Rohan C; Thompson, Reid C; Chambless, Lola B; Morone, Peter J; He, Le; Clements, Logan W; Griesenauer, Rebekah H; Kang, Hakmook; Miga, Michael I

2017-01-01

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

CUDA-based acceleration of collateral filtering in brain MR images

NASA Astrophysics Data System (ADS)

Li, Cheng-Yuan; Chang, Herng-Hua

2017-02-01

Image denoising is one of the fundamental and essential tasks within image processing. In medical imaging, finding an effective algorithm that can remove random noise in MR images is important. This paper proposes an effective noise reduction method for brain magnetic resonance (MR) images. Our approach is based on the collateral filter which is a more powerful method than the bilateral filter in many cases. However, the computation of the collateral filter algorithm is quite time-consuming. To solve this problem, we improved the collateral filter algorithm with parallel computing using GPU. We adopted CUDA, an application programming interface for GPU by NVIDIA, to accelerate the computation. Our experimental evaluation on an Intel Xeon CPU E5-2620 v3 2.40GHz with a NVIDIA Tesla K40c GPU indicated that the proposed implementation runs dramatically faster than the traditional collateral filter. We believe that the proposed framework has established a general blueprint for achieving fast and robust filtering in a wide variety of medical image denoising applications.

Concurrent application of TMS and near-infrared optical imaging: methodological considerations and potential artifacts

PubMed Central

Parks, Nathan A.

2013-01-01

The simultaneous application of transcranial magnetic stimulation (TMS) with non-invasive neuroimaging provides a powerful method for investigating functional connectivity in the human brain and the causal relationships between areas in distributed brain networks. TMS has been combined with numerous neuroimaging techniques including, electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Recent work has also demonstrated the feasibility and utility of combining TMS with non-invasive near-infrared optical imaging techniques, functional near-infrared spectroscopy (fNIRS) and the event-related optical signal (EROS). Simultaneous TMS and optical imaging affords a number of advantages over other neuroimaging methods but also involves a unique set of methodological challenges and considerations. This paper describes the methodology of concurrently performing optical imaging during the administration of TMS, focusing on experimental design, potential artifacts, and approaches to controlling for these artifacts. PMID:24065911

State of the art survey on MRI brain tumor segmentation.

PubMed

Gordillo, Nelly; Montseny, Eduard; Sobrevilla, Pilar

2013-10-01

Brain tumor segmentation consists of separating the different tumor tissues (solid or active tumor, edema, and necrosis) from normal brain tissues: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). In brain tumor studies, the existence of abnormal tissues may be easily detectable most of the time. However, accurate and reproducible segmentation and characterization of abnormalities are not straightforward. In the past, many researchers in the field of medical imaging and soft computing have made significant survey in the field of brain tumor segmentation. Both semiautomatic and fully automatic methods have been proposed. Clinical acceptance of segmentation techniques has depended on the simplicity of the segmentation, and the degree of user supervision. Interactive or semiautomatic methods are likely to remain dominant in practice for some time, especially in these applications where erroneous interpretations are unacceptable. This article presents an overview of the most relevant brain tumor segmentation methods, conducted after the acquisition of the image. Given the advantages of magnetic resonance imaging over other diagnostic imaging, this survey is focused on MRI brain tumor segmentation. Semiautomatic and fully automatic techniques are emphasized. Copyright © 2013 Elsevier Inc. All rights reserved.

In vivo mapping of current density distribution in brain tissues during deep brain stimulation (DBS)

NASA Astrophysics Data System (ADS)

Sajib, Saurav Z. K.; Oh, Tong In; Kim, Hyung Joong; Kwon, Oh In; Woo, Eung Je

2017-01-01

New methods for in vivo mapping of brain responses during deep brain stimulation (DBS) are indispensable to secure clinical applications. Assessment of current density distribution, induced by internally injected currents, may provide an alternative method for understanding the therapeutic effects of electrical stimulation. The current flow and pathway are affected by internal conductivity, and can be imaged using magnetic resonance-based conductivity imaging methods. Magnetic resonance electrical impedance tomography (MREIT) is an imaging method that can enable highly resolved mapping of electromagnetic tissue properties such as current density and conductivity of living tissues. In the current study, we experimentally imaged current density distribution of in vivo canine brains by applying MREIT to electrical stimulation. The current density maps of three canine brains were calculated from the measured magnetic flux density data. The absolute current density values of brain tissues, including gray matter, white matter, and cerebrospinal fluid were compared to assess the active regions during DBS. The resulting current density in different tissue types may provide useful information about current pathways and volume activation for adjusting surgical planning and understanding the therapeutic effects of DBS.

Confocal multispot microscope for fast and deep imaging in semicleared tissues

NASA Astrophysics Data System (ADS)

Adam, Marie-Pierre; Müllenbroich, Marie Caroline; Di Giovanna, Antonino Paolo; Alfieri, Domenico; Silvestri, Ludovico; Sacconi, Leonardo; Pavone, Francesco Saverio

2018-02-01

Although perfectly transparent specimens are imaged faster with light-sheet microscopy, less transparent samples are often imaged with two-photon microscopy leveraging its robustness to scattering; however, at the price of increased acquisition times. Clearing methods that are capable of rendering strongly scattering samples such as brain tissue perfectly transparent specimens are often complex, costly, and time intensive, even though for many applications a slightly lower level of tissue transparency is sufficient and easily achieved with simpler and faster methods. Here, we present a microscope type that has been geared toward the imaging of semicleared tissue by combining multispot two-photon excitation with rolling shutter wide-field detection to image deep and fast inside semicleared mouse brain. We present a theoretical and experimental evaluation of the point spread function and contrast as a function of shutter size. Finally, we demonstrate microscope performance in fixed brain slices by imaging dendritic spines up to 400-μm deep.

Patch Based Synthesis of Whole Head MR Images: Application to EPI Distortion Correction.

PubMed

Roy, Snehashis; Chou, Yi-Yu; Jog, Amod; Butman, John A; Pham, Dzung L

2016-10-01

Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize T 2 -w whole head (including brain, skull, eyes etc) images from T 1 -w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous B 0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T 2 -w image. We show that our synthetic T 2 -w images can be used as a template in absence of a real T 2 -w image. Our patch based method requires multiple atlases with T 1 and T 2 to be registeLowRes to a given target T 1 . Then for every patch on the target, multiple similar looking matching patches are found on the atlas T 1 images and corresponding patches on the atlas T 2 images are combined to generate a synthetic T 2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T 2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

A., Javadpour; A., Mohammadi

2016-01-01

Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629

Imaging MALDI MS of Dosed Brain Tissues Utilizing an Alternative Analyte Pre-extraction Approach

NASA Astrophysics Data System (ADS)

Quiason, Cristine M.; Shahidi-Latham, Sheerin K.

2015-06-01

Matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry has been adopted in the pharmaceutical industry as a useful tool to detect xenobiotic distribution within tissues. A unique sample preparation approach for MALDI imaging has been described here for the extraction and detection of cobimetinib and clozapine, which were previously undetectable in mouse and rat brain using a single matrix application step. Employing a combination of a buffer wash and a cyclohexane pre-extraction step prior to standard matrix application, the xenobiotics were successfully extracted and detected with an 8 to 20-fold gain in sensitivity. This alternative approach for sample preparation could serve as an advantageous option when encountering difficult to detect analytes.

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.

Interpreting CARS images of tissue within the C-H-stretching region

NASA Astrophysics Data System (ADS)

Dietzek, Benjamin; Meyer, Tobias; Medyukhina, Anna; Bergner, Norbert; Krafft, Christoph; Romeike, Bernd F. M.; Reichart, Rupert; Kalff, Rolf; Schmitt, Michael; Popp, Jürgen

2014-03-01

Single band coherent anti-Stokes Raman scattering (CARS) microscopy within the CH-stretching region is applied to detect individual cells and nuclei of human brain tissue and brain tumors - an information which allows for histopathologic grading of the tissue. The CARS image contrast within the C-H-stretching region correlated to the tissue composition. Based on the specific application example of identifying nuclei within (coherent) Raman images of neurotissue sections, we shall derive general design parameters for lasers optimally suited to serve in a clinical environment and discuss the potential of recently developed methods to analyze spectrally resolved CARS images and image segmentation algorithms.

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

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.

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

Fusing DTI and FMRI Data: A Survey of Methods and Applications

PubMed Central

Zhu, Dajiang; Zhang, Tuo; Jiang, Xi; Hu, Xintao; Chen, Hanbo; Yang, Ning; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming

2014-01-01

The relationship between brain structure and function has been one of the centers of research in neuroimaging for decades. In recent years, diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) techniques have been widely available and popular in cognitive and clinical neurosciences for examining the brain’s white matter (WM) micro-structures and gray matter (GM) functions, respectively. Given the intrinsic integration of WM/GM and the complementary information embedded in DTI/fMRI data, it is natural and well-justified to combine these two neuroimaging modalities together to investigate brain structure and function and their relationships simultaneously. In the past decade, there have been remarkable achievements of DTI/fMRI fusion methods and applications in neuroimaging and human brain mapping community. This survey paper aims to review recent advancements on methodologies and applications in incorporating multimodal DTI and fMRI data, and offer our perspectives on future research directions. We envision that effective fusion of DTI/fMRI techniques will play increasingly important roles in neuroimaging and brain sciences in the years to come. PMID:24103849

Brain shift computation using a fully nonlinear biomechanical model.

PubMed

Wittek, Adam; Kikinis, Ron; Warfield, Simon K; Miller, Karol

2005-01-01

In the present study, fully nonlinear (i.e. accounting for both geometric and material nonlinearities) patient specific finite element brain model was applied to predict deformation field within the brain during the craniotomy-induced brain shift. Deformation of brain surface was used as displacement boundary conditions. Application of the computed deformation field to align (i.e. register) the preoperative images with the intraoperative ones indicated that the model very accurately predicts the displacements of gravity centers of the lateral ventricles and tumor even for very limited information about the brain surface deformation. These results are sufficient to suggest that nonlinear biomechanical models can be regarded as one possible way of complementing medical image processing techniques when conducting nonrigid registration. Important advantage of such models over the linear ones is that they do not require unrealistic assumptions that brain deformations are infinitesimally small and brain tissue stress-strain relationship is linear.

Optical imaging of architecture and function in the living brain sheds new light on cortical mechanisms underlying visual perception.

PubMed

Grinvald, A

1992-01-01

Long standing questions related to brain mechanisms underlying perception can finally be resolved by direct visualization of the architecture and function of mammalian cortex. This advance has been accomplished with the aid of two optical imaging techniques with which one can literally see how the brain functions. The upbringing of this technology required a multi-disciplinary approach integrating brain research with organic chemistry, spectroscopy, biophysics, computer sciences, optics and image processing. Beyond the technological ramifications, recent research shed new light on cortical mechanisms underlying sensory perception. Clinical applications of this technology for precise mapping of the cortical surface of patients during neurosurgery have begun. Below is a brief summary of our own research and a description of the technical specifications of the two optical imaging techniques. Like every technique, optical imaging also suffers from severe limitations. Here we mostly emphasize some of its advantages relative to all alternative imaging techniques currently in use. The limitations are critically discussed in our recent reviews. For a series of other reviews, see Cohen (1989).

Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas.

PubMed

Vellmer, Sebastian; Tonoyan, Aram S; Suter, Dieter; Pronin, Igor N; Maximov, Ivan I

2018-02-01

Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies. Copyright © 2017. Published by Elsevier GmbH.

Bioimpedance imaging: an overview of potential clinical applications.

PubMed

Bayford, Richard; Tizzard, Andrew

2012-10-21

Electrical Impedance Tomography (EIT) is an imaging technique based on multiple bio impedance measurements to produce a map (image) of impedance or changes in impedance across a region. Its origins lay in geophysics where it is still used to today. This review highlights potential clinical applications of EIT. Beginning with a brief overview of the underlying principles behind the modality, it describes the background research leading towards the development of the application of EIT for monitoring pulmonary function, detecting and localising tumours and monitoring brain function.

Feasibility of dual-low scheme combined with iterative reconstruction technique in acute cerebral infarction volume CT whole brain perfusion imaging.

PubMed

Wang, Tao; Gong, Yi; Shi, Yibing; Hua, Rong; Zhang, Qingshan

2017-07-01

The feasibility of application of low-concentration contrast agent and low tube voltage combined with iterative reconstruction in whole brain computed tomography perfusion (CTP) imaging of patients with acute cerebral infarction was investigated. Fifty-nine patients who underwent whole brain CTP examination and diagnosed with acute cerebral infarction from September 2014 to March 2016 were selected. Patients were randomly divided into groups A and B. There were 28 cases in group A [tube voltage, 100 kV; contrast agent, iohexol (350 mg I/ml), reconstructed by filtered back projection] and 31 cases in group B [tube voltage, 80 kV; contrast agent, iodixanol (270 mg I/ml), reconstructed by algebraic reconstruction technique]. The artery CT value, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), dose length product, effective dose (ED) of radiation and brain iodine intake of both groups were measured and statistically analyzed. Two physicians carried out kappa (κ) analysis on the consistency of image quality evaluation. The difference in subjective image quality evaluation between the groups was tested by χ 2 . The differences in CT value, SNR, CNR, CTP and CT angiography subjective image quality evaluation between both groups were not statistically significant (P>0.05); the diagnosis rate of the acute infarcts between the two groups was not significantly different; while the ED and iodine intake in group B (dual low-dose group) were lower than group A. In conclusion, combination of low tube voltage and iterative reconstruction technique, and application of low-concentration contrast agent (270 mg I/ml) in whole brain CTP examination reduced ED and iodine intake without compromising image quality, thereby reducing the risk of contrast-induced nephropathy.

Whole brain myelin mapping using T1- and T2-weighted MR imaging data

PubMed Central

Ganzetti, Marco; Wenderoth, Nicole; Mantini, Dante

2014-01-01

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease. PMID:25228871

Real time in vivo imaging and measurement of serine protease activity in the mouse hippocampus using a dedicated complementary metal-oxide semiconductor imaging device.

PubMed

Ng, David C; Tamura, Hideki; Tokuda, Takashi; Yamamoto, Akio; Matsuo, Masamichi; Nunoshita, Masahiro; Ishikawa, Yasuyuki; Shiosaka, Sadao; Ohta, Jun

2006-09-30

The aim of the present study is to demonstrate the application of complementary metal-oxide semiconductor (CMOS) imaging technology for studying the mouse brain. By using a dedicated CMOS image sensor, we have successfully imaged and measured brain serine protease activity in vivo, in real-time, and for an extended period of time. We have developed a biofluorescence imaging device by packaging the CMOS image sensor which enabled on-chip imaging configuration. In this configuration, no optics are required whereby an excitation filter is applied onto the sensor to replace the filter cube block found in conventional fluorescence microscopes. The fully packaged device measures 350 microm thick x 2.7 mm wide, consists of an array of 176 x 144 pixels, and is small enough for measurement inside a single hemisphere of the mouse brain, while still providing sufficient imaging resolution. In the experiment, intraperitoneally injected kainic acid induced upregulation of serine protease activity in the brain. These events were captured in real time by imaging and measuring the fluorescence from a fluorogenic substrate that detected this activity. The entire device, which weighs less than 1% of the body weight of the mouse, holds promise for studying freely moving animals.

New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury

DTIC Science & Technology

2012-02-01

Subdural hemor- rhage (or hematoma ) is a form of traumatic brain injury, in which blood gathers between the du- ra and arachnoid mater (in meningeal...to an hour. Subdural hemorrhage (or hematoma ) is a form of traumatic brain injury, in which blood gathers between the dura and arachnoid mater (in

Neuroimaging is a novel tool to understand the impact of environmental chemicals on neurodevelopment

PubMed Central

Horton, Megan K.; Margolis, Amy E.; Tang, Cheuk; Wright, Robert

2014-01-01

Purpose of review The prevalence of childhood neurodevelopmental disorders (ND) has been increasing over the last several decades. Prenatal and early childhood exposure to environmental toxicants is increasingly recognized as contributing to the growing rate of NDs. Very little is known about the mechanistic processes by which environmental chemicals alter brain development. We review recent advances in brain imaging modalities and discuss their application in epidemiologic studies of prenatal and early childhood exposure to environmental toxicants. Recent findings Neuroimaging techniques (volumetric and functional magnetic resonance imaging (MRI), diffusor tensor imaging (DTI), magnetic resonance spectroscopy (MRS)) have opened unprecedented access to study the developing human brain. These techniques are non-invasive and free of ionization radiation making them suitable for research applications in children. Using these techniques, we now understand much about structural and functional patterns in the typically developing brain. This knowledge allows us to investigate how prenatal exposure to environmental toxicants may alter the typical developmental trajectory. Summary MRI is a powerful tool that allows in vivo visualization of brain structure and function. Used in epidemiologic studies of environmental exposure, it offers the promise to causally link exposure with behavioral and cognitive manifestations and ultimately to inform programs to reduce exposure and mitigate adverse effects of exposure. PMID:24535497

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.

Thioesters for the in vitro evaluation of agents to image brain cholinesterases.

PubMed

Macdonald, Ian R; Jollymore, Courtney T; Reid, G Andrew; Pottie, Ian R; Martin, Earl; Darvesh, Sultan

2013-06-01

Cholinesterases are associated with pathology characteristic of conditions such as Alzheimer's disease and are therefore, considered targets for neuroimaging. Ester derivatives of N-methylpiperidinol are promising potential imaging agents; however, methodology is lacking for evaluating these compounds in vitro. Here, we report the synthesis and evaluation of a series of N-methylpiperidinyl thioesters, possessing comparable properties to their corresponding esters, which can be directly evaluated for cholinesterase kinetics and histochemical distribution in human brain tissue. N-methylpiperidinyl esters and thioesters were synthesized and they demonstrated comparable cholinesterase kinetics. Furthermore, thioesters were capable, using histochemical method, to visualize cholinesterase activity in human brain tissue. N-methylpiperidinyl thioesters can be rapidly evaluated for cholinesterase kinetics and visualization of enzyme distribution in brain tissue which may facilitate development of cholinesterase imaging agents for application to conditions such as Alzheimer's disease.

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.

A versatile new technique to clear mouse and human brain

NASA Astrophysics Data System (ADS)

Costantini, Irene; Di Giovanna, Antonino Paolo; Allegra Mascaro, Anna Letizia; Silvestri, Ludovico; Müllenbroich, Marie Caroline; Sacconi, Leonardo; Pavone, Francesco S.

2015-07-01

Large volumes imaging with microscopic resolution is limited by light scattering. In the last few years based on refractive index matching, different clearing approaches have been developed. Organic solvents and water-based optical clearing agents have been used for optical clearing of entire mouse brain. Although these methods guarantee high transparency and preservation of the fluorescence, though present other non-negligible limitations. Tissue transformation by CLARITY allows high transparency, whole brain immunolabelling and structural and molecular preservation. This method however requires a highly expensive refractive index matching solution limiting practical applicability. In this work we investigate the effectiveness of a water-soluble clearing agent, the 2,2'-thiodiethanol (TDE) to clear mouse and human brain. TDE does not quench the fluorescence signal, is compatible with immunostaining and does not introduce any deformation at sub-cellular level. The not viscous nature of the TDE make it a suitable agent to perform brain slicing during serial two-photon (STP) tomography. In fact, by improving penetration depth it reduces tissue slicing, decreasing the acquisition time and cutting artefacts. TDE can also be used as a refractive index medium for CLARITY. The potential of this method has been explored by imaging a whole transgenic mouse brain with the light sheet microscope. Moreover we apply this technique also on blocks of dysplastic human brain tissue transformed with CLARITY and labeled with different antibody. This clearing approach significantly expands the application of single and two-photon imaging, providing a new useful method for quantitative morphological analysis of structure in mouse and human brain.

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.

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.

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

Region specific optimization of continuous linear attenuation coefficients based on UTE (RESOLUTE): application to PET/MR brain imaging

NASA Astrophysics Data System (ADS)

Ladefoged, Claes N.; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E.; Andersen, Flemming L.

2015-10-01

The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [18F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R2* values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within  ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.

Incorporating modern neuroscience findings to improve brain-computer interfaces: tracking auditory attention.

PubMed

Wronkiewicz, Mark; Larson, Eric; Lee, Adrian Kc

2016-10-01

Brain-computer interface (BCI) technology allows users to generate actions based solely on their brain signals. However, current non-invasive BCIs generally classify brain activity recorded from surface electroencephalography (EEG) electrodes, which can hinder the application of findings from modern neuroscience research. In this study, we use source imaging-a neuroimaging technique that projects EEG signals onto the surface of the brain-in a BCI classification framework. This allowed us to incorporate prior research from functional neuroimaging to target activity from a cortical region involved in auditory attention. Classifiers trained to detect attention switches performed better with source imaging projections than with EEG sensor signals. Within source imaging, including subject-specific anatomical MRI information (instead of using a generic head model) further improved classification performance. This source-based strategy also reduced accuracy variability across three dimensionality reduction techniques-a major design choice in most BCIs. Our work shows that source imaging provides clear quantitative and qualitative advantages to BCIs and highlights the value of incorporating modern neuroscience knowledge and methods into BCI systems.

Robust biological parametric mapping: an improved technique for multimodal brain image analysis

NASA Astrophysics Data System (ADS)

Yang, Xue; Beason-Held, Lori; Resnick, Susan M.; Landman, Bennett A.

2011-03-01

Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics. Recently, biological parametric mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

PubMed

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Classification of brain MRI with big data and deep 3D convolutional neural networks

NASA Astrophysics Data System (ADS)

Wegmayr, Viktor; Aitharaju, Sai; Buhmann, Joachim

2018-02-01

Our ever-aging society faces the growing problem of neurodegenerative diseases, in particular dementia. Magnetic Resonance Imaging provides a unique tool for non-invasive investigation of these brain diseases. However, it is extremely difficult for neurologists to identify complex disease patterns from large amounts of three-dimensional images. In contrast, machine learning excels at automatic pattern recognition from large amounts of data. In particular, deep learning has achieved impressive results in image classification. Unfortunately, its application to medical image classification remains difficult. We consider two reasons for this difficulty: First, volumetric medical image data is considerably scarcer than natural images. Second, the complexity of 3D medical images is much higher compared to common 2D images. To address the problem of small data set size, we assemble the largest dataset ever used for training a deep 3D convolutional neural network to classify brain images as healthy (HC), mild cognitive impairment (MCI) or Alzheimers disease (AD). We use more than 20.000 images from subjects of these three classes, which is almost 9x the size of the previously largest data set. The problem of high dimensionality is addressed by using a deep 3D convolutional neural network, which is state-of-the-art in large-scale image classification. We exploit its ability to process the images directly, only with standard preprocessing, but without the need for elaborate feature engineering. Compared to other work, our workflow is considerably simpler, which increases clinical applicability. Accuracy is measured on the ADNI+AIBL data sets, and the independent CADDementia benchmark.

Imaging-Genetics Applications in Child Psychiatry

ERIC Educational Resources Information Center

Pine, Daniel S.; Ernst, Monique; Leibenluft, Ellen

2010-01-01

Objective: To place imaging-genetics research in the context of child psychiatry. Method: A conceptual overview is provided, followed by discussion of specific research examples. Results: Imaging-genetics research is described linking brain function to two specific genes, for the serotonin-reuptake-transporter protein and a monoamine oxidase…

Advances in functional brain imaging technology and developmental neuro-psychology: their applications in the Jungian analytic domain.

PubMed

Petchkovsky, Leon

2017-06-01

Analytical psychology shares with many other psychotherapies the important task of repairing the consequences of developmental trauma. The majority of analytic patients come from compromised early developmental backgrounds: they may have experienced neglect, abuse, or failures of empathic resonance from their carers. Functional brain imagery techniques including Quantitative Electroencephalogram (QEEG), and functional Magnetic Resonance Imagery (fMRI), allow us to track mental processes in ways beyond verbal reportage and introspection. This independent perspective is useful for developing new psychodynamic hypotheses, testing current ones, providing diagnostic markers, and monitoring treatment progress. Jung, with the Word Association Test, grasped these principles 100 years ago. Brain imaging techniques have contributed to powerful recent advances in our understanding of neurodevelopmental processes in the first three years of life. If adequate nurturance is compromised, a range of difficulties may emerge. This has important implications for how we understand and treat our psychotherapy clients. The paper provides an overview of functional brain imaging and advances in developmental neuropsychology, and looks at applications of some of these findings (including neurofeedback) in the Jungian psychotherapy domain. © 2017, The Society of Analytical Psychology.

Machine learning for neuroimaging with scikit-learn.

PubMed

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.

Machine learning for neuroimaging with scikit-learn

PubMed Central

Abraham, Alexandre; Pedregosa, Fabian; Eickenberg, Michael; Gervais, Philippe; Mueller, Andreas; Kossaifi, Jean; Gramfort, Alexandre; Thirion, Bertrand; Varoquaux, Gaël

2014-01-01

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. PMID:24600388

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 in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.

SyMRI of the Brain

PubMed Central

Hagiwara, Akifumi; Warntjes, Marcel; Hori, Masaaki; Andica, Christina; Nakazawa, Misaki; Kumamaru, Kanako Kunishima; Abe, Osamu; Aoki, Shigeki

2017-01-01

Abstract Conventional magnetic resonance images are usually evaluated using the image signal contrast between tissues and not based on their absolute signal intensities. Quantification of tissue parameters, such as relaxation rates and proton density, would provide an absolute scale; however, these methods have mainly been performed in a research setting. The development of rapid quantification, with scan times in the order of 6 minutes for full head coverage, has provided the prerequisites for clinical use. The aim of this review article was to introduce a specific quantification method and synthesis of contrast-weighted images based on the acquired absolute values, and to present automatic segmentation of brain tissues and measurement of myelin based on the quantitative values, along with application of these techniques to various brain diseases. The entire technique is referred to as “SyMRI” in this review. SyMRI has shown promising results in previous studies when used for multiple sclerosis, brain metastases, Sturge-Weber syndrome, idiopathic normal pressure hydrocephalus, meningitis, and postmortem imaging. PMID:28257339

[Application of diffusion tensor imaging fractography in minimally invasive surgery of brain tumors].

PubMed

Yang, Lei; Zhang, Mao-zhi; Zhang, Wei; Zhao, Yuan-li; Zhao, Ji-zong

2006-05-23

To investigate the effects and prospect of application of diffusion tensor imaging (DTI) fractography in minimally invasive surgery of brain tumors. DTI fractography was performed in 52 patients with malignant brain tumors. Based on the DTI fractography results, 34 of the 52 patients underwent operation under neuro-navigation, and 18 of the 52 patients underwent operation routine minimally invasive craniotomy and tumor resection without neuro-navigation. The rate of total tumor resection was 86.5% (45/52). The mortality was 1.9% (1/52). The disability rate was 11.5% (6/52). No case needed the second operation. DTI fractography has raised the minimally invasive neurosurgery to the level of protecting the nuclei and nerve tracts and guiding intra-operative management of infiltration of deep-seated tumors, especially when combined with neuro-navigation and interventional MRI.

Determination of surgical variables for a brain shift correction pipeline using an Android application

NASA Astrophysics Data System (ADS)

Vijayan, Rohan; Conley, Rebekah H.; Thompson, Reid C.; Clements, Logan W.; Miga, Michael I.

2016-03-01

Brain shift describes the deformation that the brain undergoes from mechanical and physiological effects typically during a neurosurgical or neurointerventional procedure. With respect to image guidance techniques, brain shift has been shown to compromise the fidelity of these approaches. In recent work, a computational pipeline has been developed to predict "brain shift" based on preoperatively determined surgical variables (such as head orientation), and subsequently correct preoperative images to more closely match the intraoperative state of the brain. However, a clinical workflow difficulty in the execution of this pipeline has been acquiring the surgical variables by the neurosurgeon prior to surgery. In order to simplify and expedite this process, an Android, Java-based application designed for tablets was developed to provide the neurosurgeon with the ability to orient 3D computer graphic models of the patient's head, determine expected location and size of the craniotomy, and provide the trajectory into the tumor. These variables are exported for use as inputs for the biomechanical models of the preoperative computing phase for the brain shift correction pipeline. The accuracy of the application's exported data was determined by comparing it to data acquired from the physical execution of the surgeon's plan on a phantom head. Results indicated good overlap of craniotomy predictions, craniotomy centroid locations, and estimates of patient's head orientation with respect to gravity. However, improvements in the app interface and mock surgical setup are needed to minimize error.

Mindcontrol: A web application for brain segmentation quality control.

PubMed

Keshavan, Anisha; Datta, Esha; M McDonough, Ian; Madan, Christopher R; Jordan, Kesshi; Henry, Roland G

2018-04-15

Tissue classification plays a crucial role in the investigation of normal neural development, brain-behavior relationships, and the disease mechanisms of many psychiatric and neurological illnesses. Ensuring the accuracy of tissue classification is important for quality research and, in particular, the translation of imaging biomarkers to clinical practice. Assessment with the human eye is vital to correct various errors inherent to all currently available segmentation algorithms. Manual quality assurance becomes methodologically difficult at a large scale - a problem of increasing importance as the number of data sets is on the rise. To make this process more efficient, we have developed Mindcontrol, an open-source web application for the collaborative quality control of neuroimaging processing outputs. The Mindcontrol platform consists of a dashboard to organize data, descriptive visualizations to explore the data, an imaging viewer, and an in-browser annotation and editing toolbox for data curation and quality control. Mindcontrol is flexible and can be configured for the outputs of any software package in any data organization structure. Example configurations for three large, open-source datasets are presented: the 1000 Functional Connectomes Project (FCP), the Consortium for Reliability and Reproducibility (CoRR), and the Autism Brain Imaging Data Exchange (ABIDE) Collection. These demo applications link descriptive quality control metrics, regional brain volumes, and thickness scalars to a 3D imaging viewer and editing module, resulting in an easy-to-implement quality control protocol that can be scaled for any size and complexity of study. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field.

PubMed

Chang, Catie; Raven, Erika P; Duyn, Jeff H

2016-05-13

Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and trade-offs associated with UHF imaging, focusing on the application to studying brain-heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain-heart interactions. © 2016 The Author(s).

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

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.

Importance of Multimodal MRI in Characterizing Brain Tissue and Its Potential Application for Individual Age Prediction.

PubMed

Cherubini, Andrea; Caligiuri, Maria Eugenia; Peran, Patrice; Sabatini, Umberto; Cosentino, Carlo; Amato, Francesco

2016-09-01

This study presents a voxel-based multiple regression analysis of different magnetic resonance image modalities, including anatomical T1-weighted, T2(*) relaxometry, and diffusion tensor imaging. Quantitative parameters sensitive to complementary brain tissue alterations, including morphometric atrophy, mineralization, microstructural damage, and anisotropy loss, were compared in a linear physiological aging model in 140 healthy subjects (range 20-74 years). The performance of different predictors and the identification of the best biomarker of age-induced structural variation were compared without a priori anatomical knowledge. The best quantitative predictors in several brain regions were iron deposition and microstructural damage, rather than macroscopic tissue atrophy. Age variations were best resolved with a combination of markers, suggesting that multiple predictors better capture age-induced tissue alterations. The results of the linear model were used to predict apparent age in different regions of individual brain. This approach pointed to a number of novel applications that could potentially help highlighting areas particularly vulnerable to disease.

Assessment of variability in cerebral vasculature for neuro-anatomical surgery planning in rodent brain

NASA Astrophysics Data System (ADS)

Rangarajan, J. R.; Van Kuyck, K.; Himmelreich, U.; Nuttin, B.; Maes, F.; Suetens, P.

2011-03-01

Clinical and pre-clinical studies show that deep brain stimulation (DBS) of targeted brain regions by neurosurgical techniques ameliorate psychiatric disorder such as anorexia nervosa. Neurosurgical interventions in preclinical rodent brain are mostly accomplished manually with a 2D atlas. Considering both the large number of animals subjected to stereotactic surgical experiments and the associated imaging cost, feasibility of sophisticated pre-operative imaging based surgical path planning and/or robotic guidance is limited. Here, we spatially normalize vasculature information and assess the intra-strain variability in cerebral vasculature for a neurosurgery planning. By co-registering and subsequently building a probabilistic vasculature template in a standard space, we evaluate the risk of a user defined electrode trajectory damaging a blood vessel on its path. The use of such a method may not only be confined to DBS therapy in small animals, but also could be readily applicable to a wide range of stereotactic small animal surgeries like targeted injection of contrast agents and cell labeling applications.

Dynamic in vivo imaging of small animal brain using pulsed laser diode-based photoacoustic tomography system

NASA Astrophysics Data System (ADS)

Upputuri, Paul Kumar; Pramanik, Manojit

2017-09-01

We demonstrate dynamic in vivo imaging using a low-cost portable pulsed laser diode (PLD)-based photoacoustic tomography system. The system takes advantage of an 803-nm PLD having high-repetition rate ˜7000 Hz combined with a fast-scanning single-element ultrasound transducer leading to a 5 s cross-sectional imaging. Cortical vasculature is imaged in scan time of 5 s with high signal-to-noise ratio ˜48. To examine the ability for dynamic imaging, we monitored the fast uptake and clearance process of indocyanine green in the rat brain. The system will find applications to study neurofunctional activities, characterization of pharmacokinetic, and biodistribution profiles in the development process of drugs or imaging agents.

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.

Functional Magnetic Resonance Imaging in Alzheimer' Disease Drug Development.

PubMed

Holiga, Stefan; Abdulkadir, Ahmed; Klöppel, Stefan; Dukart, Juergen

2018-01-01

While now commonly applied for studying human brain function the value of functional magnetic resonance imaging in drug development has only recently been recognized. Here we describe the different functional magnetic resonance imaging techniques applied in Alzheimer's disease drug development with their applications, implementation guidelines, and potential pitfalls.

Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments

NASA Astrophysics Data System (ADS)

Giannoni, Luca; Lange, Frédéric; Tachtsidis, Ilias

2018-04-01

Hyperspectral imaging (HSI) technologies have been used extensively in medical research, targeting various biological phenomena and multiple tissue types. Their high spectral resolution over a wide range of wavelengths enables acquisition of spatial information corresponding to different light-interacting biological compounds. This review focuses on the application of HSI to monitor brain tissue metabolism and hemodynamics in life sciences. Different approaches involving HSI have been investigated to assess and quantify cerebral activity, mainly focusing on: (1) mapping tissue oxygen delivery through measurement of changes in oxygenated (HbO2) and deoxygenated (HHb) hemoglobin; and (2) the assessment of the cerebral metabolic rate of oxygen (CMRO2) to estimate oxygen consumption by brain tissue. Finally, we introduce future perspectives of HSI of brain metabolism, including its potential use for imaging optical signals from molecules directly involved in cellular energy production. HSI solutions can provide remarkable insight in understanding cerebral tissue metabolism and oxygenation, aiding investigation on brain tissue physiological processes.

Blood-Brain Barrier Permeable Gold Nanoparticles: An Efficient Delivery Platform for Enhanced Malignant Glioma Therapy and Imaging

PubMed Central

Cheng, Yu; Dai, Qing; Morshed, Ramin; Fan, Xiaobing; Wegscheid, Michelle L.; Wainwright, Derek A.; Han, Yu; Zhang, Lingjiao; Auffinger, Brenda; Tobias, Alex L.; Rincón, Esther; Thaci, Bart; Ahmed, Atique U.; Warnke, Peter; He, Chuan

2014-01-01

The blood-brain barrier (BBB) remains a formidable obstacle in medicine, preventing efficient penetration of chemotherapeutic and diagnostic agents to malignant gliomas. Here, we demonstrate that a transactivator of transcription (TAT) peptide-modified gold nanoparticle platform (TAT-Au NP) with a 5 nm core size is capable of crossing the BBB efficiently and delivering cargoes such as the anticancer drug doxorubicin (Dox) and Gd3+ contrast agents to brain tumor tissues. Treatment of mice bearing intracranial glioma xenografts with pH-sensitive Dox-conjugated TAT-Au NPs via a single intravenous administration leads to significant survival benefit when compared to the free Dox. Furthermore, we demonstrate that TAT-Au NPs are capable of delivering Gd3+ chelates for enhanced brain tumor imaging with a prolonged retention time of Gd3+ when compared to the free Gd3+ chelates. Collectively, these results show promising applications of the TAT-Au NPs for enhanced malignant brain tumor therapy and non-invasive imaging. PMID:25104165

Promising application of dynamic nuclear polarization for in vivo (13)C MR imaging.

PubMed

Yen, Yi-Fen; Nagasawa, Kiyoshi; Nakada, Tsutomu

2011-01-01

Use of hyperpolarized (13)C in magnetic resonance (MR) imaging is a new technique that enhances signal tens of thousands-fold. Recent in vivo animal studies of metabolic imaging that used hyperpolarized (13)C demonstrated its potential in many applications for disease indication, metabolic profiling, and treatment monitoring. We review the basic physics for dynamic nuclear polarization (DNP) and in vivo studies reported in prostate cancer research, hepatocellular carcinoma research, diabetes and cardiac applications, brain metabolism, and treatment response as well as investigations of various DNP (13)C substrates.

The self-regulating brain and neurofeedback: Experimental science and clinical promise.

PubMed

Thibault, Robert T; Lifshitz, Michael; Raz, Amir

2016-01-01

Neurofeedback, one of the primary examples of self-regulation, designates a collection of techniques that train the brain and help to improve its function. Since coming on the scene in the 1960s, electroencephalography-neurofeedback has become a treatment vehicle for a host of mental disorders; however, its clinical effectiveness remains controversial. Modern imaging technologies of the living human brain (e.g., real-time functional magnetic resonance imaging) and increasingly rigorous research protocols that utilize such methodologies begin to shed light on the underlying mechanisms that may facilitate more effective clinical applications. In this paper we focus on recent technological advances in the field of human brain imaging and discuss how these modern methods may influence the field of neurofeedback. Toward this end, we outline the state of the evidence and sketch out future directions to further explore the potential merits of this contentious therapeutic prospect. Copyright © 2015 Elsevier Ltd. All rights reserved.

In vitro and in vivo studies on the transport of PEGylated silica nanoparticles across the blood-brain barrier.

PubMed

Liu, Dan; Lin, Bingqian; Shao, Wei; Zhu, Zhi; Ji, Tianhai; Yang, Chaoyong

2014-02-12

Transport of PEGylated silica nanoparticles (PSiNPs) with diameters of 100, 50, and 25 nm across the blood-brain barrier (BBB) was evaluated using an in vitro BBB model based on mouse cerebral endothelial cells (bEnd.3) cultured on transwell inserts within a chamber. In vivo animal experiments were further performed by noninvasive in vivo imaging and ex vivo optical imaging after injection via carotid artery. Confocal fluorescence studies were carried out to evaluate the uptake of PSiNPs by brain endothelial cells. The results showed that PSiNPs can traverse the BBB in vitro and in vivo. The transport efficiency of PSiNPs across BBB was found to be size-dependent, with increased particle size resulting in decreased efficiency. This work points to the potential application of small sized silica nanoparticles in brain imaging or drug delivery.

Augmented reality-guided neurosurgery: accuracy and intraoperative application of an image projection technique.

PubMed

Besharati Tabrizi, Leila; Mahvash, Mehran

2015-07-01

An augmented reality system has been developed for image-guided neurosurgery to project images with regions of interest onto the patient's head, skull, or brain surface in real time. The aim of this study was to evaluate system accuracy and to perform the first intraoperative application. Images of segmented brain tumors in different localizations and sizes were created in 10 cases and were projected to a head phantom using a video projector. Registration was performed using 5 fiducial markers. After each registration, the distance of the 5 fiducial markers from the visualized tumor borders was measured on the virtual image and on the phantom. The difference was considered a projection error. Moreover, the image projection technique was intraoperatively applied in 5 patients and was compared with a standard navigation system. Augmented reality visualization of the tumors succeeded in all cases. The mean time for registration was 3.8 minutes (range 2-7 minutes). The mean projection error was 0.8 ± 0.25 mm. There were no significant differences in accuracy according to the localization and size of the tumor. Clinical feasibility and reliability of the augmented reality system could be proved intraoperatively in 5 patients (projection error 1.2 ± 0.54 mm). The augmented reality system is accurate and reliable for the intraoperative projection of images to the head, skull, and brain surface. The ergonomic advantage of this technique improves the planning of neurosurgical procedures and enables the surgeon to use direct visualization for image-guided neurosurgery.

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 with therapeutic agents. Experimental studies demonstrated that GNPs might be versatile, persistent, and safe contrast agents for multimodality imaging, thus enhancing the tumor edges pre-, intra-, and post-operatively improving microscopic precision. The diagnostic GNPs might also be used for multiple therapeutic approaches, namely as "theranostic" NPs.

Multiphoton Intravital Calcium Imaging.

PubMed

Cheetham, Claire E J

2018-06-26

Multiphoton intravital calcium imaging is a powerful technique that enables high-resolution longitudinal monitoring of cellular and subcellular activity hundreds of microns deep in the living organism. This unit addresses the application of 2-photon microscopy to imaging of genetically encoded calcium indicators (GECIs) in the mouse brain. The protocols in this unit enable real-time intravital imaging of intracellular calcium concentration simultaneously in hundreds of neurons, or at the resolution of single synapses, as mice respond to sensory stimuli or perform behavioral tasks. Protocols are presented for implantation of a cranial imaging window to provide optical access to the brain and for 2-photon image acquisition. Protocols for implantation of both open skull and thinned skull windows for single or multi-session imaging are described. © 2018 by John Wiley & Sons, Inc. © 2018 John Wiley & Sons, Inc.

A Novel Application for the Cavalieri Principle: A Stereological and Methodological Study

PubMed Central

Altunkaynak, Berrin Zuhal; Altunkaynak, Eyup; Unal, Deniz; Unal, Bunyamin

2009-01-01

Objective The Cavalieri principle was applied to consecutive pathology sections that were photographed at the same magnification and used to estimate tissue volumes via superimposing a point counting grid on these images. The goal of this study was to perform the Cavalieri method quickly and practically. Materials and Methods In this study, 10 adult female Sprague Dawley rats were used. Brain tissue was removed and sampled both systematically and randomly. Brain volumes were estimated using two different methods. First, all brain slices were scanned with an HP ScanJet 3400C scanner, and their images were shown on a PC monitor. Brain volume was then calculated based on these images. Second, all brain slices were photographed in 10× magnification with a microscope camera, and brain volumes were estimated based on these micrographs. Results There was no statistically significant difference between the volume measurements of the two techniques (P>0.05; Paired Samples t Test). Conclusion This study demonstrates that personal computer scanning of serial tissue sections allows for easy and reliable volume determination based on the Cavalieri method. PMID:25610077

A novel application for the cavalieri principle: a stereological and methodological study.

PubMed

Altunkaynak, Berrin Zuhal; Altunkaynak, Eyup; Unal, Deniz; Unal, Bunyamin

2009-08-01

The Cavalieri principle was applied to consecutive pathology sections that were photographed at the same magnification and used to estimate tissue volumes via superimposing a point counting grid on these images. The goal of this study was to perform the Cavalieri method quickly and practically. In this study, 10 adult female Sprague Dawley rats were used. Brain tissue was removed and sampled both systematically and randomly. Brain volumes were estimated using two different methods. First, all brain slices were scanned with an HP ScanJet 3400C scanner, and their images were shown on a PC monitor. Brain volume was then calculated based on these images. Second, all brain slices were photographed in 10× magnification with a microscope camera, and brain volumes were estimated based on these micrographs. There was no statistically significant difference between the volume measurements of the two techniques (P>0.05; Paired Samples t Test). This study demonstrates that personal computer scanning of serial tissue sections allows for easy and reliable volume determination based on the Cavalieri method.

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation

PubMed Central

Habas, Piotr A.; Kim, Kio; Corbett-Detig, James M.; Rousseau, Francois; Glenn, Orit A.; Barkovich, A. James; Studholme, Colin

2010-01-01

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation. PMID:20600970

Image processing applications: From particle physics to society

NASA Astrophysics Data System (ADS)

Sotiropoulou, C.-L.; Luciano, P.; Gkaitatzis, S.; Citraro, S.; Giannetti, P.; Dell'Orso, M.

2017-01-01

We present an embedded system for extremely efficient real-time pattern recognition execution, enabling technological advancements with both scientific and social impact. It is a compact, fast, low consumption processing unit (PU) based on a combination of Field Programmable Gate Arrays (FPGAs) and the full custom associative memory chip. The PU has been developed for real time tracking in particle physics experiments, but delivers flexible features for potential application in a wide range of fields. It has been proposed to be used in accelerated pattern matching execution for Magnetic Resonance Fingerprinting (biomedical applications), in real time detection of space debris trails in astronomical images (space applications) and in brain emulation for image processing (cognitive image processing). We illustrate the potentiality of the PU for the new applications.

Magnetic resonance elastography (MRE) of the human brain: technique, findings and clinical applications

NASA Astrophysics Data System (ADS)

Hiscox, Lucy V.; Johnson, Curtis L.; Barnhill, Eric; McGarry, Matt D. J.; Huston 3rd, John; van Beek, Edwin J. R.; Starr, John M.; Roberts, Neil

2016-12-01

Neurological disorders are one of the most important public health concerns in developed countries. Established brain imaging techniques such as magnetic resonance imaging (MRI) and x-ray computerised tomography (CT) have been essential in the identification and diagnosis of a wide range of disorders, although usually are insufficient in sensitivity for detecting subtle pathological alterations to the brain prior to the onset of clinical symptoms—at a time when prognosis for treatment is more favourable. The mechanical properties of biological tissue provide information related to the strength and integrity of the cellular microstructure. In recent years, mechanical properties of the brain have been visualised and measured non-invasively with magnetic resonance elastography (MRE), a particularly sensitive medical imaging technique that may increase the potential for early diagnosis. This review begins with an introduction to the various methods used for the acquisition and analysis of MRE data. A systematic literature search is then conducted to identify studies that have specifically utilised MRE to investigate the human brain. Through the conversion of MRE-derived measurements to shear stiffness (kPa) and, where possible, the loss tangent (rad), a summary of results for global brain tissue and grey and white matter across studies is provided for healthy participants, as potential baseline values to be used in future clinical investigations. In addition, the extent to which MRE has revealed significant alterations to the brain in patients with neurological disorders is assessed and discussed in terms of known pathophysiology. The review concludes by predicting the trends for future MRE research and applications in neuroscience.

Grid Computing Application for Brain Magnetic Resonance Image Processing

NASA Astrophysics Data System (ADS)

Valdivia, F.; Crépeault, B.; Duchesne, S.

2012-02-01

This work emphasizes the use of grid computing and web technology for automatic post-processing of brain magnetic resonance images (MRI) in the context of neuropsychiatric (Alzheimer's disease) research. Post-acquisition image processing is achieved through the interconnection of several individual processes into pipelines. Each process has input and output data ports, options and execution parameters, and performs single tasks such as: a) extracting individual image attributes (e.g. dimensions, orientation, center of mass), b) performing image transformations (e.g. scaling, rotation, skewing, intensity standardization, linear and non-linear registration), c) performing image statistical analyses, and d) producing the necessary quality control images and/or files for user review. The pipelines are built to perform specific sequences of tasks on the alphanumeric data and MRIs contained in our database. The web application is coded in PHP and allows the creation of scripts to create, store and execute pipelines and their instances either on our local cluster or on high-performance computing platforms. To run an instance on an external cluster, the web application opens a communication tunnel through which it copies the necessary files, submits the execution commands and collects the results. We present result on system tests for the processing of a set of 821 brain MRIs from the Alzheimer's Disease Neuroimaging Initiative study via a nonlinear registration pipeline composed of 10 processes. Our results show successful execution on both local and external clusters, and a 4-fold increase in performance if using the external cluster. However, the latter's performance does not scale linearly as queue waiting times and execution overhead increase with the number of tasks to be executed.

Leveraging Clinical Imaging Archives for Radiomics: Reliability of Automated Methods for Brain Volume Measurement.

PubMed

Adduru, Viraj R; Michael, Andrew M; Helguera, Maria; Baum, Stefi A; Moore, Gregory J

2017-09-01

Purpose To validate the use of thick-section clinically acquired magnetic resonance (MR) imaging data for estimating total brain volume (TBV), gray matter (GM) volume (GMV), and white matter (WM) volume (WMV) by using three widely used automated toolboxes: SPM ( www.fil.ion.ucl.ac.uk/spm/ ), FreeSurfer ( surfer.nmr.mgh.harvard.edu ), and FSL (FMRIB software library; Oxford Centre for Functional MR Imaging of the Brain, Oxford, England, https://fsl.fmrib.ox.ac.uk/fsl ). Materials and Methods MR images from a clinical archive were used and data were deidentified. The three methods were applied to estimate brain volumes from thin-section research-quality brain MR images and routine thick-section clinical MR images acquired from the same 38 patients (age range, 1-71 years; mean age, 22 years; 11 women). By using these automated methods, TBV, GMV, and WMV were estimated. Thin- versus thick-section volume comparisons were made for each method by using intraclass correlation coefficients (ICCs). Results SPM exhibited excellent ICCs (0.97, 0.85, and 0.83 for TBV, GMV, and WMV, respectively). FSL exhibited ICCs of 0.69, 0.51, and 0.60 for TBV, GMV, and WMV, respectively, but they were lower than with SPM. FreeSurfer exhibited excellent ICC of 0.63 only for TBV. Application of SPM's voxel-based morphometry on the modulated images of thin-section images and interpolated thick-section images showed fair to excellent ICCs (0.37-0.98) for the majority of brain regions (88.47% [306924 of 346916 voxels] of WM and 80.35% [377 282 of 469 502 voxels] of GM). Conclusion Thick-section clinical-quality MR images can be reliably used for computing quantitative brain metrics such as TBV, GMV, and WMV by using SPM. © RSNA, 2017 Online supplemental material is available for this article.

The possibility of application of spiral brain computed tomography to traumatic brain injury.

PubMed

Lim, Daesung; Lee, Soo Hoon; Kim, Dong Hoon; Choi, Dae Seub; Hong, Hoon Pyo; Kang, Changwoo; Jeong, Jin Hee; Kim, Seong Chun; Kang, Tae-Sin

2014-09-01

The spiral computed tomography (CT) with the advantage of low radiation dose, shorter test time required, and its multidimensional reconstruction is accepted as an essential diagnostic method for evaluating the degree of injury in severe trauma patients and establishment of therapeutic plans. However, conventional sequential CT is preferred for the evaluation of traumatic brain injury (TBI) over spiral CT due to image noise and artifact. We aimed to compare the diagnostic power of spiral facial CT for TBI to that of conventional sequential brain CT. We evaluated retrospectively the images of 315 traumatized patients who underwent both brain CT and facial CT simultaneously. The hemorrhagic traumatic brain injuries such as epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage, and contusional hemorrhage were evaluated in both images. Statistics were performed using Cohen's κ to compare the agreement between 2 imaging modalities and sensitivity, specificity, positive predictive value, and negative predictive value of spiral facial CT to conventional sequential brain CT. Almost perfect agreement was noted regarding hemorrhagic traumatic brain injuries between spiral facial CT and conventional sequential brain CT (Cohen's κ coefficient, 0.912). To conventional sequential brain CT, sensitivity, specificity, positive predictive value, and negative predictive value of spiral facial CT were 92.2%, 98.1%, 95.9%, and 96.3%, respectively. In TBI, the diagnostic power of spiral facial CT was equal to that of conventional sequential brain CT. Therefore, expanded spiral facial CT covering whole frontal lobe can be applied to evaluate TBI in the future. Copyright © 2014 Elsevier Inc. All rights reserved.

Classroom Applications of Top-Down and Bottom-Up Processing

ERIC Educational Resources Information Center

Lovrich, Deborah

2007-01-01

Recent research in cognitive neuroscience has yielded a more comprehensive understanding of brain function. Some of these diagnostic techniques include the event-related potential, which depicts brain electrical activity, and magnetic resonance imaging and positron emission tomography, which are particularly sensitive to the delineation of brain…

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.

[Surface coils for magnetic-resonance images].

PubMed

Rodríguez-González, Alfredo Odón; Amador-Baheza, Ricardo; Rojas-Jasso, Rafael; Barrios-Alvarez, Fernando Alejandro

2005-01-01

Since the introduction of magnetic resonance imaging in Mexico, the development of this important medical imaging technology has been almost non-existing in our country. The very first surface coil prototypes for clinical applications in magnetic resonance imaging has been developed at the Center of Research in Medical Imaging and Instrumentation of the Universidad Autónoma Metropolitana Iztapalapa (Metropolitan Autonomous University, Campus Iztapalapa). Two surface coil prototypes were built: a) a circular-shaped coil and b) a square-shaped coil for multiple regions of the body, such as heart, brain, knee, hands, and ankles. These coils were tested on the 1.5T imager of the ABC Hospital-Tacubaya, located in Mexico City. Brain images of healthy volunteers were obtained in different orientations: sagittal, coronal, and axial. Since images showed a good-enough clinical quality for diagnosis, it is fair to say that these coil prototypes can be used in the clinical environment, and with small modifications, they can be made compatible with almost any commercial scanner. This type of development can offer new alternatives for further collaboration between the research centers and the radiology community, in the search of new applications and developments of this imaging technique.

A generative probabilistic model and discriminative extensions for brain lesion segmentation – with application to tumor and stroke

PubMed Central

Menze, Bjoern H.; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-André; Székely, Gabor; Ayache, Nicholas; Golland, Polina

2016-01-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM) to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as “tumor core” or “fluid-filled structure”, but without a one-to-one correspondence to the hypo-or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the generative-discriminative model to be one of the top ranking methods in the BRATS evaluation. PMID:26599702

A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation--With Application to Tumor and Stroke.

PubMed

Menze, Bjoern H; Van Leemput, Koen; Lashkari, Danial; Riklin-Raviv, Tammy; Geremia, Ezequiel; Alberts, Esther; Gruber, Philipp; Wegener, Susanne; Weber, Marc-Andre; Szekely, Gabor; Ayache, Nicholas; Golland, Polina

2016-04-01

We introduce a generative probabilistic model for segmentation of brain lesions in multi-dimensional images that generalizes the EM segmenter, a common approach for modelling brain images using Gaussian mixtures and a probabilistic tissue atlas that employs expectation-maximization (EM), to estimate the label map for a new image. Our model augments the probabilistic atlas of the healthy tissues with a latent atlas of the lesion. We derive an estimation algorithm with closed-form EM update equations. The method extracts a latent atlas prior distribution and the lesion posterior distributions jointly from the image data. It delineates lesion areas individually in each channel, allowing for differences in lesion appearance across modalities, an important feature of many brain tumor imaging sequences. We also propose discriminative model extensions to map the output of the generative model to arbitrary labels with semantic and biological meaning, such as "tumor core" or "fluid-filled structure", but without a one-to-one correspondence to the hypo- or hyper-intense lesion areas identified by the generative model. We test the approach in two image sets: the publicly available BRATS set of glioma patient scans, and multimodal brain images of patients with acute and subacute ischemic stroke. We find the generative model that has been designed for tumor lesions to generalize well to stroke images, and the extended discriminative -discriminative model to be one of the top ranking methods in the BRATS evaluation.

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.

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.

NMR imaging of cell phone radiation absorption in brain tissue

PubMed Central

Gultekin, David H.; Moeller, Lothar

2013-01-01

A method is described for measuring absorbed electromagnetic energy radiated from cell phone antennae into ex vivo brain tissue. NMR images the 3D thermal dynamics inside ex vivo bovine brain tissue and equivalent gel under exposure to power and irradiation time-varying radio frequency (RF) fields. The absorbed RF energy in brain tissue converts into Joule heat and affects the nuclear magnetic shielding and the Larmor precession. The resultant temperature increase is measured by the resonance frequency shift of hydrogen protons in brain tissue. This proposed application of NMR thermometry offers sufficient spatial and temporal resolution to characterize the hot spots from absorbed cell phone radiation in aqueous media and biological tissues. Specific absorption rate measurements averaged over 1 mg and 10 s in the brain tissue cover the total absorption volume. Reference measurements with fiber optic temperature sensors confirm the accuracy of the NMR thermometry. PMID:23248293

NMR imaging of cell phone radiation absorption in brain tissue.

PubMed

Gultekin, David H; Moeller, Lothar

2013-01-02

A method is described for measuring absorbed electromagnetic energy radiated from cell phone antennae into ex vivo brain tissue. NMR images the 3D thermal dynamics inside ex vivo bovine brain tissue and equivalent gel under exposure to power and irradiation time-varying radio frequency (RF) fields. The absorbed RF energy in brain tissue converts into Joule heat and affects the nuclear magnetic shielding and the Larmor precession. The resultant temperature increase is measured by the resonance frequency shift of hydrogen protons in brain tissue. This proposed application of NMR thermometry offers sufficient spatial and temporal resolution to characterize the hot spots from absorbed cell phone radiation in aqueous media and biological tissues. Specific absorption rate measurements averaged over 1 mg and 10 s in the brain tissue cover the total absorption volume. Reference measurements with fiber optic temperature sensors confirm the accuracy of the NMR thermometry.

Imaging Metals in Brain Tissue by Laser Ablation - Inductively Coupled Plasma - Mass Spectrometry (LA-ICP-MS)

PubMed Central

Hare, Dominic J.; Kysenius, Kai; Paul, Bence; Knauer, Beate; Hutchinson, Robert W.; O'Connor, Ciaran; Fryer, Fred; Hennessey, Tom P.; Bush, Ashley I.; Crouch, Peter J.; Doble, Philip A.

2017-01-01

Metals are found ubiquitously throughout an organism, with their biological role dictated by both their chemical reactivity and abundance within a specific anatomical region. Within the brain, metals have a highly compartmentalized distribution, depending on the primary function they play within the central nervous system. Imaging the spatial distribution of metals has provided unique insight into the biochemical architecture of the brain, allowing direct correlation between neuroanatomical regions and their known function with regard to metal-dependent processes. In addition, several age-related neurological disorders feature disrupted metal homeostasis, which is often confined to small regions of the brain that are otherwise difficult to analyze. Here, we describe a comprehensive method for quantitatively imaging metals in the mouse brain, using laser ablation - inductively coupled plasma - mass spectrometry (LA-ICP-MS) and specially designed image processing software. Focusing on iron, copper and zinc, which are three of the most abundant and disease-relevant metals within the brain, we describe the essential steps in sample preparation, analysis, quantitative measurements and image processing to produce maps of metal distribution within the low micrometer resolution range. This technique, applicable to any cut tissue section, is capable of demonstrating the highly variable distribution of metals within an organ or system, and can be used to identify changes in metal homeostasis and absolute levels within fine anatomical structures. PMID:28190025

Server-based Approach to Web Visualization of Integrated Three-dimensional Brain Imaging Data

PubMed Central

Poliakov, Andrew V.; Albright, Evan; Hinshaw, Kevin P.; Corina, David P.; Ojemann, George; Martin, Richard F.; Brinkley, James F.

2005-01-01

The authors describe a client-server approach to three-dimensional (3-D) visualization of neuroimaging data, which enables researchers to visualize, manipulate, and analyze large brain imaging datasets over the Internet. All computationally intensive tasks are done by a graphics server that loads and processes image volumes and 3-D models, renders 3-D scenes, and sends the renderings back to the client. The authors discuss the system architecture and implementation and give several examples of client applications that allow visualization and analysis of integrated language map data from single and multiple patients. PMID:15561787

Non-invasive neuroimaging using near-infrared light

NASA Technical Reports Server (NTRS)

Strangman, Gary; Boas, David A.; Sutton, Jeffrey P.

2002-01-01

This article reviews diffuse optical brain imaging, a technique that employs near-infrared light to non-invasively probe the brain for changes in parameters relating to brain function. We describe the general methodology, including types of measurements and instrumentation (including the tradeoffs inherent in the various instrument components), and the basic theory required to interpret the recorded data. A brief review of diffuse optical applications is included, with an emphasis on research that has been done with psychiatric populations. Finally, we discuss some practical issues and limitations that are relevant when conducting diffuse optical experiments. We find that, while diffuse optics can provide substantial advantages to the psychiatric researcher relative to the alternative brain imaging methods, the method remains substantially underutilized in this field.

Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera.

PubMed

Yoshida, Keiichiro; Nishidate, Izumi; Ishizuka, Tomohiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-05-01

In order to estimate multispectral images of the absorption and scattering properties in the cerebral cortex of in vivo rat brain, we investigated spectral reflectance images estimated by the Wiener estimation method using a digital RGB camera. A Monte Carlo simulation-based multiple regression analysis for the corresponding spectral absorbance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) was then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentrations of oxygenated hemoglobin and that of deoxygenated hemoglobin were estimated as the absorption parameters, whereas the coefficient a and the exponent b of the reduced scattering coefficient spectrum approximated by a power law function were estimated as the scattering parameters. The spectra of absorption and reduced scattering coefficients were reconstructed from the absorption and scattering parameters, and the spectral images of absorption and reduced scattering coefficients were then estimated. In order to confirm the feasibility of this method, we performed in vivo experiments on exposed rat brain. The estimated images of the absorption coefficients were dominated by the spectral characteristics of hemoglobin. The estimated spectral images of the reduced scattering coefficients had a broad scattering spectrum, exhibiting a larger magnitude at shorter wavelengths, corresponding to the typical spectrum of brain tissue published in the literature. The changes in the estimated absorption and scattering parameters during normoxia, hyperoxia, and anoxia indicate the potential applicability of the method by which to evaluate the pathophysiological conditions of in vivo brain due to the loss of tissue viability.

“Optical communication with brain cells by means of an implanted duplex micro-device with optogenetics and Ca2+ fluoroimaging”

PubMed Central

Kobayashi, Takuma; Haruta, Makito; Sasagawa, Kiyotaka; Matsumata, Miho; Eizumi, Kawori; Kitsumoto, Chikara; Motoyama, Mayumi; Maezawa, Yasuyo; Ohta, Yasumi; Noda, Toshihiko; Tokuda, Takashi; Ishikawa, Yasuyuki; Ohta, Jun

2016-01-01

To better understand the brain function based on neural activity, a minimally invasive analysis technology in a freely moving animal is necessary. Such technology would provide new knowledge in neuroscience and contribute to regenerative medical techniques and prosthetics care. An application that combines optogenetics for voluntarily stimulating nerves, imaging to visualize neural activity, and a wearable micro-instrument for implantation into the brain could meet the abovementioned demand. To this end, a micro-device that can be applied to the brain less invasively and a system for controlling the device has been newly developed in this study. Since the novel implantable device has dual LEDs and a CMOS image sensor, photostimulation and fluorescence imaging can be performed simultaneously. The device enables bidirectional communication with the brain by means of light. In the present study, the device was evaluated in an in vitro experiment using a new on-chip 3D neuroculture with an extracellular matrix gel and an in vivo experiment involving regenerative medical transplantation and gene delivery to the brain by using both photosensitive channel and fluorescent Ca2+ indicator. The device succeeded in activating cells locally by selective photostimulation, and the physiological Ca2+ dynamics of neural cells were visualized simultaneously by fluorescence imaging. PMID:26878910

“Optical communication with brain cells by means of an implanted duplex micro-device with optogenetics and Ca2+ fluoroimaging”

NASA Astrophysics Data System (ADS)

Kobayashi, Takuma; Haruta, Makito; Sasagawa, Kiyotaka; Matsumata, Miho; Eizumi, Kawori; Kitsumoto, Chikara; Motoyama, Mayumi; Maezawa, Yasuyo; Ohta, Yasumi; Noda, Toshihiko; Tokuda, Takashi; Ishikawa, Yasuyuki; Ohta, Jun

2016-02-01

To better understand the brain function based on neural activity, a minimally invasive analysis technology in a freely moving animal is necessary. Such technology would provide new knowledge in neuroscience and contribute to regenerative medical techniques and prosthetics care. An application that combines optogenetics for voluntarily stimulating nerves, imaging to visualize neural activity, and a wearable micro-instrument for implantation into the brain could meet the abovementioned demand. To this end, a micro-device that can be applied to the brain less invasively and a system for controlling the device has been newly developed in this study. Since the novel implantable device has dual LEDs and a CMOS image sensor, photostimulation and fluorescence imaging can be performed simultaneously. The device enables bidirectional communication with the brain by means of light. In the present study, the device was evaluated in an in vitro experiment using a new on-chip 3D neuroculture with an extracellular matrix gel and an in vivo experiment involving regenerative medical transplantation and gene delivery to the brain by using both photosensitive channel and fluorescent Ca2+ indicator. The device succeeded in activating cells locally by selective photostimulation, and the physiological Ca2+ dynamics of neural cells were visualized simultaneously by fluorescence imaging.

Long-term optical imaging of intrinsic signals in anesthetized and awake monkeys

NASA Astrophysics Data System (ADS)

Roe, Anna W.

2007-04-01

Some exciting new efforts to use intrinsic signal optical imaging methods for long-term studies in anesthetized and awake monkeys are reviewed. The development of such methodologies opens the door for studying behavioral states such as attention, motivation, memory, emotion, and other higher-order cognitive functions. Long-term imaging is also ideal for studying changes in the brain that accompany development, plasticity, and learning. Although intrinsic imaging lacks the temporal resolution offered by dyes, it is a high spatial resolution imaging method that does not require application of any external agents to the brain. The bulk of procedures described here have been developed in the monkey but can be applied to the study of surface structures in any in vivo preparation.

Mental Imagery: Functional Mechanisms and Clinical Applications

PubMed Central

Pearson, Joel; Naselaris, Thomas; Holmes, Emily A.; Kosslyn, Stephen M.

2015-01-01

Mental imagery research has weathered both disbelief of the phenomenon and inherent methodological limitations. Here we review recent behavioral, brain imaging, and clinical research that has reshaped our understanding of mental imagery. Research supports the claim that visual mental imagery is a depictive internal representation that functions like a weak form of perception. Brain imaging work has demonstrated that neural representations of mental and perceptual images resemble one another as early as the primary visual cortex (V1). Activity patterns in V1 encode mental images and perceptual images via a common set of low-level depictive visual features. Recent translational and clinical research reveals the pivotal role that imagery plays in many mental disorders and suggests how clinicians can utilize imagery in treatment. PMID:26412097

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.

Brain SPECT Imaging in Complex Psychiatric Cases: An Evidence-Based, Underutilized Tool

PubMed Central

Amen, Daniel G; Trujillo, Manuel; Newberg, Andrew; Willeumier, Kristen; Tarzwell, Robert; Wu, Joseph C; Chaitin, Barry

2011-01-01

Over the past 20 years brain Single Photon Emission Computed Tomography (SPECT) imaging has developed a substantial, evidence-based foundation and is now recommended by professional societies for numerous indications relevant to psychiatric practice. Unfortunately, SPECT in clinical practice is utilized by only a handful of clinicians. This article presents a rationale for a more widespread use of SPECT in clinical practice for complex cases, and includes seven clinical applications where it may help optimize patient care. PMID:21863144

Optical coherent tomography and fluorescent microscopy for the study of meningeal lymphatic systems

NASA Astrophysics Data System (ADS)

Semyachkina-Glushkovskaya, O.; Abdurashitov, A.; Namykin, A.; Fedosov, I.; Pavlov, A.; Karavaev, A.; Sindeeva, O.; Shirokov, A.; Ulanova, M.; Shushunova, N.; Khorovodov, A.; Agranovich, I.; Bodrova, A.; Sagatova, M.; Shareef, Ali Esmat; Saranceva, E.; Dvoryatkina, M.; Tuchin, V.

2018-04-01

The development of novel technologies for the imaging of meningeal lymphatic vessels is one of the amazing trends of biophotonics thanks to discovery of brain lymphatics over several years ago. However, there is the limited technologies exist for the study of lymphatics in vivo because lymphatic vessels are transparent with a low speed flow of lymph. Here we demonstrate the successful application of fluorescent microscopy for the imaging of lymphatic system in the mouse brain in vivo.

Tissue Tracking: Applications for Brain MRI Classification

DTIC Science & Technology

2007-01-01

General Hospital, Center for Morphometric Analysis.10,11 The IBSR data-sets are T1-weighted, 3D coronal brain scans after having been positionally...learned priors,” Image Processing, IEEE Transactions on 9(2), pp. 299–301, 2000. 5. P. Olver, G. Sapiro, and A. Tannenbaum, “Invariant Geometric Evolutions...MRI,” NeuroImage 22(3), pp. 1060–1075, 2004. 16. A. Zijdenbos, B. Dawant, R. Margolin, and A. Palmer, “ Morphometric analysis of white matter lesions in

Comparison of seven optical clearing methods for mouse brain

NASA Astrophysics Data System (ADS)

Wan, Peng; Zhu, Jingtan; Yu, Tingting; Zhu, Dan

2018-02-01

Recently, a variety of tissue optical clearing techniques have been developed to reduce light scattering for imaging deeper and three-dimensional reconstruction of tissue structures. Combined with optical imaging techniques and diverse labeling methods, these clearing methods have significantly promoted the development of neuroscience. However, most of the protocols were proposed aiming for specific tissue type. Though there are some comparison results, the clearing methods covered are limited and the evaluation indices are lack of uniformity, which made it difficult to select a best-fit protocol for clearing in practical applications. Hence, it is necessary to systematically assess and compare these clearing methods. In this work, we evaluated the performance of seven typical clearing methods, including 3DISCO, uDISCO, SeeDB, ScaleS, ClearT2, CUBIC and PACT, on mouse brain samples. First, we compared the clearing capability on both brain slices and whole-brains by observing brain transparency. Further, we evaluated the fluorescence preservation and the increase of imaging depth. The results showed that 3DISCO, uDISCO and PACT posed excellent clearing capability on mouse brains, ScaleS and SeeDB rendered moderate transparency, while ClearT2 was the worst. Among those methods, ScaleS was the best on fluorescence preservation, and PACT achieved the highest increase of imaging depth. This study is expected to provide important reference for users in choosing most suitable brain optical clearing method.

Direct Visualization of Neurotransmitters in Rat Brain Slices by Desorption Electrospray Ionization Mass Spectrometry Imaging (DESI - MS)

NASA Astrophysics Data System (ADS)

Fernandes, Anna Maria A. P.; Vendramini, Pedro H.; Galaverna, Renan; Schwab, Nicolas V.; Alberici, Luciane C.; Augusti, Rodinei; Castilho, Roger F.; Eberlin, Marcos N.

2016-12-01

Mass spectrometry imaging (MSI) of neurotransmitters has so far been mainly performed by matrix-assisted laser desorption/ionization (MALDI) where derivatization reagents, deuterated matrix and/or high resolution, or tandem MS have been applied to circumvent problems with interfering ion peaks from matrix and from isobaric species. We herein describe the application of desorption electrospray ionization mass spectrometry imaging (DESI)-MSI in rat brain coronal and sagittal slices for direct spatial monitoring of neurotransmitters and choline with no need of derivatization reagents and/or deuterated materials. The amino acids γ-aminobutyric (GABA), glutamate, aspartate, serine, as well as acetylcholine, dopamine, and choline were successfully imaged using a commercial DESI source coupled to a hybrid quadrupole-Orbitrap mass spectrometer. The spatial distribution of the analyzed compounds in different brain regions was determined. We conclude that the ambient matrix-free DESI-MSI is suitable for neurotransmitter imaging and could be applied in studies that involve evaluation of imbalances in neurotransmitters levels.

Positron Emission Tomography: Human Brain Function and Biochemistry.

ERIC Educational Resources Information Center

Phelps, Michael E.; Mazziotta, John C.

1985-01-01

Describes the method, present status, and application of positron emission tomography (PET), an analytical imaging technique for "in vivo" measurements of the anatomical distribution and rates of specific biochemical reactions. Measurements and image dynamic biochemistry link basic and clinical neurosciences with clinical findings…

An automatic fuzzy-based multi-temporal brain digital subtraction angiography image fusion algorithm using curvelet transform and content selection strategy.

PubMed

Momeni, Saba; Pourghassem, Hossein

2014-08-01

Recently image fusion has prominent role in medical image processing and is useful to diagnose and treat many diseases. Digital subtraction angiography is one of the most applicable imaging to diagnose brain vascular diseases and radiosurgery of brain. This paper proposes an automatic fuzzy-based multi-temporal fusion algorithm for 2-D digital subtraction angiography images. In this algorithm, for blood vessel map extraction, the valuable frames of brain angiography video are automatically determined to form the digital subtraction angiography images based on a novel definition of vessel dispersion generated by injected contrast material. Our proposed fusion scheme contains different fusion methods for high and low frequency contents based on the coefficient characteristic of wrapping second generation of curvelet transform and a novel content selection strategy. Our proposed content selection strategy is defined based on sample correlation of the curvelet transform coefficients. In our proposed fuzzy-based fusion scheme, the selection of curvelet coefficients are optimized by applying weighted averaging and maximum selection rules for the high frequency coefficients. For low frequency coefficients, the maximum selection rule based on local energy criterion is applied to better visual perception. Our proposed fusion algorithm is evaluated on a perfect brain angiography image dataset consisting of one hundred 2-D internal carotid rotational angiography videos. The obtained results demonstrate the effectiveness and efficiency of our proposed fusion algorithm in comparison with common and basic fusion algorithms.

Three‐dimensional motion corrected sensitivity encoding reconstruction for multi‐shot multi‐slice MRI: Application to neonatal brain imaging

PubMed Central

Hughes, Emer J.; Hutter, Jana; Price, Anthony N.; Hajnal, Joseph V.

2017-01-01

Purpose To introduce a methodology for the reconstruction of multi‐shot, multi‐slice magnetic resonance imaging able to cope with both within‐plane and through‐plane rigid motion and to describe its application in structural brain imaging. Theory and Methods The method alternates between motion estimation and reconstruction using a common objective function for both. Estimates of three‐dimensional motion states for each shot and slice are gradually refined by improving on the fit of current reconstructions to the partial k‐space information from multiple coils. Overlapped slices and super‐resolution allow recovery of through‐plane motion and outlier rejection discards artifacted shots. The method is applied to T 2 and T 1 brain scans acquired in different views. Results The procedure has greatly diminished artifacts in a database of 1883 neonatal image volumes, as assessed by image quality metrics and visual inspection. Examples showing the ability to correct for motion and robustness against damaged shots are provided. Combination of motion corrected reconstructions for different views has shown further artifact suppression and resolution recovery. Conclusion The proposed method addresses the problem of rigid motion in multi‐shot multi‐slice anatomical brain scans. Tests on a large collection of potentially corrupted datasets have shown a remarkable image quality improvement. Magn Reson Med 79:1365–1376, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. PMID:28626962

Localization of brain tumors with Indium-111 labeled somatostatin analogue and iodine-123-methyl tyrosine

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

Kroiss, A.; Boeck, F.; Auinger, C.

1994-05-01

The aim of this study was to compare the visualization of brain tumors with Iodine-123-Methyl Tyrosine (l-123-MT) and Indium-111-Octreotide (ln-111-Oc). We used l-123-MT (FZ-Seibersdorf), administering 222 MBq and planar images were performed 10min and 1hr after application. In 5 pts SPECT images were performed too. Not earlier than 48hrs 244 MBq In-111-Oc (OctreoScan{sup {reg_sign}}, Mallincrodt) were injected and planar images 4 and 24 hrs after application performed. In 9 pts SPECT images were performed (4hrs p.appl.). A digital Anger camera was used for data acquisition and processing (APEX 409A, Elscint). A total of 12 pts (8 male, 4 female agemore » ranging from 45-71 yrs) were investigated. using a region of interest technique tumor-to-brain tissue rations (T/BT) were calculated. Diagnosis of tumor was established by neurosurgical procedures. 6 pts with glioblastoma showed a high uptake with in-111-Oc (T/BT 1.7 {plus_minus}0.5) and also with l-123-MT T/BT: 1.5{plus_minus}0.4 (10 {prime}) and 1.45 {plus_minus}0.45 (1h). In 2 menigiomas the images with ln-111-Oc were very good (T/BT: 3.1, 3.6 (4hr pl)) and were negative with l-123-MT. In 4 metastases we found a low uptake in 2 pts with l-123-MT T/BT (10{prime}): 1.3 and 1.25; T/BT (1h): 1.25 and 1.2 and ln-111-Oc (T/BT (4h pl): 1.6 and 1.4). These were pts with brain metastases of adeno carcinoma. In two pts with brain metastases of small cell lung cancer we found good images with both substances I-123-MT T/BT: 1.6 and 1.7 (1h) and ln-111-Oc T/BT: 2.5 and 2.6 (4h pl). In summary, glioblastoma showed concordant images with both substances and also metastases, meningiomas showed discordant images. SPECT acquisition is possible with both substances and sometimes advisable.« less

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

Neuroimaging is a novel tool to understand the impact of environmental chemicals on neurodevelopment.

PubMed

Horton, Megan K; Margolis, Amy E; Tang, Cheuk; Wright, Robert

2014-04-01

The prevalence of childhood neurodevelopmental disorders has been increasing over the last several decades. Prenatal and early childhood exposure to environmental toxicants is increasingly recognized as contributing to the growing rate of neurodevelopmental disorders. Very little information is known about the mechanistic processes by which environmental chemicals alter brain development. We review the recent advances in brain imaging modalities and discuss their application in epidemiologic studies of prenatal and early childhood exposure to environmental toxicants. Neuroimaging techniques (volumetric and functional MRI, diffusor tensor imaging, and magnetic resonance spectroscopy) have opened unprecedented access to study the developing human brain. These techniques are noninvasive and free of ionization radiation making them suitable for research applications in children. Using these techniques, we now understand much about structural and functional patterns in the typically developing brain. This knowledge allows us to investigate how prenatal exposure to environmental toxicants may alter the typical developmental trajectory. MRI is a powerful tool that allows in-vivo visualization of brain structure and function. Used in epidemiologic studies of environmental exposure, it offers the promise to causally link exposure with behavioral and cognitive manifestations and ultimately to inform programs to reduce exposure and mitigate adverse effects of exposure.

Multifunctional Nanoparticles for Brain Tumor Diagnosis and Therapy

PubMed Central

Cheng, Yu; Morshed, Ramin; Auffinger, Brenda; Tobias, Alex L.; Lesniak, Maciej S.

2013-01-01

Brain tumors are a diverse group of neoplasms that often carry a poor prognosis for patients. Despite tremendous efforts to develop diagnostic tools and therapeutic avenues, the treatment of brain tumors remains a formidable challenge in the field of neuro-oncology. Physiological barriers including the blood-brain barrier result in insufficient accumulation of therapeutic agents at the site of a tumor, preventing adequate destruction of malignant cells. Furthermore, there is a need for improvements in brain tumor imaging to allow for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional nanoparticles offer the potential to improve upon many of these issues and may lead to breakthroughs in brain tumor management. In this review, we discuss the diagnostic and therapeutic applications of nanoparticles for brain tumors with an emphasis on innovative approaches in tumor targeting, tumor imaging, and therapeutic agent delivery. Clinically feasible nanoparticle administration strategies for brain tumor patients are also examined. Furthermore, we address the barriers towards clinical implementation of multifunctional nanoparticles in the context of brain tumor management. PMID:24060923

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

PubMed Central

Sunkin, Susan M.; Ng, Lydia; Lau, Chris; Dolbeare, Tim; Gilbert, Terri L.; Thompson, Carol L.; Hawrylycz, Michael; Dang, Chinh

2013-01-01

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal. PMID:23193282

Dual-modality brain PET-CT image segmentation based on adaptive use of functional and anatomical information.

PubMed

Xia, Yong; Eberl, Stefan; Wen, Lingfeng; Fulham, Michael; Feng, David Dagan

2012-01-01

Dual medical imaging modalities, such as PET-CT, are now a routine component of clinical practice. Medical image segmentation methods, however, have generally only been applied to single modality images. In this paper, we propose the dual-modality image segmentation model to segment brain PET-CT images into gray matter, white matter and cerebrospinal fluid. This model converts PET-CT image segmentation into an optimization process controlled simultaneously by PET and CT voxel values and spatial constraints. It is innovative in the creation and application of the modality discriminatory power (MDP) coefficient as a weighting scheme to adaptively combine the functional (PET) and anatomical (CT) information on a voxel-by-voxel basis. Our approach relies upon allowing the modality with higher discriminatory power to play a more important role in the segmentation process. We compared the proposed approach to three other image segmentation strategies, including PET-only based segmentation, combination of the results of independent PET image segmentation and CT image segmentation, and simultaneous segmentation of joint PET and CT images without an adaptive weighting scheme. Our results in 21 clinical studies showed that our approach provides the most accurate and reliable segmentation for brain PET-CT images. Copyright © 2011 Elsevier Ltd. All rights reserved.

Simpler images, better results

NASA Astrophysics Data System (ADS)

Chance, Britton

1999-03-01

The very rapid development of optical technology has followed a pattern similar to that of nuclear magnetic resonance: first, spectroscopy and then imaging. The accomplishments in spectroscopy have been significant--among them, early detection of hematomas and quantitative oximetry (assuming that time and frequency domain instruments are used). Imaging has progressed somewhat later. The first images were obtained in Japan and USA a few years ago, particularly of parietal stimulation of the human brain. Since then, rapid applications to breast and limb, together with higher resolution of the brain now make NIR imaging of functional activation and tumor detection readily available, reliable and affordable devices. The lecture has to do with the applications of imaging to these three areas, particularly to prefrontal imaging of cognitive function, of breast tumor detection, and of localized muscle activation in exercise. The imaging resolution achievable in functional activation appears to be FWHM of 4 mm. The time required for an image is a few seconds or even much less. Breast image detection at 50 microsecond(s) ec/pixel results in images obtainable in a few seconds or shorter times (bandwidths of the kHz are available). Finally, imaging of the body organs is under study in this laboratory, particularly in the in utero fetus. It appears that the photon migration theory now leads to the development of a wide number of images for human subject tissue spectroscopy and imaging.

Nonlinear optical and multiphoton processes for in situ manipulation and conversion of photons: applications to energy and healthcare (Conference Presentation)

NASA Astrophysics Data System (ADS)

Prasad, Paras N.

2017-02-01

Chiral control of nonlinear optical functions holds a great promise for a wide range of applications including optical signal processing, bio-sensing and chiral bio-imaging. In chiral polyfluorene thin films, we demonstrated extremely large chiral nonlinearity. The physics of manipulating excitation dynamics for photon transformation will be discussed, along with nanochemistry control of upconversion in hierarchically built organic chromophore coupled-core-multiple shell nanostructures which enable introduce new, organic-inorganic energy transfer routes for broadband light harvesting and increased upconversion efficiency via multistep cascaded energy transfer. We are pursuing the applications of photon conversion technology in IR harvesting for photovoltaics, high contrast bioimaging, photoacoustic imaging, photodynamic therapy, and optogenetics. An important application is in Brain research and Neurophotonics for functional mapping and modulation of brain activities. Another new direction pursued is magnetic field control of light in in a chiral polymer nanocomposite to achieve large magneto-optic coefficient which can enable sensing of extremely weak magnetic field due to brain waves. Finally, we will consider the thought provoking concept of utilizing photons to quantify, through magneto-optics, and augment - through nanoptogenetics, the cognitive states, thus paving the path way to a quantified human paradigm.

Imaging of convection enhanced delivery of toxins in humans.

PubMed

Mehta, Ankit I; Choi, Bryan D; Raghavan, Raghu; Brady, Martin; Friedman, Allan H; Bigner, Darell D; Pastan, Ira; Sampson, John H

2011-03-01

Drug delivery of immunotoxins to brain tumors circumventing the blood brain barrier is a significant challenge. Convection-enhanced delivery (CED) circumvents the blood brain barrier through direct intracerebral application using a hydrostatic pressure gradient to percolate therapeutic compounds throughout the interstitial spaces of infiltrated brain and tumors. The efficacy of CED is determined through the distribution of the therapeutic agent to the targeted region. The vast majority of patients fail to receive a significant amount of coverage of the area at risk for tumor recurrence. Understanding this challenge, it is surprising that so little work has been done to monitor the delivery of therapeutic agents using this novel approach. Here we present a review of imaging in convection enhanced delivery monitoring of toxins in humans, and discuss future challenges in the field.

Imaging of Convection Enhanced Delivery of Toxins in Humans

PubMed Central

Mehta, Ankit I.; Choi, Bryan D.; Raghavan, Raghu; Brady, Martin; Friedman, Allan H.; Bigner, Darell D.; Pastan, Ira; Sampson, John H.

2011-01-01

Drug delivery of immunotoxins to brain tumors circumventing the blood brain barrier is a significant challenge. Convection-enhanced delivery (CED) circumvents the blood brain barrier through direct intracerebral application using a hydrostatic pressure gradient to percolate therapeutic compounds throughout the interstitial spaces of infiltrated brain and tumors. The efficacy of CED is determined through the distribution of the therapeutic agent to the targeted region. The vast majority of patients fail to receive a significant amount of coverage of the area at risk for tumor recurrence. Understanding this challenge, it is surprising that so little work has been done to monitor the delivery of therapeutic agents using this novel approach. Here we present a review of imaging in convection enhanced delivery monitoring of toxins in humans, and discuss future challenges in the field. PMID:22069706

Generative diffeomorphic modelling of large MRI data sets for probabilistic template construction.

PubMed

Blaiotta, Claudia; Freund, Patrick; Cardoso, M Jorge; Ashburner, John

2018-02-01

In this paper we present a hierarchical generative model of medical image data, which can capture simultaneously the variability of both signal intensity and anatomical shapes across large populations. Such a model has a direct application for learning average-shaped probabilistic tissue templates in a fully automated manner. While in principle the generality of the proposed Bayesian approach makes it suitable to address a wide range of medical image computing problems, our work focuses primarily on neuroimaging applications. In particular we validate the proposed method on both real and synthetic brain MR scans including the cervical cord and demonstrate that it yields accurate alignment of brain and spinal cord structures, as compared to state-of-the-art tools for medical image registration. At the same time we illustrate how the resulting tissue probability maps can readily be used to segment, bias correct and spatially normalise unseen data, which are all crucial pre-processing steps for MR imaging studies. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Neuroscience and Education: Issues and Challenges for Curriculum

ERIC Educational Resources Information Center

Clement, Neville D.; Lovat, Terence

2012-01-01

The burgeoning knowledge of the human brain generated by the proliferation of new brain imaging technology from in recent decades has posed questions about the potential for this new knowledge of neural processing to be translated into "usable knowledge" that teachers can employ in their practical curriculum work. The application of the findings…

Optically enhanced blood-brain-barrier crossing of plasmonic-active nanoparticles in preclinical brain tumor animal models

NASA Astrophysics Data System (ADS)

Yuan, Hsiangkuo; Wilson, Christy M.; Li, Shuqin; Fales, Andrew M.; Liu, Yang; Grant, Gerald; Vo-Dinh, Tuan

2014-02-01

Nanotechnology provides tremendous biomedical opportunities for cancer diagnosis, imaging, and therapy. In contrast to conventional chemotherapeutic agents where their actual target delivery cannot be easily imaged, integrating imaging and therapeutic properties into one platform facilitates the understanding of pharmacokinetic profiles, and enables monitoring of the therapeutic process in each individual. Such a concept dubbed "theranostics" potentiates translational research and improves precision medicine. One particular challenging application of theranostics involves imaging and controlled delivery of nanoplatforms across blood-brain-barrier (BBB) into brain tissues. Typically, the BBB hinders paracellular flux of drug molecules into brain parenchyma. BBB disrupting agents (e.g. mannitol, focused ultrasound), however, suffer from poor spatial confinement. It has been a challenge to design a nanoplatform not only acts as a contrast agent but also improves the BBB permeation. In this study, we demonstrated the feasibility of plasmonic gold nanoparticles as both high-resolution optical contrast agent and focalized tumor BBB permeation-inducing agent. We specifically examined the microscopic distribution of nanoparticles in tumor brain animal models. We observed that most nanoparticles accumulated at the tumor periphery or perivascular spaces. Nanoparticles were present in both endothelial cells and interstitial matrices. This study also demonstrated a novel photothermal-induced BBB permeation. Fine-tuning the irradiating energy induced gentle disruption of the vascular integrity, causing short-term extravasation of nanomaterials but without hemorrhage. We conclude that our gold nanoparticles are a powerful biocompatible contrast agent capable of inducing focal BBB permeation, and therefore envision a strong potential of plasmonic gold nanoparticle in future brain tumor imaging and therapy.

A non-contact time-domain scanning brain imaging system: first in-vivo results

NASA Astrophysics Data System (ADS)

Mazurenka, M.; Di Sieno, L.; Boso, G.; Contini, D.; Pifferi, A.; Dalla Mora, A.; Tosi, A.; Wabnitz, H.; Macdonald, R.

2013-06-01

We present results of first in-vivo tests of an optical non-contact scanning imaging system, intended to study oxidative metabolism related processes in biological tissue by means of time-resolved near-infrared spectroscopy. Our method is a novel realization of the short source-detector separation approach and based on a fast-gated single-photon avalanche diode to detect late photons only. The scanning system is built in quasi-confocal configuration and utilizes polarizationsensitive detection. It scans an area of 4×4 cm2, recording images with 32×32 pixels, thus creating a high density of source-detector pairs. To test the system we performed a range of in vivo measurements of hemodynamic changes in several types of biological tissues, i.e. skin (Valsalva maneuver), muscle (venous and arterial occlusions) and brain (motor and cognitive tasks). Task-related changes in hemoglobin concentrations were clearly detected in skin and muscle. The brain activation shows weaker, but yet detectable changes. These changes were localized in pixels near the motor cortex area (C3). However, it was found that even very short hair substantially impairs the measurement. Thus the applicability of the scanner is limited to hairless parts of body. The results of our first in-vivo tests prove the feasibility of non-contact scanning imaging as a first step towards development of a prototype for biological tissue imaging for various medical applications.

The Puzzle of Neuroimaging and Psychiatric Diagnosis: Technology and Nosology in an Evolving Discipline

PubMed Central

Farah, Martha J.; Gillihan, Seth J.

2013-01-01

Brain imaging provides ever more sensitive measures of structure and function relevant to human psychology and has revealed correlates for virtually every psychiatric disorder. Yet it plays no accepted role in psychiatric diagnosis beyond ruling out medical factors such as tumors or traumatic brain injuries. Why is brain imaging not used in the diagnosis of primary psychiatric disorders, such as depression, bipolar disease, schizophrenia, and ADHD? The present article addresses this question. It reviews the state of the art in psychiatric imaging, including diagnostic and other applications, and explains the nonutility of diagnostic imaging in terms of aspects of both the current state of imaging and the current nature of psychiatric nosology. The likely future path by which imaging-based diagnoses will be incorporated into psychiatry is also discussed. By reviewing one well-known attempt to use SPECT-scanning in psychiatric diagnosis, the article examines a real-world practice that illustrates several related points: the appeal of the idea of image-assisted diagnosis for physicians, patients and families, despite a lack of proven effectiveness, and the mismatch between the categories and dimensions of current nosology and those suggested by imaging. PMID:23505613

Crowdsourcing Precision Cerebrovascular Health: Imaging and Cloud Seeding A Million Brains Initiative™.

PubMed

Liebeskind, David S

2016-01-01

Crowdsourcing, an unorthodox approach in medicine, creates an unusual paradigm to study precision cerebrovascular health, eliminating the relative isolation and non-standardized nature of current imaging data infrastructure, while shifting emphasis to the astounding capacity of big data in the cloud. This perspective envisions the use of imaging data of the brain and vessels to orient and seed A Million Brains Initiative™ that may leapfrog incremental advances in stroke and rapidly provide useful data to the sizable population around the globe prone to the devastating effects of stroke and vascular substrates of dementia. Despite such variability in the type of data available and other limitations, the data hierarchy logically starts with imaging and can be enriched with almost endless types and amounts of other clinical and biological data. Crowdsourcing allows an individual to contribute to aggregated data on a population, while preserving their right to specific information about their own brain health. The cloud now offers endless storage, computing prowess, and neuroimaging applications for postprocessing that is searchable and scalable. Collective expertise is a windfall of the crowd in the cloud and particularly valuable in an area such as cerebrovascular health. The rise of precision medicine, rapidly evolving technological capabilities of cloud computing and the global imperative to limit the public health impact of cerebrovascular disease converge in the imaging of A Million Brains Initiative™. Crowdsourcing secure data on brain health may provide ultimate generalizability, enable focused analyses, facilitate clinical practice, and accelerate research efforts.

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

The teen brain: insights from neuroimaging.

PubMed

Giedd, Jay N

2008-04-01

Few parents of a teenager are surprised to hear that the brain of a 16-year-old is different from the brain of an 8-year-old. Yet to pin down these differences in a rigorous scientific way has been elusive. Magnetic resonance imaging, with the capacity to provide exquisitely accurate quantifications of brain anatomy and physiology without the use of ionizing radiation, has launched a new era of adolescent neuroscience. Longitudinal studies of subjects from ages 3-30 years demonstrate a general pattern of childhood peaks of gray matter followed by adolescent declines, functional and structural increases in connectivity and integrative processing, and a changing balance between limbic/subcortical and frontal lobe functions, extending well into young adulthood. Although overinterpretation and premature application of neuroimaging findings for diagnostic purposes remains a risk, converging data from multiple imaging modalities is beginning to elucidate the implications of these brain changes on cognition, emotion, and behavior.

Predicting consumer behavior: using novel mind-reading approaches.

PubMed

Calvert, Gemma A; Brammer, Michael J

2012-01-01

Advances in machine learning as applied to functional magnetic resonance imaging (fMRI) data offer the possibility of pretesting and classifying marketing communications using unbiased pattern recognition algorithms. By using these algorithms to analyze brain responses to brands, products, or existing marketing communications that either failed or succeeded in the marketplace and identifying the patterns of brain activity that characterize success or failure, future planned campaigns or new products can now be pretested to determine how well the resulting brain responses match the desired (successful) pattern of brain activity without the need for verbal feedback. This major advance in signal processing is poised to revolutionize the application of these brain-imaging techniques in the marketing sector by offering greater accuracy of prediction in terms of consumer acceptance of new brands, products, and campaigns at a speed that makes them accessible as routine pretesting tools that will clearly demonstrate return on investment.

Creating an anthropomorphic digital MR phantom—an extensible tool for comparing and evaluating quantitative imaging algorithms

NASA Astrophysics Data System (ADS)

Bosca, Ryan J.; Jackson, Edward F.

2016-01-01

Assessing and mitigating the various sources of bias and variance associated with image quantification algorithms is essential to the use of such algorithms in clinical research and practice. Assessment is usually accomplished with grid-based digital reference objects (DRO) or, more recently, digital anthropomorphic phantoms based on normal human anatomy. Publicly available digital anthropomorphic phantoms can provide a basis for generating realistic model-based DROs that incorporate the heterogeneity commonly found in pathology. Using a publicly available vascular input function (VIF) and digital anthropomorphic phantom of a normal human brain, a methodology was developed to generate a DRO based on the general kinetic model (GKM) that represented realistic and heterogeneously enhancing pathology. GKM parameters were estimated from a deidentified clinical dynamic contrast-enhanced (DCE) MRI exam. This clinical imaging volume was co-registered with a discrete tissue model, and model parameters estimated from clinical images were used to synthesize a DCE-MRI exam that consisted of normal brain tissues and a heterogeneously enhancing brain tumor. An example application of spatial smoothing was used to illustrate potential applications in assessing quantitative imaging algorithms. A voxel-wise Bland-Altman analysis demonstrated negligible differences between the parameters estimated with and without spatial smoothing (using a small radius Gaussian kernel). In this work, we reported an extensible methodology for generating model-based anthropomorphic DROs containing normal and pathological tissue that can be used to assess quantitative imaging algorithms.

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain

PubMed Central

Huppert, Theodore J.; Diamond, Solomon G.; Franceschini, Maria A.; Boas, David A.

2009-01-01

Near-infrared spectroscopy (NIRS) is a noninvasive neuroimaging tool for studying evoked hemodynamic changes within the brain. By this technique, changes in the optical absorption of light are recorded over time and are used to estimate the functionally evoked changes in cerebral oxyhemoglobin and deoxyhemoglobin concentrations that result from local cerebral vascular and oxygen metabolic effects during brain activity. Over the past three decades this technology has continued to grow, and today NIRS studies have found many niche applications in the fields of psychology, physiology, and cerebral pathology. The growing popularity of this technique is in part associated with a lower cost and increased portability of NIRS equipment when compared with other imaging modalities, such as functional magnetic resonance imaging and positron emission tomography. With this increasing number of applications, new techniques for the processing, analysis, and interpretation of NIRS data are continually being developed. We review some of the time-series and functional analysis techniques that are currently used in NIRS studies, we describe the practical implementation of various signal processing techniques for removing physiological, instrumental, and motion-artifact noise from optical data, and we discuss the unique aspects of NIRS analysis in comparison with other brain imaging modalities. These methods are described within the context of the MATLAB-based graphical user interface program, HomER, which we have developed and distributed to facilitate the processing of optical functional brain data. PMID:19340120

Imaging outcome measures for progressive multiple sclerosis trials

PubMed Central

Moccia, Marcello; de Stefano, Nicola; Barkhof, Frederik

2017-01-01

Imaging markers that are reliable, reproducible and sensitive to neurodegenerative changes in progressive multiple sclerosis (MS) can enhance the development of new medications with a neuroprotective mode-of-action. Accordingly, in recent years, a considerable number of imaging biomarkers have been included in phase 2 and 3 clinical trials in primary and secondary progressive MS. Brain lesion count and volume are markers of inflammation and demyelination and are important outcomes even in progressive MS trials. Brain and, more recently, spinal cord atrophy are gaining relevance, considering their strong association with disability accrual; ongoing improvements in analysis methods will enhance their applicability in clinical trials, especially for cord atrophy. Advanced magnetic resonance imaging (MRI) techniques (e.g. magnetization transfer ratio (MTR), diffusion tensor imaging (DTI), spectroscopy) have been included in few trials so far and hold promise for the future, as they can reflect specific pathological changes targeted by neuroprotective treatments. Position emission tomography (PET) and optical coherence tomography have yet to be included. Applications, limitations and future perspectives of these techniques in clinical trials in progressive MS are discussed, with emphasis on measurement sensitivity, reliability and sample size calculation. PMID:29041865

Dynamic in vivo imaging of small animal brain using pulsed laser diode-based photoacoustic tomography system.

PubMed

Upputuri, Paul Kumar; Pramanik, Manojit

2017-09-01

We demonstrate dynamic in vivo imaging using a low-cost portable pulsed laser diode (PLD)-based photoacoustic tomography system. The system takes advantage of an 803-nm PLD having high-repetition rate ∼7000  Hz combined with a fast-scanning single-element ultrasound transducer leading to a 5 s cross-sectional imaging. Cortical vasculature is imaged in scan time of 5 s with high signal-to-noise ratio ∼48. To examine the ability for dynamic imaging, we monitored the fast uptake and clearance process of indocyanine green in the rat brain. The system will find applications to study neurofunctional activities, characterization of pharmacokinetic, and biodistribution profiles in the development process of drugs or imaging agents. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Seeing the forest and trees: whole-body and whole-brain imaging for circadian biology.

PubMed

Ode, K L; Ueda, H R

2015-09-01

Recent advances in methods for making mammalian organs translucent have made possible whole-body fluorescent imaging with single-cell resolution. Because organ-clearing methods can be used to image the heterogeneous nature of cell populations, they are powerful tools to investigate the hierarchical organization of the cellular circadian clock, and how the clock synchronizes a variety of physiological activities. In particular, methods compatible with genetically encoded fluorescent reporters have the potential to detect circadian activity in different brain regions and the circadian-phase distribution across the whole body. In this review, we summarize the current methods and strategy for making organs translucent (removal of lipids, decolourization of haemoglobin and adjusting the refractive index of the specimen). We then discuss possible applications to circadian biology. For example, the coupling of circadian rhythms among different brain regions, brain activity in sleep-wake cycles and the role of migrating cells such as immune cells and cancer cells in chronopharmacology. © 2015 John Wiley & Sons Ltd.

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.

[Detection of cerebral hypoperfusion using single photon emission computed tomography image analysis and statistical parametric mapping in patients with Parkinson's disease or progressive supranuclear palsy].

PubMed

Harada, Kengo; Saeki, Hiroshi; Matsuya, Eiji; Okita, Izumi

2013-11-01

We carried out differential diagnosis of brain blood flow images using single-photon emission computed tomography (SPECT) for patients with Parkinson's disease (PD) or progressive supranuclear paralysis (PSP) using statistical parametric mapping (SPM) and to whom we had applied anatomical standardization. We studied two groups and compared brain blood flow images using SPECT (N-isopropyl-4-iodoamphetamine [(123)I] hydrochloride injection, 222 MGq dosage i.v.). A total of 27 patients were studied using SPM: 18 with PD and 9 with PSP; humming bird sign on MRI was from moderate to medium. The decline of brain bloodstream in the PSP group was more notable in the midbrain, near the domain where the humming bird sign was observable, than in the PD group. The observable differences in brain bloodstream decline in the midbrain of PSP and PD patients suggest the potential usefulness of this technique's clinical application to distinction diagnosis.

Cryo-imaging of fluorescently labeled single cells in a mouse

NASA Astrophysics Data System (ADS)

Steyer, Grant J.; Roy, Debashish; Salvado, Olivier; Stone, Meredith E.; Wilson, David L.

2009-02-01

We developed a cryo-imaging system to provide single-cell detection of fluorescently labeled cells in mouse, with particular applicability to stem cells and metastatic cancer. The Case cryoimaging system consists of a fluorescence microscope, robotic imaging positioner, customized cryostat, PC-based control system, and visualization/analysis software. The system alternates between sectioning (10-40 μm) and imaging, collecting color brightfield and fluorescent blockface image volumes >60GB. In mouse experiments, we imaged quantum-dot labeled stem cells, GFP-labeled cancer and stem cells, and cell-size fluorescent microspheres. To remove subsurface fluorescence, we used a simplified model of light-tissue interaction whereby the next image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing entropy of subtracted images. Tissue specific attenuation parameters were found [uT : heart (267 +/- 47.6 μm), liver (218 +/- 27.1 μm), brain (161 +/- 27.4 μm)] to be within the range of estimates in the literature. "Next image" processing removed subsurface fluorescence equally well across multiple tissues (brain, kidney, liver, adipose tissue, etc.), and analysis of 200 microsphere images in the brain gave 97+/-2% reduction of subsurface fluorescence. Fluorescent signals were determined to arise from single cells based upon geometric and integrated intensity measurements. Next image processing greatly improved axial resolution, enabled high quality 3D volume renderings, and improved enumeration of single cells with connected component analysis by up to 24%. Analysis of image volumes identified metastatic cancer sites, found homing of stem cells to injury sites, and showed microsphere distribution correlated with blood flow patterns. We developed and evaluated cryo-imaging to provide single-cell detection of fluorescently labeled cells in mouse. Our cryo-imaging system provides extreme (>60GB), micron-scale, fluorescence, and bright field image data. Here we describe our image preprocessing, analysis, and visualization techniques. Processing improves axial resolution, reduces subsurface fluorescence by 97%, and enables single cell detection and counting. High quality 3D volume renderings enable us to evaluate cell distribution patterns. Applications include the myriad of biomedical experiments using fluorescent reporter gene and exogenous fluorophore labeling of cells in applications such as stem cell regenerative medicine, cancer, tissue engineering, etc.

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

Diffuse Optical Tomography for Brain Imaging: Theory

NASA Astrophysics Data System (ADS)

Yuan, Zhen; Jiang, Huabei

Diffuse optical tomography (DOT) is a noninvasive, nonionizing, and inexpensive imaging technique that uses near-infrared light to probe tissue optical properties. Regional variations in oxy- and deoxy-hemoglobin concentrations as well as blood flow and oxygen consumption can be imaged by monitoring spatiotemporal variations in the absorption spectra. For brain imaging, this provides DOT unique abilities to directly measure the hemodynamic, metabolic, and neuronal responses to cells (neurons), and tissue and organ activations with high temporal resolution and good tissue penetration. DOT can be used as a stand-alone modality or can be integrated with other imaging modalities such as fMRI/MRI, PET/CT, and EEG/MEG in studying neurophysiology and pathology. This book chapter serves as an introduction to the basic theory and principles of DOT for neuroimaging. It covers the major aspects of advances in neural optical imaging including mathematics, physics, chemistry, reconstruction algorithm, instrumentation, image-guided spectroscopy, neurovascular and neurometabolic coupling, and clinical applications.

Geometric Metamorphosis

PubMed Central

Niethammer, Marc; Hart, Gabriel L.; Pace, Danielle F.; Vespa, Paul M.; Irimia, Andrei; Van Horn, John D.; Aylward, Stephen R.

2013-01-01

Standard image registration methods do not account for changes in image appearance. Hence, metamorphosis approaches have been developed which jointly estimate a space deformation and a change in image appearance to construct a spatio-temporal trajectory smoothly transforming a source to a target image. For standard metamorphosis, geometric changes are not explicitly modeled. We propose a geometric metamorphosis formulation, which explains changes in image appearance by a global deformation, a deformation of a geometric model, and an image composition model. This work is motivated by the clinical challenge of predicting the long-term effects of traumatic brain injuries based on time-series images. This work is also applicable to the quantification of tumor progression (e.g., estimating its infiltrating and displacing components) and predicting chronic blood perfusion changes after stroke. We demonstrate the utility of the method using simulated data as well as scans from a clinical traumatic brain injury patient. PMID:21995083

White matter fiber tracking computation based on diffusion tensor imaging for clinical applications.

PubMed

Dellani, Paulo R; Glaser, Martin; Wille, Paulo R; Vucurevic, Goran; Stadie, Axel; Bauermann, Thomas; Tropine, Andrei; Perneczky, Axel; von Wangenheim, Aldo; Stoeter, Peter

2007-03-01

Fiber tracking allows the in vivo reconstruction of human brain white matter fiber trajectories based on magnetic resonance diffusion tensor imaging (MR-DTI), but its application in the clinical routine is still in its infancy. In this study, we present a new software for fiber tracking, developed on top of a general-purpose DICOM (digital imaging and communications in medicine) framework, which can be easily integrated into existing picture archiving and communication system (PACS) of radiological institutions. Images combining anatomical information and the localization of different fiber tract trajectories can be encoded and exported in DICOM and Analyze formats, which are valuable resources in the clinical applications of this method. Fiber tracking was implemented based on existing line propagation algorithms, but it includes a heuristic for fiber crossings in the case of disk-shaped diffusion tensors. We successfully performed fiber tracking on MR-DTI data sets from 26 patients with different types of brain lesions affecting the corticospinal tracts. In all cases, the trajectories of the central spinal tract (pyramidal tract) were reconstructed and could be applied at the planning phase of the surgery as well as in intraoperative neuronavigation.

Accelerated echo-planar J-resolved spectroscopic imaging in the human brain using compressed sensing: a pilot validation in obstructive sleep apnea.

PubMed

Sarma, M K; Nagarajan, R; Macey, P M; Kumar, R; Villablanca, J P; Furuyama, J; Thomas, M A

2014-06-01

Echo-planar J-resolved spectroscopic imaging is a fast spectroscopic technique to record the biochemical information in multiple regions of the brain, but for clinical applications, time is still a constraint. Investigations of neural injury in obstructive sleep apnea have revealed structural changes in the brain, but determining the neurochemical changes requires more detailed measurements across multiple brain regions, demonstrating a need for faster echo-planar J-resolved spectroscopic imaging. Hence, we have extended the compressed sensing reconstruction of prospectively undersampled 4D echo-planar J-resolved spectroscopic imaging to investigate metabolic changes in multiple brain locations of patients with obstructive sleep apnea and healthy controls. Nonuniform undersampling was imposed along 1 spatial and 1 spectral dimension of 4D echo-planar J-resolved spectroscopic imaging, and test-retest reliability of the compressed sensing reconstruction of the nonuniform undersampling data was tested by using a brain phantom. In addition, 9 patients with obstructive sleep apnea and 11 healthy controls were investigated by using a 3T MR imaging/MR spectroscopy scanner. Significantly reduced metabolite differences were observed between patients with obstructive sleep apnea and healthy controls in multiple brain regions: NAA/Cr in the left hippocampus; total Cho/Cr and Glx/Cr in the right hippocampus; total NAA/Cr, taurine/Cr, scyllo-Inositol/Cr, phosphocholine/Cr, and total Cho/Cr in the occipital gray matter; total NAA/Cr and NAA/Cr in the medial frontal white matter; and taurine/Cr and total Cho/Cr in the left frontal white matter regions. The 4D echo-planar J-resolved spectroscopic imaging technique using the nonuniform undersampling-based acquisition and compressed sensing reconstruction in patients with obstructive sleep apnea and healthy brain is feasible in a clinically suitable time. In addition to brain metabolite changes previously reported by 1D MR spectroscopy, our results show changes of additional metabolites in patients with obstructive sleep apnea compared with healthy controls. © 2014 by American Journal of Neuroradiology.

Review of MR Elastography Applications and Recent Developments

PubMed Central

Glaser, Kevin J.; Manduca, Armando; Ehman, Richard L.

2012-01-01

The technique of MR elastography (MRE) has emerged as a useful modality for quantitatively imaging the mechanical properties of soft tissues in vivo. Recently, MRE has been introduced as a clinical tool for evaluating chronic liver disease, but many other potential applications are being explored. These applications include measuring tissue changes associated with diseases of the liver, breast, brain, heart, and skeletal muscle including both focal lesions (e.g., hepatic, breast, and brain tumors) and diffuse diseases (e.g., fibrosis and multiple sclerosis). The purpose of this review article is to summarize some of the recent developments of MRE and to highlight some emerging applications. PMID:22987755

Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present, and Future.

PubMed

Iima, Mami; Le Bihan, Denis

2016-01-01

The concept of diffusion magnetic resonance (MR) imaging emerged in the mid-1980s, together with the first images of water diffusion in the human brain, as a way to probe tissue structure at a microscopic scale, although the images were acquired at a millimetric scale. Since then, diffusion MR imaging has become a pillar of modern clinical imaging. Diffusion MR imaging has mainly been used to investigate neurologic disorders. A dramatic application of diffusion MR imaging has been acute brain ischemia, providing patients with the opportunity to receive suitable treatment at a stage when brain tissue might still be salvageable, thus avoiding terrible handicaps. On the other hand, it was found that water diffusion is anisotropic in white matter, because axon membranes limit molecular movement perpendicularly to the nerve fibers. This feature can be exploited to produce stunning maps of the orientation in space of the white matter tracts and brain connections in just a few minutes. Diffusion MR imaging is now also rapidly expanding in oncology, for the detection of malignant lesions and metastases, as well as monitoring. Water diffusion is usually largely decreased in malignant tissues, and body diffusion MR imaging, which does not require any tracer injection, is rapidly becoming a modality of choice to detect, characterize, or even stage malignant lesions, especially for breast or prostate cancer. After a brief summary of the key methodological concepts beyond diffusion MR imaging, this article will give a review of the clinical literature, mainly focusing on current outstanding issues, followed by some innovative proposals for future improvements. © RSNA, 2016

A New Variational Method for Bias Correction and Its Applications to Rodent Brain Extraction.

PubMed

Chang, Huibin; Huang, Weimin; Wu, Chunlin; Huang, Su; Guan, Cuntai; Sekar, Sakthivel; Bhakoo, Kishore Kumar; Duan, Yuping

2017-03-01

Brain extraction is an important preprocessing step for further analysis of brain MR images. Significant intensity inhomogeneity can be observed in rodent brain images due to the high-field MRI technique. Unlike most existing brain extraction methods that require bias corrected MRI, we present a high-order and L 0 regularized variational model for bias correction and brain extraction. The model is composed of a data fitting term, a piecewise constant regularization and a smooth regularization, which is constructed on a 3-D formulation for medical images with anisotropic voxel sizes. We propose an efficient multi-resolution algorithm for fast computation. At each resolution layer, we solve an alternating direction scheme, all subproblems of which have the closed-form solutions. The method is tested on three T2 weighted acquisition configurations comprising a total of 50 rodent brain volumes, which are with the acquisition field strengths of 4.7 Tesla, 9.4 Tesla and 17.6 Tesla, respectively. On one hand, we compare the results of bias correction with N3 and N4 in terms of the coefficient of variations on 20 different tissues of rodent brain. On the other hand, the results of brain extraction are compared against manually segmented gold standards, BET, BSE and 3-D PCNN based on a number of metrics. With the high accuracy and efficiency, our proposed method can facilitate automatic processing of large-scale brain studies.

Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration.

PubMed

Wittek, Adam; Joldes, Grand; Couton, Mathieu; Warfield, Simon K; Miller, Karol

2010-12-01

Long computation times of non-linear (i.e. accounting for geometric and material non-linearity) biomechanical models have been regarded as one of the key factors preventing application of such models in predicting organ deformation for image-guided surgery. This contribution presents real-time patient-specific computation of the deformation field within the brain for six cases of brain shift induced by craniotomy (i.e. surgical opening of the skull) using specialised non-linear finite element procedures implemented on a graphics processing unit (GPU). In contrast to commercial finite element codes that rely on an updated Lagrangian formulation and implicit integration in time domain for steady state solutions, our procedures utilise the total Lagrangian formulation with explicit time stepping and dynamic relaxation. We used patient-specific finite element meshes consisting of hexahedral and non-locking tetrahedral elements, together with realistic material properties for the brain tissue and appropriate contact conditions at the boundaries. The loading was defined by prescribing deformations on the brain surface under the craniotomy. Application of the computed deformation fields to register (i.e. align) the preoperative and intraoperative images indicated that the models very accurately predict the intraoperative deformations within the brain. For each case, computing the brain deformation field took less than 4 s using an NVIDIA Tesla C870 GPU, which is two orders of magnitude reduction in computation time in comparison to our previous study in which the brain deformation was predicted using a commercial finite element solver executed on a personal computer. Copyright © 2010 Elsevier Ltd. All rights reserved.

Optical Coherence Tomography for Brain Imaging

NASA Astrophysics Data System (ADS)

Liu, Gangjun; Chen, Zhongping

Recently, there has been growing interest in using OCT for brain imaging. A feasibility study of OCT for guiding deep brain probes has found that OCT can differentiate the white matter and gray matter because the white matter tends to have a higher peak reflectivity and steeper attenuation rate compared to gray matter. In vivo 3D visualization of the layered organization of a rat olfactory bulb with OCT has been demonstrated. OCT has been used for single myelin fiber imaging in living rodents without labeling. The refractive index in the rat somatosensory cortex has also been measured with OCT. In addition, functional extension of OCT, such as Doppler-OCT (D-OCT), polarization sensitive-OCT (PS-OCT), and phase-resolved-OCT (PR-OCT), can image and quantify physiological parameters in addition to the morphological structure image. Based on the scattering changes during neural activity, OCT has been used to measure the functional activation in neuronal tissues. PS-OCT, which combines polarization sensitive detection with OCT to determine tissue birefringence, has been used for the localization of nerve fiber bundles and the mapping of micrometer-scale fiber pathways in the brain. D-OCT, also named optical Doppler tomography (ODT), combines the Doppler principle with OCT to obtain high resolution tomographic images of moving constituents in highly scattering biological tissues. D-OCT has been successfully used to image cortical blood flow and map the blood vessel network for brain research. In this chapter, the principle and technology of OCT and D-OCT are reviewed and examples of potential applications are described.

A Nested Phosphorus and Proton Coil Array for Brain Magnetic Resonance Imaging and Spectroscopy

PubMed Central

Brown, Ryan; Lakshmanan, Karthik; Madelin, Guillaume; Parasoglou, Prodromos

2015-01-01

A dual-nuclei radiofrequency coil array was constructed for phosphorus and proton magnetic resonance imaging and spectroscopy of the human brain at 7 Tesla. An eight-channel transceive degenerate birdcage phosphorus module was implemented to provide whole-brain coverage and significant sensitivity improvement over a standard dual-tuned loop coil. A nested eight-channel proton module provided adequate sensitivity for anatomical localization without substantially sacrificing performance on the phosphorus module. The developed array enabled phosphorus spectroscopy, a saturation transfer technique to calculate the global creatine kinase forward reaction rate, and single-metabolite whole-brain imaging with 1.4 cm nominal isotropic resolution in 15 min (2.3 cm actual resolution), while additionally enabling 1 mm isotropic proton imaging. This study demonstrates that a multi-channel array can be utilized for phosphorus and proton applications with improved coverage and/or sensitivity over traditional single-channel coils. The efficient multi-channel coil array, time-efficient pulse sequences, and the enhanced signal strength available at ultra-high fields can be combined to allow volumetric assessment of the brain and could provide new insights into the underlying energy metabolism impairment in several neurodegenerative conditions, such as Alzheimer’s and Parkinson’s diseases, as well as mental disorders such as schizophrenia. PMID:26375209

A nested phosphorus and proton coil array for brain magnetic resonance imaging and spectroscopy.

PubMed

Brown, Ryan; Lakshmanan, Karthik; Madelin, Guillaume; Parasoglou, Prodromos

2016-01-01

A dual-nuclei radiofrequency coil array was constructed for phosphorus and proton magnetic resonance imaging and spectroscopy of the human brain at 7T. An eight-channel transceive degenerate birdcage phosphorus module was implemented to provide whole-brain coverage and significant sensitivity improvement over a standard dual-tuned loop coil. A nested eight-channel proton module provided adequate sensitivity for anatomical localization without substantially sacrificing performance on the phosphorus module. The developed array enabled phosphorus spectroscopy, a saturation transfer technique to calculate the global creatine kinase forward reaction rate, and single-metabolite whole-brain imaging with 1.4cm nominal isotropic resolution in 15min (2.3cm actual resolution), while additionally enabling 1mm isotropic proton imaging. This study demonstrates that a multi-channel array can be utilized for phosphorus and proton applications with improved coverage and/or sensitivity over traditional single-channel coils. The efficient multi-channel coil array, time-efficient pulse sequences, and the enhanced signal strength available at ultra-high fields can be combined to allow volumetric assessment of the brain and could provide new insights into the underlying energy metabolism impairment in several neurodegenerative conditions, such as Alzheimer's and Parkinson's diseases, as well as mental disorders such as schizophrenia. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Optical pathology of human brain metastasis of lung cancer using combined resonance Raman and spatial frequency spectroscopies

NASA Astrophysics Data System (ADS)

Zhou, Yan; Liu, Cheng-hui; Pu, Yang; Cheng, Gangge; Zhou, Lixin; Chen, Jun; Zhu, Ke; Alfano, Robert R.

2016-03-01

Raman spectroscopy has become widely used for diagnostic purpose of breast, lung and brain cancers. This report introduced a new approach based on spatial frequency spectra analysis of the underlying tissue structure at different stages of brain tumor. Combined spatial frequency spectroscopy (SFS), Resonance Raman (RR) spectroscopic method is used to discriminate human brain metastasis of lung cancer from normal tissues for the first time. A total number of thirty-one label-free micrographic images of normal and metastatic brain cancer tissues obtained from a confocal micro- Raman spectroscopic system synchronously with examined RR spectra of the corresponding samples were collected from the identical site of tissue. The difference of the randomness of tissue structures between the micrograph images of metastatic brain tumor tissues and normal tissues can be recognized by analyzing spatial frequency. By fitting the distribution of the spatial frequency spectra of human brain tissues as a Gaussian function, the standard deviation, σ, can be obtained, which was used to generate a criterion to differentiate human brain cancerous tissues from the normal ones using Support Vector Machine (SVM) classifier. This SFS-SVM analysis on micrograph images presents good results with sensitivity (85%), specificity (75%) in comparison with gold standard reports of pathology and immunology. The dual-modal advantages of SFS combined with RR spectroscopy method may open a new way in the neuropathology applications.

TH-A-18C-09: Ultra-Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma

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

Sisniega, A; Zbijewski, W; Stayman, J

Purpose: Application of cone-beam CT (CBCT) to low-contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high-quality CBCT imaging of acute brain injury. Methods: The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC-GPU), with baseline simulation speed of ~1e7 photons/sec. MC-GPU is accelerated by a novel, GPU-optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced formore » additional speed-up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source-detector distance 700 mm, source-axis distance 480 mm). Results: For a fixed run-time of ~1 sec/projection, GPU-optimized VR reduces the noise in MC-GPU scatter estimates by a factor of 4. For scatter correction, MC-GPU with VR is executed with 4-fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run-time per scan, and de-noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast-tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction. Conclusion: Acceleration of MC-GPU achieved through GPU-optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications. Research grant from Carestream Health. JY is an employee of Carestream Health.« less

Cancer Imaging Phenomics Software Suite: Application to Brain and Breast Cancer | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The transition of oncologic imaging from its “industrial era” to it is “information era” demands analytical methods that 1) extract information from this data that is clinically and biologically relevant; 2) integrate imaging, clinical, and genomic data via rigorous statistical and computational methodologies in order to derive models valuable for understanding cancer mechanisms, diagnosis, prognostic assessment, response evaluation, and personalized treatment management; 3) are available to the biomedical community for easy use and application, with the aim of understanding, diagnosing, an

Development of representative magnetic resonance imaging-based atlases of the canine brain and evaluation of three methods for atlas-based segmentation.

PubMed

Milne, Marjorie E; Steward, Christopher; Firestone, Simon M; Long, Sam N; O'Brien, Terrence J; Moffat, Bradford A

2016-04-01

To develop representative MRI atlases of the canine brain and to evaluate 3 methods of atlas-based segmentation (ABS). 62 dogs without clinical signs of epilepsy and without MRI evidence of structural brain disease. The MRI scans from 44 dogs were used to develop 4 templates on the basis of brain shape (brachycephalic, mesaticephalic, dolichocephalic, and combined mesaticephalic and dolichocephalic). Atlas labels were generated by segmenting the brain, ventricular system, hippocampal formation, and caudate nuclei. The MRI scans from the remaining 18 dogs were used to evaluate 3 methods of ABS (manual brain extraction and application of a brain shape-specific template [A], automatic brain extraction and application of a brain shape-specific template [B], and manual brain extraction and application of a combined template [C]). The performance of each ABS method was compared by calculation of the Dice and Jaccard coefficients, with manual segmentation used as the gold standard. Method A had the highest mean Jaccard coefficient and was the most accurate ABS method assessed. Measures of overlap for ABS methods that used manual brain extraction (A and C) ranged from 0.75 to 0.95 and compared favorably with repeated measures of overlap for manual extraction, which ranged from 0.88 to 0.97. Atlas-based segmentation was an accurate and repeatable method for segmentation of canine brain structures. It could be performed more rapidly than manual segmentation, which should allow the application of computer-assisted volumetry to large data sets and clinical cases and facilitate neuroimaging research and disease diagnosis.

Human brain imaging at 9.4 T using a tunable patch antenna for transmission.

PubMed

Hoffmann, Jens; Shajan, G; Budde, Juliane; Scheffler, Klaus; Pohmann, Rolf

2013-05-01

For human brain imaging at ultrahigh fields, the traveling wave concept can provide a more uniform B1+ field over a larger field of view with improved patient comfort compared to conventional volume coils. It suffers, however, from limited transmit efficiency and receive sensitivity and is not readily applicable in systems where the radiofrequency shield is too narrow to allow for unattenuated wave propagation. Here, the near field of a capacitively adjustable patch antenna for excitation is combined with a receive-only array at 9.4 T. The antenna is designed in compact size and placed in close proximity to the subject to improve the transmit efficiency in narrow bores. Experimental and numerical comparisons to conventional microstrip arrays reveal improved B1+ homogeneity and longitudinal coverage, but at the cost of elevated local specific absorption rate. High-resolution functional and anatomical images demonstrate the use of this setup for in vivo human brain imaging at 9.4 T. Copyright © 2012 Wiley Periodicals, Inc.

Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images

PubMed Central

Osadebey, Michael; Pedersen, Marius; Arnold, Douglas; Wendel-Mitoraj, Katrina

2017-01-01

Abstract. We describe a postacquisition, attribute-based quality assessment method for brain magnetic resonance imaging (MRI) images. It is based on the application of Bayes theory to the relationship between entropy and image quality attributes. The entropy feature image of a slice is segmented into low- and high-entropy regions. For each entropy region, there are three separate observations of contrast, standard deviation, and sharpness quality attributes. A quality index for a quality attribute is the posterior probability of an entropy region given any corresponding region in a feature image where quality attribute is observed. Prior belief in each entropy region is determined from normalized total clique potential (TCP) energy of the slice. For TCP below the predefined threshold, the prior probability for a region is determined by deviation of its percentage composition in the slice from a standard normal distribution built from 250 MRI volume data provided by Alzheimer’s Disease Neuroimaging Initiative. For TCP above the threshold, the prior is computed using a mathematical model that describes the TCP–noise level relationship in brain MRI images. Our proposed method assesses the image quality of each entropy region and the global image. Experimental results demonstrate good correlation with subjective opinions of radiologists for different types and levels of quality distortions. PMID:28630885

Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

PubMed

Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

2015-09-01

Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

7T: Physics, safety, and potential clinical applications.

PubMed

Kraff, Oliver; Quick, Harald H

2017-12-01

With more than 60 installed magnetic resonance imaging (MRI) systems worldwide operating at a magnetic field strength of 7T or higher, ultrahigh-field (UHF) MRI has been established as a platform for clinically oriented research in recent years. Profound technical and methodological developments have helped overcome the inherent physical challenges of UHF radiofrequency (RF) signal homogenization in the human body. The ongoing development of dedicated RF coil arrays was pivotal in realizing UHF body MRI, beyond mere brain imaging applications. Another precondition to clinical application of 7T MRI is the safety testing of implants and the establishment of safety concepts. Against this backdrop, 7T MRI and MR spectroscopy (MRS) recently have demonstrated capabilities and potentials for clinical diagnostics in a variety of studies. This article provides an overview of the immanent physical challenges of 7T UHF MRI and discusses recent technical solutions and safety concepts. Furthermore, recent clinically oriented studies are highlighted that span a broad application spectrum from 7T UHF brain to body MRI. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1573-1589. © 2017 International Society for Magnetic Resonance in Medicine.

Impact of SQUIDs on functional imaging in neuroscience

NASA Astrophysics Data System (ADS)

Della Penna, Stefania; Pizzella, Vittorio; Romani, Gian Luca

2014-04-01

This paper provides an overview on the basic principles and applications of magnetoencephalography (MEG), a technique that requires the use of many SQUIDs and thus represents one of the most important applications of superconducting electronics. Since the development of the first SQUID magnetometers, it was clear that these devices could be used to measure the ultra-low magnetic signals associated with the bioelectric activity of the neurons of the human brain. Forty years on from the first measurement of magnetic alpha rhythm by David Cohen, MEG has become a fundamental tool for the investigation of brain functions. The simple localization of cerebral sources activated by sensory stimulation performed in the early years has been successively expanded to the identification of the sequence of neuronal pool activations, thus decrypting information of the hierarchy underlying cerebral processing. This goal has been achieved thanks to the development of complex instrumentation, namely whole head MEG systems, allowing simultaneous measurement of magnetic fields all over the scalp with an exquisite time resolution. The latest trends in MEG, such as the study of brain networks, i.e. how the brain organizes itself in a coherent and stable way, are discussed. These sound applications together with the latest technological developments aimed at implementing systems able to record MEG signals and magnetic resonance imaging (MRI) of the head with the same set-up pave the way to high performance systems for brain functional investigation in the healthy and the sick population.

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.

Robust skull stripping using multiple MR image contrasts insensitive to pathology.

PubMed

Roy, Snehashis; Butman, John A; Pham, Dzung L

2017-02-01

Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T 1 -w MR images of normal brains, especially because high resolution T 1 -w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T 1 -w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR), 2 where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T 1 -w, T 2 -w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases. Copyright © 2016 Elsevier Inc. All rights reserved.

ABrIL - Advanced Brain Imaging Lab : a cloud based computation environment for cooperative neuroimaging projects.

PubMed

Neves Tafula, Sérgio M; Moreira da Silva, Nádia; Rozanski, Verena E; Silva Cunha, João Paulo

2014-01-01

Neuroscience is an increasingly multidisciplinary and highly cooperative field where neuroimaging plays an important role. Neuroimaging rapid evolution is demanding for a growing number of computing resources and skills that need to be put in place at every lab. Typically each group tries to setup their own servers and workstations to support their neuroimaging needs, having to learn from Operating System management to specific neuroscience software tools details before any results can be obtained from each setup. This setup and learning process is replicated in every lab, even if a strong collaboration among several groups is going on. In this paper we present a new cloud service model - Brain Imaging Application as a Service (BiAaaS) - and one of its implementation - Advanced Brain Imaging Lab (ABrIL) - in the form of an ubiquitous virtual desktop remote infrastructure that offers a set of neuroimaging computational services in an interactive neuroscientist-friendly graphical user interface (GUI). This remote desktop has been used for several multi-institution cooperative projects with different neuroscience objectives that already achieved important results, such as the contribution to a high impact paper published in the January issue of the Neuroimage journal. The ABrIL system has shown its applicability in several neuroscience projects with a relatively low-cost, promoting truly collaborative actions and speeding up project results and their clinical applicability.

Spatial Frequency Domain Imaging: Applications in Preclinical Models of Alzheimer's Disease

NASA Astrophysics Data System (ADS)

Lin, Alexander Justin

A clinical challenge in Alzheimer's disease (AD) is diagnosing and treating patients earlier, before symptoms of cognitive dysfunction occur. A good screening test would be sensitive to the AD brain pathology, safe, and cost-effective. Diffuse optical imaging, which measures how non-ionizing light is absorbed and scattered in tissue, may fulfill these three parameters. We imaged the brains of transgenic AD mouse models in vivo with a quantitative, camera-based, diffuse optical imaging technology called spatial frequency domain imaging (SFDI) to characterize near-infrared (650-970nm) optical biomarkers of AD. Compared to age-matched control mice, we found a decrease in light absorption --- due to lower oxygenated and total hemoglobin concentrations in the brain --- correlating to decreased blood vessel volume and density in histology. Light scattering also increased in AD mice, correlating to brain structural changes caused by neuron loss and activation of inflammatory cells. Furthermore, inhaled gas challenges revealed brain vascular function was diminished. To investigate how AD affects the small changes in blood perfusion caused by increased brain activity, we built a new SFDI system from a commercial light-emitting diode microprojector and off-the-shelf optical components and cameras to measure optical properties in the visible range (460-632nm). Our measurements showed a reduced amplitude and duration of blood vessel dilation to increased brain activity in the AD mice. Altogether, this work increased our understanding of AD pathogenesis, explored optical biomarkers of AD, and improved technology access to other research labs. These results and technologies can further be used to facilitate longitudinal drug therapy trials in mice and provide a roadmap to diffuse optical spectroscopy studies in humans.

A correlative optical microscopy and scanning electron microscopy approach to locating nanoparticles in brain tumors.

PubMed

Kempen, Paul J; Kircher, Moritz F; de la Zerda, Adam; Zavaleta, Cristina L; Jokerst, Jesse V; Mellinghoff, Ingo K; Gambhir, Sanjiv S; Sinclair, Robert

2015-01-01

The growing use of nanoparticles in biomedical applications, including cancer diagnosis and treatment, demands the capability to exactly locate them within complex biological systems. In this work a correlative optical and scanning electron microscopy technique was developed to locate and observe multi-modal gold core nanoparticle accumulation in brain tumor models. Entire brain sections from mice containing orthotopic brain tumors injected intravenously with nanoparticles were imaged using both optical microscopy to identify the brain tumor, and scanning electron microscopy to identify the individual nanoparticles. Gold-based nanoparticles were readily identified in the scanning electron microscope using backscattered electron imaging as bright spots against a darker background. This information was then correlated to determine the exact location of the nanoparticles within the brain tissue. The nanoparticles were located only in areas that contained tumor cells, and not in the surrounding healthy brain tissue. This correlative technique provides a powerful method to relate the macro- and micro-scale features visible in light microscopy with the nanoscale features resolvable in scanning electron microscopy. Copyright © 2014 Elsevier Ltd. All rights reserved.

Laser imaging for clinical applications

NASA Astrophysics Data System (ADS)

Van Houten, John P.; Cheong, Wai-Fung; Kermit, Eben L.; King, Richard A.; Spilman, Stanley D.; Benaron, David A.

1995-03-01

Medical optical imaging (MOI) uses light emitted into opaque tissues in order to determine the interior structure and chemical content. These optical techniques have been developed in an attempt to prospectively identify impending brain injuries before they become irreversible, thus allowing injury to be avoided or minimized. Optical imaging and spectroscopy center around the simple idea that light passes through the body in small amounts, and emerges bearing clues about tissues through which it passed. Images can be reconstructed from such data, and this is the basis of optical tomography. Over the past few years, techniques have been developed to allow construction of images from such optical data at the bedside. We have used a time-of-flight system reported earlier to monitor oxygenation and image hemorrhage in neonatal brain. This article summarizes the problems that we believe can be addressed by such techniques, and reports on some of our early results.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Targeted delivery of antibody-based therapeutic and imaging agents to CNS tumors: Crossing the blood-brain-barrier divide

PubMed Central

Chacko, Ann-Marie; Li, Chunsheng; Pryma, Daniel A.; Brem, Steven; Coukos, George; Muzykantov, Vladimir R.

2014-01-01

Introduction Brain tumors are inherently difficult to treat in large part due to the cellular blood-brain barriers (BBB) that limit the delivery of therapeutics to the tumor tissue from the systemic circulation. Virtually no large-molecules, including antibody-based proteins, can penetrate the BBB. With antibodies fast becoming attractive ligands for highly specific molecular targeting to tumor antigens, a variety of methods are being investigated to enhance the access of these agents to intracranial tumors for imaging or therapeutic applications. Areas covered This review describes the characteristics of the BBB and the vasculature in brain tumors, described as the blood-brain tumor barrier (BBTB). Antibodies targeted to molecular markers of CNS tumors will be highlighted, and current strategies for enhancing the delivery of antibodies across these cellular barriers into the brain parenchyma to the tumor will be discussed. Non-invasive imaging approaches to assess BBB/BBTB permeability and/or antibody targeting will be presented as a means of guiding the optimal delivery of targeted agents to brain tumors. Expert Opinion Pre-clinical and clinical studies highlight the potential of several approaches in increasing brain tumor delivery across the blood-brain barrier divide. However, each carries its own risks and challenges. There is tremendous potential in using neuroimaging strategies to assist in understanding and defining the challenges to translating and optimizing molecularly-targeted antibody delivery to CNS tumors to improve clinical outcomes. PMID:23751126

Image-Guided Intraoperative Cortical Deformation Recovery Using Game Theory: Application to Neocortical Epilepsy Surgery

PubMed Central

DeLorenzo, Christine; Papademetris, Xenophon; Staib, Lawrence H.; Vives, Kenneth P.; Spencer, Dennis D.; Duncan, James S.

2010-01-01

During neurosurgery, nonrigid brain deformation prevents preoperatively-acquired images from accurately depicting the intraoperative brain. Stereo vision systems can be used to track intraoperative cortical surface deformation and update preoperative brain images in conjunction with a biomechanical model. However, these stereo systems are often plagued with calibration error, which can corrupt the deformation estimation. In order to decouple the effects of camera calibration from the surface deformation estimation, a framework that can solve for disparate and often competing variables is needed. Game theory, which was developed to handle decision making in this type of competitive environment, has been applied to various fields from economics to biology. In this paper, game theory is applied to cortical surface tracking during neocortical epilepsy surgery and used to infer information about the physical processes of brain surface deformation and image acquisition. The method is successfully applied to eight in vivo cases, resulting in an 81% decrease in mean surface displacement error. This includes a case in which some of the initial camera calibration parameters had errors of 70%. Additionally, the advantages of using a game theoretic approach in neocortical epilepsy surgery are clearly demonstrated in its robustness to initial conditions. PMID:20129844

Coronal in vivo forward-imaging of rat brain morphology with an ultra-small optical coherence tomography fiber probe

NASA Astrophysics Data System (ADS)

Xie, Yijing; Bonin, Tim; Löffler, Susanne; Hüttmann, Gereon; Tronnier, Volker; Hofmann, Ulrich G.

2013-02-01

A well-established navigation method is one of the key conditions for successful brain surgery: it should be accurate, safe and online operable. Recent research shows that optical coherence tomography (OCT) is a potential solution for this application by providing a high resolution and small probe dimension. In this study a fiber-based spectral-domain OCT system utilizing a super-luminescent-diode with the center wavelength of 840 nm providing 14.5 μm axial resolution was used. A composite 125 μm diameter detecting probe with a gradient index (GRIN) fiber fused to a single mode fiber was employed. Signals were reconstructed into grayscale images by horizontally aligning A-scans from the same trajectory with different depths. The reconstructed images can display brain morphology along the entire trajectory. For scans of typical white matter, the signals showed a higher reflection of light intensity with lower penetration depth as well as a steeper attenuation rate compared to the scans typical for gray matter. Micro-structures such as axon bundles (70 μm) in the caudate nucleus are visible in the reconstructed images. This study explores the potential of OCT to be a navigation modality in brain surgery.

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

Brain Slice Staining and Preparation for Three-Dimensional Super-Resolution Microscopy

PubMed Central

German, Christopher L.; Gudheti, Manasa V.; Fleckenstein, Annette E.; Jorgensen, Erik M.

2018-01-01

Localization microscopy techniques – such as photoactivation localization microscopy (PALM), fluorescent PALM (FPALM), ground state depletion (GSD), and stochastic optical reconstruction microscopy (STORM) – provide the highest precision for single molecule localization currently available. However, localization microscopy has been largely limited to cell cultures due to the difficulties that arise in imaging thicker tissue sections. Sample fixation and antibody staining, background fluorescence, fluorophore photoinstability, light scattering in thick sections, and sample movement create significant challenges for imaging intact tissue. We have developed a sample preparation and image acquisition protocol to address these challenges in rat brain slices. The sample preparation combined multiple fixation steps, saponin permeabilization, and tissue clarification. Together, these preserve intracellular structures, promote antibody penetration, reduce background fluorescence and light scattering, and allow acquisition of images deep in a 30 μm thick slice. Image acquisition challenges were resolved by overlaying samples with a permeable agarose pad and custom-built stainless steel imaging adapter, and sealing the imaging chamber. This approach kept slices flat, immobile, bathed in imaging buffer, and prevented buffer oxidation during imaging. Using this protocol, we consistently obtained single molecule localizations of synaptic vesicle and active zone proteins in three-dimensions within individual synaptic terminals of the striatum in rat brain slices. These techniques may be easily adapted to the preparation and imaging of other tissues, substantially broadening the application of super-resolution imaging. PMID:28924666

A simple one-step method to prepare fluorescent carbon dots and their potential application in non-invasive glioma imaging

NASA Astrophysics Data System (ADS)

Ruan, Shaobo; Qian, Jun; Shen, Shun; Zhu, Jianhua; Jiang, Xinguo; He, Qin; Gao, Huile

2014-08-01

Fluorescent carbon dots (CD) possess impressive potential in bioimaging because of their low photobleaching, absence of optical blinking and good biocompatibility. However, their relatively short excitation/emission wavelengths restrict their application in in vivo imaging. In the present study, a kind of CD was prepared by a simple heat treatment method using glycine as the only precursor. The diameter of CD was lower than 5 nm, and the highest emission wavelength was 500 nm. However, at 600 nm, there was still a relatively strong fluorescent emission, suggesting CD could be used for in vivo imaging. Additionally, several experiments demonstrated that CD possessed good serum stability and low cytotoxicity. In vitro, CD could be taken up into C6 glioma cells in a time- and concentration-dependent manner, with both endosomes and mitochondria involved. In vivo, CD could be used for non-invasive glioma imaging because of its high accumulation in the glioma site of the brain, which was demonstrated by both in vivo imaging and ex vivo tissue imaging. Furthermore, the fluorescent distribution in tissue slices also showed CD distributed in glioma with high intensity, while with a low intensity in normal brain tissue. In conclusion, CD were prepared using a simple method with relatively long excitation and emission wavelengths and could be used for non-invasive glioma imaging.Fluorescent carbon dots (CD) possess impressive potential in bioimaging because of their low photobleaching, absence of optical blinking and good biocompatibility. However, their relatively short excitation/emission wavelengths restrict their application in in vivo imaging. In the present study, a kind of CD was prepared by a simple heat treatment method using glycine as the only precursor. The diameter of CD was lower than 5 nm, and the highest emission wavelength was 500 nm. However, at 600 nm, there was still a relatively strong fluorescent emission, suggesting CD could be used for in vivo imaging. Additionally, several experiments demonstrated that CD possessed good serum stability and low cytotoxicity. In vitro, CD could be taken up into C6 glioma cells in a time- and concentration-dependent manner, with both endosomes and mitochondria involved. In vivo, CD could be used for non-invasive glioma imaging because of its high accumulation in the glioma site of the brain, which was demonstrated by both in vivo imaging and ex vivo tissue imaging. Furthermore, the fluorescent distribution in tissue slices also showed CD distributed in glioma with high intensity, while with a low intensity in normal brain tissue. In conclusion, CD were prepared using a simple method with relatively long excitation and emission wavelengths and could be used for non-invasive glioma imaging. Electronic supplementary information (ESI) available. See DOI: 10.1039/c4nr02657h

Transferrin liposomes of docetaxel for brain-targeted cancer applications: formulation and brain theranostics.

PubMed

Sonali; Singh, Rahul Pratap; Singh, Nitesh; Sharma, Gunjan; Vijayakumar, Mahalingam R; Koch, Biplob; Singh, Sanjay; Singh, Usha; Dash, Debabrata; Pandey, Bajarangprasad L; Muthu, Madaswamy S

2016-05-01

Diagnosis and therapy of brain cancer was often limited due to low permeability of delivery materials across the blood-brain barrier (BBB) and their poor penetration into the brain tissue. This study explored the possibility of utilizing theranostic d-alpha-tocopheryl polyethylene glycol 1000 succinate mono-ester (TPGS) liposomes as nanocarriers for minimally invasive brain-targeted imaging and therapy (brain theranostics). The aim of this work was to formulate transferrin conjugated TPGS coated theranostic liposomes, which contain both docetaxel and quantum dots (QDs) for imaging and therapy of brain cancer. The theranostic liposomes with and without transferrin decoration were prepared and characterized for their particle size, polydispersity, morphology, drug encapsulation efficiency, in-vitro release study and brain theranostics. The particle sizes of the non-targeted and targeted theranostic liposomes were found below 200 nm. Nearly, 71% of drug encapsulation efficiency was achieved with liposomes. The drug release from transferrin conjugated theranostic liposomes was sustained for more than 72 h with 70% of drug release. The in-vivo results indicated that transferrin receptor-targeted theranostic liposomes could be a promising carrier for brain theranostics due to nano-sized delivery and its permeability which provided an improved and prolonged brain targeting of docetaxel and QDs in comparison to the non-targeted preparations.

Applications of magnetic resonance image segmentation in neurology

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Lahtinen, Antti J.; Dastidar, Prasun; Ryymin, Pertti; Laarne, Paeivi; Malmivuo, Jaakko; Laasonen, Erkki; Frey, Harry; Eskola, Hannu

1999-05-01

After the introduction of digital imagin devices in medicine computerized tissue recognition and classification have become important in research and clinical applications. Segmented data can be applied among numerous research fields including volumetric analysis of particular tissues and structures, construction of anatomical modes, 3D visualization, and multimodal visualization, hence making segmentation essential in modern image analysis. In this research project several PC based software were developed in order to segment medical images, to visualize raw and segmented images in 3D, and to produce EEG brain maps in which MR images and EEG signals were integrated. The software package was tested and validated in numerous clinical research projects in hospital environment.

Deep 3D convolution neural network for CT brain hemorrhage classification

NASA Astrophysics Data System (ADS)

Jnawali, Kamal; Arbabshirani, Mohammad R.; Rao, Navalgund; Patel, Alpen A.

2018-02-01

Intracranial hemorrhage is a critical conditional with the high mortality rate that is typically diagnosed based on head computer tomography (CT) images. Deep learning algorithms, in particular, convolution neural networks (CNN), are becoming the methodology of choice in medical image analysis for a variety of applications such as computer-aided diagnosis, and segmentation. In this study, we propose a fully automated deep learning framework which learns to detect brain hemorrhage based on cross sectional CT images. The dataset for this work consists of 40,367 3D head CT studies (over 1.5 million 2D images) acquired retrospectively over a decade from multiple radiology facilities at Geisinger Health System. The proposed algorithm first extracts features using 3D CNN and then detects brain hemorrhage using the logistic function as the last layer of the network. Finally, we created an ensemble of three different 3D CNN architectures to improve the classification accuracy. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve of the ensemble of three architectures was 0.87. Their results are very promising considering the fact that the head CT studies were not controlled for slice thickness, scanner type, study protocol or any other settings. Moreover, the proposed algorithm reliably detected various types of hemorrhage within the skull. This work is one of the first applications of 3D CNN trained on a large dataset of cross sectional medical images for detection of a critical radiological condition

Individualized statistical learning from medical image databases: application to identification of brain lesions.

PubMed

Erus, Guray; Zacharaki, Evangelia I; Davatzikos, Christos

2014-04-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a "target-specific" feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject's images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an "estimability" criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. Copyright © 2014 Elsevier B.V. All rights reserved.

Synthesis of nanostructured barium phosphate and its application in micro-computed tomography of mouse brain vessels in ex vivo

NASA Astrophysics Data System (ADS)

Zhu, Bangshang; Yuan, Falei; Yuan, Xiaoya; Bo, Yang; Wang, Yongting; Yang, Guo-Yuan; Drummen, Gregor P. C.; Zhu, Xinyuan

2014-02-01

Micro-computed tomography (micro-CT) is a powerful tool for visualizing the vascular systems of tissues, organs, or entire small animals. Vascular contrast agents play a vital role in micro-CT imaging in order to obtain clear and high-quality images. In this study, a new kind of nanostructured barium phosphate was fabricated and used as a contrast agent for ex vivo micro-CT imaging of blood vessels in the mouse brain. Nanostructured barium phosphate was synthesized through a simple wet precipitation method using Ba(NO3)2, and (NH4)2HPO4 as starting materials. The physiochemical properties of barium phosphate were characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, and thermal analysis. Furthermore, the impact of the produced nanostructures on cell viability was evaluated via the MTT assay, which generally showed low to moderate cytotoxicity. Finally, the animal test images demonstrated that the use of nanostructured barium phosphate as a contrast agent in Micro-CT imaging produced sharp images with excellent contrast. Both major vessels and the microvasculature were clearly observable in the imaged mouse brain. Overall, the results indicate that nanostructured barium phosphate is a potential and useful vascular contrast agent for micro-CT imaging.

Individualized Statistical Learning from Medical Image Databases: Application to Identification of Brain Lesions

PubMed Central

Erus, Guray; Zacharaki, Evangelia I.; Davatzikos, Christos

2014-01-01

This paper presents a method for capturing statistical variation of normal imaging phenotypes, with emphasis on brain structure. The method aims to estimate the statistical variation of a normative set of images from healthy individuals, and identify abnormalities as deviations from normality. A direct estimation of the statistical variation of the entire volumetric image is challenged by the high-dimensionality of images relative to smaller sample sizes. To overcome this limitation, we iteratively sample a large number of lower dimensional subspaces that capture image characteristics ranging from fine and localized to coarser and more global. Within each subspace, a “target-specific” feature selection strategy is applied to further reduce the dimensionality, by considering only imaging characteristics present in a test subject’s images. Marginal probability density functions of selected features are estimated through PCA models, in conjunction with an “estimability” criterion that limits the dimensionality of estimated probability densities according to available sample size and underlying anatomy variation. A test sample is iteratively projected to the subspaces of these marginals as determined by PCA models, and its trajectory delineates potential abnormalities. The method is applied to segmentation of various brain lesion types, and to simulated data on which superiority of the iterative method over straight PCA is demonstrated. PMID:24607564

Positron Emission Tomography (PET) and Positron Scanning

Science.gov Websites

National Laboratory 'Positron Emission Tomography ... [is a medical imaging technique that] can track human brain.' Edited excerpts from from Medical Applications of Non-Medical Research: Applications Technical Report, November 1988 High-resolution PET (Positron Emission Tomography) for Medical Science

Assessment of FUS-Tissue Interactions In Vivo

NASA Astrophysics Data System (ADS)

Haritonova, Alyona V.

Focused ultrasound (FUS) has been proposed for a variety of minimally invasive therapeutic applications, including tumor ablation, neuromodulation, targeted drug delivery and blood brain barrier opening. To date, FUS beams have been primarily monitored through MR and ultrasound diagnostic imaging modalities. The recent introduction of real-time dual-mode ultrasound array (DMUA) systems offers a new paradigm for the guidance of therapeutic focused ultrasound. The DMUA approach allows for inherent registration between the therapeutic and imaging coordinate systems. In this thesis we investigated the use of ultrasound-based thermography to assess FUS-tissue interactions. Specifically, we focused on two aspects of image-guided therapy: 1) monitoring and localization of FUS-tissue interactions, and 2) tissue damage assessment. Towards this end, we presented first experimental results of ultrasound-guided transcranial FUS in a rat brain, both ex vivo and in vivo. DMUA imaging was used to monitor and localize FUS-tissue thermal interactions in real-time. The transcranial echo data allowed for a reliable estimation of temperature change in brain tissue, which had never been done before using ultrasound image guidance. Despite some measurable distortion and loss in focusing gain, transcranial FUS beams at 3.2 MHz were localized axially and laterally. This confirms the results obtained using DMUA-based transcranial ultrasound thermography. A high degree of focusing with the DMUA was then successfully leveraged to perform localized tissue damage assessment in both ex vivo and in vivo. The experimental results presented in this thesis demonstrate some of the unique aspects of image guidance using DMUAs, especially when FUS is subject to significant distortions as in transcranial applications.

[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.

[From Brownian motion to mind imaging: diffusion MRI].

PubMed

Le Bihan, Denis

2006-11-01

The success of diffusion MRI, which was introduced in the mid 1980s is deeply rooted in the powerful concept that during their random, diffusion-driven movements water molecules probe tissue structure at a microscopic scale well beyond the usual image resolution. The observation of these movements thus provides valuable information on the structure and the geometric organization of tissues. The most successful application of diffusion MRI has been in brain ischemia, following the discovery that water diffusion drops at a very early stage of the ischemic event. Diffusion MRI provides some patients with the opportunity to receive suitable treatment at a very acute stage when brain tissue might still be salvageable. On the other hand, diffusion is modulated by the spatial orientation of large bundles of myelinated axons running in parallel through in brain white matter. This feature can be exploited to map out the orientation in space of the white matter tracks and to visualize the connections between different parts of the brain on an individual basis. Furthermore, recent data suggest that diffusion MRI may also be used to visualize rapid dynamic tissue changes, such as neuronal swelling, associated with cortical activation, offering a new and direct approach to brain functional imaging.

Unveiling molecular events in the brain by noninvasive imaging.

PubMed

Klohs, Jan; Rudin, Markus

2011-10-01

Neuroimaging allows researchers and clinicians to noninvasively assess structure and function of the brain. With the advances of imaging modalities such as magnetic resonance, nuclear, and optical imaging; the design of target-specific probes; and/or the introduction of reporter gene assays, these technologies are now capable of visualizing cellular and molecular processes in vivo. Undoubtedly, the system biological character of molecular neuroimaging, which allows for the study of molecular events in the intact organism, will enhance our understanding of physiology and pathophysiology of the brain and improve our ability to diagnose and treat diseases more specifically. Technical/scientific challenges to be faced are the development of highly sensitive imaging modalities, the design of specific imaging probe molecules capable of penetrating the CNS and reporting on endogenous cellular and molecular processes, and the development of tools for extracting quantitative, biologically relevant information from imaging data. Today, molecular neuroimaging is still an experimental approach with limited clinical impact; this is expected to change within the next decade. This article provides an overview of molecular neuroimaging approaches with a focus on rodent studies documenting the exploratory state of the field. Concepts are illustrated by discussing applications related to the pathophysiology of Alzheimer's disease.

Cy5.5 conjugated MnO nanoparticles for magnetic resonance/near-infrared fluorescence dual-modal imaging of brain gliomas.

PubMed

Chen, Ning; Shao, Chen; Li, Shuai; Wang, Zihao; Qu, Yanming; Gu, Wei; Yu, Chunjiang; Ye, Ling

2015-11-01

The fusion of molecular and anatomical modalities facilitates more reliable and accurate detection of tumors. Herein, we prepared the PEG-Cy5.5 conjugated MnO nanoparticles (MnO-PEG-Cy5.5 NPs) with magnetic resonance (MR) and near-infrared fluorescence (NIRF) imaging modalities. The applicability of MnO-PEG-Cy5.5 NPs as a dual-modal (MR/NIRF) imaging nanoprobe for the detection of brain gliomas was investigated. In vivo MR contrast enhancement of the MnO-PEG-Cy5.5 nanoprobe in the tumor region was demonstrated. Meanwhile, whole-body NIRF imaging of glioma bearing nude mouse exhibited distinct tumor localization upon injection of MnO-PEG-Cy5.5 NPs. Moreover, ex vivo CLSM imaging of the brain slice hosting glioma indicated the preferential accumulation of MnO-PEG-Cy5.5 NPs in the glioma region. Our results therefore demonstrated the potential of MnO-PEG-Cy5.5 NPs as a dual-modal (MR/NIRF) imaging nanoprobe in improving the diagnostic efficacy by simultaneously providing anatomical information from deep inside the body and more sensitive information at the cellular level. Copyright © 2015 Elsevier Inc. All rights reserved.

Three-Photon Luminescence of Gold Nanorods and Its Applications for High Contrast Tissue and Deep In Vivo Brain Imaging

PubMed Central

Wang, Shaowei; Xi, Wang; Cai, Fuhong; Zhao, Xinyuan; Xu, Zhengping; Qian, Jun; He, Sailing

2015-01-01

Gold nanoparticles can be used as contrast agents for bio-imaging applications. Here we studied multi-photon luminescence (MPL) of gold nanorods (GNRs), under the excitation of femtosecond (fs) lasers. GNRs functionalized with polyethylene glycol (PEG) molecules have high chemical and optical stability, and can be used as multi-photon luminescent nanoprobes for deep in vivo imaging of live animals. We have found that the depth of in vivo imaging is dependent upon the transmission and focal capability of the excitation light interacting with the GNRs. Our study focused on the comparison of MPL from GNRs with two different aspect ratios, as well as their ex vivo and in vivo imaging effects under 760 nm and 1000 nm excitation, respectively. Both of these wavelengths were located at an optically transparent window of biological tissue (700-1000 nm). PEGylated GNRs, which were intravenously injected into mice via the tail vein and accumulated in major organs and tumor tissue, showed high image contrast due to distinct three-photon luminescence (3PL) signals upon irradiation of a 1000 nm fs laser. Concerning in vivo mouse brain imaging, the 3PL imaging depth of GNRs under 1000 nm fs excitation could reach 600 μm, which was approximately 170 μm deeper than the two-photon luminescence (2PL) imaging depth of GNRs with a fs excitation of 760 nm. PMID:25553113

Deep brain stimulation as a functional scalpel.

PubMed

Broggi, G; Franzini, A; Tringali, G; Ferroli, P; Marras, C; Romito, L; Maccagnano, E

2006-01-01

Since 1995, at the Istituto Nazionale Neurologico "Carlo Besta" in Milan (INNCB,) 401 deep brain electrodes were implanted to treat several drug-resistant neurological syndromes (Fig. 1). More than 200 patients are still available for follow-up and therapeutical considerations. In this paper our experience is reviewed and pioneered fields are highlighted. The reported series of patients extends the use of deep brain stimulation beyond the field of Parkinson's disease to new fields such as cluster headache, disruptive behaviour, SUNCt, epilepsy and tardive dystonia. The low complication rate, the reversibility of the procedure and the available image guided surgery tools will further increase the therapeutic applications of DBS. New therapeutical applications are expected for this functional scalpel.

Visualization of hemodynamics and light scattering in exposed brain of rat using multispectral image reconstruction based on Wiener estimation method

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Ishizuka, Tomohiro; Yoshida, Keiichiro; Kawauchi, Satoko; Sato, Shunichi; Sato, Manabu

2015-07-01

We investigate a method to estimate the spectral images of reduced scattering coefficients and the absorption coefficients of in vivo exposed brain tissues in the range from visible to near-infrared wavelength (500-760 nm) based on diffuse reflectance spectroscopy using a digital RGB camera. In the proposed method, the multi-spectral reflectance images of in vivo exposed brain are reconstructed from the digital red, green, blue images using the Wiener estimation algorithm. The Monte Carlo simulation-based multiple regression analysis for the absorbance spectra is then used to specify the absorption and scattering parameters of brain tissue. In this analysis, the concentration of oxygenated hemoglobin and that of deoxygenated hemoglobin are estimated as the absorption parameters whereas the scattering amplitude a and the scattering power b in the expression of μs'=aλ-b as the scattering parameters, respectively. The spectra of absorption and reduced scattering coefficients are reconstructed from the absorption and scattering parameters, and finally, the spectral images of absorption and reduced scattering coefficients are estimated. We performed simultaneous recordings of spectral diffuse reflectance images and of the electrophysiological signals for in vivo exposed rat brain during the cortical spreading depression evoked by the topical application of KCl. Changes in the total hemoglobin concentration and the tissue oxygen saturation imply the temporary change in cerebral blood flow during CSD. Change in the reduced scattering coefficient was observed before the profound increase in the total hemoglobin concentration, and its occurrence was synchronized with the negative dc shift of the local field potential.

A digital 3D atlas of the marmoset brain based on multi-modal MRI.

PubMed

Liu, Cirong; Ye, Frank Q; Yen, Cecil Chern-Chyi; Newman, John D; Glen, Daniel; Leopold, David A; Silva, Afonso C

2018-04-01

The common marmoset (Callithrix jacchus) is a New-World monkey of growing interest in neuroscience. Magnetic resonance imaging (MRI) is an essential tool to unveil the anatomical and functional organization of the marmoset brain. To facilitate identification of regions of interest, it is desirable to register MR images to an atlas of the brain. However, currently available atlases of the marmoset brain are mainly based on 2D histological data, which are difficult to apply to 3D imaging techniques. Here, we constructed a 3D digital atlas based on high-resolution ex-vivo MRI images, including magnetization transfer ratio (a T1-like contrast), T2w images, and multi-shell diffusion MRI. Based on the multi-modal MRI images, we manually delineated 54 cortical areas and 16 subcortical regions on one hemisphere of the brain (the core version). The 54 cortical areas were merged into 13 larger cortical regions according to their locations to yield a coarse version of the atlas, and also parcellated into 106 sub-regions using a connectivity-based parcellation method to produce a refined atlas. Finally, we compared the new atlas set with existing histology atlases and demonstrated its applications in connectome studies, and in resting state and stimulus-based fMRI. The atlas set has been integrated into the widely-distributed neuroimaging data analysis software AFNI and SUMA, providing a readily usable multi-modal template space with multi-level anatomical labels (including labels from the Paxinos atlas) that can facilitate various neuroimaging studies of marmosets. Published by Elsevier Inc.

High-performance radiofrequency coils for (23)Na MRI: brain and musculoskeletal applications.

PubMed

Wiggins, Graham C; Brown, Ryan; Lakshmanan, Karthik

2016-02-01

(23)Na RF coil design for brain and MSK applications presents a number of challenges, including poor coil loading for arrays of small coils and SNR penalties associated with providing (1)H capability with the same coil. The basics of RF coil design are described, as well as a review of historical approaches to dual tuning. There follows a review of published high performance coil designs for MSK and brain imaging. Several coil designs have been demonstrated at 7T and 3T which incorporate close-fitting receive arrays and in some cases design features which provide (1)H imaging with little penalty to (23)Na sensitivity. The "nested coplanar loop" approach is examined, in which small transmit-receive (1)H elements are placed within each (23)Na loop, presenting only a small perturbation to (23)Na performance and minimizing RF shielding issues. Other designs incorporating transmit-receive arrays for (23)Na and (1)H are discussed including a 9.4 T (23)Na/(1)H brain coil. Great gains in (23)Na SNR have been made with many of these designs, but simultaneously achieving high performance for 1H remains elusive. Copyright © 2015 John Wiley & Sons, Ltd.

Artificial neural network detects human uncertainty

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Frolov, Nikita S.; Maksimenko, Vladimir A.; Makarov, Vladimir V.; Koronovskii, Alexey A.; Garcia-Prieto, Juan; Antón-Toro, Luis Fernando; Maestú, Fernando; Pisarchik, Alexander N.

2018-03-01

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.

Towards the utilization of EEG as a brain imaging tool.

PubMed

Michel, Christoph M; Murray, Micah M

2012-06-01

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications. Copyright © 2011 Elsevier Inc. All rights reserved.

Quantification of Load Dependent Brain Activity in Parametric N-Back Working Memory Tasks using Pseudo-continuous Arterial Spin Labeling (pCASL) Perfusion Imaging.

PubMed

Zou, Qihong; Gu, Hong; Wang, Danny J J; Gao, Jia-Hong; Yang, Yihong

2011-04-01

Brain activation and deactivation induced by N-back working memory tasks and their load effects have been extensively investigated using positron emission tomography (PET) and blood-oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI). However, the underlying mechanisms of BOLD fMRI are still not completely understood and PET imaging requires injection of radioactive tracers. In this study, a pseudo-continuous arterial spin labeling (pCASL) perfusion imaging technique was used to quantify cerebral blood flow (CBF), a well understood physiological index reflective of cerebral metabolism, in N-back working memory tasks. Using pCASL, we systematically investigated brain activation and deactivation induced by the N-back working memory tasks and further studied the load effects on brain activity based on quantitative CBF. Our data show increased CBF in the fronto-parietal cortices, thalamus, caudate, and cerebellar regions, and decreased CBF in the posterior cingulate cortex and medial prefrontal cortex, during the working memory tasks. Most of the activated/deactivated brain regions show an approximately linear relationship between CBF and task loads (0, 1, 2 and 3 back), although several regions show non-linear relationships (quadratic and cubic). The CBF-based spatial patterns of brain activation/deactivation and load effects from this study agree well with those obtained from BOLD fMRI and PET techniques. These results demonstrate the feasibility of ASL techniques to quantify human brain activity during high cognitive tasks, suggesting its potential application to assessing the mechanisms of cognitive deficits in neuropsychiatric and neurological disorders.

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.

Spatial cluster analysis of nanoscopically mapped serotonin receptors for classification of fixed brain tissue

NASA Astrophysics Data System (ADS)

Sams, Michael; Silye, Rene; Göhring, Janett; Muresan, Leila; Schilcher, Kurt; Jacak, Jaroslaw

2014-01-01

We present a cluster spatial analysis method using nanoscopic dSTORM images to determine changes in protein cluster distributions within brain tissue. Such methods are suitable to investigate human brain tissue and will help to achieve a deeper understanding of brain disease along with aiding drug development. Human brain tissue samples are usually treated postmortem via standard fixation protocols, which are established in clinical laboratories. Therefore, our localization microscopy-based method was adapted to characterize protein density and protein cluster localization in samples fixed using different protocols followed by common fluorescent immunohistochemistry techniques. The localization microscopy allows nanoscopic mapping of serotonin 5-HT1A receptor groups within a two-dimensional image of a brain tissue slice. These nanoscopically mapped proteins can be confined to clusters by applying the proposed statistical spatial analysis. Selected features of such clusters were subsequently used to characterize and classify the tissue. Samples were obtained from different types of patients, fixed with different preparation methods, and finally stored in a human tissue bank. To verify the proposed method, samples of a cryopreserved healthy brain have been compared with epitope-retrieved and paraffin-fixed tissues. Furthermore, samples of healthy brain tissues were compared with data obtained from patients suffering from mental illnesses (e.g., major depressive disorder). Our work demonstrates the applicability of localization microscopy and image analysis methods for comparison and classification of human brain tissues at a nanoscopic level. Furthermore, the presented workflow marks a unique technological advance in the characterization of protein distributions in brain tissue sections.

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.

Dynamic perfusion CT in brain tumors.

PubMed

Yeung, Timothy Pok Chi; Bauman, Glenn; Yartsev, Slav; Fainardi, Enrico; Macdonald, David; Lee, Ting-Yim

2015-12-01

Dynamic perfusion CT (PCT) is an imaging technique for assessing the vascular supply and hemodynamics of brain tumors by measuring blood flow, blood volume, and permeability-surface area product. These PCT parameters provide information complementary to histopathologic assessments and have been used for grading brain tumors, distinguishing high-grade gliomas from other brain lesions, differentiating true progression from post-treatment effects, and predicting prognosis after treatments. In this review, the basic principles of PCT are described, and applications of PCT of brain tumors are discussed. The advantages and current challenges, along with possible solutions, of PCT are presented. Copyright © 2015. Published by Elsevier Ireland Ltd.

Target-specific contrast agents for magnetic resonance microscopy

PubMed Central

Blackwell, Megan L.; Farrar, Christian T.; Fischl, Bruce; Rosen, Bruce R.

2009-01-01

High-resolution ex vivo magnetic resonance (MR) imaging can be used to delineate prominent architectonic features in the human brain, but increased contrast is required to visualize more subtle distinctions. To aid MR sensitivity to cell density and myelination, we have begun the development of target-specific paramagnetic contrast agents. This work details the first application of luxol fast blue (LFB), an optical stain for myelin, as a white matter-selective MR contrast agent for human ex vivo brain tissue. Formalin-fixed human visual cortex was imaged with an isotropic resolution between 80 and 150 μm at 4.7 and 14 T before and after en bloc staining with LFB. Longitudinal (R1) and transverse (R2) relaxation rates in LFB-stained tissue increased proportionally with myelination at both field strengths. Changes in R1 resulted in larger contrast-to-noise ratios (CNR), per unit time, on T1-weighted images between more myelinated cortical layers (IV–VI) and adjacent, superficial layers (I–III) at both field strengths. Specifically, CNR for LFB-treated samples increased by 229±13% at 4.7 T and 269±25% at 14 T when compared to controls. Also, additional cortical layers (IVca, IVd, and Va) were resolvable in 14T-MR images of LFB-treated samples but not in control samples. After imaging, samples were sliced in 40-micron sections, mounted, and photographed. Both the macroscopic and microscopic distributions of LFB were found to mimic those of traditional histological preparations. Our results suggest target-specific contrast agents will enable more detailed MR images with applications in imaging pathological ex vivo samples and constructing better MR atlases from ex vivo brains. PMID:19385012

Embedded sparse representation of fMRI data via group-wise dictionary optimization

NASA Astrophysics Data System (ADS)

Zhu, Dajiang; Lin, Binbin; Faskowitz, Joshua; Ye, Jieping; Thompson, Paul M.

2016-03-01

Sparse learning enables dimension reduction and efficient modeling of high dimensional signals and images, but it may need to be tailored to best suit specific applications and datasets. Here we used sparse learning to efficiently represent functional magnetic resonance imaging (fMRI) data from the human brain. We propose a novel embedded sparse representation (ESR), to identify the most consistent dictionary atoms across different brain datasets via an iterative group-wise dictionary optimization procedure. In this framework, we introduced additional criteria to make the learned dictionary atoms more consistent across different subjects. We successfully identified four common dictionary atoms that follow the external task stimuli with very high accuracy. After projecting the corresponding coefficient vectors back into the 3-D brain volume space, the spatial patterns are also consistent with traditional fMRI analysis results. Our framework reveals common features of brain activation in a population, as a new, efficient fMRI analysis method.

Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

PubMed Central

Shimizu, Yu; Yoshimoto, Junichiro; Takamura, Masahiro; Okada, Go; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area. PMID:28700672

Neurovascular abnormalities in brain disorders: highlights with angiogenesis and magnetic resonance imaging studies.

PubMed

Chen, Chiao-Chi V; Chen, Yu-Chen; Hsiao, Han-Yun; Chang, Chen; Chern, Yijuang

2013-07-05

The coupling between neuronal activity and vascular responses is controlled by the neurovascular unit (NVU), which comprises multiple cell types. Many different types of dysfunction in these cells may impair the proper control of vascular responses by the NVU. Magnetic resonance imaging, which is the most powerful tool available to investigate neurovascular structures or functions, will be discussed in the present article in relation to its applications and discoveries. Because aberrant angiogenesis and vascular remodeling have been increasingly reported as being implicated in brain pathogenesis, this review article will refer to this hallmark event when suitable.

Feasibility of in Vivo SAXS Imaging for Detection of Alzheiemer's Disease

NASA Astrophysics Data System (ADS)

Choi, Mina

Small-angle x-ray scattering (SAXS) imaging has been proposed as a technique to characterize and selectively image structures based on electron density structure which allows for discriminating materials based on their scatter cross sections. This dissertation explores the feasibility of SAXS imaging for the detection of Alzheimer's disease (AD) amyloid plaques. The inherent scatter cross sections of amyloid plaque serve as biomarkers in vivo without the need of injected molecular tags. SAXS imaging can also assist in a better understanding of how these biomarkers play a role in Alzheimer's disease which in turn can lead to the development of more effective disease-modifying therapies. I implement simulations of x-ray transport using Monte Carlo methods for SAXS imaging enabling accurate calculation of radiation dose and image quality in SAXS-computed tomography (CT). I describe SAXS imaging phantoms with tissue-mimicking material and embedded scatter targets as a way of demonstrating the characteristics of SAXS imaging. I also performed a comprehensive study of scattering cross sections of brain tissue from measurements of ex-vivo sections of a wild-type mouse brain and reported generalized cross sections of gray matter, white matter, and corpus callosum obtained and registered by planar SAXS imaging. Finally, I demonstrate the ability of SAXS imaging to locate an amyloid fibril pellet within a brain section. This work contributes to novel application of SAXS imaging for Alzheimer's disease detection and studies its feasibility as an imaging tool for AD biomarkers.

Not single brain areas but a network is involved in language: Applications in presurgical planning.

PubMed

Alemi, Razieh; Batouli, Seyed Amir Hossein; Behzad, Ebrahim; Ebrahimpoor, Mitra; Oghabian, Mohammad Ali

2018-02-01

Language is an important human function, and is a determinant of the quality of life. In conditions such as brain lesions, disruption of the language function may occur, and lesion resection is a solution for that. Presurgical planning to determine the language-related brain areas would enhance the chances of language preservation after the operation; however, availability of a normative language template is essential. In this study, using data from 60 young individuals who were meticulously checked for mental and physical health, and using fMRI and robust imaging and data analysis methods, functional brain maps for the language production, perception and semantic were produced. The obtained templates showed that the language function should be considered as the product of the collaboration of a network of brain regions, instead of considering only few brain areas to be involved in that. This study has important clinical applications, and extends our knowledge on the neuroanatomy of the language function. Copyright © 2018 Elsevier B.V. All rights reserved.

[Application of anoptomagnetic probe Gd-DO3A-EA-FITC in imaging and analyzing the brain interstitial space].

PubMed

Li, Y Q; Sheng, Y; Liang, L; Zhao, Y; Li, H Y; Bai, N; Wang, T; Yuan, L; Han, H B

2018-04-18

To investigate the application of the optical magnetic bimodal molecular probe Gd-DO3A-ethylthiouret-fluorescein isothiocyanate (Gd -DO3A-EA-FITC) in brain tissue imaging and brain interstitial space (ISS). In the study, 24 male SD rats were randomly divided into 3 groups, including magnetic probe group (n=6), optical probe group (n=6) and optical magnetic bimodal probe group (n=12), then the optical magnetic bimodal probe group was divided equally into magnetic probe subgroup (n=6) and optical probe subgroup (n=6). Referencing the brain stereotaxic atlas, the coronal globus pallidus as center level, the probes including gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA), fluorescein isothiocyanate (FITC) and Gd-DO3A-EA-FITC of 2 μL (10 mmol/L) were injected into the caudate nucleus respectively, magnetic resonance imaging (MRI) was performed in the magnetic probe group and magnetic probe subgroup to image the dynamic diffusion and distribution of the probes in the brain ISS, a self-developed brain ISS image processing system was used to measure the diffusion coefficient, clearance, volume fraction and half-time in these two groups. Laser scanning confocal microscope (LSCM) was performed in vitro in the optical probe group and optical probe subgroup for fluorescence imaging at the time points 2 hours after the injection of the probe, and the distribution in the oblique sagittal slice was compared with the result of the first two groups. For the magnetic probe group and magnetic probe subgroup, there were the same imaging results between the probes of Gd-DTPA and Gd-DO3A-EA-FITC. The diffusion parameters of Gd-DTPA and Gd-DO3A-EA-FITC were as follows: the average diffusion coefficients [(3.31±0.11)×10 -4 mm 2 /s vs. (3.37±0.15)×10 -4 mm 2 /s, t=0.942, P=0.360], the clearance [(3.04±0.37) mmol/L vs. (2.90±0.51) mmol/L, t=0.640, P=0.531], the volume fractions (17.18%±0.14% vs. 17.31%±0.15%, t=1.961, P=0.068), the half-time [(86.58±3.31) min vs. (84.61±2.38) min, t=1.412, P=0.177], the diffusion areas [(23.25±0.68) mm 2 vs. (22.71±1.00) mm 2 , t=1.100, P=0.297]. The statistical analysis of each brain was made by t test, and the diffusion parameters were not statistically significant. Moreover, for the optical probe group and optical probe subgroup, the diffusion area of Gd-DO3A-EA-FITC [(22.61±1.16) mm 2 ] was slightly larger than that of FITC [(22.10±1.29) mm 2 ], the statistical analysis of each brain was made by t test, and the diffusion parameters were not statistically significant (t=0.713, P=0.492). Gd-DO3A-EA-FITC shows the same imaging results as the traditional GD-DTPA, and it can be used in measuring brain ISS.

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.

Technical Note: Development of a 3D printed subresolution sandwich phantom for validation of brain SPECT analysis

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

Negus, Ian S.; Holmes, Robin B.; Thorne, Gareth C.

Purpose: To make an adaptable, head shaped radionuclide phantom to simulate molecular imaging of the brain using clinical acquisition and reconstruction protocols. This will allow the characterization and correction of scanner characteristics, and improve the accuracy of clinical image analysis, including the application of databases of normal subjects. Methods: A fused deposition modeling 3D printer was used to create a head shaped phantom made up of transaxial slabs, derived from a simulated MRI dataset. The attenuation of the printed polylactide (PLA), measured by means of the Hounsfield unit on CT scanning, was set to match that of the brain bymore » adjusting the proportion of plastic filament and air (fill ratio). Transmission measurements were made to verify the attenuation of the printed slabs. The radionuclide distribution within the phantom was created by adding {sup 99m}Tc pertechnetate to the ink cartridge of a paper printer and printing images of gray and white matter anatomy, segmented from the same MRI data. The complete subresolution sandwich phantom was assembled from alternate 3D printed slabs and radioactive paper sheets, and then imaged on a dual headed gamma camera to simulate an HMPAO SPECT scan. Results: Reconstructions of phantom scans successfully used automated ellipse fitting to apply attenuation correction. This removed the variability inherent in manual application of attenuation correction and registration inherent in existing cylindrical phantom designs. The resulting images were assessed visually and by count profiles and found to be similar to those from an existing elliptical PMMA phantom. Conclusions: The authors have demonstrated the ability to create physically realistic HMPAO SPECT simulations using a novel head-shaped 3D printed subresolution sandwich method phantom. The phantom can be used to validate all neurological SPECT imaging applications. A simple modification of the phantom design to use thinner slabs would make it suitable for use in PET.« less

Graphene-based carbon-layered electrode array technology for neural imaging and optogenetic applications

PubMed Central

Park, Dong-Wook; Schendel, Amelia A.; Mikael, Solomon; Brodnick, Sarah K.; Richner, Thomas J.; Ness, Jared P.; Hayat, Mohammed R.; Atry, Farid; Frye, Seth T.; Pashaie, Ramin; Thongpang, Sanitta; Ma, Zhenqiang; Williams, Justin C.

2014-01-01

Neural micro-electrode arrays that are transparent over a broad wavelength spectrum from ultraviolet to infrared could allow for simultaneous electrophysiology and optical imaging, as well as optogenetic modulation of the underlying brain tissue. The long-term biocompatibility and reliability of neural micro-electrodes also require their mechanical flexibility and compliance with soft tissues. Here we present a graphene-based, carbon-layered electrode array (CLEAR) device, which can be implanted on the brain surface in rodents for high-resolution neurophysiological recording. We characterize optical transparency of the device at >90% transmission over the ultraviolet to infrared spectrum and demonstrate its utility through optical interface experiments that use this broad spectrum transparency. These include optogenetic activation of focal cortical areas directly beneath electrodes, in vivo imaging of the cortical vasculature via fluorescence microscopy and 3D optical coherence tomography. This study demonstrates an array of interfacing abilities of the CLEAR device and its utility for neural applications. PMID:25327513

Brain imaging and behavioral outcome in traumatic brain injury.

PubMed

Bigler, E D

1996-09-01

Brain imaging studies have become an essential diagnostic assessment procedure in evaluating the effects of traumatic brain injury (TBI). Such imaging studies provide a wealth of information about structural and functional deficits following TBI. But how pathologic changes identified by brain imaging methods relate to neurobehavioral outcome is not as well known. Thus, the focus of this article is on brain imaging findings and outcome following TBI. The article starts with an overview of current research dealing with the cellular pathology associated with TBI. Understanding the cellular elements of pathology permits extrapolation to what is observed with brain imaging. Next, this article reviews the relationship of brain imaging findings to underlying pathology and how that pathology relates to neurobehavioral outcome. The brain imaging techniques of magnetic resonance imaging, computerized tomography, and single photon emission computed tomography are reviewed. Various image analysis procedures, and how such findings relate to neuropsychological testing, are discussed. The importance of brain imaging in evaluating neurobehavioral deficits following brain injury is stressed.

Robust tissue-air volume segmentation of MR images based on the statistics of phase and magnitude: Its applications in the display of susceptibility-weighted imaging of the brain.

PubMed

Du, Yiping P; Jin, Zhaoyang

2009-10-01

To develop a robust algorithm for tissue-air segmentation in magnetic resonance imaging (MRI) using the statistics of phase and magnitude of the images. A multivariate measure based on the statistics of phase and magnitude was constructed for tissue-air volume segmentation. The standard deviation of first-order phase difference and the standard deviation of magnitude were calculated in a 3 x 3 x 3 kernel in the image domain. To improve differentiation accuracy, the uniformity of phase distribution in the kernel was also calculated and linear background phase introduced by field inhomogeneity was corrected. The effectiveness of the proposed volume segmentation technique was compared to a conventional approach that uses the magnitude data alone. The proposed algorithm was shown to be more effective and robust in volume segmentation in both synthetic phantom and susceptibility-weighted images of human brain. Using our proposed volume segmentation method, veins in the peripheral regions of the brain were well depicted in the minimum-intensity projection of the susceptibility-weighted images. Using the additional statistics of phase, tissue-air volume segmentation can be substantially improved compared to that using the statistics of magnitude data alone. (c) 2009 Wiley-Liss, Inc.

Structural neuroimaging in neuropsychology: History and contemporary applications.

PubMed

Bigler, Erin D

2017-11-01

Neuropsychology's origins began long before there were any in vivo methods to image the brain. That changed with the advent of computed tomography in the 1970s and magnetic resonance imaging in the early 1980s. Now computed tomography and magnetic resonance imaging are routinely a part of neuropsychological investigations with an increasing number of sophisticated methods for image analysis. This review examines the history of neuroimaging utilization in neuropsychological investigations, highlighting the basic methods that go into image quantification and the various metrics that can be derived. Neuroimaging methods and limitations for identify what constitutes a lesion are discussed. Likewise, the influence of various demographic and developmental factors that influence quantification of brain structure are reviewed. Neuroimaging is an integral part of 21st Century neuropsychology. The importance of neuroimaging to advancing neuropsychology is emphasized. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

An improved monomeric infrared fluorescent protein for neuronal and tumour brain imaging.

PubMed

Yu, Dan; Gustafson, William Clay; Han, Chun; Lafaye, Céline; Noirclerc-Savoye, Marjolaine; Ge, Woo-Ping; Thayer, Desiree A; Huang, Hai; Kornberg, Thomas B; Royant, Antoine; Jan, Lily Yeh; Jan, Yuh Nung; Weiss, William A; Shu, Xiaokun

2014-05-15

Infrared fluorescent proteins (IFPs) are ideal for in vivo imaging, and monomeric versions of these proteins can be advantageous as protein tags or for sensor development. In contrast to GFP, which requires only molecular oxygen for chromophore maturation, phytochrome-derived IFPs incorporate biliverdin (BV) as the chromophore. However, BV varies in concentration in different cells and organisms. Here we engineered cells to express the haeme oxygenase responsible for BV biosynthesis and a brighter monomeric IFP mutant (IFP2.0). Together, these tools improve the imaging capabilities of IFP2.0 compared with monomeric IFP1.4 and dimeric iRFP. By targeting IFP2.0 to the plasma membrane, we demonstrate robust labelling of neuronal processes in Drosophila larvae. We also show that this strategy improves the sensitivity when imaging brain tumours in whole mice. Our work shows promise in the application of IFPs for protein labelling and in vivo imaging.

Surface shape analysis with an application to brain surface asymmetry in schizophrenia.

PubMed

Brignell, Christopher J; Dryden, Ian L; Gattone, S Antonio; Park, Bert; Leask, Stuart; Browne, William J; Flynn, Sean

2010-10-01

Some methods for the statistical analysis of surface shapes and asymmetry are introduced. We focus on a case study where magnetic resonance images of the brain are available from groups of 30 schizophrenia patients and 38 controls, and we investigate large-scale brain surface shape differences. Key aspects of shape analysis are to remove nuisance transformations by registration and to identify which parts of one object correspond with the parts of another object. We introduce maximum likelihood and Bayesian methods for registering brain images and providing large-scale correspondences of the brain surfaces. Brain surface size-and-shape analysis is considered using random field theory, and also dimension reduction is carried out using principal and independent components analysis. Some small but significant differences are observed between the the patient and control groups. We then investigate a particular type of asymmetry called torque. Differences in asymmetry are observed between the control and patient groups, which add strength to other observations in the literature. Further investigations of the midline plane location in the 2 groups and the fitting of nonplanar curved midlines are also considered.

The multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) high performance computing infrastructure: applications in neuroscience and neuroinformatics research

PubMed Central

Goscinski, Wojtek J.; McIntosh, Paul; Felzmann, Ulrich; Maksimenko, Anton; Hall, Christopher J.; Gureyev, Timur; Thompson, Darren; Janke, Andrew; Galloway, Graham; Killeen, Neil E. B.; Raniga, Parnesh; Kaluza, Owen; Ng, Amanda; Poudel, Govinda; Barnes, David G.; Nguyen, Toan; Bonnington, Paul; Egan, Gary F.

2014-01-01

The Multi-modal Australian ScienceS Imaging and Visualization Environment (MASSIVE) is a national imaging and visualization facility established by Monash University, the Australian Synchrotron, the Commonwealth Scientific Industrial Research Organization (CSIRO), and the Victorian Partnership for Advanced Computing (VPAC), with funding from the National Computational Infrastructure and the Victorian Government. The MASSIVE facility provides hardware, software, and expertise to drive research in the biomedical sciences, particularly advanced brain imaging research using synchrotron x-ray and infrared imaging, functional and structural magnetic resonance imaging (MRI), x-ray computer tomography (CT), electron microscopy and optical microscopy. The development of MASSIVE has been based on best practice in system integration methodologies, frameworks, and architectures. The facility has: (i) integrated multiple different neuroimaging analysis software components, (ii) enabled cross-platform and cross-modality integration of neuroinformatics tools, and (iii) brought together neuroimaging databases and analysis workflows. MASSIVE is now operational as a nationally distributed and integrated facility for neuroinfomatics and brain imaging research. PMID:24734019

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.

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 method utilizing the diagnostic information from neurologists and a corner matching method based on the uncertainty and structure of brain medical images. Additionally, we present a similarity calculation method for brain image classification. Experimental results on two brain image sets show the proposed corner-based brain medical image classifier outperforms the state-of-the-art studies.

[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 surface model is the most common method of three-dimensional display. However, the volume rendering method may be more effective for imaging regions such as the brain.

Coordination Chemistry of Bifunctional Chemical Agents Designed for Applications in 64Cu PET Imaging for Alzheimer's Disease.

PubMed

Sharma, Anuj K; Schultz, Jason W; Prior, John T; Rath, Nigam P; Mirica, Liviu M

2017-11-20

Positron emission tomography (PET) is emerging as one of the most important diagnostic tools for brain imaging, yet the most commonly used radioisotopes in PET imaging, 11 C and 18 F, have short half-lives, and their usage is thus somewhat limited. By comparison, the 64 Cu radionuclide has a half-life of 12.7 h, which is ideal for administering and imaging purposes. In spite of appreciable research efforts, high-affinity copper chelators suitable for brain imaging applications are still lacking. Herein, we present the synthesis and characterization of a series of bifunctional compounds (BFCs) based on macrocyclic 1,4,7-triazacyclononane and 2,11-diaza[3.3](2,6)pyridinophane ligand frameworks that exhibit a high affinity for Cu 2+ ions. In addition, these BFCs contain a 2-phenylbenzothiazole fragment that is known to interact tightly with amyloid β fibrillar aggregates. Determination of the protonation constants (pK a values) and stability constants (log β values) of these BFCs, as well as characterization of the isolated copper complexes using X-ray crystallography, electron paramagnetic resonance spectroscopy, and electrochemical studies, suggests that these BFCs exhibit desirable properties for the development of novel 64 Cu PET imaging agents for Alzheimer's disease.

Multi-brain fusion and applications to intelligence analysis

NASA Astrophysics Data System (ADS)

Stoica, A.; Matran-Fernandez, A.; Andreou, D.; Poli, R.; Cinel, C.; Iwashita, Y.; Padgett, C.

2013-05-01

In a rapid serial visual presentation (RSVP) images are shown at an extremely rapid pace. Yet, the images can still be parsed by the visual system to some extent. In fact, the detection of specific targets in a stream of pictures triggers a characteristic electroencephalography (EEG) response that can be recognized by a brain-computer interface (BCI) and exploited for automatic target detection. Research funded by DARPA's Neurotechnology for Intelligence Analysts program has achieved speed-ups in sifting through satellite images when adopting this approach. This paper extends the use of BCI technology from individual analysts to collaborative BCIs. We show that the integration of information in EEGs collected from multiple operators results in performance improvements compared to the single-operator case.

Functional connectomics from a "big data" perspective.

PubMed

Xia, Mingrui; He, Yong

2017-10-15

In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field. Copyright © 2017 Elsevier Inc. All rights reserved.

A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using 18F-FMISO-PET

PubMed Central

Rockne, Russell C.; Trister, Andrew D.; Jacobs, Joshua; Hawkins-Daarud, Andrea J.; Neal, Maxwell L.; Hendrickson, Kristi; Mrugala, Maciej M.; Rockhill, Jason K.; Kinahan, Paul; Krohn, Kenneth A.; Swanson, Kristin R.

2015-01-01

Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient's disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia in vivo determined by [18F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model–data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning. PMID:25540239

Optimization of flow-sensitive alternating inversion recovery (FAIR) for perfusion functional MRI of rodent brain.

PubMed

Nasrallah, Fatima A; Lee, Eugene L Q; Chuang, Kai-Hsiang

2012-11-01

Arterial spin labeling (ASL) MRI provides a noninvasive method to image perfusion, and has been applied to map neural activation in the brain. Although pulsed labeling methods have been widely used in humans, continuous ASL with a dedicated neck labeling coil is still the preferred method in rodent brain functional MRI (fMRI) to maximize the sensitivity and allow multislice acquisition. However, the additional hardware is not readily available and hence its application is limited. In this study, flow-sensitive alternating inversion recovery (FAIR) pulsed ASL was optimized for fMRI of rat brain. A practical challenge of FAIR is the suboptimal global inversion by the transmit coil of limited dimensions, which results in low effective labeling. By using a large volume transmit coil and proper positioning to optimize the body coverage, the perfusion signal was increased by 38.3% compared with positioning the brain at the isocenter. An additional 53.3% gain in signal was achieved using optimized repetition and inversion times compared with a long TR. Under electrical stimulation to the forepaws, a perfusion activation signal change of 63.7 ± 6.3% can be reliably detected in the primary somatosensory cortices using single slice or multislice echo planar imaging at 9.4 T. This demonstrates the potential of using pulsed ASL for multislice perfusion fMRI in functional and pharmacological applications in rat brain. Copyright © 2012 John Wiley & Sons, Ltd.

Application of radiosurgical techniques to produce a primate model of brain lesions

PubMed Central

Kunimatsu, Jun; Miyamoto, Naoki; Ishikawa, Masayori; Shirato, Hiroki; Tanaka, Masaki

2015-01-01

Behavioral analysis of subjects with discrete brain lesions provides important information about the mechanisms of various brain functions. However, it is generally difficult to experimentally produce discrete lesions in deep brain structures. Here we show that a radiosurgical technique, which is used as an alternative treatment for brain tumors and vascular malformations, is applicable to create non-invasive lesions in experimental animals for the research in systems neuroscience. We delivered highly focused radiation (130–150 Gy at ISO center) to the frontal eye field (FEF) of macaque monkeys using a clinical linear accelerator (LINAC). The effects of irradiation were assessed by analyzing oculomotor performance along with magnetic resonance (MR) images before and up to 8 months following irradiation. In parallel with tissue edema indicated by MR images, deficits in saccadic and smooth pursuit eye movements were observed during several days following irradiation. Although initial signs of oculomotor deficits disappeared within a month, damage to the tissue and impaired eye movements gradually developed during the course of the subsequent 6 months. Postmortem histological examinations showed necrosis and hemorrhages within a large area of the white matter and, to a lesser extent, in the adjacent gray matter, which was centered at the irradiated target. These results indicated that the LINAC system was useful for making brain lesions in experimental animals, while the suitable radiation parameters to generate more focused lesions need to be further explored. We propose the use of a radiosurgical technique for establishing animal models of brain lesions, and discuss the possible uses of this technique for functional neurosurgical treatments in humans. PMID:25964746

Application of State-Space Smoothing to fMRI Data for Calculation of Lagged Transinformation between Human Brain Activations

NASA Astrophysics Data System (ADS)

Watanabe, Jobu

2009-09-01

Mutual information can be given a directional sense by introducing a time lag in one of the variables. In an author's previous study, to investigate the network dynamics of human brain regions, lagged transinformation (LTI) was introduced using time delayed mutual information. The LTI makes it possible to quantify the time course of dynamic information transfer between regions in the temporal domain. The LTI was applied to functional magnetic resonance imaging (fMRI) data involved in neural processing of the transformation and comparison from three-dimensional (3D) visual information to a two-dimensional (2D) location to calculate directed information flows between the activated brain regions. In the present study, for more precise estimation of LTI, Kalman filter smoothing was applied to the same fMRI data. Because the smoothing method exploits the full length of the time series data for the estimation, its application increases the precision. Large information flows were found from the bilateral prefrontal cortices to the parietal cortices. The results suggest that information of the 3D images stored as working memory was retrieved and transferred from the prefrontal cortices to the parietal cortices for comparison with information of the 2D images.

Development and Design of Next-Generation Head-Mounted Ambulatory Microdose Positron-Emission Tomography (AM-PET) System.

PubMed

Melroy, Samantha; Bauer, Christopher; McHugh, Matthew; Carden, Garret; Stolin, Alexander; Majewski, Stan; Brefczynski-Lewis, Julie; Wuest, Thorsten

2017-05-19

Several applications exist for a whole brain positron-emission tomography (PET) brain imager designed as a portable unit that can be worn on a patient's head. Enabled by improvements in detector technology, a lightweight, high performance device would allow PET brain imaging in different environments and during behavioral tasks. Such a wearable system that allows the subjects to move their heads and walk-the Ambulatory Microdose PET (AM-PET)-is currently under development. This imager will be helpful for testing subjects performing selected activities such as gestures, virtual reality activities and walking. The need for this type of lightweight mobile device has led to the construction of a proof of concept portable head-worn unit that uses twelve silicon photomultiplier (SiPM) PET module sensors built into a small ring which fits around the head. This paper is focused on the engineering design of mechanical support aspects of the AM-PET project, both of the current device as well as of the coming next-generation devices. The goal of this work is to optimize design of the scanner and its mechanics to improve comfort for the subject by reducing the effect of weight, and to enable diversification of its applications amongst different research activities.

Development and Design of Next-Generation Head-Mounted Ambulatory Microdose Positron-Emission Tomography (AM-PET) System

PubMed Central

Melroy, Samantha; Bauer, Christopher; McHugh, Matthew; Carden, Garret; Stolin, Alexander; Majewski, Stan; Brefczynski-Lewis, Julie; Wuest, Thorsten

2017-01-01

Several applications exist for a whole brain positron-emission tomography (PET) brain imager designed as a portable unit that can be worn on a patient’s head. Enabled by improvements in detector technology, a lightweight, high performance device would allow PET brain imaging in different environments and during behavioral tasks. Such a wearable system that allows the subjects to move their heads and walk—the Ambulatory Microdose PET (AM-PET)—is currently under development. This imager will be helpful for testing subjects performing selected activities such as gestures, virtual reality activities and walking. The need for this type of lightweight mobile device has led to the construction of a proof of concept portable head-worn unit that uses twelve silicon photomultiplier (SiPM) PET module sensors built into a small ring which fits around the head. This paper is focused on the engineering design of mechanical support aspects of the AM-PET project, both of the current device as well as of the coming next-generation devices. The goal of this work is to optimize design of the scanner and its mechanics to improve comfort for the subject by reducing the effect of weight, and to enable diversification of its applications amongst different research activities. PMID:28534848

In vivo imaging of brain infarct with the novel fluorescent probe PSVue 794 in a rat middle cerebral artery occlusion-reperfusion model.

PubMed

Chu, Chun; Huang, Xiaofang; Chen, Chiung-Tong; Zhao, Yuanli; Luo, Jin J; Gray, Brian D; Pak, Koon Y; Dun, Nae J

2013-01-01

The utility of PSVue 794 (PS794), a near-infrared fluorescent dye conjugated to a bis[zinc (II)-dipicolylamine] (Zn-DPA) targeting moiety, in imaging brain infarct was assessed in a rat middle cerebral artery occlusion-reperfusion model. Following reperfusion, 1 mM PS794 solution was administered intravenously via a tail vein. Fluorescence images were captured between 6 to 72 hours postinjection using a LI-COR Biosciences Pearl Imaging System. Strong fluorescence signals, which may represent the infarct core, were detected in the right hemisphere, ipsilateral to the injured site, and weaker signals in areas surrounding the core. In ischemia-reperfusion rats injected with a control dye not linked to a targeting agent, fluorescence was distributed diffusely throughout the brain. To address the issue of whether Zn-DPA targets apoptotic/necrotic cells, HT22 mouse hippocampal neurons were cultured in either Dulbecco's Modified Eagle's Medium, serum-deprived medium, Hank's Balanced Salt Solution, or L-glutamate (10 mM)-containing medium for up to 33 hours. Cells were then double-labeled with PSVue 480 (Zn-DPA conjugated to fluorescein isothiocyanate) and propidium iodide, which labels necrotic cells. Microscopic examination revealed that PS480 targeted apoptotic and necrotic cells. The result indicates that PS794 is applicable to in vivo imaging of brain infarct and that Zn-DPA selectively targets apoptotic/necrotic cells.

Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging

PubMed Central

Ohayon, Shay; Caravaca-Aguirre, Antonio; Piestun, Rafael; DiCarlo, James J.

2018-01-01

A major open challenge in neuroscience is the ability to measure and perturb neural activity in vivo from well defined neural sub-populations at cellular resolution anywhere in the brain. However, limitations posed by scattering and absorption prohibit non-invasive multi-photon approaches for deep (>2mm) structures, while gradient refractive index (GRIN) endoscopes are relatively thick and can cause significant damage upon insertion. Here, we present a novel micro-endoscope design to image neural activity at arbitrary depths via an ultra-thin multi-mode optical fiber (MMF) probe that has 5–10X thinner diameter than commercially available micro-endoscopes. We demonstrate micron-scale resolution, multi-spectral and volumetric imaging. In contrast to previous approaches, we show that this method has an improved acquisition speed that is sufficient to capture rapid neuronal dynamics in-vivo in rodents expressing a genetically encoded calcium indicator (GCaMP). Our results emphasize the potential of this technology in neuroscience applications and open up possibilities for cellular resolution imaging in previously unreachable brain regions. PMID:29675297

3D deformable image matching: a hierarchical approach over nested subspaces

NASA Astrophysics Data System (ADS)

Musse, Olivier; Heitz, Fabrice; Armspach, Jean-Paul

2000-06-01

This paper presents a fast hierarchical method to perform dense deformable inter-subject matching of 3D MR Images of the brain. To recover the complex morphological variations in neuroanatomy, a hierarchy of 3D deformations fields is estimated, by minimizing a global energy function over a sequence of nested subspaces. The nested subspaces, generated from a single scaling function, consist of deformation fields constrained at different scales. The highly non linear energy function, describing the interactions between the target and the source images, is minimized using a coarse-to-fine continuation strategy over this hierarchy. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR Images from different individuals. The method has shown efficient in putting into correspondence the principle anatomical structures of the brain. An application to atlas-based MRI segmentation, by transporting a labeled segmentation map on patient data, is also presented.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors.

PubMed

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A

2015-11-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional.

Intensity-Curvature Measurement Approaches for the Diagnosis of Magnetic Resonance Imaging Brain Tumors

PubMed Central

Ciulla, Carlo; Veljanovski, Dimitar; Rechkoska Shikoska, Ustijana; Risteski, Filip A.

2015-01-01

This research presents signal-image post-processing techniques called Intensity-Curvature Measurement Approaches with application to the diagnosis of human brain tumors detected through Magnetic Resonance Imaging (MRI). Post-processing of the MRI of the human brain encompasses the following model functions: (i) bivariate cubic polynomial, (ii) bivariate cubic Lagrange polynomial, (iii) monovariate sinc, and (iv) bivariate linear. The following Intensity-Curvature Measurement Approaches were used: (i) classic-curvature, (ii) signal resilient to interpolation, (iii) intensity-curvature measure and (iv) intensity-curvature functional. The results revealed that the classic-curvature, the signal resilient to interpolation and the intensity-curvature functional are able to add additional information useful to the diagnosis carried out with MRI. The contribution to the MRI diagnosis of our study are: (i) the enhanced gray level scale of the tumor mass and the well-behaved representation of the tumor provided through the signal resilient to interpolation, and (ii) the visually perceptible third dimension perpendicular to the image plane provided through the classic-curvature and the intensity-curvature functional. PMID:26644943

Time reversal for ultrasonic transcranial surgery and echographic imaging

NASA Astrophysics Data System (ADS)

Tanter, Mickael; Aubry, Jean-Francois; Vignon, Francois; Fink, Mathias

2005-09-01

High-intensity focused ultrasound (HIFU) is able to induce non-invasively controlled and selective destruction of tissues by focusing ultrasonic beams within organs, analogous to a magnifying glass that concentrates enough sunlight to burn a hole in paper. The brain is an attractive organ in which to perform ultrasonic tissue ablation, but such an application has been hampered by the strong defocusing effect of the skull bone. Our group has been involved in this topic for several years, providing proofs of concept and proposing technological solutions to this problem. Thanks to a high-power time-reversal mirror, presented here are in vivo thermal lesions induced through the skull of 12 sheep. Thermal lesions were confirmed by T2-weighted magnetic resonance post-treatment images and histological examination. These results provide striking evidence that noninvasive ultrasound brain surgery is feasible. A recent approach for high-resolution brain ultrasonic imaging will also be discussed with a skull aberration correction technique based on twin arrays technology. The correction of transcranial ultrasonic images is implemented on a new generation of time-reversal mirrors relying on a fully programmable transmit and receive beamformer.

Design and Evaluation of a Hybrid Radiofrequency Applicator for Magnetic Resonance Imaging and RF Induced Hyperthermia: Electromagnetic Field Simulations up to 14.0 Tesla and Proof-of-Concept at 7.0 Tesla

PubMed Central

Winter, Lukas; Özerdem, Celal; Hoffmann, Werner; Santoro, Davide; Müller, Alexander; Waiczies, Helmar; Seemann, Reiner; Graessl, Andreas; Wust, Peter; Niendorf, Thoralf

2013-01-01

This work demonstrates the feasibility of a hybrid radiofrequency (RF) applicator that supports magnetic resonance (MR) imaging and MR controlled targeted RF heating at ultrahigh magnetic fields (B0≥7.0T). For this purpose a virtual and an experimental configuration of an 8-channel transmit/receive (TX/RX) hybrid RF applicator was designed. For TX/RX bow tie antenna electric dipoles were employed. Electromagnetic field simulations (EMF) were performed to study RF heating versus RF wavelength (frequency range: 64 MHz (1.5T) to 600 MHz (14.0T)). The experimental version of the applicator was implemented at B0 = 7.0T. The applicators feasibility for targeted RF heating was evaluated in EMF simulations and in phantom studies. Temperature co-simulations were conducted in phantoms and in a human voxel model. Our results demonstrate that higher frequencies afford a reduction in the size of specific absorption rate (SAR) hotspots. At 7T (298 MHz) the hybrid applicator yielded a 50% iso-contour SAR (iso-SAR-50%) hotspot with a diameter of 43 mm. At 600 MHz an iso-SAR-50% hotspot of 26 mm in diameter was observed. RF power deposition per RF input power was found to increase with B0 which makes targeted RF heating more efficient at higher frequencies. The applicator was capable of generating deep-seated temperature hotspots in phantoms. The feasibility of 2D steering of a SAR/temperature hotspot to a target location was demonstrated by the induction of a focal temperature increase (ΔT = 8.1 K) in an off-center region of the phantom. Temperature simulations in the human brain performed at 298 MHz showed a maximum temperature increase to 48.6C for a deep-seated hotspot in the brain with a size of (19×23×32)mm3 iso-temperature-90%. The hybrid applicator provided imaging capabilities that facilitate high spatial resolution brain MRI. To conclude, this study outlines the technical underpinnings and demonstrates the basic feasibility of an 8-channel hybrid TX/RX applicator that supports MR imaging, MR thermometry and targeted RF heating in one device. PMID:23613896

Design and evaluation of a hybrid radiofrequency applicator for magnetic resonance imaging and RF induced hyperthermia: electromagnetic field simulations up to 14.0 Tesla and proof-of-concept at 7.0 Tesla.

PubMed

Winter, Lukas; Özerdem, Celal; Hoffmann, Werner; Santoro, Davide; Müller, Alexander; Waiczies, Helmar; Seemann, Reiner; Graessl, Andreas; Wust, Peter; Niendorf, Thoralf

2013-01-01

This work demonstrates the feasibility of a hybrid radiofrequency (RF) applicator that supports magnetic resonance (MR) imaging and MR controlled targeted RF heating at ultrahigh magnetic fields (B0≥7.0T). For this purpose a virtual and an experimental configuration of an 8-channel transmit/receive (TX/RX) hybrid RF applicator was designed. For TX/RX bow tie antenna electric dipoles were employed. Electromagnetic field simulations (EMF) were performed to study RF heating versus RF wavelength (frequency range: 64 MHz (1.5T) to 600 MHz (14.0T)). The experimental version of the applicator was implemented at B0 = 7.0T. The applicators feasibility for targeted RF heating was evaluated in EMF simulations and in phantom studies. Temperature co-simulations were conducted in phantoms and in a human voxel model. Our results demonstrate that higher frequencies afford a reduction in the size of specific absorption rate (SAR) hotspots. At 7T (298 MHz) the hybrid applicator yielded a 50% iso-contour SAR (iso-SAR-50%) hotspot with a diameter of 43 mm. At 600 MHz an iso-SAR-50% hotspot of 26 mm in diameter was observed. RF power deposition per RF input power was found to increase with B0 which makes targeted RF heating more efficient at higher frequencies. The applicator was capable of generating deep-seated temperature hotspots in phantoms. The feasibility of 2D steering of a SAR/temperature hotspot to a target location was demonstrated by the induction of a focal temperature increase (ΔT = 8.1 K) in an off-center region of the phantom. Temperature simulations in the human brain performed at 298 MHz showed a maximum temperature increase to 48.6C for a deep-seated hotspot in the brain with a size of (19×23×32)mm(3) iso-temperature-90%. The hybrid applicator provided imaging capabilities that facilitate high spatial resolution brain MRI. To conclude, this study outlines the technical underpinnings and demonstrates the basic feasibility of an 8-channel hybrid TX/RX applicator that supports MR imaging, MR thermometry and targeted RF heating in one device.

Nanosensors for the Chemical Imaging of Acetylcholine Using Magnetic Resonance Imaging.

PubMed

Luo, Yi; Kim, Eric H; Flask, Chris A; Clark, Heather A

2018-06-06

A suite of imaging tools for detecting specific chemicals in the central nervous system could accelerate the understanding of neural signaling events critical to brain function and disease. Here, we introduce a class of nanoparticle sensors for the highly specific detection of acetylcholine in the living brain using magnetic resonance imaging. The nanosensor is composed of acetylcholine-catalyzing enzymes and pH-sensitive gadolinium contrast agents co-localized onto the surface of polymer nanoparticles, which leads to changes in T 1 relaxation rate (1/ T 1 ). The mechanism of the sensor involves the enzymatic hydrolysis of acetylcholine leading to a localized decrease in pH which is detected by the pH-sensitive gadolinium chelate. The concomitant change in 1/ T 1 in vitro measured a 20% increase from 0 to 10 μM acetylcholine concentration. The applicability of the nanosensors in vivo was demonstrated in the rat medial prefrontal cortex showing distinct changes in 1/ T 1 induced by pharmacological stimuli. The highly specific acetylcholine nanosensor we present here offers a promising strategy for detection of cholinergic neurotransmission and will facilitate our understanding of brain function through chemical imaging.

A Scalable Framework For Segmenting Magnetic Resonance Images

PubMed Central

Hore, Prodip; Goldgof, Dmitry B.; Gu, Yuhua; Maudsley, Andrew A.; Darkazanli, Ammar

2009-01-01

A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data. PMID:20046893

Brain-Compatible Learning: Principles and Applications in Athletic Training

PubMed Central

2003-01-01

Objective: To discuss the principles of brain-compatible learning research and provide insights into how this research may be applied in athletic training education to benefit the profession. Background: In the past decade, new brain-imaging techniques have allowed us to observe the brain while it is learning. The field of neuroscience has produced a body of empirical data that provides a new understanding of how we learn. This body of data has implications in education, although the direct study of these implications is in its infancy. Description: An overview of how the brain learns at a cellular level is provided, followed by a discussion of the principles of brain-compatible learning. Applications of these principles and implications for the field of athletic training education are also offered. Application: Many educational-reform fads have garnered attention in the past. Brain-compatible learning will not likely be one of those, as its origin is in neuroscience, not education. Brain-compatible learning is not an educational-reform movement. It does not prescribe how to run your classroom or offer specific techniques to use. Rather, it provides empirical data about how the brain learns and suggests guidelines to be considered while preparing lessons for your students. These guidelines may be incorporated into every educational setting, with every type of curriculum and every age group. The field of athletic training lends itself well to many of the basic principles of brain-compatible learning. PMID:16558681

Resting state brain networks and their implications in neurodegenerative disease

NASA Astrophysics Data System (ADS)

Sohn, William S.; Yoo, Kwangsun; Kim, Jinho; Jeong, Yong

2012-10-01

Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer's disease, frontotemporal lobar degeneration and Parkinson's disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.

Multimodality cellular and molecular imaging of concomitant tumour enhancement in a syngeneic mouse model of breast cancer metastasis.

PubMed

Parkins, Katie M; Dubois, Veronica P; Hamilton, Amanda M; Makela, Ashley V; Ronald, John A; Foster, Paula J

2018-06-12

The mechanisms that influence metastatic growth rates are poorly understood. One mechanism of interest known as concomitant tumour resistance (CTR) can be defined as the inhibition of metastasis by existing tumour mass. Conversely, the presence of a primary tumour has also been shown to increase metastatic outgrowth, termed concomitant tumour enhancement (CTE). The majority of studies evaluating CTR/CTE in preclinical models have relied on endpoint histological evaluation of tumour burden. The goal of this research was to use conventional magnetic resonance imaging (MRI), cellular MRI, and bioluminescence imaging to study the impact of a primary tumour on the development of brain metastases in a syngeneic mouse model. Here, we report that the presence of a 4T1 primary tumour significantly enhances total brain tumour burden in Balb/C mice. Using in vivo BLI/MRI we could determine this was not related to differences in initial arrest or clearance of viable cells in the brain, which suggests that the presence of a primary tumour can increase the proliferative growth of brain metastases in this model. The continued application of our longitudinal cellular and molecular imaging tools will yield a better understanding of the mechanism(s) by which this physiological inhibition (CTR) and/or enhancement (CTE) occurs.

Mapping brain function in freely moving subjects

PubMed Central

Holschneider, Daniel P.; Maarek, Jean-Michel I.

2014-01-01

Expression of many fundamental mammalian behaviors such as, for example, aggression, mating, foraging or social behaviors, depend on locomotor activity. A central dilemma in the functional neuroimaging of these behaviors has been the fact that conventional neuroimaging techniques generally rely on immobilization of the subject, which extinguishes all but the simplest activity. Ideally, imaging could occur in freely moving subjects, while presenting minimal interference with the subject’s natural behavior. Here we provide an overview of several approaches that have been undertaken in the past to achieve this aim in both tethered and freely moving animals, as well as in nonrestrained human subjects. Applications of specific radiotracers to single photon emission computed tomography and positron emission tomography are discussed in which brain activation is imaged after completion of the behavioral task and capture of the tracer. Potential applications to clinical neuropsychiatry are discussed, as well as challenges inherent to constraint-free functional neuroimaging. Future applications of these methods promise to increase our understanding of the neural circuits underlying mammalian behavior in health and disease. PMID:15465134

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.

PET Imaging - from Physics to Clinical Molecular Imaging

NASA Astrophysics Data System (ADS)

Majewski, Stan

2008-03-01

From the beginnings many years ago in a few physics laboratories and first applications as a research brain function imager, PET became lately a leading molecular imaging modality used in diagnosis, staging and therapy monitoring of cancer, as well as has increased use in assessment of brain function (early diagnosis of Alzheimer's, etc) and in cardiac function. To assist with anatomic structure map and with absorption correction CT is often used with PET in a duo system. Growing interest in the last 5-10 years in dedicated organ specific PET imagers (breast, prostate, brain, etc) presents again an opportunity to the particle physics instrumentation community to contribute to the important field of medical imaging. In addition to the bulky standard ring structures, compact, economical and high performance mobile imagers are being proposed and build. The latest development in standard PET imaging is introduction of the well known TOF concept enabling clearer tomographic pictures of the patient organs. Development and availability of novel photodetectors such as Silicon PMT immune to magnetic fields offers an exciting opportunity to use PET in conjunction with MRI and fMRI. As before with avalanche photodiodes, particle physics community plays a leading role in developing these devices. The presentation will mostly focus on present and future opportunities for better PET designs based on new technologies and methods: new scintillators, photodetectors, readout, software.

Contrast-enhanced T1-weighted fluid-attenuated inversion-recovery BLADE magnetic resonance imaging of the brain: an alternative to spin-echo technique for detection of brain lesions in the unsedated pediatric patient?

PubMed

Alibek, Sedat; Adamietz, Boris; Cavallaro, Alexander; Stemmer, Alto; Anders, Katharina; Kramer, Manuel; Bautz, Werner; Staatz, Gundula

2008-08-01

We compared contrast-enhanced T1-weighted magnetic resonance (MR) imaging of the brain using different types of data acquisition techniques: periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER, BLADE) imaging versus standard k-space sampling (conventional spin-echo pulse sequence) in the unsedated pediatric patient with focus on artifact reduction, overall image quality, and lesion detectability. Forty-eight pediatric patients (aged 3 months to 18 years) were scanned with a clinical 1.5-T whole body MR scanner. Cross-sectional contrast-enhanced T1-weighted spin-echo sequence was compared to a T1-weighted dark-fluid fluid-attenuated inversion-recovery (FLAIR) BLADE sequence for qualitative and quantitative criteria (image artifacts, image quality, lesion detectability) by two experienced radiologists. Imaging protocols were matched for imaging parameters. Reader agreement was assessed using the exact Bowker test. BLADE images showed significantly less pulsation and motion artifacts than the standard T1-weighted spin-echo sequence scan. BLADE images showed statistically significant lower signal-to-noise ratio but higher contrast-to-noise ratios with superior gray-white matter contrast. All lesions were demonstrated on FLAIR BLADE imaging, and one false-positive lesion was visible in spin-echo sequence images. BLADE MR imaging at 1.5 T is applicable for central nervous system imaging of the unsedated pediatric patient, reduces motion and pulsation artifacts, and minimizes the need for sedation or general anesthesia without loss of relevant diagnostic information.

Testosterone affects language areas of the adult human brain.

PubMed

Hahn, Andreas; Kranz, Georg S; Sladky, Ronald; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Vanicek, Thomas; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F; Lanzenberger, Rupert

2016-05-01

Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high-dose hormone application in adult female-to-male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging. Voxel-based morphometry analysis showed decreased gray matter volume with increasing levels of bioavailable testosterone exclusively in Broca's and Wernicke's areas. Particularly, this may link known sex differences in language performance to the influence of testosterone on relevant brain regions. Using probabilistic tractography, we further observed that longitudinal changes in testosterone negatively predicted changes in mean diffusivity of the corresponding structural connection passing through the extreme capsule. Considering a related increase in myelin staining in rodents, this potentially reflects a strengthening of the fiber tract particularly involved in language comprehension. Finally, functional images at resting-state were evaluated, showing increased functional connectivity between the two brain regions with increasing testosterone levels. These findings suggest testosterone-dependent neuroplastic adaptations in adulthood within language-specific brain regions and connections. Importantly, deteriorations in gray matter volume seem to be compensated by enhancement of corresponding structural and functional connectivity. Hum Brain Mapp 37:1738-1748, 2016. © 2016 Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

In vivo imaging and analysis of cerebrovascular hemodynamic responses and tissue oxygenation in the mouse brain.

PubMed

Kisler, Kassandra; Lazic, Divna; Sweeney, Melanie D; Plunkett, Shane; El Khatib, Mirna; Vinogradov, Sergei A; Boas, David A; Sakadži, Sava; Zlokovic, Berislav V

2018-06-01

Cerebrovascular dysfunction has an important role in the pathogenesis of multiple brain disorders. Measurement of hemodynamic responses in vivo can be challenging, particularly as techniques are often not described in sufficient detail and vary between laboratories. We present a set of standardized in vivo protocols that describe high-resolution two-photon microscopy and intrinsic optical signal (IOS) imaging to evaluate capillary and arteriolar responses to a stimulus, regional hemodynamic responses, and oxygen delivery to the brain. The protocol also describes how to measure intrinsic NADH fluorescence to understand how blood O 2 supply meets the metabolic demands of activated brain tissue, and to perform resting-state absolute oxygen partial pressure (pO 2 ) measurements of brain tissue. These methods can detect cerebrovascular changes at far higher resolution than MRI techniques, although the optical nature of these techniques limits their achievable imaging depths. Each individual procedure requires 1-2 h to complete, with two to three procedures typically performed per animal at a time. These protocols are broadly applicable in studies of cerebrovascular function in healthy and diseased brain in any of the existing mouse models of neurological and vascular disorders. All these procedures can be accomplished by a competent graduate student or experienced technician, except the two-photon measurement of absolute pO 2 level, which is better suited to a more experienced, postdoctoral-level researcher.

Concept of an upright wearable positron emission tomography imager in humans.

PubMed

Bauer, Christopher E; Brefczynski-Lewis, Julie; Marano, Gary; Mandich, Mary-Beth; Stolin, Alexander; Martone, Peter; Lewis, James W; Jaliparthi, Gangadhar; Raylman, Raymond R; Majewski, Stan

2016-09-01

Positron Emission Tomography (PET) is traditionally used to image patients in restrictive positions, with few devices allowing for upright, brain-dedicated imaging. Our team has explored the concept of wearable PET imagers which could provide functional brain imaging of freely moving subjects. To test feasibility and determine future considerations for development, we built a rudimentary proof-of-concept prototype (Helmet_PET) and conducted tests in phantoms and four human volunteers. Twelve Silicon Photomultiplier-based detectors were assembled in a ring with exterior weight support and an interior mechanism that could be adjustably fitted to the head. We conducted brain phantom tests as well as scanned four patients scheduled for diagnostic F(18-) FDG PET/CT imaging. For human subjects the imager was angled such that field of view included basal ganglia and visual cortex to test for typical resting-state pattern. Imaging in two subjects was performed ~4 hr after PET/CT imaging to simulate lower injected F(18-) FDG dose by taking advantage of the natural radioactive decay of the tracer (F(18) half-life of 110 min), with an estimated imaging dosage of 25% of the standard. We found that imaging with a simple lightweight ring of detectors was feasible using a fraction of the standard radioligand dose. Activity levels in the human participants were quantitatively similar to standard PET in a set of anatomical ROIs. Typical resting-state brain pattern activation was demonstrated even in a 1 min scan of active head rotation. To our knowledge, this is the first demonstration of imaging a human subject with a novel wearable PET imager that moves with robust head movements. We discuss potential research and clinical applications that will drive the design of a fully functional device. Designs will need to consider trade-offs between a low weight device with high mobility and a heavier device with greater sensitivity and larger field of view.

Imaging of Neuronal Activity in Awake Mice by Measurements of Flavoprotein Autofluorescence Corrected for Cerebral Blood Flow.

PubMed

Takahashi, Manami; Urushihata, Takuya; Takuwa, Hiroyuki; Sakata, Kazumi; Takado, Yuhei; Shimizu, Eiji; Suhara, Tetsuya; Higuchi, Makoto; Ito, Hiroshi

2017-01-01

Green fluorescence imaging (e.g., flavoprotein autofluorescence imaging, FAI) can be used to measure neuronal activity and oxygen metabolism in living brains without expressing fluorescence proteins. It is useful for understanding the mechanism of various brain functions and their abnormalities in age-related brain diseases. However, hemoglobin in cerebral blood vessels absorbs green fluorescence, hampering accurate assessments of brain function in animal models with cerebral blood vessel dysfunctions and subsequent cerebral blood flow (CBF) alterations. In the present study, we developed a new method to correct FAI signals for hemoglobin-dependent green fluorescence reductions by simultaneous measurements of green fluorescence and intrinsic optical signals. Intrinsic optical imaging enabled evaluations of light absorption and scatters by hemoglobin, which could then be applied to corrections of green fluorescence intensities. Using this method, enhanced flavoprotein autofluorescence by sensory stimuli was successfully detected in the brains of awake mice, despite increases of CBF, and hemoglobin interference. Moreover, flavoprotein autofluorescence could be properly quantified in a resting state and during sensory stimulation by a CO 2 inhalation challenge, which modified vascular responses without overtly affecting neuronal activities. The flavoprotein autofluorescence signal data obtained here were in good agreement with the previous findings from a condition with drug-induced blockade of cerebral vasodilation, justifying the current assaying methodology. Application of this technology to studies on animal models of brain diseases with possible changes of CBF, including age-related neurological disorders, would provide better understanding of the mechanisms of neurovascular coupling in pathological circumstances.

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

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

Diagnostic imaging of herpes simplex virus encephalitis using a radiolabeled antiviral drug: autoradiographic assessment in an animal model

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

Saito, Y.; Rubenstein, R.; Price, R.W.

1984-06-01

To develop a new approach to the diagnosis of herpes simplex encephalitis, we used a radiolabeled antiviral drug, 2'-fluoro-5-methyl-1-beta-D-arabinosyluracil labeled with carbon 14 ((14C)FMAU), as a probe for selectively imaging brain infection in a rat model by quantitative autoradiography. A high correlation was found between focal infection, as defined by immunoperoxidase viral antigen staining, and increased regional (14C)FMAU uptake in brain sections. Two potential sources of false-positive imaging were defined: high concentrations of drug in the choroid plexus because of its higher permeability compared with brain, and drug sequestration by proliferating uninfected cell populations. Our results support the soundness ofmore » the proposed strategy of using a labeled antiviral drug that is selectively phosphorylated by herpes simplex virus type 1 thymidine kinase in conjunction with scanning methods for human diagnosis, and also define some of the factors that must be taken into account when planning clinical application.« less

Automatic selection of localized region-based active contour models using image content analysis applied to brain tumor segmentation.

PubMed

Ilunga-Mbuyamba, Elisee; Avina-Cervantes, Juan Gabriel; Cepeda-Negrete, Jonathan; Ibarra-Manzano, Mario Alberto; Chalopin, Claire

2017-12-01

Brain tumor segmentation is a routine process in a clinical setting and provides useful information for diagnosis and treatment planning. Manual segmentation, performed by physicians or radiologists, is a time-consuming task due to the large quantity of medical data generated presently. Hence, automatic segmentation methods are needed, and several approaches have been introduced in recent years including the Localized Region-based Active Contour Model (LRACM). There are many popular LRACM, but each of them presents strong and weak points. In this paper, the automatic selection of LRACM based on image content and its application on brain tumor segmentation is presented. Thereby, a framework to select one of three LRACM, i.e., Local Gaussian Distribution Fitting (LGDF), localized Chan-Vese (C-V) and Localized Active Contour Model with Background Intensity Compensation (LACM-BIC), is proposed. Twelve visual features are extracted to properly select the method that may process a given input image. The system is based on a supervised approach. Applied specifically to Magnetic Resonance Imaging (MRI) images, the experiments showed that the proposed system is able to correctly select the suitable LRACM to handle a specific image. Consequently, the selection framework achieves better accuracy performance than the three LRACM separately. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function

PubMed Central

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D.

2009-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings. PMID:19834575

A Parallel Independent Component Analysis Approach to Investigate Genomic Influence on Brain Function.

PubMed

Liu, Jingyu; Demirci, Oguz; Calhoun, Vince D

2008-01-01

Relationships between genomic data and functional brain images are of great interest but require new analysis approaches to integrate the high-dimensional data types. This letter presents an extension of a technique called parallel independent component analysis (paraICA), which enables the joint analysis of multiple modalities including interconnections between them. We extend our earlier work by allowing for multiple interconnections and by providing important overfitting controls. Performance was assessed by simulations under different conditions, and indicated reliable results can be extracted by properly balancing overfitting and underfitting. An application to functional magnetic resonance images and single nucleotide polymorphism array produced interesting findings.

The development, past achievements, and future directions of brain PET

PubMed Central

Jones, Terry; Rabiner, Eugenii A

2012-01-01

The early developments of brain positron emission tomography (PET), including the methodological advances that have driven progress, are outlined. The considerable past achievements of brain PET have been summarized in collaboration with contributing experts in specific clinical applications including cerebrovascular disease, movement disorders, dementia, epilepsy, schizophrenia, addiction, depression and anxiety, brain tumors, drug development, and the normal healthy brain. Despite a history of improving methodology and considerable achievements, brain PET research activity is not growing and appears to have diminished. Assessments of the reasons for decline are presented and strategies proposed for reinvigorating brain PET research. Central to this is widening the access to advanced PET procedures through the introduction of lower cost cyclotron and radiochemistry technologies. The support and expertize of the existing major PET centers, and the recruitment of new biologists, bio-mathematicians and chemists to the field would be important for such a revival. New future applications need to be identified, the scope of targets imaged broadened, and the developed expertize exploited in other areas of medical research. Such reinvigoration of the field would enable PET to continue making significant contributions to advance the understanding of the normal and diseased brain and support the development of advanced treatments. PMID:22434067

A 3D MRI-based atlas of a lizard brain.

PubMed

Hoops, Daniel; Desfilis, Ester; Ullmann, Jeremy F P; Janke, Andrew L; Stait-Gardner, Timothy; Devenyi, Gabriel A; Price, William S; Medina, Loreta; Whiting, Martin J; Keogh, J Scott

2018-06-22

Magnetic resonance imaging (MRI) is an established technique for neuroanatomical analysis, being particularly useful in the medical sciences. However, the application of MRI to evolutionary neuroscience is still in its infancy. Few magnetic resonance brain atlases exist outside the standard model organisms in neuroscience and no magnetic resonance atlas has been produced for any reptile brain. A detailed understanding of reptilian brain anatomy is necessary to elucidate the evolutionary origin of enigmatic brain structures such as the cerebral cortex. Here, we present a magnetic resonance atlas for the brain of a representative squamate reptile, the Australian tawny dragon (Agamidae: Ctenophorus decresii), which has been the object of numerous ecological and behavioral studies. We used a high-field 11.74T magnet, a paramagnetic contrasting-enhancing agent and minimum-deformation modeling of the brains of thirteen adult male individuals. From this, we created a high-resolution three-dimensional model of a lizard brain . The 3D-MRI model can be freely downloaded and allows a better comprehension of brain areas, nuclei, and fiber tracts, facilitating comparison with other species and setting the basis for future comparative evolution imaging studies. The MRI model of a tawny dragon brain (Ctenophorus decresii) can be viewed online and downloaded using the Wiley Biolucida Server at wiley.biolucida.net. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

A combined MR and CT study for precise quantitative analysis of the avian brain

NASA Astrophysics Data System (ADS)

Jirak, Daniel; Janacek, Jiri; Kear, Benjamin P.

2015-10-01

Brain size is widely used as a measure of behavioural complexity and sensory-locomotive capacity in avians but has largely relied upon laborious dissections, endoneurocranial tissue displacement, and physical measurement to derive comparative volumes. As an alternative, we present a new precise calculation method based upon coupled magnetic resonance (MR) imaging and computed tomography (CT). Our approach utilizes a novel interactive Fakir probe cross-referenced with an automated CT protocol to efficiently generate total volumes and surface areas of the brain tissue and endoneurocranial space, as well as the discrete cephalic compartments. We also complemented our procedures by using sodium polytungstate (SPT) as a contrast agent. This greatly enhanced CT applications but did not degrade MR quality and is therefore practical for virtual brain tissue reconstructions employing multiple imaging modalities. To demonstrate our technique, we visualized sex-based brain size differentiation in a sample set of Ring-necked pheasants (Phasianus colchicus). This revealed no significant variance in relative volume or surface areas of the primary brain regions. Rather, a trend towards isometric enlargement of the total brain and endoneurocranial space was evidenced in males versus females, thus advocating a non-differential sexually dimorphic pattern of brain size increase amongst these facultatively flying birds.

High-speed pre-clinical brain imaging using pulsed laser diode based photoacoustic tomography (PLD-PAT) system

NASA Astrophysics Data System (ADS)

Upputuri, Paul Kumar; Pramanik, Manojit

2016-03-01

Photoacoustic tomography (PAT) is a promising biomedical imaging modality for small animal imaging, breast cancer imaging, monitoring of vascularisation, tumor angiogenesis, blood oxygenation, total haemoglobin concentration etc. The existing PAT systems that uses Q-switched Nd:YAG and OPO nanosecond lasers have limitations in clinical applications because they are expensive, non-potable and not suitable for real-time imaging due to their low pulse repetition rate. Low-energy pulsed near-infrared diode laser which are low-cost, compact, and light-weight (<200 grams), can be used as an alternate. In this work, we present a photoacoustic tomography system with a pulsed laser diode (PLD) that can nanosecond pulses with pulse energy 1.3 mJ/pulse at ~803 nm wavelength and 7000 Hz repetition rate. The PLD is integrated inside a single-detector circular scanning geometric system. To verify the high speed imaging capabilities of the PLD-PAT system, we performed in vivo experimental results on small animal brain imaging using this system. The proposed system is portable, low-cost and can provide real-time imaging.

Local/non-local regularized image segmentation using graph-cuts: application to dynamic and multispectral MRI.

PubMed

Hanson, Erik A; Lundervold, Arvid

2013-11-01

Multispectral, multichannel, or time series image segmentation is important for image analysis in a wide range of applications. Regularization of the segmentation is commonly performed using local image information causing the segmented image to be locally smooth or piecewise constant. A new spatial regularization method, incorporating non-local information, was developed and tested. Our spatial regularization method applies to feature space classification in multichannel images such as color images and MR image sequences. The spatial regularization involves local edge properties, region boundary minimization, as well as non-local similarities. The method is implemented in a discrete graph-cut setting allowing fast computations. The method was tested on multidimensional MRI recordings from human kidney and brain in addition to simulated MRI volumes. The proposed method successfully segment regions with both smooth and complex non-smooth shapes with a minimum of user interaction.

fMRI Brain-Computer Interface: A Tool for Neuroscientific Research and Treatment

PubMed Central

Sitaram, Ranganatha; Caria, Andrea; Veit, Ralf; Gaber, Tilman; Rota, Giuseppina; Kuebler, Andrea; Birbaumer, Niels

2007-01-01

Brain-computer interfaces based on functional magnetic resonance imaging (fMRI-BCI) allow volitional control of anatomically specific regions of the brain. Technological advancement in higher field MRI scanners, fast data acquisition sequences, preprocessing algorithms, and robust statistical analysis are anticipated to make fMRI-BCI more widely available and applicable. This noninvasive technique could potentially complement the traditional neuroscientific experimental methods by varying the activity of the neural substrates of a region of interest as an independent variable to study its effects on behavior. If the neurobiological basis of a disorder (e.g., chronic pain, motor diseases, psychopathy, social phobia, depression) is known in terms of abnormal activity in certain regions of the brain, fMRI-BCI can be targeted to modify activity in those regions with high specificity for treatment. In this paper, we review recent results of the application of fMRI-BCI to neuroscientific research and psychophysiological treatment. PMID:18274615

Brain Imaging and Behavioral Outcome in Traumatic Brain Injury.

ERIC Educational Resources Information Center

Bigler, Erin D.

1996-01-01

This review explores the cellular pathology associated with traumatic brain injury (TBI) and its relation to neurobehavioral outcomes, the relationship of brain imaging findings to underlying pathology, brain imaging techniques, various image analysis procedures and how they relate to neuropsychological testing, and the importance of brain imaging…

Functional magnetic resonance imaging.

PubMed

Buchbinder, Bradley R

2016-01-01

Functional magnetic resonance imaging (fMRI) maps the spatiotemporal distribution of neural activity in the brain under varying cognitive conditions. Since its inception in 1991, blood oxygen level-dependent (BOLD) fMRI has rapidly become a vital methodology in basic and applied neuroscience research. In the clinical realm, it has become an established tool for presurgical functional brain mapping. This chapter has three principal aims. First, we review key physiologic, biophysical, and methodologic principles that underlie BOLD fMRI, regardless of its particular area of application. These principles inform a nuanced interpretation of the BOLD fMRI signal, along with its neurophysiologic significance and pitfalls. Second, we illustrate the clinical application of task-based fMRI to presurgical motor, language, and memory mapping in patients with lesions near eloquent brain areas. Integration of BOLD fMRI and diffusion tensor white-matter tractography provides a road map for presurgical planning and intraoperative navigation that helps to maximize the extent of lesion resection while minimizing the risk of postoperative neurologic deficits. Finally, we highlight several basic principles of resting-state fMRI and its emerging translational clinical applications. Resting-state fMRI represents an important paradigm shift, focusing attention on functional connectivity within intrinsic cognitive networks. © 2016 Elsevier B.V. All rights reserved.

Imaging screening of catastrophic neurological events using a software tool: preliminary results.

PubMed

Fernandes, A P; Gomes, A; Veiga, J; Ermida, D; Vardasca, T

2015-05-01

In Portugal, as in most countries, the most frequent organ donors are brain-dead donors. To answer the increasing need for transplants, donation programs have been implemented. The goal is to recognize virtually all the possible and potential brain-dead donors admitted to hospitals. The aim of this work was to describe preliminary results of a software application designed to identify devastating neurological injury victims who may progress to brain death and can be possible organ donors. This was an observational, longitudinal study with retrospective data collection. The software application is an automatic algorithm based on natural language processing for selected keywords/expressions present in the cranio-encephalic computerized tomography (CE CT) scan reports to identify catastrophic neurological situations, with e-mail notification to the Transplant Coordinator (TC). The first 7 months of this application were analyzed and compared with the standard clinical evaluation methodology. The imaging identification tool showed a sensitivity of 77% and a specificity of 66%; predictive positive value (PPV) was 0.8 and predictive negative value (PNV) was 0.7 for the identification of catastrophic neurological events. The methodology proposed in this work seems promising in improving the screening efficiency of critical neurological events. Copyright © 2015 Elsevier Inc. All rights reserved.

Brain tumor segmentation based on local independent projection-based classification.

PubMed

Huang, Meiyan; Yang, Wei; Wu, Yao; Jiang, Jun; Chen, Wufan; Feng, Qianjin

2014-10-01

Brain tumor segmentation is an important procedure for early tumor diagnosis and radiotherapy planning. Although numerous brain tumor segmentation methods have been presented, enhancing tumor segmentation methods is still challenging because brain tumor MRI images exhibit complex characteristics, such as high diversity in tumor appearance and ambiguous tumor boundaries. To address this problem, we propose a novel automatic tumor segmentation method for MRI images. This method treats tumor segmentation as a classification problem. Additionally, the local independent projection-based classification (LIPC) method is used to classify each voxel into different classes. A novel classification framework is derived by introducing the local independent projection into the classical classification model. Locality is important in the calculation of local independent projections for LIPC. Locality is also considered in determining whether local anchor embedding is more applicable in solving linear projection weights compared with other coding methods. Moreover, LIPC considers the data distribution of different classes by learning a softmax regression model, which can further improve classification performance. In this study, 80 brain tumor MRI images with ground truth data are used as training data and 40 images without ground truth data are used as testing data. The segmentation results of testing data are evaluated by an online evaluation tool. The average dice similarities of the proposed method for segmenting complete tumor, tumor core, and contrast-enhancing tumor on real patient data are 0.84, 0.685, and 0.585, respectively. These results are comparable to other state-of-the-art methods.

In vivo monitoring of glial scar proliferation on chronically implanted neural electrodes by fiber optical coherence tomography

PubMed Central

Xie, Yijing; Martini, Nadja; Hassler, Christina; Kirch, Robert D.; Stieglitz, Thomas; Seifert, Andreas; Hofmann, Ulrich G.

2014-01-01

In neural prosthetics and stereotactic neurosurgery, intracortical electrodes are often utilized for delivering therapeutic electrical pulses, and recording neural electrophysiological signals. Unfortunately, neuroinflammation impairs the neuron-electrode-interface by developing a compact glial encapsulation around the implants in long term. At present, analyzing this immune reaction is only feasible with post-mortem histology; currently no means for specific in vivo monitoring exist and most applicable imaging modalities can not provide information in deep brain regions. Optical coherence tomography (OCT) is a well established imaging modality for in vivo studies, providing cellular resolution and up to 1.2 mm imaging depth in brain tissue. A fiber based spectral domain OCT was shown to be capable of minimally invasive brain imaging. In the present study, we propose to use a fiber based spectral domain OCT to monitor the progression of the tissue's immune response through scar encapsulation progress in a rat animal model. A fine fiber catheter was implanted in rat brain together with a flexible polyimide microelectrode in sight both of which acts as a foreign body and induces the brain tissue immune reaction. OCT signals were collected from animals up to 12 weeks after implantation and thus gliotic scarring in vivo monitored for that time. Preliminary data showed a significant enhancement of the OCT backscattering signal during the first 3 weeks after implantation, and increased attenuation factor of the sampled tissue due to the glial scar formation. PMID:25191264

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Metabolic Brain Network Analysis of Hypothyroidism Symptom Based on [18F]FDG-PET of Rats.

PubMed

Wan, Hongkai; Tan, Ziyu; Zheng, Qiang; Yu, Jing

2018-03-12

Recent researches have demonstrated the value of using 2-deoxy-2-[ 18 F]fluoro-D-glucose ([ 18 F]FDG) positron emission tomography (PET) imaging to reveal the hypothyroidism-related damages in local brain regions. However, the influence of hypothyroidism on the entire brain network is barely studied. This study focuses on the application of graph theory on analyzing functional brain networks of the hypothyroidism symptom. For both the hypothyroidism and the control groups of Wistar rats, the functional brain networks were constructed by thresholding the glucose metabolism correlation matrices of 58 brain regions. The network topological properties (including the small-world properties and the nodal centralities) were calculated and compared between the two groups. We found that the rat brains, like human brains, have typical properties of the small-world network in both the hypothyroidism and the control groups. However, the hypothyroidism group demonstrated lower global efficiency and decreased local cliquishness of the brain network, indicating hypothyroidism-related impairment to the brain network. The hypothyroidism group also has decreased nodal centrality in the left posterior hippocampus, the right hypothalamus, pituitary, pons, and medulla. This observation accorded with the hypothyroidism-related functional disorder of hypothalamus-pituitary-thyroid (HPT) feedback regulation mechanism. Our research quantitatively confirms that hypothyroidism hampers brain cognitive function by causing impairment to the brain network of glucose metabolism. This study reveals the feasibility and validity of applying graph theory method to preclinical [ 18 F]FDG-PET images and facilitates future study on human subjects.

Image-guided intracranial cannula placement for awake in vivo microdialysis in nonhuman primates

NASA Astrophysics Data System (ADS)

Chen, Antong; Bone, Ashleigh; Hines, Catherine D. G.; Dogdas, Belma; Montgomery, Tamara O.; Michener, Maria; Winkelmann, Christopher T.; Ghafurian, Soheil; Lubbers, Laura S.; Renger, John; Bagchi, Ansuman; Uslaner, Jason M.; Johnson, Colena; Zariwala, Hatim A.

2016-03-01

Intracranial microdialysis is used for sampling neurochemicals and large peptides along with their metabolites from the interstitial fluid (ISF) of the brain. The ability to perform this in nonhuman primates (NHP) e.g., rhesus could improve the prediction of pharmacokinetic (PK) and pharmacodynamics (PD) action of drugs in human. However, microdialysis in rhesus brains is not as routinely performed as in rodents. One challenge is that the precise intracranial probe placement in NHP brains is difficult due to the richness of the anatomical structure and the variability of the size and shape of brains across animals. Also, a repeatable and reproducible ISF sampling from the same animal is highly desirable when combined with cognitive behaviors or other longitudinal study end points. Toward that end, we have developed a semi-automatic flexible neurosurgical method employing MR and CT imaging to (a) derive coordinates for permanent guide cannula placement in mid-brain structures and (b) fabricate a customized recording chamber to implant above the skull for enclosing and safeguarding access to the cannula for repeated experiments. In order to place the intracranial guide cannula in each subject, the entry points in the skull and the depth in the brain were derived using co-registered images acquired from MR and CT scans. The anterior/posterior (A/P) and medial-lateral (M/L) rotation in the pose of the animal was corrected in the 3D image to appropriately represent the pose used in the stereotactic frame. An array of implanted fiducial markers was used to transform stereotactic coordinates to the images. The recording chamber was custom fabricated using computer-aided design (CAD), such that it would fit the contours of the individual skull with minimum error. The chamber also helped in guiding the cannula through the entry points down a trajectory into the depth of the brain. We have validated our method in four animals and our results indicate average placement error of cannula to be 1.20 +/- 0.68 mm of the targeted positions. The approach employed here for derivation of the coordinates, surgical implantation and post implant validation is built using traditional access to surgical and imaging methods without the necessity of intra-operative imaging. The validation of our method lends support to its wider application in most nonhuman primate laboratories with onsite MR and CT imaging capabilities.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

In Vivo Fiber-Optic Raman Mapping Of Metastases In Mouse Brains

NASA Astrophysics Data System (ADS)

Stelling, A.; Kirsch, M.; Steiner, G.; Krafft, C.; Schackert, G.; Salzer, R.

2010-08-01

Vibrational spectroscopy, in particular Raman spectroscopy, has potential applications in the field of in vivo diagnostics. Raman and FT-IR spectroscopy analyze the complete biochemical information at any given pixel within the visual field. Here we demonstrate the feasibility of performing Raman spectroscopic measurements on living mice brains using a fiber-optic probe with a nominal spatial resolution of 60 μm. The objectives of this study were to 1) evaluate preclinical models, namely murine brain slices containing experimental tumors, 2) optimize the preparation of pristine brain tissue to obtain reference information, to 3) optimize the conditions for introducing a fiber-optic probe to acquire Raman maps in vivo, and 4) to transfer results obtained from human brain tumors to an animal model. Disseminated brain metastases of malignant melanomas were induced by injecting tumor cells into the carotid artery of mice. The procedure mimicked hematogenous tumor spread in one brain hemisphere while the other hemisphere remained tumor free. Three series of sections were prepared consecutively from whole mouse brains: pristine, 2-mm thick sections for Raman mapping and dried, thin sections for FT-IR imaging, hematoxylin and eosin-stained thin sections for histopathological assessment. Raman maps were collected serially using a spectrometer coupled to a fiber-optic probe. FT-IR images were recorded using a spectrometer with a multi-channel detector. The FT-IR images and the Raman maps were evaluated by multivariate data analysis. The results obtained from the thin section studies were employed to guide measurements of murine brains in vivo. Raman maps with an acquisition time of over an hour could be performed on the living animals. No damage to the tissue was observed.

Engineered core-shell magnetic nanoparticle for MR dual-modal tracking and safe magnetic manipulation of ependymal cells in live rodents

NASA Astrophysics Data System (ADS)

Peng, Yung-Kang; Lui, Cathy N. P.; Chen, Yu-Wei; Chou, Shang-Wei; Chou, Pi-Tai; Yung, Ken K. L.; Edman Tsang, S. C.

2018-01-01

Tagging recognition group(s) on superparamagnetic iron oxide is known to aid localisation (imaging), stimulation and separation of biological entities using magnetic resonance imaging (MRI) and magnetic agitation/separation (MAS) techniques. Despite the wide applicability of iron oxide nanoparticles in T 2-weighted MRI and MAS, the quality of the images and safe manipulation of the exceptionally delicate neural cells in a live brain are currently the key challenges. Here, we demonstrate the engineered manganese oxide clusters-iron oxide core-shell nanoparticle as an MR dual-modal contrast agent for neural stem cells (NSCs) imaging and magnetic manipulation in live rodents. As a result, using this engineered nanoparticle and associated technologies, identification, stimulation and transportation of labelled potentially multipotent NSCs from a specific location of a live brain to another by magnetic means for self-healing therapy can therefore be made possible.

Calcium neuroimaging in behaving zebrafish larvae using a turn-key light field camera

NASA Astrophysics Data System (ADS)

Cruz Perez, Carlos; Lauri, Antonella; Symvoulidis, Panagiotis; Cappetta, Michele; Erdmann, Arne; Westmeyer, Gil Gregor

2015-09-01

Reconstructing a three-dimensional scene from multiple simultaneously acquired perspectives (the light field) is an elegant scanless imaging concept that can exceed the temporal resolution of currently available scanning-based imaging methods for capturing fast cellular processes. We tested the performance of commercially available light field cameras on a fluorescent microscopy setup for monitoring calcium activity in the brain of awake and behaving reporter zebrafish larvae. The plenoptic imaging system could volumetrically resolve diverse neuronal response profiles throughout the zebrafish brain upon stimulation with an aversive odorant. Behavioral responses of the reporter fish could be captured simultaneously together with depth-resolved neuronal activity. Overall, our assessment showed that with some optimizations for fluorescence microscopy applications, commercial light field cameras have the potential of becoming an attractive alternative to custom-built systems to accelerate molecular imaging research on cellular dynamics.

Calcium neuroimaging in behaving zebrafish larvae using a turn-key light field camera.

PubMed

Perez, Carlos Cruz; Lauri, Antonella; Symvoulidis, Panagiotis; Cappetta, Michele; Erdmann, Arne; Westmeyer, Gil Gregor

2015-09-01

Reconstructing a three-dimensional scene from multiple simultaneously acquired perspectives (the light field) is an elegant scanless imaging concept that can exceed the temporal resolution of currently available scanning-based imaging methods for capturing fast cellular processes. We tested the performance of commercially available light field cameras on a fluorescent microscopy setup for monitoring calcium activity in the brain of awake and behaving reporter zebrafish larvae. The plenoptic imaging system could volumetrically resolve diverse neuronal response profiles throughout the zebrafish brain upon stimulation with an aversive odorant. Behavioral responses of the reporter fish could be captured simultaneously together with depth-resolved neuronal activity. Overall, our assessment showed that with some optimizations for fluorescence microscopy applications, commercial light field cameras have the potential of becoming an attractive alternative to custom-built systems to accelerate molecular imaging research on cellular dynamics.

Camelid single-domain antibodies: A versatile tool for in vivo imaging of extracellular and intracellular brain targets.

PubMed

Li, Tengfei; Vandesquille, Matthias; Koukouli, Fani; Dudeffant, Clémence; Youssef, Ihsen; Lenormand, Pascal; Ganneau, Christelle; Maskos, Uwe; Czech, Christian; Grueninger, Fiona; Duyckaerts, Charles; Dhenain, Marc; Bay, Sylvie; Delatour, Benoît; Lafaye, Pierre

2016-12-10

Detection of intracerebral targets with imaging probes is challenging due to the non-permissive nature of blood-brain barrier (BBB). The present work describes two novel single-domain antibodies (VHHs or nanobodies) that specifically recognize extracellular amyloid deposits and intracellular tau neurofibrillary tangles, the two core lesions of Alzheimer's disease (AD). Following intravenous administration in transgenic mouse models of AD, in vivo real-time two-photon microscopy showed gradual extravasation of the VHHs across the BBB, diffusion in the parenchyma and labeling of amyloid deposits and neurofibrillary tangles. Our results demonstrate that VHHs can be used as specific BBB-permeable probes for both extracellular and intracellular brain targets and suggest new avenues for therapeutic and diagnostic applications in neurology. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluation of blood-brain barrier-stealth nanocomposites for in situ glioblastoma theranostics applications

NASA Astrophysics Data System (ADS)

Su, Chia-Hao; Tsai, Ching-Yi; Tomanek, Boguslaw; Chen, Wei-Yu; Cheng, Fong-Yu

2016-04-01

The blood-brain barrier (BBB) is a physiological structure of the blood vessels in the brain. The BBB efficiently traps most therapeutic drugs in the blood vessels and stops them from entering the brain tissue, resulting in a decreased therapeutic efficiency. In this study, we developed BBB-stealth nanocomposites composed of iron oxide (Fe3O4) nanoparticles (NPs) as a safe nanocarrier for glioblastoma therapy. We showed the antitumor activity of Dox/alg-Fe3O4 NPs using in vitro and in vivo tests. We demonstrated that G23-alg-Fe3O4 NPs crossed the BBB and entered the brain. In situ glioblastoma tumor-bearing mice were used to successfully evaluate the antitumor activity of G23-Dox/alg-Fe3O4 NPs. Magnetic resonance imaging (MRI) and bioluminescence imaging (BLI) confirmed the BBB crossing. The BBB-stealth nanocomposites show great potential for a proof-of-concept clinical trial as a theranostics platform for human brain tumor therapy.The blood-brain barrier (BBB) is a physiological structure of the blood vessels in the brain. The BBB efficiently traps most therapeutic drugs in the blood vessels and stops them from entering the brain tissue, resulting in a decreased therapeutic efficiency. In this study, we developed BBB-stealth nanocomposites composed of iron oxide (Fe3O4) nanoparticles (NPs) as a safe nanocarrier for glioblastoma therapy. We showed the antitumor activity of Dox/alg-Fe3O4 NPs using in vitro and in vivo tests. We demonstrated that G23-alg-Fe3O4 NPs crossed the BBB and entered the brain. In situ glioblastoma tumor-bearing mice were used to successfully evaluate the antitumor activity of G23-Dox/alg-Fe3O4 NPs. Magnetic resonance imaging (MRI) and bioluminescence imaging (BLI) confirmed the BBB crossing. The BBB-stealth nanocomposites show great potential for a proof-of-concept clinical trial as a theranostics platform for human brain tumor therapy. Electronic supplementary information (ESI) available: Experimental details. See DOI: 10.1039/c6nr00280c

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

PubMed Central

Kim, Hak Yeong; Seo, Kain; Jeon, Hong Jin; Lee, Unjoo; Lee, Hyosang

2017-01-01

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies. PMID:28835022

A hybrid skull-stripping algorithm based on adaptive balloon snake models

NASA Astrophysics Data System (ADS)

Liu, Hung-Ting; Sheu, Tony W. H.; Chang, Herng-Hua

2013-02-01

Skull-stripping is one of the most important preprocessing steps in neuroimage analysis. We proposed a hybrid algorithm based on an adaptive balloon snake model to handle this challenging task. The proposed framework consists of two stages: first, the fuzzy possibilistic c-means (FPCM) is used for voxel clustering, which provides a labeled image for the snake contour initialization. In the second stage, the contour is initialized outside the brain surface based on the FPCM result and evolves under the guidance of the balloon snake model, which drives the contour with an adaptive inward normal force to capture the boundary of the brain. The similarity indices indicate that our method outperformed the BSE and BET methods in skull-stripping the MR image volumes in the IBSR data set. Experimental results show the effectiveness of this new scheme and potential applications in a wide variety of skull-stripping applications.

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 crucial step in order to obtain improved activity estimates. Presence of local errors in both MLACF and CBAC based reconstructions would require the use of a normal database for clinical assessment. However, further work is required in order to assess the clinical advantage of MLACF over CBAC based method.

Dynamic Multi-Coil Shimming of the Human Brain at 7 Tesla

PubMed Central

Juchem, Christoph; Nixon, Terence W.; McIntyre, Scott; Boer, Vincent O.; Rothman, Douglas L.; de Graaf, Robin A.

2011-01-01

High quality magnetic field homogenization of the human brain (i.e. shimming) for MR imaging and spectroscopy is a demanding task. The susceptibility differences between air and tissue are a longstanding problem as they induce complex field distortions in the prefrontal cortex and the temporal lobes. To date, the theoretical gains of high field MR have only been realized partially in the human brain due to limited magnetic field homogeneity. A novel shimming technique for the human brain is presented that is based on the combination of non-orthogonal basis fields from 48 individual, circular coils. Custom-built amplifier electronics enabled the dynamic application of the multi-coil shim fields in a slice-specific fashion. Dynamic multi-coil (DMC) shimming is shown to eliminate most of the magnetic field inhomogeneity apparent in the human brain at 7 Tesla and provided improved performance compared to state-of-the-art dynamic shim updating with zero through third order spherical harmonic functions. The novel technique paves the way for high field MR applications of the human brain for which excellent magnetic field homogeneity is a prerequisite. PMID:21824794

Neuroimaging of Cerebrovascular Disease in the Aging Brain

PubMed Central

Gupta, Ajay; Nair, Sreejit; Schweitzer, Andrew D.; Kishore, Sirish; Johnson, Carl E.; Comunale, Joseph P.; Tsiouris, Apostolos J.; Sanelli, Pina C.

2012-01-01

Cerebrovascular disease remains a significant public health burden with its greatest impact on the elderly population. Advances in neuroimaging techniques allow detailed and sophisticated evaluation of many manifestations of cerebrovascular disease in the brain parenchyma as well as in the intracranial and extracranial vasculature. These tools continue to contribute to our understanding of the multifactorial processes that occur in the age-dependent development of cerebrovascular disease. Structural abnormalities related to vascular disease in the brain and vessels have been well characterized with CT and MRI based techniques. We review some of the pathophysiologic mechanisms in the aging brain and cerebral vasculature and the related structural abnormalities detectable on neuroimaging, including evaluation of age-related white matter changes, atherosclerosis of the cerebral vasculature, and cerebral infarction. In addition, newer neuroimaging techniques, such as diffusion tensor imaging, perfusion techniques, and assessment of cerebrovascular reserve, are also reviewed, as these techniques can detect physiologic alterations which complement the morphologic changes that cause cerebrovascular disease in the aging brain.Further investigation of these advanced imaging techniques has potential application to the understanding and diagnosis of cerebrovascular disease in the elderly. PMID:23185721

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

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

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

Decoding brain cancer dynamics: a quantitative histogram-based approach using temporal MRI

NASA Astrophysics Data System (ADS)

Zhou, Mu; Hall, Lawrence O.; Goldgof, Dmitry B.; Russo, Robin; Gillies, Robert J.; Gatenby, Robert A.

2015-03-01

Brain tumor heterogeneity remains a challenge for probing brain cancer evolutionary dynamics. In light of evolution, it is a priority to inspect the cancer system from a time-domain perspective since it explicitly tracks the dynamics of cancer variations. In this paper, we study the problem of exploring brain tumor heterogeneity from temporal clinical magnetic resonance imaging (MRI) data. Our goal is to discover evidence-based knowledge from such temporal imaging data, where multiple clinical MRI scans from Glioblastoma multiforme (GBM) patients are generated during therapy. In particular, we propose a quantitative histogram-based approach that builds a prediction model to measure the difference in histograms obtained from pre- and post-treatment. The study could significantly assist radiologists by providing a metric to identify distinctive patterns within each tumor, which is crucial for the goal of providing patient-specific treatments. We examine the proposed approach for a practical application - clinical survival group prediction. Experimental results show that our approach achieved 90.91% accuracy.

A Statistical Analysis of Brain Morphology Using Wild Bootstrapping

PubMed Central

Ibrahim, Joseph G.; Tang, Niansheng; Rowe, Daniel B.; Hao, Xuejun; Bansal, Ravi; Peterson, Bradley S.

2008-01-01

Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. PMID:17649909

A wireless beta-microprobe based on pixelated silicon for in vivo brain studies in freely moving rats

NASA Astrophysics Data System (ADS)

Märk, J.; Benoit, D.; Balasse, L.; Benoit, M.; Clémens, J. C.; Fieux, S.; Fougeron, D.; Graber-Bolis, J.; Janvier, B.; Jevaud, M.; Genoux, A.; Gisquet-Verrier, P.; Menouni, M.; Pain, F.; Pinot, L.; Tourvielle, C.; Zimmer, L.; Morel, C.; Laniece, P.

2013-07-01

The investigation of neurophysiological mechanisms underlying the functional specificity of brain regions requires the development of technologies that are well adjusted to in vivo studies in small animals. An exciting challenge remains the combination of brain imaging and behavioural studies, which associates molecular processes of neuronal communications to their related actions. A pixelated intracerebral probe (PIXSIC) presents a novel strategy using a submillimetric probe for beta+ radiotracer detection based on a pixelated silicon diode that can be stereotaxically implanted in the brain region of interest. This fully autonomous detection system permits time-resolved high sensitivity measurements of radiotracers with additional imaging features in freely moving rats. An application-specific integrated circuit (ASIC) allows for parallel signal processing of each pixel and enables the wireless operation. All components of the detector were tested and characterized. The beta+ sensitivity of the system was determined with the probe dipped into radiotracer solutions. Monte Carlo simulations served to validate the experimental values and assess the contribution of gamma noise. Preliminary implantation tests on anaesthetized rats proved PIXSIC's functionality in brain tissue. High spatial resolution allows for the visualization of radiotracer concentration in different brain regions with high temporal resolution.

Multi-Coil Shimming of the Mouse Brain

PubMed Central

Juchem, Christoph; Brown, Peter B.; Nixon, Terence W.; McIntyre, Scott; Rothman, Douglas L.; de Graaf, Robin A.

2011-01-01

MR imaging and spectroscopy allow the non-invasive measurement of brain function and physiology, but excellent magnetic field homogeneity is required for meaningful results. The homogenization of the magnetic field distribution in the mouse brain (i.e. shimming) is a difficult task due to complex susceptibility-induced field distortions combined with the small size of the object. To date, the achievement of satisfactory whole brain shimming in the mouse remains a major challenge. The magnetic fields generated by a set of 48 circular coils (diameter 13 mm) that were arranged in a cylinder-shaped pattern of 32 mm diameter and driven with individual dynamic current ranges of ±1 A are shown to be capable of substantially reducing the field distortions encountered in the mouse brain at 9.4 Tesla. Static multi-coil shim fields allowed the reduction of the standard deviation of Larmor frequencies by 31% compared to second order spherical harmonics shimming and a 66% narrowing was achieved with the slice-specific application of the multi-coil shimming with a dynamic approach. For gradient echo imaging, multi-coil shimming minimized shim-related signal voids in the brain periphery and allowed overall signal gains of up to 51% compared to spherical harmonics shimming. PMID:21442653

Current approaches to the treatment of metastatic brain tumours

PubMed Central

Owonikoko, Taofeek K.; Arbiser, Jack; Zelnak, Amelia; Shu, Hui-Kuo G.; Shim, Hyunsuk; Robin, Adam M.; Kalkanis, Steven N.; Whitsett, Timothy G.; Salhia, Bodour; Tran, Nhan L.; Ryken, Timothy; Moore, Michael K.; Egan, Kathleen M.; Olson, Jeffrey J.

2014-01-01

Metastatic tumours involving the brain overshadow primary brain neoplasms in frequency and are an important complication in the overall management of many cancers. Importantly, advances are being made in understanding the molecular biology underlying the initial development and eventual proliferation of brain metastases. Surgery and radiation remain the cornerstones of the therapy for symptomatic lesions; however, image-based guidance is improving surgical technique to maximize the preservation of normal tissue, while more sophisticated approaches to radiation therapy are being used to minimize the long-standing concerns over the toxicity of whole-brain radiation protocols used in the past. Furthermore, the burgeoning knowledge of tumour biology has facilitated the entry of systemically administered therapies into the clinic. Responses to these targeted interventions have ranged from substantial toxicity with no control of disease to periods of useful tumour control with no decrement in performance status of the treated individual. This experience enables recognition of the limits of targeted therapy, but has also informed methods to optimize this approach. This Review focuses on the clinically relevant molecular biology of brain metastases, and summarizes the current applications of these data to imaging, surgery, radiation therapy, cytotoxic chemotherapy and targeted therapy. PMID:24569448

Multiplexed aberration measurement for deep tissue imaging in vivo

PubMed Central

Wang, Chen; Liu, Rui; Milkie, Daniel E.; Sun, Wenzhi; Tan, Zhongchao; Kerlin, Aaron; Chen, Tsai-Wen; Kim, Douglas S.; Ji, Na

2014-01-01

We describe a multiplexed aberration measurement method that modulates the intensity or phase of light rays at multiple pupil segments in parallel to determine their phase gradients. Applicable to fluorescent-protein-labeled structures of arbitrary complexity, it allows us to obtain diffraction-limited resolution in various samples in vivo. For the strongly scattering mouse brain, a single aberration correction improves structural and functional imaging of fine neuronal processes over a large imaging volume. PMID:25128976

Highest Resolution In Vivo Human Brain MRI Using Prospective Motion Correction

PubMed Central

Stucht, Daniel; Danishad, K. Appu; Schulze, Peter; Godenschweger, Frank; Zaitsev, Maxim; Speck, Oliver

2015-01-01

High field MRI systems, such as 7 Tesla (T) scanners, can deliver higher signal to noise ratio (SNR) than lower field scanners and thus allow for the acquisition of data with higher spatial resolution, which is often demanded by users in the fields of clinical and neuroscientific imaging. However, high resolution scans may require long acquisition times, which in turn increase the discomfort for the subject and the risk of subject motion. Even with a cooperative and trained subject, involuntary motion due to heartbeat, swallowing, respiration and changes in muscle tone can cause image artifacts that reduce the effective resolution. In addition, scanning with higher resolution leads to increased sensitivity to even very small movements. Prospective motion correction (PMC) at 3T and 7T has proven to increase image quality in case of subject motion. Although the application of prospective motion correction is becoming more popular, previous articles focused on proof of concept studies and technical descriptions, whereas this paper briefly describes the technical aspects of the optical tracking system, marker fixation and cross calibration and focuses on the application of PMC to very high resolution imaging without intentional motion. In this study we acquired in vivo MR images at 7T using prospective motion correction during long acquisitions. As a result, we present images among the highest, if not the highest resolution of in vivo human brain MRI ever acquired. PMID:26226146

Characterization of the resting-state brain network topology in the 6-hydroxydopamine rat model of Parkinson’s disease

PubMed Central

Simmons, Camilla; Mesquita, Michel B.; Wood, Tobias C.; Williams, Steve C. R.; Vernon, Anthony C.; Cash, Diana

2017-01-01

Resting-state functional MRI (rsfMRI) is an imaging technology that has recently gained attention for its ability to detect disruptions in functional brain networks in humans, including in patients with Parkinson’s disease (PD), revealing early and widespread brain network abnormalities. This methodology is now readily applicable to experimental animals offering new possibilities for cross-species translational imaging. In this context, we herein describe the application of rsfMRI to the unilaterally-lesioned 6-hydroxydopamine (6-OHDA) rat, a robust experimental model of the dopamine depletion implicated in PD. Using graph theory to analyse the rsfMRI data, we were able to provide meaningful and translatable measures of integrity, influence and segregation of the underlying functional brain architecture. Specifically, we confirm that rats share a similar functional brain network topology as observed in humans, characterised by small-worldness and modularity. Interestingly, we observed significantly reduced functional connectivity in the 6-OHDA rats, primarily in the ipsilateral (lesioned) hemisphere as evidenced by significantly lower node degree, local efficiency and clustering coefficient in the motor, orbital and sensorimotor cortices. In contrast, we found significantly, and bilaterally, increased thalamic functional connectivity in the lesioned rats. The unilateral deficits in the cortex are consistent with the unilateral nature of this model and further support the validity of the rsfMRI technique in rodents. We thereby provide a methodological framework for the investigation of brain networks in other rodent experimental models of PD, as well as of animal models in general, for cross-comparison with human data. PMID:28249008

Magnetic resonance spectroscopy of the human brain

NASA Astrophysics Data System (ADS)

Strózik-Kotlorz, D.

2014-01-01

I give a brief description of the magnetic resonance spectroscopy (MRS) in the human brain examinations. MRS allows a noninvasive chemical analysis of the brain using a standard high field MR system. Nowadays, the dominant form of MR brain spectroscopy is proton spectroscopy. Two main techniques of MRS, which utilize the chemical shift of metabolites in the external magnetic field, are SVS (single voxel) and CSI (single slice). The major peaks in the spectrum of a normal brain include NAA, Cr, Cho and m-Ins, which are neuronal, energetic, membrane turnover and glial markers, respectively. In disease, two pathological metabolites can be found in the brain spectra: Lac, which is end product of anaerobic glycolysis and Lip, which is a marker of membrane breakdown, occurring in necrosis. The common way to analyze clinical spectra is to determine metabolite ratios, e.g. NAA/Cr, Cho/Cr, Cho/NAA. This analysis permits a safe and noninvasive examination of the brain tissue as each disease state has its own characteristic spectroscopic image. MRS is a valuable diagnostic tool in such clinical applications as detecting brain tumors and differentiating tumors from inflammatory and infectious processes. Proton MRS is also very helpful in diagnostic of ischemic lesions, Alzheimer's disease and hepatic encephalopathy. The MRS brain spectra should always be correlated with the Magnetic Resonance Imaging (MRI) results and alone cannot make neurological diagnosis.

Application of Thinned-Skull Cranial Window to Mouse Cerebral Blood Flow Imaging Using Optical Microangiography

PubMed Central

Wang, Ruikang K.

2014-01-01

In vivo imaging of mouse brain vasculature typically requires applying skull window opening techniques: open-skull cranial window or thinned-skull cranial window. We report non-invasive 3D in vivo cerebral blood flow imaging of C57/BL mouse by the use of ultra-high sensitive optical microangiography (UHS-OMAG) and Doppler optical microangiography (DOMAG) techniques to evaluate two cranial window types based on their procedures and ability to visualize surface pial vessel dynamics. Application of the thinned-skull technique is found to be effective in achieving high quality images for pial vessels for short-term imaging, and has advantages over the open-skull technique in available imaging area, surgical efficiency, and cerebral environment preservation. In summary, thinned-skull cranial window serves as a promising tool in studying hemodynamics in pial microvasculature using OMAG or other OCT blood flow imaging modalities. PMID:25426632

Integrated one- and two-photon scanned oblique plane illumination (SOPi) microscopy for rapid volumetric imaging

NASA Astrophysics Data System (ADS)

Kumar, Manish; Kishore, Sandeep; Nasenbeny, Jordan; McLean, David L.; Kozorovitskiy, Yevgenia

2018-05-01

Versatile, sterically accessible imaging systems capable of in vivo rapid volumetric functional and structural imaging deep in the brain continue to be a limiting factor in neuroscience research. Towards overcoming this obstacle, we present integrated one- and two-photon scanned oblique plane illumination (SOPi) microscopy which uses a single front-facing microscope objective to provide light-sheet scanning based rapid volumetric imaging capability at subcellular resolution. Our planar scan-mirror based optimized light-sheet architecture allows for non-distorted scanning of volume samples, simplifying accurate reconstruction of the imaged volume. Integration of both one-photon (1P) and two-photon (2P) light-sheet microscopy in the same system allows for easy selection between rapid volumetric imaging and higher resolution imaging in scattering media. Using SOPi, we demonstrate deep, large volume imaging capability inside scattering mouse brain sections and rapid imaging speeds up to 10 volumes per second in zebrafish larvae expressing genetically encoded fluorescent proteins GFP or GCaMP6s. SOPi flexibility and steric access makes it adaptable for numerous imaging applications and broadly compatible with orthogonal techniques for actuating or interrogating neuronal structure and activity.

Integrated one- and two-photon scanned oblique plane illumination (SOPi) microscopy for rapid volumetric imaging.

PubMed

Kumar, Manish; Kishore, Sandeep; Nasenbeny, Jordan; McLean, David L; Kozorovitskiy, Yevgenia

2018-05-14

Versatile, sterically accessible imaging systems capable of in vivo rapid volumetric functional and structural imaging deep in the brain continue to be a limiting factor in neuroscience research. Towards overcoming this obstacle, we present integrated one- and two-photon scanned oblique plane illumination (SOPi, /sōpī/) microscopy which uses a single front-facing microscope objective to provide light-sheet scanning based rapid volumetric imaging capability at subcellular resolution. Our planar scan-mirror based optimized light-sheet architecture allows for non-distorted scanning of volume samples, simplifying accurate reconstruction of the imaged volume. Integration of both one-photon (1P) and two-photon (2P) light-sheet microscopy in the same system allows for easy selection between rapid volumetric imaging and higher resolution imaging in scattering media. Using SOPi, we demonstrate deep, large volume imaging capability inside scattering mouse brain sections and rapid imaging speeds up to 10 volumes per second in zebrafish larvae expressing genetically encoded fluorescent proteins GFP or GCaMP6s. SOPi's flexibility and steric access makes it adaptable for numerous imaging applications and broadly compatible with orthogonal techniques for actuating or interrogating neuronal structure and activity.

Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images

PubMed Central

Peters, James F.; Ramanna, Sheela; Tozzi, Arturo; İnan, Ebubekir

2017-01-01

We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain. PMID:28203153

Bold-Independent Computational Entropy Assesses Functional Donut-Like Structures in Brain fMRI Images.

PubMed

Peters, James F; Ramanna, Sheela; Tozzi, Arturo; İnan, Ebubekir

2017-01-01

We introduce a novel method for the measurement of information level in fMRI (functional Magnetic Resonance Imaging) neural data sets, based on image subdivision in small polygons equipped with different entropic content. We show how this method, called maximal nucleus clustering (MNC), is a novel, fast and inexpensive image-analysis technique, independent from the standard blood-oxygen-level dependent signals. MNC facilitates the objective detection of hidden temporal patterns of entropy/information in zones of fMRI images generally not taken into account by the subjective standpoint of the observer. This approach befits the geometric character of fMRIs. The main purpose of this study is to provide a computable framework for fMRI that not only facilitates analyses, but also provides an easily decipherable visualization of structures. This framework commands attention because it is easily implemented using conventional software systems. In order to evaluate the potential applications of MNC, we looked for the presence of a fourth dimension's distinctive hallmarks in a temporal sequence of 2D images taken during spontaneous brain activity. Indeed, recent findings suggest that several brain activities, such as mind-wandering and memory retrieval, might take place in the functional space of a four dimensional hypersphere, which is a double donut-like structure undetectable in the usual three dimensions. We found that the Rényi entropy is higher in MNC areas than in the surrounding ones, and that these temporal patterns closely resemble the trajectories predicted by the possible presence of a hypersphere in the brain.

NIR time domain diffuse optical tomography experiments on human forearm

NASA Astrophysics Data System (ADS)

Zhao, Huijuan; Gao, Feng; Tanikawa, Yukari; Homma, Kazuhiro; Yamada, Yukio

2003-07-01

To date, the applications of near infrared (NIR) diffusion optical tomography (DOT) are mostly focused on the potential of imaging woman breast, human head hemodynamics and neonatal head. For the neonates, who are suffered from ischaemia or hemorrhages in brain, bedside monitoring of the cerebral perfusion situation, e.g., the blood oxygen saturation and blood volume, is necessary for avoiding permanent injure. NIR DOT is on the promising tools because it is noninvasive, smaller in size, and moveable. Prior to achieving the ultimate goal of imaging infant brain and woman breast using DOT, in this paper, the developed methodologies are justified by imaging in vivo human forearms. The absolute absorption- and scattering-coefficient images revealed the inner structure of the forearm and the bones were clearly distinguished from the muscle. The differential images showed the changes in oxy-hemoglobin, deoxy-hemoglobin and blood volume during the hand-gripping exercises, which are consistent with the physiological process reported on literatures.

Hyperpolarized xenon magnetic resonance of the lung and the brain

NASA Astrophysics Data System (ADS)

Venkatesh, Arvind Krishnamachari

2001-04-01

Hyperpolarized noble gas Magnetic Resonance Imaging (MRI) is a new diagnostic modality that has been used successfully for lung imaging. Xenon is soluble in blood and inhaled xenon is transported to the brain via circulating blood. Xenon also accumulates in the lipid rich white matter of the brain. Hyperpolarized xenon can hence be used as a tissue- sensitive probe of brain function. The goals of this study were to identify the NMR resonances of xenon in the rat brain and evaluate the role of hyperpolarized xenon for brain MRI. We have developed systems to produce sufficient volumes of hyperpolarized xenon for in vivo brain experiments. The specialized instrumentation developed include an apparatus for optical pump-cell manufacture and high purity gas manifolds for filling cells. A hyperpolarized gas delivery system was designed to ventilate small animals with hyperpolarized xenon for transport to the brain. The T1 of xenon dissolved in blood indicates that the lifetime of xenon in the blood is sufficient for significant magnetization to be transferred to distal tissues. A variety of carrier agents for intravenous delivery of hyperpolarized xenon were tested for transport to distal tissues. Using our new gas delivery system, high SNR 129Xe images of rat lungs were obtained. Spectroscopy with hyperpolarized xenon indicated that xenon was transported from the lungs to the blood and tissues with intact magnetization. After preliminary studies that indicated the feasibility for in vivo rat brain studies, experiments were performed with adult rats and young rats with different stages of white matter development. Both in vivo and in vitro experiments showed the prominence of one peak from xenon in the rat brain, which was assigned to brain lipids. Cerebral brain perfusion was calculated from the wash-out of the hyperpolarized xenon signal in the brain. An increase in brain perfusion during maturation was observed. These experiments showed that hyperpolarized xenon MRI can be used to develop unique approaches to studying white matter and gray matter in the brain. Some of the possible applications of hyperpolarized xenon MRI in the brain are clinical diagnosis of white matter diseases, functional MRI (fMRI) and measurement of cerebral blood perfusion.

Simulation of realistic abnormal SPECT brain perfusion images: application in semi-quantitative analysis

NASA Astrophysics Data System (ADS)

Ward, T.; Fleming, J. S.; Hoffmann, S. M. A.; Kemp, P. M.

2005-11-01

Simulation is useful in the validation of functional image analysis methods, particularly when considering the number of analysis techniques currently available lacking thorough validation. Problems exist with current simulation methods due to long run times or unrealistic results making it problematic to generate complete datasets. A method is presented for simulating known abnormalities within normal brain SPECT images using a measured point spread function (PSF), and incorporating a stereotactic atlas of the brain for anatomical positioning. This allows for the simulation of realistic images through the use of prior information regarding disease progression. SPECT images of cerebral perfusion have been generated consisting of a control database and a group of simulated abnormal subjects that are to be used in a UK audit of analysis methods. The abnormality is defined in the stereotactic space, then transformed to the individual subject space, convolved with a measured PSF and removed from the normal subject image. The dataset was analysed using SPM99 (Wellcome Department of Imaging Neuroscience, University College, London) and the MarsBaR volume of interest (VOI) analysis toolbox. The results were evaluated by comparison with the known ground truth. The analysis showed improvement when using a smoothing kernel equal to system resolution over the slightly larger kernel used routinely. Significant correlation was found between effective volume of a simulated abnormality and the detected size using SPM99. Improvements in VOI analysis sensitivity were found when using the region median over the region mean. The method and dataset provide an efficient methodology for use in the comparison and cross validation of semi-quantitative analysis methods in brain SPECT, and allow the optimization of analysis parameters.

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia

PubMed Central

Castro, Eduardo; Martínez-Ramón, Manel; Pearlson, Godfrey; Sui, Jing; Calhoun, Vince D.

2011-01-01

Pattern classification of brain imaging data can enable the automatic detection of differences in cognitive processes of specific groups of interest. Furthermore, it can also give neuroanatomical information related to the regions of the brain that are most relevant to detect these differences by means of feature selection procedures, which are also well-suited to deal with the high dimensionality of brain imaging data. This work proposes the application of recursive feature elimination using a machine learning algorithm based on composite kernels to the classification of healthy controls and patients with schizophrenia. This framework, which evaluates nonlinear relationships between voxels, analyzes whole-brain fMRI data from an auditory task experiment that is segmented into anatomical regions and recursively eliminates the uninformative ones based on their relevance estimates, thus yielding the set of most discriminative brain areas for group classification. The collected data was processed using two analysis methods: the general linear model (GLM) and independent component analysis (ICA). GLM spatial maps as well as ICA temporal lobe and default mode component maps were then input to the classifier. A mean classification accuracy of up to 95% estimated with a leave-two-out cross-validation procedure was achieved by doing multi-source data classification. In addition, it is shown that the classification accuracy rate obtained by using multi-source data surpasses that reached by using single-source data, hence showing that this algorithm takes advantage of the complimentary nature of GLM and ICA. PMID:21723948

An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM).

PubMed

Feng, Xiang; Deistung, Andreas; Dwyer, Michael G; Hagemeier, Jesper; Polak, Paul; Lebenberg, Jessica; Frouin, Frédérique; Zivadinov, Robert; Reichenbach, Jürgen R; Schweser, Ferdinand

2017-06-01

Accurate and robust segmentation of subcortical gray matter (SGM) nuclei is required in many neuroimaging applications. FMRIB's Integrated Registration and Segmentation Tool (FIRST) is one of the most popular software tools for automated subcortical segmentation based on T 1 -weighted (T1w) images. In this work, we demonstrate that FIRST tends to produce inaccurate SGM segmentation results in the case of abnormal brain anatomy, such as present in atrophied brains, due to a poor spatial match of the subcortical structures with the training data in the MNI space as well as due to insufficient contrast of SGM structures on T1w images. Consequently, such deviations from the average brain anatomy may introduce analysis bias in clinical studies, which may not always be obvious and potentially remain unidentified. To improve the segmentation of subcortical nuclei, we propose to use FIRST in combination with a special Hybrid image Contrast (HC) and Non-Linear (nl) registration module (HC-nlFIRST), where the hybrid image contrast is derived from T1w images and magnetic susceptibility maps to create subcortical contrast that is similar to that in the Montreal Neurological Institute (MNI) template. In our approach, a nonlinear registration replaces FIRST's default linear registration, yielding a more accurate alignment of the input data to the MNI template. We evaluated our method on 82 subjects with particularly abnormal brain anatomy, selected from a database of >2000 clinical cases. Qualitative and quantitative analyses revealed that HC-nlFIRST provides improved segmentation compared to the default FIRST method. Copyright © 2017 Elsevier Inc. All rights reserved.

Supervised novelty detection in brain tissue classification with an application to white matter hyperintensities

NASA Astrophysics Data System (ADS)

Kuijf, Hugo J.; Moeskops, Pim; de Vos, Bob D.; Bouvy, Willem H.; de Bresser, Jeroen; Biessels, Geert Jan; Viergever, Max A.; Vincken, Koen L.

2016-03-01

Novelty detection is concerned with identifying test data that differs from the training data of a classifier. In the case of brain MR images, pathology or imaging artefacts are examples of untrained data. In this proof-of-principle study, we measure the behaviour of a classifier during the classification of trained labels (i.e. normal brain tissue). Next, we devise a measure that distinguishes normal classifier behaviour from abnormal behavior that occurs in the case of a novelty. This will be evaluated by training a kNN classifier on normal brain tissue, applying it to images with an untrained pathology (white matter hyperintensities (WMH)), and determine if our measure is able to identify abnormal classifier behaviour at WMH locations. For our kNN classifier, behaviour is modelled as the mean, median, or q1 distance to the k nearest points. Healthy tissue was trained on 15 images; classifier behaviour was trained/tested on 5 images with leave-one-out cross-validation. For each trained class, we measure the distribution of mean/median/q1 distances to the k nearest point. Next, for each test voxel, we compute its Z-score with respect to the measured distribution of its predicted label. We consider a Z-score >=4 abnormal behaviour of the classifier, having a probability due to chance of 0.000032. Our measure identified >90% of WMH volume and also highlighted other non-trained findings. The latter being predominantly vessels, cerebral falx, brain mask errors, choroid plexus. This measure is generalizable to other classifiers and might help in detecting unexpected findings or novelties by measuring classifier behaviour.

Magnetic Resonance Elastography of the Brain using Multi-Shot Spiral Readouts with Self-Navigated Motion Correction

PubMed Central

Johnson, Curtis L.; McGarry, Matthew D. J.; Van Houten, Elijah E. W.; Weaver, John B.; Paulsen, Keith D.; Sutton, Bradley P.; Georgiadis, John G.

2012-01-01

MRE has been introduced in clinical practice as a possible surrogate for mechanical palpation, but its application to study the human brain in vivo has been limited by low spatial resolution and the complexity of the inverse problem associated with biomechanical property estimation. Here, we report significant improvements in brain MRE data acquisition by reporting images with high spatial resolution and signal-to-noise ratio as quantified by octahedral shear strain metrics. Specifically, we have developed a sequence for brain MRE based on multi-shot, variable-density spiral imaging and three-dimensional displacement acquisition, and implemented a correction scheme for any resulting phase errors. A Rayleigh damped model of brain tissue mechanics was adopted to represent the parenchyma, and was integrated via a finite element-based iterative inversion algorithm. A multi-resolution phantom study demonstrates the need for obtaining high-resolution MRE data when estimating focal mechanical properties. Measurements on three healthy volunteers demonstrate satisfactory resolution of grey and white matter, and mechanical heterogeneities correspond well with white matter histoarchitecture. Together, these advances enable MRE scans that result in high-fidelity, spatially-resolved estimates of in vivo brain tissue mechanical properties, improving upon lower resolution MRE brain studies which only report volume averaged stiffness values. PMID:23001771

Rapid and reversible enhancement of blood–brain barrier permeability using lysophosphatidic acid

PubMed Central

On, Ngoc H; Savant, Sanjot; Toews, Myron; Miller, Donald W

2013-01-01

The present study characterizes the effects of lysophosphatidic acid (LPA) on blood–brain barrier (BBB) permeability focusing specifically on the time of onset, duration, and magnitude of LPA-induced changes in cerebrovascular permeability in the mouse using both magnetic resonance imaging (MRI) and near infrared fluorescence imaging (NIFR). Furthermore, potential application of LPA for enhanced drug delivery to the brain was also examined by measuring the brain accumulation of radiolabeled methotrexate. Exposure of primary cultured brain microvessel endothelial cells (BMECs) to LPA produced concentration-dependent increases in permeability that were completely abolished by clostridium toxin B. Administration of LPA disrupted BBB integrity and enhanced the permeability of small molecular weight marker gadolinium diethylenetriaminepentaacetate (Gd-DTPA) contrast agent, the large molecular weight permeability marker, IRdye800cwPEG, and the P-glycoprotein efflux transporter probe, Rhodamine 800 (R800). The increase in BBB permeability occurred within 3 minutes after LPA injection and barrier integrity was restored within 20 minutes. A decreased response to LPA on large macromolecule BBB permeability was observed after repeated administration. The administration of LPA also resulted in 20-fold enhancement of radiolabeled methotrexate in the brain. These studies indicate that administration of LPA in combination with therapeutic agents may increase drug delivery to the brain. PMID:24045401

Key clinical benefits of neuroimaging at 7T.

PubMed

Trattnig, Siegfried; Springer, Elisabeth; Bogner, Wolfgang; Hangel, Gilbert; Strasser, Bernhard; Dymerska, Barbara; Cardoso, Pedro Lima; Robinson, Simon Daniel

2018-03-01

The growing interest in ultra-high field MRI, with more than 35.000 MR examinations already performed at 7T, is related to improved clinical results with regard to morphological as well as functional and metabolic capabilities. Since the signal-to-noise ratio increases with the field strength of the MR scanner, the most evident application at 7T is to gain higher spatial resolution in the brain compared to 3T. Of specific clinical interest for neuro applications is the cerebral cortex at 7T, for the detection of changes in cortical structure, like the visualization of cortical microinfarcts and cortical plaques in Multiple Sclerosis. In imaging of the hippocampus, even subfields of the internal hippocampal anatomy and pathology may be visualized with excellent spatial resolution. Using Susceptibility Weighted Imaging, the plaque-vessel relationship and iron accumulations in Multiple Sclerosis can be visualized, which may provide a prognostic factor of disease. Vascular imaging is a highly promising field for 7T which is dealt with in a separate dedicated article in this special issue. The static and dynamic blood oxygenation level-dependent contrast also increases with the field strength, which significantly improves the accuracy of pre-surgical evaluation of vital brain areas before tumor removal. Improvement in acquisition and hardware technology have also resulted in an increasing number of MR spectroscopic imaging studies in patients at 7T. More recent parallel imaging and short-TR acquisition approaches have overcome the limitations of scan time and spatial resolution, thereby allowing imaging matrix sizes of up to 128×128. The benefits of these acquisition approaches for investigation of brain tumors and Multiple Sclerosis have been shown recently. Together, these possibilities demonstrate the feasibility and advantages of conducting routine diagnostic imaging and clinical research at 7T. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Evaluation of a video-based head motion tracking system for dedicated brain PET

NASA Astrophysics Data System (ADS)

Anishchenko, S.; Beylin, D.; Stepanov, P.; Stepanov, A.; Weinberg, I. N.; Schaeffer, S.; Zavarzin, V.; Shaposhnikov, D.; Smith, M. F.

2015-03-01

Unintentional head motion during Positron Emission Tomography (PET) data acquisition can degrade PET image quality and lead to artifacts. Poor patient compliance, head tremor, and coughing are examples of movement sources. Head motion due to patient non-compliance can be an issue with the rise of amyloid brain PET in dementia patients. To preserve PET image resolution and quantitative accuracy, head motion can be tracked and corrected in the image reconstruction algorithm. While fiducial markers can be used, a contactless approach is preferable. A video-based head motion tracking system for a dedicated portable brain PET scanner was developed. Four wide-angle cameras organized in two stereo pairs are used for capturing video of the patient's head during the PET data acquisition. Facial points are automatically tracked and used to determine the six degree of freedom head pose as a function of time. The presented work evaluated the newly designed tracking system using a head phantom and a moving American College of Radiology (ACR) phantom. The mean video-tracking error was 0.99±0.90 mm relative to the magnetic tracking device used as ground truth. Qualitative evaluation with the ACR phantom shows the advantage of the motion tracking application. The developed system is able to perform tracking with accuracy close to millimeter and can help to preserve resolution of brain PET images in presence of movements.

The Potential Role of Sensory Testing, Skin Biopsy, and Functional Brain Imaging as Biomarkers in Chronic Pain Clinical Trials: IMMPACT Considerations.

PubMed

Smith, Shannon M; Dworkin, Robert H; Turk, Dennis C; Baron, Ralf; Polydefkis, Michael; Tracey, Irene; Borsook, David; Edwards, Robert R; Harris, Richard E; Wager, Tor D; Arendt-Nielsen, Lars; Burke, Laurie B; Carr, Daniel B; Chappell, Amy; Farrar, John T; Freeman, Roy; Gilron, Ian; Goli, Veeraindar; Haeussler, Juergen; Jensen, Troels; Katz, Nathaniel P; Kent, Jeffrey; Kopecky, Ernest A; Lee, David A; Maixner, William; Markman, John D; McArthur, Justin C; McDermott, Michael P; Parvathenani, Lav; Raja, Srinivasa N; Rappaport, Bob A; Rice, Andrew S C; Rowbotham, Michael C; Tobias, Jeffrey K; Wasan, Ajay D; Witter, James

2017-07-01

Valid and reliable biomarkers can play an important role in clinical trials as indicators of biological or pathogenic processes or as a signal of treatment response. Currently, there are no biomarkers for pain qualified by the U.S. Food and Drug Administration or the European Medicines Agency for use in clinical trials. This article summarizes an Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials meeting in which 3 potential biomarkers were discussed for use in the development of analgesic treatments: 1) sensory testing, 2) skin punch biopsy, and 3) brain imaging. The empirical evidence supporting the use of these tests is described within the context of the 4 categories of biomarkers: 1) diagnostic, 2) prognostic, 3) predictive, and 4) pharmacodynamic. Although sensory testing, skin punch biopsy, and brain imaging are promising tools for pain in clinical trials, additional evidence is needed to further support and standardize these tests for use as biomarkers in pain clinical trials. The applicability of sensory testing, skin biopsy, and brain imaging as diagnostic, prognostic, predictive, and pharmacodynamic biomarkers for use in analgesic treatment trials is considered. Evidence in support of their use and outlining problems is presented, as well as a call for further standardization and demonstrations of validity and reliability. Copyright © 2017 American Pain Society. All rights reserved.

An automatic rat brain extraction method based on a deformable surface model.

PubMed

Li, Jiehua; Liu, Xiaofeng; Zhuo, Jiachen; Gullapalli, Rao P; Zara, Jason M

2013-08-15

The extraction of the brain from the skull in medical images is a necessary first step before image registration or segmentation. While pre-clinical MR imaging studies on small animals, such as rats, are increasing, fully automatic imaging processing techniques specific to small animal studies remain lacking. In this paper, we present an automatic rat brain extraction method, the Rat Brain Deformable model method (RBD), which adapts the popular human brain extraction tool (BET) through the incorporation of information on the brain geometry and MR image characteristics of the rat brain. The robustness of the method was demonstrated on T2-weighted MR images of 64 rats and compared with other brain extraction methods (BET, PCNN, PCNN-3D). The results demonstrate that RBD reliably extracts the rat brain with high accuracy (>92% volume overlap) and is robust against signal inhomogeneity in the images. Copyright © 2013 Elsevier B.V. All rights reserved.

High-fidelity artifact correction for cone-beam CT imaging of the brain

NASA Astrophysics Data System (ADS)

Sisniega, A.; Zbijewski, W.; Xu, J.; Dang, H.; Stayman, J. W.; Yorkston, J.; Aygun, N.; Koliatsos, V.; Siewerdsen, J. H.

2015-02-01

CT is the frontline imaging modality for diagnosis of acute traumatic brain injury (TBI), involving the detection of fresh blood in the brain (contrast of 30-50 HU, detail size down to 1 mm) in a non-contrast-enhanced exam. A dedicated point-of-care imaging system based on cone-beam CT (CBCT) could benefit early detection of TBI and improve direction to appropriate therapy. However, flat-panel detector (FPD) CBCT is challenged by artifacts that degrade contrast resolution and limit application in soft-tissue imaging. We present and evaluate a fairly comprehensive framework for artifact correction to enable soft-tissue brain imaging with FPD CBCT. The framework includes a fast Monte Carlo (MC)-based scatter estimation method complemented by corrections for detector lag, veiling glare, and beam hardening. The fast MC scatter estimation combines GPU acceleration, variance reduction, and simulation with a low number of photon histories and reduced number of projection angles (sparse MC) augmented by kernel de-noising to yield a runtime of ~4 min per scan. Scatter correction is combined with two-pass beam hardening correction. Detector lag correction is based on temporal deconvolution of the measured lag response function. The effects of detector veiling glare are reduced by deconvolution of the glare response function representing the long range tails of the detector point-spread function. The performance of the correction framework is quantified in experiments using a realistic head phantom on a testbench for FPD CBCT. Uncorrected reconstructions were non-diagnostic for soft-tissue imaging tasks in the brain. After processing with the artifact correction framework, image uniformity was substantially improved, and artifacts were reduced to a level that enabled visualization of ~3 mm simulated bleeds throughout the brain. Non-uniformity (cupping) was reduced by a factor of 5, and contrast of simulated bleeds was improved from ~7 to 49.7 HU, in good agreement with the nominal blood contrast of 50 HU. Although noise was amplified by the corrections, the contrast-to-noise ratio (CNR) of simulated bleeds was improved by nearly a factor of 3.5 (CNR = 0.54 without corrections and 1.91 after correction). The resulting image quality motivates further development and translation of the FPD-CBCT system for imaging of acute TBI.

[See the thinking brain: a story about water].

PubMed

Le Bihan, D

2008-01-01

Among the astonishing Einstein's papers from 1905, there is one which unexpectedly gave birth to a powerful method to explore the brain. Molecular diffusion was explained by Einstein on the basis of the random translational motion of molecules which results from their thermal energy. In the mid 1980s it was shown that water diffusion in the brain could be imaged using MRI. During their random displacements water molecules probe tissue structure at a microscopic scale, interacting with cell membranes and, thus, providing unique information on the functional architecture of tissues. A dramatic application of diffusion MRI has been brain ischemia, following the discovery that water diffusion drops immediately after the onset of an ischemic event, when brain cells undergo swelling through cytotoxic edema. On the other hand, water diffusion is anisotropic in white matter, because axon membranes limit molecular movement perpendicularly to the fibers. This feature can be exploited to map out the orientation in space of the white matter tracks and image brain connections. More recently, it has been shown that diffusion MRI could accurately detect cortical activation. As the diffusion response precedes by several seconds the hemodynamic response captured by BOLD fMRI, it has been suggested that water diffusion could reflect early neuronal events, such as the transient swelling of activated cortical cells. If confirmed, this discovery will represent a significant breakthrough, allowing non invasive access to a direct physiological marker of brain activation. This approach will bridge the gap between invasive optical imaging techniques in neuronal cell cultures, and current functional neuroimaging approaches in humans, which are based on indirect and remote blood flow changes.

Manifold learning of brain MRIs by deep learning.

PubMed

Brosch, Tom; Tam, Roger

2013-01-01

Manifold learning of medical images plays a potentially important role for modeling anatomical variability within a population with pplications that include segmentation, registration, and prediction of clinical parameters. This paper describes a novel method for learning the manifold of 3D brain images that, unlike most existing manifold learning methods, does not require the manifold space to be locally linear, and does not require a predefined similarity measure or a prebuilt proximity graph. Our manifold learning method is based on deep learning, a machine learning approach that uses layered networks (called deep belief networks, or DBNs) and has received much attention recently in the computer vision field due to their success in object recognition tasks. DBNs have traditionally been too computationally expensive for application to 3D images due to the large number of trainable parameters. Our primary contributions are (1) a much more computationally efficient training method for DBNs that makes training on 3D medical images with a resolution of up to 128 x 128 x 128 practical, and (2) the demonstration that DBNs can learn a low-dimensional manifold of brain volumes that detects modes of variations that correlate to demographic and disease parameters.

Forensic applications of cerebral single photon emission computed tomography in mild traumatic brain injury.

PubMed

Wortzel, Hal S; Filley, Christopher M; Anderson, C Alan; Oster, Timothy; Arciniegas, David B

2008-01-01

Traumatic brain injury (TBI) is a substantial source of mortality and morbidity world wide. Although most such injuries are relatively mild, accurate diagnosis and prognostication after mild TBI are challenging. These problems are complicated further when considered in medicolegal contexts, particularly civil litigation. Cerebral single photon emission computed tomography (SPECT) may contribute to the evaluation and treatment of persons with mild TBI. Cerebral SPECT is relatively sensitive to the metabolic changes produced by TBI. However, such changes are not specific to this condition, and their presence on cerebral SPECT imaging does not confirm a diagnosis of mild TBI. Conversely, the absence of abnormalities on cerebral SPECT imaging does not exclude a diagnosis of mild TBI, although such findings may be of prognostic value. The literature does not demonstrate consistent relationships between SPECT images and neuropsychological testing or neuropsychiatric symptoms. Using the rules of evidence shaped by Daubert v. Merrell Dow Pharmaceuticals, Inc., and its progeny to analyze the suitability of SPECT for forensic purposes, we suggest that expert testimony regarding SPECT findings should be admissible only as evidence to support clinical history, neuropsychological test results, and structural brain imaging findings and not as stand-alone diagnostic data.

[¹⁸F]Altanserin and small animal PET: impact of multidrug efflux transporters on ligand brain uptake and subsequent quantification of 5-HT₂A receptor densities in the rat brain.

PubMed

Kroll, Tina; Elmenhorst, David; Matusch, Andreas; Celik, A Avdo; Wedekind, Franziska; Weisshaupt, Angela; Beer, Simone; Bauer, Andreas

2014-01-01

The selective 5-hydroxytryptamine type 2a receptor (5-HT(2A)R) radiotracer [(18)F]altanserin is a promising ligand for in vivo brain imaging in rodents. However, [(18)F]altanserin is a substrate of P-glycoprotein (P-gp) in rats. Its applicability might therefore be constrained by both a differential expression of P-gp under pathological conditions, e.g. epilepsy, and its relatively low cerebral uptake. The aim of the present study was therefore twofold: (i) to investigate whether inhibition of multidrug transporters (MDT) is suitable to enhance the cerebral uptake of [(18)F]altanserin in vivo and (ii) to test different pharmacokinetic, particularly reference tissue-based models for exact quantification of 5-HT(2A)R densities in the rat brain. Eighteen Sprague-Dawley rats, either treated with the MDT inhibitor cyclosporine A (CsA, 50 mg/kg, n=8) or vehicle (n=10) underwent 180-min PET scans with arterial blood sampling. Kinetic analyses of tissue time-activity curves (TACs) were performed to validate invasive and non-invasive pharmacokinetic models. CsA application lead to a two- to threefold increase of [(18)F]altanserin uptake in different brain regions and showed a trend toward higher binding potentials (BP(ND)) of the radioligand. MDT inhibition led to an increased cerebral uptake of [(18)F]altanserin but did not improve the reliability of BP(ND) as a non-invasive estimate of 5-HT(2A)R. This finding is most probable caused by the heterogeneous distribution of P-gp in the rat brain and its incomplete blockade in the reference region (cerebellum). Differential MDT expressions in experimental animal models or pathological conditions are therefore likely to influence the applicability of imaging protocols and have to be carefully evaluated. © 2013.

Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis

NASA Astrophysics Data System (ADS)

Evans, Alan C.; Dai, Weiqian; Collins, D. Louis; Neelin, Peter; Marrett, Sean

1991-06-01

We describe the implementation, experience and preliminary results obtained with a 3-D computerized brain atlas for topographical and functional analysis of brain sub-regions. A volume-of-interest (VOI) atlas was produced by manual contouring on 64 adjacent 2 mm-thick MRI slices to yield 60 brain structures in each hemisphere which could be adjusted, originally by global affine transformation or local interactive adjustments, to match individual MRI datasets. We have now added a non-linear deformation (warp) capability (Bookstein, 1989) into the procedure for fitting the atlas to the brain data. Specific target points are identified in both atlas and MRI spaces which define a continuous 3-D warp transformation that maps the atlas on to the individual brain image. The procedure was used to fit MRI brain image volumes from 16 young normal volunteers. Regional volume and positional variability were determined, the latter in such a way as to assess the extent to which previous linear models of brain anatomical variability fail to account for the true variation among normal individuals. Using a linear model for atlas deformation yielded 3-D fits of the MRI data which, when pooled across subjects and brain regions, left a residual mis-match of 6 - 7 mm as compared to the non-linear model. The results indicate a substantial component of morphometric variability is not accounted for by linear scaling. This has profound implications for applications which employ stereotactic coordinate systems which map individual brains into a common reference frame: quantitative neuroradiology, stereotactic neurosurgery and cognitive mapping of normal brain function with PET. In the latter case, the combination of a non-linear deformation algorithm would allow for accurate measurement of individual anatomic variations and the inclusion of such variations in inter-subject averaging methodologies used for cognitive mapping with PET.

CO2 induced pHi changes in the brain of polar fish: a TauCEST application.

PubMed

Wermter, Felizitas C; Maus, Bastian; Pörtner, Hans-O; Dreher, Wolfgang; Bock, Christian

2018-06-22

Chemical exchange saturation transfer (CEST) from taurine to water (TauCEST) can be used for in vivo mapping of taurine concentrations as well as for measurements of relative changes in intracellular pH (pH i ) at temperatures below 37°C. Therefore, TauCEST offers the opportunity to investigate acid-base regulation and neurological disturbances of ectothermic animals living at low temperatures, and in particular to study the impact of ocean acidification (OA) on neurophysiological changes of fish. Here, we report the first in vivo application of TauCEST imaging. Thus, the study aimed to investigate the TauCEST effect in a broad range of temperatures (1-37°C) and pH (5.5-8.0), motivated by the high taurine concentration measured in the brains of polar fish. The in vitro data show that the TauCEST effect is especially detectable in the low temperature range and strictly monotonic for the relevant pH range (6.8-7.5). To investigate the specificity of TauCEST imaging for the brain of polar cod (Boreogadus saida) at 1.5°C simulations were carried out, indicating a taurine contribution of about 65% to the in vivo expected CEST effect, if experimental parameters are optimized. B. saida was acutely exposed to three different CO 2 concentrations in the sea water (control normocapnia; comparatively moderate hypercapnia OA m  = 3300 μatm; high hypercapnia OA h  = 4900 μatm). TauCEST imaging of the brain showed a significant increase in the TauCEST effect under the different CO 2 concentrations of about 1.5-3% in comparison with control measurements, indicative of changes in pH i or metabolite concentration. Consecutive recordings of 1 H MR spectra gave no support for a concentration induced change of the in vivo observed TauCEST effect. Thus, the in vivo application of TauCEST offers the possibility of mapping relative changes in pH i in the brain of polar cod during exposure to CO 2 . © 2018 John Wiley & Sons, Ltd.

Simulation of High-Resolution Magnetic Resonance Images on the IBM Blue Gene/L Supercomputer Using SIMRI

DOE PAGES

Baum, K. G.; Menezes, G.; Helguera, M.

2011-01-01

Medical imaging system simulators are tools that provide a means to evaluate system architecture and create artificial image sets that are appropriate for specific applications. We have modified SIMRI, a Bloch equation-based magnetic resonance image simulator, in order to successfully generate high-resolution 3D MR images of the Montreal brain phantom using Blue Gene/L systems. Results show that redistribution of the workload allows an anatomically accurate 256 3 voxel spin-echo simulation in less than 5 hours when executed on an 8192-node partition of a Blue Gene/L system.

Simulation of High-Resolution Magnetic Resonance Images on the IBM Blue Gene/L Supercomputer Using SIMRI.

PubMed

Baum, K G; Menezes, G; Helguera, M

2011-01-01

Medical imaging system simulators are tools that provide a means to evaluate system architecture and create artificial image sets that are appropriate for specific applications. We have modified SIMRI, a Bloch equation-based magnetic resonance image simulator, in order to successfully generate high-resolution 3D MR images of the Montreal brain phantom using Blue Gene/L systems. Results show that redistribution of the workload allows an anatomically accurate 256(3) voxel spin-echo simulation in less than 5 hours when executed on an 8192-node partition of a Blue Gene/L system.

Multifunctional nanomaterials for advanced molecular imaging and cancer therapy

NASA Astrophysics Data System (ADS)

Subramaniam, Prasad

Nanotechnology offers tremendous potential for use in biomedical applications, including cancer and stem cell imaging, disease diagnosis and drug delivery. The development of nanosystems has aided in understanding the molecular mechanisms of many diseases and permitted the controlled nanoscale manipulation of biological phenomena. In recent years, many studies have focused on the use of several kinds of nanomaterials for cancer and stem cell imaging and also for the delivery of anticancer therapeutics to tumor cells. However, the proper diagnosis and treatment of aggressive tumors such as brain and breast cancer requires highly sensitive diagnostic agents, in addition to the ability to deliver multiple therapeutics using a single platform to the target cells. Addressing these challenges, novel multifunctional nanomaterial-based platforms that incorporate multiple therapeutic and diagnostic agents, with superior molecular imaging and targeting capabilities, has been presented in this work. The initial part of this work presents the development of novel nanomaterials with superior optical properties for efficiently delivering soluble cues such as small interfering RNA (siRNA) into brain cancer cells with minimal toxicity. Specifically, this section details the development of non-toxic quantums dots for the imaging and delivery of siRNA into brain cancer and mesenchymal stem cells, with the hope of using these quantum dots as multiplexed imaging and delivery vehicles. The use of these quantum dots could overcome the toxicity issues associated with the use of conventional quantum dots, enabled the imaging of brain cancer and stem cells with high efficiency and allowed for the delivery of siRNA to knockdown the target oncogene in brain cancer cells. The latter part of this thesis details the development of nanomaterial-based drug delivery platforms for the co-delivery of multiple anticancer drugs to brain tumor cells. In particular, this part of the thesis focuses on the synthesis and use of a biodegradable dendritic polypeptide-based nanocarrier for the delivery of multiple anticancer drugs and siRNA to brain tumor cells. The co-delivery of important anticancer agents using a single platform was shown to increase the efficacy of the drugs manyfold, ensuring the cancer cell-specific delivery and minimizing dose limiting toxicities of the individual drugs. This would be of immense importance when used in vivo.

Computer-assisted detection of epileptiform focuses on SPECT images

NASA Astrophysics Data System (ADS)

Grzegorczyk, Dawid; Dunin-Wąsowicz, Dorota; Mulawka, Jan J.

2010-09-01

Epilepsy is a common nervous system disease often related to consciousness disturbances and muscular spasm which affects about 1% of the human population. Despite major technological advances done in medicine in the last years there was no sufficient progress towards overcoming it. Application of advanced statistical methods and computer image analysis offers the hope for accurate detection and later removal of an epileptiform focuses which are the cause of some types of epilepsy. The aim of this work was to create a computer system that would help to find and diagnose disorders of blood circulation in the brain This may be helpful for the diagnosis of the epileptic seizures onset in the brain.

In vivo Magnetic Resonance Imaging of Tumor Protease Activity

PubMed Central

Haris, Mohammad; Singh, Anup; Mohammed, Imran; Ittyerah, Ranjit; Nath, Kavindra; Nanga, Ravi Prakash Reddy; Debrosse, Catherine; Kogan, Feliks; Cai, Kejia; Poptani, Harish; Reddy, Damodar; Hariharan, Hari; Reddy, Ravinder

2014-01-01

Increased expression of cathepsins has diagnostic as well as prognostic value in several types of cancer. Here, we demonstrate a novel magnetic resonance imaging (MRI) method, which uses poly-L-glutamate (PLG) as an MRI probe to map cathepsin expression in vivo, in a rat brain tumor model. This noninvasive, high-resolution and non-radioactive method exploits the differences in the CEST signals of PLG in the native form and cathepsin mediated cleaved form. The method was validated in phantoms with known physiological concentrations, in tumor cells and in an animal model of brain tumor along with immunohistochemical analysis. Potential applications in tumor diagnosis and evaluation of therapeutic response are outlined. PMID:25124082

Testosterone affects language areas of the adult human brain

PubMed Central

Hahn, Andreas; Kranz, Georg S.; Sladky, Ronald; Kaufmann, Ulrike; Ganger, Sebastian; Hummer, Allan; Seiger, Rene; Spies, Marie; Vanicek, Thomas; Winkler, Dietmar; Kasper, Siegfried; Windischberger, Christian; Swaab, Dick F.

2016-01-01

Abstract Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high‐dose hormone application in adult female‐to‐male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging. Voxel‐based morphometry analysis showed decreased gray matter volume with increasing levels of bioavailable testosterone exclusively in Broca's and Wernicke's areas. Particularly, this may link known sex differences in language performance to the influence of testosterone on relevant brain regions. Using probabilistic tractography, we further observed that longitudinal changes in testosterone negatively predicted changes in mean diffusivity of the corresponding structural connection passing through the extreme capsule. Considering a related increase in myelin staining in rodents, this potentially reflects a strengthening of the fiber tract particularly involved in language comprehension. Finally, functional images at resting‐state were evaluated, showing increased functional connectivity between the two brain regions with increasing testosterone levels. These findings suggest testosterone‐dependent neuroplastic adaptations in adulthood within language‐specific brain regions and connections. Importantly, deteriorations in gray matter volume seem to be compensated by enhancement of corresponding structural and functional connectivity. Hum Brain Mapp 37:1738–1748, 2016. © 2016 Wiley Periodicals, Inc. PMID:26876303

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

Application of a novel metal artifact correction algorithm in flat-panel CT after coil embolization of brain aneurysms: intraindividual comparison.

PubMed

Buhk, J-H; Groth, M; Sehner, S; Fiehler, J; Schmidt, N O; Grzyska, U

2013-09-01

To evaluate a novel algorithm for correcting beam hardening artifacts caused by metal implants in computed tomography performed on a C-arm angiography system equipped with a flat panel (FP-CT). 16 datasets of cerebral FP-CT acquisitions after coil embolization of brain aneurysms in the context of acute subarachnoid hemorrhage have been reconstructed by applying a soft tissue kernel with and without a novel reconstruction filter for metal artifact correction. Image reading was performed in multiplanar reformations (MPR) in average mode on a dedicated radiological workplace in comparison to the preinterventional native multisection CT (MS-CT) scan serving as the anatomic gold standard. Two independent radiologists performed image scoring following a defined scale in direct comparison of the image data with and without artifact correction. For statistical analysis, a random intercept model was calculated. The inter-rater agreement was very high (ICC = 86.3 %). The soft tissue image quality and visualization of the CSF spaces at the level of the implants was substantially improved. The additional metal artifact correction algorithm did not induce impairment of the subjective image quality in any other brain regions. Adding metal artifact correction to FP-CT in an acute postinterventional setting helps to visualize the close vicinity of the aneurysm at a generally consistent image quality. © Georg Thieme Verlag KG Stuttgart · New York.

Photoacoustic imaging of living mouse brain vasculature using hollow gold nanospheres.

PubMed

Lu, Wei; Huang, Qian; Ku, Geng; Wen, Xiaoxia; Zhou, Min; Guzatov, Dmitry; Brecht, Peter; Su, Richard; Oraevsky, Alexander; Wang, Lihong V; Li, Chun

2010-03-01

Photoacoustic tomography (PAT) also referred to as optoacoustic tomography (OAT) is a hybrid imaging modality that employs nonionizing optical radiation and ultrasonic detection. Here, we describe the application of a new class of optical contrast agents based on mesoscopic hollow gold nanospheres (HAuNS) to PAT. HAuNS are approximately 40 nm in diameter with a hollow interior and consist of a thin gold wall. They display strong resonance absorption tuned to the near-infrared (NIR) range, with an absorption peak at 800 nm, whose photoacoustic efficiency is significantly greater than that of blood. Following surface conjugation with thiolated poly(ethylene glycol), the pegylated HAuNS (PEG-HAuNS) had distribution and elimination half-lives of 1.38 +/- 0.38 and 71.82 +/- 30.46 h, respectively. Compared with PAT images based on the intrinsic optical contrast in nude mice, the PAT images acquired within 2 h after intravenous administration of PEG-HAuNS showed the brain vasculature with greater clarity and detail. The image depicted brain blood vessels as small as approximately 100 mum in diameter using PEG-HAuNS as contrast agents. Preliminary results showed no acute toxicity to the liver, spleen, or kidneys in mice following a single imaging dose of PEG-HAuNS. Our results indicate that PEG-HAuNS are promising contrast agents for PAT, with high spatial resolution and enhanced sensitivity. Copyright 2009 Elsevier Ltd. All rights reserved.

New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury

DTIC Science & Technology

2013-02-01

magnet based ), the development of novel high-speed parallel imaging detection systems, and work on advanced adaptive reconstruction methods ...signal many times within the acquisition time . We present here a new method for 3D OMRI based on b-SSFP at a constant field of 6.5 mT that provides up...developing injury-sensitive MRI based on the detection of free radicals associat- ed with injury using the Overhauser effect and subsequently imaging that

In vivo multiphoton microscopy beyond 1 mm in the brain

NASA Astrophysics Data System (ADS)

Miller, David R.; Medina, Flor A.; Hassan, Ahmed; Perillo, Evan P.; Hagan, Kristen; Kazmi, S. M. Shams; Zemelman, Boris V.; Dunn, Andrew K.

2017-02-01

We perform high-resolution, non-invasive, in vivo deep-tissue imaging of the mouse neocortex using multiphoton microscopy with a high repetition rate optical parametric amplifier laser source tunable between λ=1,100 and 1,400 nm. We demonstrate an imaging depth of 1,200 μm in vasculature and 1,160 μm in neurons. We also demonstrate deep-tissue imaging using Indocyanine Green (ICG), which is FDA approved and a promising route to translate multiphoton microscopy to human applications.

SPECT-imaging of activity-dependent changes in regional cerebral blood flow induced by electrical and optogenetic self-stimulation in mice.

PubMed

Kolodziej, Angela; Lippert, Michael; Angenstein, Frank; Neubert, Jenni; Pethe, Annette; Grosser, Oliver S; Amthauer, Holger; Schroeder, Ulrich H; Reymann, Klaus G; Scheich, Henning; Ohl, Frank W; Goldschmidt, Jürgen

2014-12-01

Electrical and optogenetic methods for brain stimulation are widely used in rodents for manipulating behavior and analyzing functional connectivities in neuronal circuits. High-resolution in vivo imaging of the global, brain-wide, activation patterns induced by these stimulations has remained challenging, in particular in awake behaving mice. We here mapped brain activation patterns in awake, intracranially self-stimulating mice using a novel protocol for single-photon emission computed tomography (SPECT) imaging of regional cerebral blood flow (rCBF). Mice were implanted with either electrodes for electrical stimulation of the medial forebrain bundle (mfb-microstim) or with optical fibers for blue-light stimulation of channelrhodopsin-2 expressing neurons in the ventral tegmental area (vta-optostim). After training for self-stimulation by current or light application, respectively, mice were implanted with jugular vein catheters and intravenously injected with the flow tracer 99m-technetium hexamethylpropyleneamine oxime (99mTc-HMPAO) during seven to ten minutes of intracranial self-stimulation or ongoing behavior without stimulation. The 99mTc-brain distributions were mapped in anesthetized animals after stimulation using multipinhole SPECT. Upon self-stimulation rCBF strongly increased at the electrode tip in mfb-microstim mice. In vta-optostim mice peak activations were found outside the stimulation site. Partly overlapping brain-wide networks of activations and deactivations were found in both groups. When testing all self-stimulating mice against all controls highly significant activations were found in the rostromedial nucleus accumbens shell. SPECT-imaging of rCBF using intravenous tracer-injection during ongoing behavior is a new tool for imaging regional brain activation patterns in awake behaving rodents providing higher spatial and temporal resolutions than 18F-2-fluoro-2-dexoyglucose positron emission tomography. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

PubMed Central

Jain, Saurabh; Sima, Diana M.; Sanaei Nezhad, Faezeh; Hangel, Gilbert; Bogner, Wolfgang; Williams, Stephen; Van Huffel, Sabine; Maes, Frederik; Smeets, Dirk

2017-01-01

Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects. Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI. Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications. PMID:28197066

Design and deployment of a large brain-image database for clinical and nonclinical research

NASA Astrophysics Data System (ADS)

Yang, Guo Liang; Lim, Choie Cheio Tchoyoson; Banukumar, Narayanaswami; Aziz, Aamer; Hui, Francis; Nowinski, Wieslaw L.

2004-04-01

An efficient database is an essential component of organizing diverse information on image metadata and patient information for research in medical imaging. This paper describes the design, development and deployment of a large database system serving as a brain image repository that can be used across different platforms in various medical researches. It forms the infrastructure that links hospitals and institutions together and shares data among them. The database contains patient-, pathology-, image-, research- and management-specific data. The functionalities of the database system include image uploading, storage, indexing, downloading and sharing as well as database querying and management with security and data anonymization concerns well taken care of. The structure of database is multi-tier client-server architecture with Relational Database Management System, Security Layer, Application Layer and User Interface. Image source adapter has been developed to handle most of the popular image formats. The database has a user interface based on web browsers and is easy to handle. We have used Java programming language for its platform independency and vast function libraries. The brain image database can sort data according to clinically relevant information. This can be effectively used in research from the clinicians" points of view. The database is suitable for validation of algorithms on large population of cases. Medical images for processing could be identified and organized based on information in image metadata. Clinical research in various pathologies can thus be performed with greater efficiency and large image repositories can be managed more effectively. The prototype of the system has been installed in a few hospitals and is working to the satisfaction of the clinicians.

Atlas-based fuzzy connectedness segmentation and intensity nonuniformity correction applied to brain MRI.

PubMed

Zhou, Yongxin; Bai, Jing

2007-01-01

A framework that combines atlas registration, fuzzy connectedness (FC) segmentation, and parametric bias field correction (PABIC) is proposed for the automatic segmentation of brain magnetic resonance imaging (MRI). First, the atlas is registered onto the MRI to initialize the following FC segmentation. Original techniques are proposed to estimate necessary initial parameters of FC segmentation. Further, the result of the FC segmentation is utilized to initialize a following PABIC algorithm. Finally, we re-apply the FC technique on the PABIC corrected MRI to get the final segmentation. Thus, we avoid expert human intervention and provide a fully automatic method for brain MRI segmentation. Experiments on both simulated and real MRI images demonstrate the validity of the method, as well as the limitation of the method. Being a fully automatic method, it is expected to find wide applications, such as three-dimensional visualization, radiation therapy planning, and medical database construction.

Recent technological advances in pediatric brain tumor surgery.

PubMed

Zebian, Bassel; Vergani, Francesco; Lavrador, José Pedro; Mukherjee, Soumya; Kitchen, William John; Stagno, Vita; Chamilos, Christos; Pettorini, Benedetta; Mallucci, Conor

2017-01-01

X-rays and ventriculograms were the first imaging modalities used to localize intracranial lesions including brain tumors as far back as the 1880s. Subsequent advances in preoperative radiological localization included computed tomography (CT; 1971) and MRI (1977). Since then, other imaging modalities have been developed for clinical application although none as pivotal as CT and MRI. Intraoperative technological advances include the microscope, which has allowed precise surgery under magnification and improved lighting, and the endoscope, which has improved the treatment of hydrocephalus and allowed biopsy and complete resection of intraventricular, pituitary and pineal region tumors through a minimally invasive approach. Neuronavigation, intraoperative MRI, CT and ultrasound have increased the ability of the neurosurgeon to perform safe and maximal tumor resection. This may be facilitated by the use of fluorescing agents, which help define the tumor margin, and intraoperative neurophysiological monitoring, which helps identify and protect eloquent brain.

Molecular Magnetic Resonance Imaging of Endothelial Activation in the Central Nervous System

PubMed Central

Gauberti, Maxime; Fournier, Antoine P.; Docagne, Fabian; Vivien, Denis; Martinez de Lizarrondo, Sara

2018-01-01

Endothelial cells of the central nervous system over-express surface proteins during neurological disorders, either as a cause, or a consequence, of the disease. Since the cerebral vasculature is easily accessible by large contrast-carrying particles, it constitutes a target of choice for molecular magnetic resonance imaging (MRI). In this review, we highlight the most recent advances in molecular MRI of brain endothelial activation and focus on the development of micro-sized particles of iron oxide (MPIO) targeting adhesion molecules including intercellular adhesion molecule 1 (ICAM-1), vascular cell adhesion molecule 1 (VCAM-1), P-Selectin and E-Selectin. We also discuss the perspectives and challenges for the clinical application of this technology in neurovascular disorders (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, diabetes mellitus), neuroinflammatory disorders (multiple sclerosis, brain infectious diseases, sepsis), neurodegenerative disorders (Alzheimer's disease, vascular dementia, aging) and brain cancers (primitive neoplasms, metastasis). PMID:29507614

Functional imaging of cerebral blood flow and glucose metabolism in Parkinson's disease and Huntington's disease.

PubMed

Ma, Yilong; Eidelberg, David

2007-01-01

Brain imaging of cerebral blood flow and glucose metabolism has been playing key roles in describing pathophysiology of Parkinson's disease (PD) and Huntington's disease (HD), respectively. Many biomarkers have been developed in recent years to investigate the abnormality in molecular substrate, track the time course of disease progression, and evaluate the efficacy of novel experimental therapeutics. A growing body of literature has emerged on neurobiology of these two movement disorders in resting states and in response to brain activation tasks. In this paper, we review the latest applications of these approaches in patients and normal volunteers at rest conditions. The discussions focus on brain mapping studies with univariate and multivariate statistical analyses on a voxel basis. In particular, we present data to validate the reproducibility and reliability of unique spatial covariance patterns related with PD and HD.

Chemical reactivation of resin-embedded pHuji adds red for simultaneous two-color imaging with EGFP

PubMed Central

Guo, Wenyan; Liu, Xiuli; Liu, Yurong; Gang, Yadong; He, Xiaobin; Jia, Yao; Yin, Fangfang; Li, Pei; Huang, Fei; Zhou, Hongfu; Wang, Xiaojun; Gong, Hui; Luo, Qingming; Xu, Fuqiang; Zeng, Shaoqun

2017-01-01

The pH-sensitive fluorescent proteins enabling chemical reactivation in resin are useful tools for fluorescence microimaging. EGFP or EYFP is good for such applications. For simultaneous two-color imaging, a suitable red fluorescent protein is an urgent need. Here a pH-sensitive red fluorescent protein, pHuji, is selected and verified to remain pH-sensitive in HM20 resin. We observe 183% fluorescence intensity of pHuji in resin-embeded mouse brain and 29.08-fold fluorescence intensity of reactivated pHuji compared to the quenched state. pHuji and EGFP can be quenched and chemically reactivated simultaneously in resin, thus enabling simultaneous two-color micro-optical sectioning tomography of resin-embedded mouse brain. This method may greatly facilitate the visualization of neuronal morphology and neural circuits to promote understanding of the structure and function of the brain. PMID:28717566

Chemical reactivation of resin-embedded pHuji adds red for simultaneous two-color imaging with EGFP.

PubMed

Guo, Wenyan; Liu, Xiuli; Liu, Yurong; Gang, Yadong; He, Xiaobin; Jia, Yao; Yin, Fangfang; Li, Pei; Huang, Fei; Zhou, Hongfu; Wang, Xiaojun; Gong, Hui; Luo, Qingming; Xu, Fuqiang; Zeng, Shaoqun

2017-07-01

The pH-sensitive fluorescent proteins enabling chemical reactivation in resin are useful tools for fluorescence microimaging. EGFP or EYFP is good for such applications. For simultaneous two-color imaging, a suitable red fluorescent protein is an urgent need. Here a pH-sensitive red fluorescent protein, pHuji, is selected and verified to remain pH-sensitive in HM20 resin. We observe 183% fluorescence intensity of pHuji in resin-embeded mouse brain and 29.08-fold fluorescence intensity of reactivated pHuji compared to the quenched state. pHuji and EGFP can be quenched and chemically reactivated simultaneously in resin, thus enabling simultaneous two-color micro-optical sectioning tomography of resin-embedded mouse brain. This method may greatly facilitate the visualization of neuronal morphology and neural circuits to promote understanding of the structure and function of the brain.

Enhanced spectral domain optical coherence tomography for pathological and functional studies

NASA Astrophysics Data System (ADS)

Yuan, Zhijia

Optical coherence tomography (OCT) is a novel technique that enables noninvasive or minimally invasive, cross-sectional imaging of biological tissue at sub-10mum spatial resolution and up to 2-3mm imaging depth. Numerous technological advances have emerged in recent years that have shown great potential to develop OCT into a powerful imaging and diagnostic tools. In particular, the implementation of Fourier-domain OCT (FDOCT) is a major step forward that leads to greatly improved imaging rate and image fidelity of OCT. This dissertation summarizes the work that focuses on enhancing the performances and functionalities of spectral radar based FDOCT (SDOCT) for pathological and functional applications. More specifically, chapters 1-4 emphasize on the development of SDOCT and its utility in pathological studies, including cancer diagnosis. The principle of SDOCT is first briefly outlined, followed by the design of our bench-top SDOCT systems with emphasis on spectral linear interpolation, calibration and system dispersion compensation. For ultrahigh-resolution SDOCT, time-lapse image registration and frame averaging is introduced to effectively reduce speckle noise and uncover subcellular details, showing great promise for enhancing the diagnosis of carcinoma in situ. To overcome the image depth limitation of OCT, a dual-modal imaging method combing SDOCT with high-frequency ultrasound is proposed and examined in animal cancer models to enhance the sensitivity and staging capabilities for bladder cancer diagnosis. Chapters 5-7 summarize the work on developing Doppler SDOCT for functional studies. Digital-frequency-ramping OCT (DFR-OCT) is developed in the study, which has demonstrated the ability to significantly improve the signal-to-noise ratio and thus sensitivity for retrieving subsurface blood flow imaging. New DFR algorithms and imaging processing methods are discussed to further enhance cortical CBF imaging. Applications of DFR-OCT for brain functional studies are presented and laser speckle imaging is combined to enable quantitative cerebral blood flow (CBF) imaging at high spatiotemporal resolutions. An angiography-enhanced Doppler optical coherence tomography (aDFR-OCT) was also demonstrated to enable quantitative imaging of capillary changes for brain functional studies. Lastly, future work on technological development and potential biomedical applications is briefly outlined.

High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications.

PubMed

Fernandez-Miranda, Juan C; Pathak, Sudhir; Engh, Johnathan; Jarbo, Kevin; Verstynen, Timothy; Yeh, Fang-Cheng; Wang, Yibao; Mintz, Arlan; Boada, Fernando; Schneider, Walter; Friedlander, Robert

2012-08-01

High-definition fiber tracking (HDFT) is a novel combination of processing, reconstruction, and tractography methods that can track white matter fibers from cortex, through complex fiber crossings, to cortical and subcortical targets with subvoxel resolution. To perform neuroanatomical validation of HDFT and to investigate its neurosurgical applications. Six neurologically healthy adults and 36 patients with brain lesions were studied. Diffusion spectrum imaging data were reconstructed with a Generalized Q-Ball Imaging approach. Fiber dissection studies were performed in 20 human brains, and selected dissection results were compared with tractography. HDFT provides accurate replication of known neuroanatomical features such as the gyral and sulcal folding patterns, the characteristic shape of the claustrum, the segmentation of the thalamic nuclei, the decussation of the superior cerebellar peduncle, the multiple fiber crossing at the centrum semiovale, the complex angulation of the optic radiations, the terminal arborization of the arcuate tract, and the cortical segmentation of the dorsal Broca area. From a clinical perspective, we show that HDFT provides accurate structural connectivity studies in patients with intracerebral lesions, allowing qualitative and quantitative white matter damage assessment, aiding in understanding lesional patterns of white matter structural injury, and facilitating innovative neurosurgical applications. High-grade gliomas produce significant disruption of fibers, and low-grade gliomas cause fiber displacement. Cavernomas cause both displacement and disruption of fibers. Our HDFT approach provides an accurate reconstruction of white matter fiber tracts with unprecedented detail in both the normal and pathological human brain. Further studies to validate the clinical findings are needed.

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.

Functional magnetic resonance imaging in clinical practice: State of the art and science.

PubMed

Barras, Christen D; Asadi, Hamed; Baldeweg, Torsten; Mancini, Laura; Yousry, Tarek A; Bisdas, Sotirios

2016-11-01

Functional magnetic resonance imaging (fMRI) has become a mainstream neuroimaging modality in the assessment of patients being evaluated for brain tumour and epilepsy surgeries. Thus, it is important for doctors in primary care settings to be well acquainted with the present and potential future applications, as well as limitations, of this modality. The objective of this article is to introduce the theoretical principles and state-of-the-art clinical applications of fMRI in brain tumour and epilepsy surgery, with a focus on the implications for clinical primary care. fMRI enables non-invasive functional mapping of specific cortical tasks (eg motor, language, memory-based, visual), revealing information about functional localisation, anatomical variation in cortical function, and disease effects and adaptations, including the fascinating phenomenon of brain plasticity. fMRI is currently ordered by specialist neurologists and neurosurgeons for the purposes of pre-surgical assessment, and within the context of an experienced multidisciplinary team to prepare, conduct and interpret the scan. With an increasing number of patients undergoing fMRI, general practitioners can expect questions about the current and emerging role of fMRI in clinical care from these patients and their families.

Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders.

PubMed

Takamura, T; Hanakawa, T

2017-07-01

Although functional magnetic resonance imaging (fMRI) has long been used to assess task-related brain activity in neuropsychiatric disorders, it has not yet become a widely available clinical tool. Resting-state fMRI (rs-fMRI) has been the subject of recent attention in the fields of basic and clinical neuroimaging research. This method enables investigation of the functional organization of the brain and alterations of resting-state networks (RSNs) in patients with neuropsychiatric disorders. Rs-fMRI does not require participants to perform a demanding task, in contrast to task fMRI, which often requires participants to follow complex instructions. Rs-fMRI has a number of advantages over task fMRI for application with neuropsychiatric patients, for example, although applications of task fMR to participants for healthy are easy. However, it is difficult to apply these applications to patients with psychiatric and neurological disorders, because they may have difficulty in performing demanding cognitive task. Here, we review the basic methodology and analysis techniques relevant to clinical studies, and the clinical applications of the technique for examining neuropsychiatric disorders, focusing on mood disorders (major depressive disorder and bipolar disorder) and dementia (Alzheimer's disease and mild cognitive impairment).

Intranasal insulin modulates intrinsic reward and prefrontal circuitry of the human brain in lean women.

PubMed

Kullmann, Stephanie; Frank, Sabine; Heni, Martin; Ketterer, Caroline; Veit, Ralf; Häring, Hans-Ulrich; Fritsche, Andreas; Preissl, Hubert

2013-01-01

There is accumulating evidence that food consumption is controlled by a wide range of brain circuits outside of the homeostatic system. Activation in these brain circuits may override the homeostatic system and also contribute to the enormous increase of obesity. However, little is known about the influence of hormonal signals on the brain's non-homeostatic system. Thus, selective insulin action in the brain was investigated by using intranasal application. We performed 'resting-state' functional magnetic resonance imaging in 17 healthy lean female subjects to assess intrinsic brain activity by fractional amplitude of low-frequency fluctuations (fALFF) before, 30 and 90 min after application of intranasal insulin. Here, we showed that insulin modulates intrinsic brain activity in the hypothalamus and orbitofrontal cortex. Furthermore, we could show that the prefrontal and anterior cingulate cortex response to insulin is associated with body mass index. This demonstrates that hormonal signals as insulin may reduce food intake by modifying the reward and prefrontal circuitry of the human brain, thereby potentially decreasing the rewarding properties of food. Due to the alarming increase in obesity worldwide, it is of great importance to identify neural mechanisms of interaction between the homeostatic and non-homeostatic system to generate new targets for obesity therapy. Copyright © 2012 S. Karger AG, Basel.

Review of optical coherence tomography in oncology

NASA Astrophysics Data System (ADS)

Wang, Jianfeng; Xu, Yang; Boppart, Stephen A.

2017-12-01

The application of optical coherence tomography (OCT) in the field of oncology has been prospering over the past decade. OCT imaging has been used to image a broad spectrum of malignancies, including those arising in the breast, brain, bladder, the gastrointestinal, respiratory, and reproductive tracts, the skin, and oral cavity, among others. OCT imaging has initially been applied for guiding biopsies, for intraoperatively evaluating tumor margins and lymph nodes, and for the early detection of small lesions that would often not be visible on gross examination, tasks that align well with the clinical emphasis on early detection and intervention. Recently, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This paper reviews the evolution of OCT technologies for the clinical application of OCT in surgical and noninvasive interventional oncology procedures and concludes with a discussion of the future directions for OCT technologies, with particular emphasis on their applications in oncology.

Automating cell detection and classification in human brain fluorescent microscopy images using dictionary learning and sparse coding.

PubMed

Alegro, Maryana; Theofilas, Panagiotis; Nguy, Austin; Castruita, Patricia A; Seeley, William; Heinsen, Helmut; Ushizima, Daniela M; Grinberg, Lea T

2017-04-15

Immunofluorescence (IF) plays a major role in quantifying protein expression in situ and understanding cell function. It is widely applied in assessing disease mechanisms and in drug discovery research. Automation of IF analysis can transform studies using experimental cell models. However, IF analysis of postmortem human tissue relies mostly on manual interaction, often subjected to low-throughput and prone to error, leading to low inter and intra-observer reproducibility. Human postmortem brain samples challenges neuroscientists because of the high level of autofluorescence caused by accumulation of lipofuscin pigment during aging, hindering systematic analyses. We propose a method for automating cell counting and classification in IF microscopy of human postmortem brains. Our algorithm speeds up the quantification task while improving reproducibility. Dictionary learning and sparse coding allow for constructing improved cell representations using IF images. These models are input for detection and segmentation methods. Classification occurs by means of color distances between cells and a learned set. Our method successfully detected and classified cells in 49 human brain images. We evaluated our results regarding true positive, false positive, false negative, precision, recall, false positive rate and F1 score metrics. We also measured user-experience and time saved compared to manual countings. We compared our results to four open-access IF-based cell-counting tools available in the literature. Our method showed improved accuracy for all data samples. The proposed method satisfactorily detects and classifies cells from human postmortem brain IF images, with potential to be generalized for applications in other counting tasks. Copyright © 2017 Elsevier B.V. All rights reserved.

Thermal camera used for the assessment of metabolism and functions of the rat brain

NASA Astrophysics Data System (ADS)

Kastek, Mariusz; Piatkowski, Tadeusz; Polakowski, Henryk; Kaczmarska, Katarzyna; Czernicki, Zbigniew; Koźniewska, Ewa; Przykaza, Lukasz

2014-05-01

Motivation to undertake research on brain surface temperature in clinical practice is based on a strong conviction that the enormous progress in thermal imaging techniques and camera design has a great application potential. Intraoperative imaging of pathological changes and functionally important areas of the brain is not yet fully resolved in neurosurgery and remains a challenge. Extensive knowledge of the complex mechanisms controlling homeostasis (thermodynamic status of an organism being a part of it ) and laws of physics (which are the foundations of thermography), make this method very good and a simple imaging tool in comparison with other modern techniques, such as computed tomography, magnetic resonance imaging and angiography. Measurements of temperature distribution across the brain surface were performed on four rats (Wistar strain) weighing approximately 300 g each. Animals have remained under general anesthesia typically conducted using isoflurane. The brain was unveiled (the dura mater remained untouched) through the skin incision and removal of the bone cranial vault. Cerebrocortical microflow was measured using laser-Doppler flow meter. Arterial blood pressure was also measured in rat femoral artery. From the above data the cerebrovascular resistance index was calculated. Cerebral flow was modified by increasing the CO2 concentration in the inspired air to 5% for the duration of 6 minutes. Another change in cerebral flow was induced by periodic closing of right middle cerebral artery. Artery occlusion was performed by introducing a filament for a period of 15 minutes, then an artery was opened again. Measurements were carried out before, during and after the artery occlusion. Paper presents results and methodology of measurements.

Brain tissue segmentation based on DTI data

PubMed Central

Liu, Tianming; Li, Hai; Wong, Kelvin; Tarokh, Ashley; Guo, Lei; Wong, Stephen T.C.

2008-01-01

We present a method for automated brain tissue segmentation based on the multi-channel fusion of diffusion tensor imaging (DTI) data. The method is motivated by the evidence that independent tissue segmentation based on DTI parametric images provides complementary information of tissue contrast to the tissue segmentation based on structural MRI data. This has important applications in defining accurate tissue maps when fusing structural data with diffusion data. In the absence of structural data, tissue segmentation based on DTI data provides an alternative means to obtain brain tissue segmentation. Our approach to the tissue segmentation based on DTI data is to classify the brain into two compartments by utilizing the tissue contrast existing in a single channel. Specifically, because the apparent diffusion coefficient (ADC) values in the cerebrospinal fluid (CSF) are more than twice that of gray matter (GM) and white matter (WM), we use ADC images to distinguish CSF and non-CSF tissues. Additionally, fractional anisotropy (FA) images are used to separate WM from non-WM tissues, as highly directional white matter structures have much larger fractional anisotropy values. Moreover, other channels to separate tissue are explored, such as eigenvalues of the tensor, relative anisotropy (RA), and volume ratio (VR). We developed an approach based on the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm that combines these two-class maps to obtain a complete tissue segmentation map of CSF, GM, and WM. Evaluations are provided to demonstrate the performance of our approach. Experimental results of applying this approach to brain tissue segmentation and deformable registration of DTI data and spoiled gradient-echo (SPGR) data are also provided. PMID:17804258

Noninvasive diffusive optical imaging of the auditory response to birdsong in the zebra finch

PubMed Central

Lee, James V.; Maclin, Edward L.; Low, Kathy A.; Gratton, Gabriele; Fabiani, Monica; Clayton, David F.

2013-01-01

Songbirds communicate by learned vocalizations with concomitant changes in neurophysiological and genomic activities in discrete parts of the brain. Here we tested a novel implementation of diffusive optical imaging (also known as diffuse optical imaging, DOI) for monitoring brain physiology associated with vocal signal perception. DOI noninvasively measures brain activity using red and near-infrared light delivered through optic fibers (optodes) resting on the scalp. DOI does not harm subjects, so it raises the possibility of repeatedly measuring brain activity and the effects of accumulated experience in the same subject over an entire life span, all while leaving tissue intact for further study. We developed a custom-made apparatus for interfacing optodes to the zebra finch (Taeniopygia guttata) head using 3D modeling software and rapid prototyping technology, and applied it to record responses to presentations of birdsong in isoflurane-anesthetized zebra finches. We discovered a subtle but significant difference between the hemoglobin spectra of zebra finches and mammals which has a major impact in how hemodynamic responses are interpreted in the zebra finch. Our measured responses to birdsong playback were robust, highly repeatable, and readily observed in single trials. Responses were complex in shape and closely paralleled responses described in mammals. They were localized to the caudal medial portion of the brain, consistent with response localization from prior gene expression, electrophysiological, and functional magnetic resonance imaging studies. These results define an approach for collecting neurophysiological data from songbirds that should be applicable to diverse species and adaptable for studies in awake behaving animals. PMID:23322445

Sensor, signal, and image informatics - state of the art and current topics.

PubMed

Lehmann, T M; Aach, T; Witte, H

2006-01-01

The number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments. Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CT-based diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different time-frequency approaches for signal analysis, an application to Cochlea implants, where time-frequency analysis is applied for controlling the replacement system, recent trends for fusion of different modalities, and the role of BSA as part of a brain machine interfaces. To cover the broad spectrum of publications in the field of biomedical image processing, six papers are focused. Important topics are content-based image retrieval in medical applications, automatic classification of tongue photographs from traditional Chinese medicine, brain perfusion analysis in single photon emission computed tomography (SPECT), model-based visualization of vascular trees, and virtual surgery, where enhanced visualization and haptic feedback techniques are combined with a sphere-filled model of the organ. The selected papers emphasize the five fields forming the chain of biomedical data processing: (1) data acquisition, (2) data reconstruction and pre-processing, (3) data handling, (4) data analysis, and (5) data visualization. Fields 1 and 2 form the sensor informatics, while fields 2 to 5 form signal or image informatics with respect to the nature of the data considered. Biomedical data acquisition and pre-processing, as well as data handling, analysis and visualization aims at providing reliable tools for decision support that improve the quality of health care. Comprehensive evaluation of the processing methods and their reliable integration in routine applications are future challenges in the field of sensor, signal and image informatics.

Brain Injury in Neonates with Complex Congenital Heart Disease: What Is the Predictive Value of MRI in the Fetal Period?

PubMed

Brossard-Racine, M; du Plessis, A; Vezina, G; Robertson, R; Donofrio, M; Tworetzky, W; Limperopoulos, C

2016-07-01

Brain injury in neonates with congenital heart disease is an important predictor of adverse neurodevelopmental outcome. Impaired brain development in congenital heart disease may have a prenatal origin, but the sensitivity and specificity of fetal brain MR imaging for predicting neonatal brain lesions are currently unknown. We sought to determine the value of conventional fetal MR imaging for predicting abnormal findings on neonatal preoperative MR imaging in neonates with complex congenital heart disease. MR imaging studies were performed in 103 fetuses with confirmed congenital heart disease (mean gestational age, 31.57 ± 3.86 weeks) and were repeated postnatally before cardiac surgery (mean age, 6.8 ± 12.2 days). Each MR imaging study was read by a pediatric neuroradiologist. Brain abnormalities were detected in 17/103 (16%) fetuses by fetal MR imaging and in 33/103 (32%) neonates by neonatal MR imaging. Only 9/33 studies with abnormal neonatal findings were preceded by abnormal findings on fetal MR imaging. The sensitivity and specificity of conventional fetal brain MR imaging for predicting neonatal brain abnormalities were 27% and 89%, respectively. Brain abnormalities detected by in utero MR imaging in fetuses with congenital heart disease are associated with higher risk of postnatal preoperative brain injury. However, a substantial proportion of anomalies on postnatal MR imaging were not present on fetal MR imaging; this result is likely due to the limitations of conventional fetal MR imaging and the emergence of new lesions that occurred after the fetal studies. Postnatal brain MR imaging studies are needed to confirm the presence of injury before open heart surgery. © 2016 by American Journal of Neuroradiology.

Enhanced Sensitivity for High Spatial Resolution Lipid Analysis by Negative Ion Mode MALDI Imaging Mass Spectrometry

PubMed Central

Angel, Peggi M.; Spraggins, Jeffrey M.; Baldwin, H. Scott; Caprioli, Richard

2012-01-01

We have achieved enhanced lipid imaging to a ~10 μm spatial resolution using negative ion mode matrix assisted laser desorption ionization (MALDI) imaging mass spectrometry, sublimation of 2,5-dihydroxybenzoic acid as the MALDI matrix and a sample preparation protocol that uses aqueous washes. We report on the effect of treating tissue sections by washing with volatile buffers at different pHs prior to negative ion mode lipid imaging. The results show that washing with ammonium formate, pH 6.4, or ammonium acetate, pH 6.7, significantly increases signal intensity and number of analytes recorded from adult mouse brain tissue sections. Major lipid species measured were glycerophosphoinositols, glycerophosphates, glycerolphosphoglycerols, glycerophosphoethanolamines, glycerophospho-serines, sulfatides, and gangliosides. Ion images from adult mouse brain sections that compare washed and unwashed sections are presented and show up to fivefold increases in ion intensity for washed tissue. The sample preparation protocol has been found to be applicable across numerous organ types and significantly expands the number of lipid species detectable by imaging mass spectrometry at high spatial resolution. PMID:22243218

Optimization of white matter tractography for pre-surgical planning and image-guided surgery.

PubMed

Arfanakis, Konstantinos; Gui, Minzhi; Lazar, Mariana

2006-01-01

Accurate localization of white matter fiber tracts in relation to brain tumors is a goal of critical importance to the neurosurgical community. White matter fiber tractography by means of diffusion tensor magnetic resonance imaging (DTI) is the only non-invasive method that can provide estimates of brain connectivity. However, conventional tractography methods are based on data acquisition techniques that suffer from image distortions and artifacts. Thus, a large percentage of white matter fiber bundles are distorted, and/or terminated early, while others are completely undetected. This severely limits the potential of fiber tractography in pre-surgical planning and image-guided surgery. In contrast, Turboprop-DTI is a technique that provides images with significantly fewer distortions and artifacts than conventional DTI data acquisition methods. The purpose of this study was to evaluate fiber tracking results obtained from Turboprop-DTI data. It was demonstrated that Turboprop may be a more appropriate DTI data acquisition technique for tracing white matter fibers than conventional DTI methods, especially in applications such as pre-surgical planning and image-guided surgery.

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.

OdorMapComparer: an application for quantitative analyses and comparisons of fMRI brain odor maps.

PubMed

Liu, Nian; Xu, Fuqiang; Miller, Perry L; Shepherd, Gordon M

2007-01-01

Brain odor maps are reconstructed flat images that describe the spatial activity patterns in the glomerular layer of the olfactory bulbs in animals exposed to different odor stimuli. We have developed a software application, OdorMapComparer, to carry out quantitative analyses and comparisons of the fMRI odor maps. This application is an open-source window program that first loads two odor map images being compared. It allows image transformations including scaling, flipping, rotating, and warping so that the two images can be appropriately aligned to each other. It performs simple subtraction, addition, and average of signals in the two images. It also provides comparative statistics including the normalized correlation (NC) and spatial correlation coefficient. Experimental studies showed that the rodent fMRI odor maps for aliphatic aldehydes displayed spatial activity patterns that are similar in gross outlines but somewhat different in specific subregions. Analyses with OdorMapComparer indicate that the similarity between odor maps decreases with increasing difference in the length of carbon chains. For example, the map of butanal is more closely related to that of pentanal (with a NC = 0.617) than to that of octanal (NC = 0.082), which is consistent with animal behavioral studies. The study also indicates that fMRI odor maps are statistically odor-specific and repeatable across both the intra- and intersubject trials. OdorMapComparer thus provides a tool for quantitative, statistical analyses and comparisons of fMRI odor maps in a fashion that is integrated with the overall odor mapping techniques.

FDG-PET imaging in mild traumatic brain injury: a critical review

PubMed Central

Byrnes, Kimberly R.; Wilson, Colin M.; Brabazon, Fiona; von Leden, Ramona; Jurgens, Jennifer S.; Oakes, Terrence R.; Selwyn, Reed G.

2013-01-01

Traumatic brain injury (TBI) affects an estimated 1.7 million people in the United States and is a contributing factor to one third of all injury related deaths annually. According to the CDC, approximately 75% of all reported TBIs are concussions or considered mild in form, although the number of unreported mild TBIs (mTBI) and patients not seeking medical attention is unknown. Currently, classification of mTBI or concussion is a clinical assessment since diagnostic imaging is typically inconclusive due to subtle, obscure, or absent changes in anatomical or physiological parameters measured using standard magnetic resonance (MR) or computed tomography (CT) imaging protocols. Molecular imaging techniques that examine functional processes within the brain, such as measurement of glucose uptake and metabolism using [18F]fluorodeoxyglucose and positron emission tomography (FDG-PET), have the ability to detect changes after mTBI. Recent technological improvements in the resolution of PET systems, the integration of PET with magnetic resonance imaging (MRI), and the availability of normal healthy human databases and commercial image analysis software contribute to the growing use of molecular imaging in basic science research and advances in clinical imaging. This review will discuss the technological considerations and limitations of FDG-PET, including differentiation between glucose uptake and glucose metabolism and the significance of these measurements. In addition, the current state of FDG-PET imaging in assessing mTBI in clinical and preclinical research will be considered. Finally, this review will provide insight into potential critical data elements and recommended standardization to improve the application of FDG-PET to mTBI research and clinical practice. PMID:24409143

Towards Effective Non-Invasive Brain-Computer Interfaces Dedicated to Gait Rehabilitation Systems

PubMed Central

Castermans, Thierry; Duvinage, Matthieu; Cheron, Guy; Dutoit, Thierry

2014-01-01

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation. PMID:24961699

PET Mapping for Brain-Computer Interface Stimulation of the Ventroposterior Medial Nucleus of the Thalamus in Rats with Implanted Electrodes.

PubMed

Zhu, Yunqi; Xu, Kedi; Xu, Caiyun; Zhang, Jiacheng; Ji, Jianfeng; Zheng, Xiaoxiang; Zhang, Hong; Tian, Mei

2016-07-01

Brain-computer interface (BCI) technology has great potential for improving the quality of life for neurologic patients. This study aimed to use PET mapping for BCI-based stimulation in a rat model with electrodes implanted in the ventroposterior medial (VPM) nucleus of the thalamus. PET imaging studies were conducted before and after stimulation of the right VPM. Stimulation induced significant orienting performance. (18)F-FDG uptake increased significantly in the paraventricular thalamic nucleus, septohippocampal nucleus, olfactory bulb, left crus II of the ansiform lobule of the cerebellum, and bilaterally in the lateral septum, amygdala, piriform cortex, endopiriform nucleus, and insular cortex, but it decreased in the right secondary visual cortex, right simple lobule of the cerebellum, and bilaterally in the somatosensory cortex. This study demonstrated that PET mapping after VPM stimulation can identify specific brain regions associated with orienting performance. PET molecular imaging may be an important approach for BCI-based research and its clinical applications. © 2016 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Transducer design and characterization for dorsal-based ultrasound exposure and two-photon imaging of in vivo blood-brain barrier disruption in a rat model.

PubMed

Nhan, Tam; Burgess, Alison; Hynynen, Kullervo

2013-07-01

Focused ultrasound (FUS) and microbubbles have been used effectively for transient, noninvasive blood¿ brain barrier disruption (BBBD). The use of two-photon microscopy (2PM) imaging of BBBD can provide valuable insights into the associated cellular mechanisms and fundamental biological effects. Coupling a thin ring-shaped transducer to a coverslip offers a robust solution for simultaneous dorsal application of FUS for BBBD and in vivo 2PM imaging of the cerebral microvasculature under treatment conditions. Two modes of vibration (thickness and height) from the transducer configuration were investigated for BBBD in an animal model. With the transducer operating in the thickness mode at 1.2 MHz frequency, shallow and localized BBBD near the cortical surface of animal brain was detected via 2PM and confirmed by Evans blue (EB) extravasation. Acoustic pressures ranging from 0.2 to 0.8 MPa were tested and the probability for successful BBBD was identified. Two distinct types of disruption characterized by different leakage kinetics were observed and appeared to be dependent on acoustic pressure.

TheHiveDB image data management and analysis framework.

PubMed

Muehlboeck, J-Sebastian; Westman, Eric; Simmons, Andrew

2014-01-06

The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative.

TheHiveDB image data management and analysis framework

PubMed Central

Muehlboeck, J-Sebastian; Westman, Eric; Simmons, Andrew

2014-01-01

The hive database system (theHiveDB) is a web-based brain imaging database, collaboration, and activity system which has been designed as an imaging workflow management system capable of handling cross-sectional and longitudinal multi-center studies. It can be used to organize and integrate existing data from heterogeneous projects as well as data from ongoing studies. It has been conceived to guide and assist the researcher throughout the entire research process, integrating all relevant types of data across modalities (e.g., brain imaging, clinical, and genetic data). TheHiveDB is a modern activity and resource management system capable of scheduling image processing on both private compute resources and the cloud. The activity component supports common image archival and management tasks as well as established pipeline processing (e.g., Freesurfer for extraction of scalar measures from magnetic resonance images). Furthermore, via theHiveDB activity system algorithm developers may grant access to virtual machines hosting versioned releases of their tools to collaborators and the imaging community. The application of theHiveDB is illustrated with a brief use case based on organizing, processing, and analyzing data from the publically available Alzheimer Disease Neuroimaging Initiative. PMID:24432000

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

Deep into the Brain: Artificial Intelligence in Stroke Imaging

PubMed Central

Lee, Eun-Jae; Kim, Yong-Hwan; Kim, Namkug; Kang, Dong-Wha

2017-01-01

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives. PMID:29037014

Magnetic resonance imaging of the central nervous system

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

Not Available

1988-02-26

This report reviews the current applications of magnetic resonance imaging of the central nervous system. Since its introduction into the clinical environment in the early 1980's, this technology has had a major impact on the practice of neurology. It has proved to be superior to computed tomography for imaging many diseases of the brain and spine. In some instances it has clearly replaced computed tomography. It is likely that it will replace myelography for the assessment of cervicomedullary junction and spinal regions. The magnetic field strengths currently used appear to be entirely safe for clinical application in neurology except inmore » patients with cardiac pacemakers or vascular metallic clips. Some shortcomings of magnetic resonance imaging include its expense, the time required for scanning, and poor visualization of cortical bone.« less

Deep into the Brain: Artificial Intelligence in Stroke Imaging.

PubMed

Lee, Eun-Jae; Kim, Yong-Hwan; Kim, Namkug; Kang, Dong-Wha

2017-09-01

Artificial intelligence (AI), a computer system aiming to mimic human intelligence, is gaining increasing interest and is being incorporated into many fields, including medicine. Stroke medicine is one such area of application of AI, for improving the accuracy of diagnosis and the quality of patient care. For stroke management, adequate analysis of stroke imaging is crucial. Recently, AI techniques have been applied to decipher the data from stroke imaging and have demonstrated some promising results. In the very near future, such AI techniques may play a pivotal role in determining the therapeutic methods and predicting the prognosis for stroke patients in an individualized manner. In this review, we offer a glimpse at the use of AI in stroke imaging, specifically focusing on its technical principles, clinical application, and future perspectives.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Demeclocycline as a contrast agent for detecting brain neoplasms using confocal microscopy

NASA Astrophysics Data System (ADS)

Wirth, Dennis; Smith, Thomas W.; Moser, Richard; Yaroslavsky, Anna N.

2015-04-01

Complete resection of brain tumors improves life expectancy and quality. Thus, there is a strong need for high-resolution detection and microscopically controlled removal of brain neoplasms. The goal of this study was to test demeclocycline as a contrast enhancer for the intraoperative detection of brain tumors. We have imaged benign and cancerous brain tumors using multimodal confocal microscopy. The tumors investigated included pituitary adenoma, meningiomas, glioblastomas, and metastatic brain cancers. Freshly excised brain tissues were stained in 0.75 mg ml-1 aqueous solution of demeclocyline. Reflectance images were acquired at 402 nm. Fluorescence signals were excited at 402 nm and registered between 500 and 540 nm. After imaging, histological sections were processed from the imaged specimens and compared to the optical images. Fluorescence images highlighted normal and cancerous brain cells, while reflectance images emphasized the morphology of connective tissue. The optical and histological images were in accordance with each other for all types of tumors investigated. Demeclocyline shows promise as a contrast agent for intraoperative detection of brain tumors.

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.

Plasticity following early-life brain injury: Insights from quantitative MRI.

PubMed

Fiori, Simona; Guzzetta, Andrea

2015-03-01

Over the last decade, the application of novel advanced neuroimaging techniques to study congenital brain damage has provided invaluable insights into the mechanisms underlying early neuroplasticity. The concept that is clearly emerging, both from human and nun-human studies, is that functional reorganization in the immature brain is substantially different from that of the more mature, developed brain. This applies to the reorganization of language, the sensorimotor system, and the visual system. The rapid implementation and development of higher order imaging methods will offer increased, currently unavailable knowledge about the specific mechanisms of cerebral plasticity in infancy, which is essential to support the development of early therapeutic interventions aimed at supporting and enhancing functional reorganization during a time of greatest potential brain plasticity. Copyright © 2015. Published by Elsevier Inc.

Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging☆

PubMed Central

Oishi, Kenichi; Faria, Andreia V.; Yoshida, Shoko; Chang, Linda; Mori, Susumu

2013-01-01

The development of the brain is structure-specific, and the growth rate of each structure differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used to evaluate brain development because of the high spatial resolution and contrast that enable the observation of structure-specific developmental status. Currently, most clinical MRIs are evaluated qualitatively to assist in the clinical decision-making and diagnosis. The clinical MRI report usually does not provide quantitative values that can be used to monitor developmental status. Recently, the importance of image quantification to detect and evaluate mild-to-moderate anatomical abnormalities has been emphasized because these alterations are possibly related to several psychiatric disorders and learning disabilities. In the research arena, structural MRI and diffusion tensor imaging (DTI) have been widely applied to quantify brain development of the pediatric population. To interpret the values from these MR modalities, a “growth percentile chart,” which describes the mean and standard deviation of the normal developmental curve for each anatomical structure, is required. Although efforts have been made to create such a growth percentile chart based on MRI and DTI, one of the greatest challenges is to standardize the anatomical boundaries of the measured anatomical structures. To avoid inter- and intra-reader variability about the anatomical boundary definition, and hence, to increase the precision of quantitative measurements, an automated structure parcellation method, customized for the neonatal and pediatric population, has been developed. This method enables quantification of multiple MR modalities using a common analytic framework. In this paper, the attempt to create an MRI- and a DTI-based growth percentile chart, followed by an application to investigate developmental abnormalities related to cerebral palsy, Williams syndrome, and Rett syndrome, have been introduced. Future directions include multimodal image analysis and personalization for clinical application. PMID:23796902

Imaging laminar structures in the gray matter with diffusion MRI.

PubMed

Assaf, Yaniv

2018-01-05

The cortical layers define the architecture of the gray matter and its neuroanatomical regions and are essential for brain function. Abnormalities in cortical layer development, growth patterns, organization, or size can affect brain physiology and cognition. Unfortunately, while large population studies are underway that will greatly increase our knowledge about these processes, current non-invasive techniques for characterizing the cortical layers remain inadequate. For decades, high-resolution T1 and T2 Weighted Magnetic Resonance Imaging (MRI) have been the method-of-choice for gray matter and layer characterization. In the past few years, however, diffusion MRI has shown increasing promise for its unique insights into the fine structure of the cortex. Several different methods, including surface analysis, connectivity exploration, and sub-voxel component modeling, are now capable of exploring the diffusion characteristics of the cortex. In this review, we will discuss current advances in the application of diffusion imaging for cortical characterization and its unique features, with a particular emphasis on its spatial resolution, arguably its greatest limitation. In addition, we will explore the relationship between the diffusion MRI signal and the cellular components of the cortex, as visualized by histology. While the obstacles facing the widespread application of cortical diffusion imaging remain daunting, the information it can reveal may prove invaluable. Within the next few years, we predict a surge in the application of this technique and a concomitant expansion of our knowledge of cortical layers. Copyright © 2018 Elsevier Inc. All rights reserved.

Induction of Social Behavior in Zebrafish: Live Versus Computer Animated Fish as Stimuli

PubMed Central

Qin, Meiying; Wong, Albert; Seguin, Diane

2014-01-01

Abstract The zebrafish offers an excellent compromise between system complexity and practical simplicity and has been suggested as a translational research tool for the analysis of human brain disorders associated with abnormalities of social behavior. Unlike laboratory rodents zebrafish are diurnal, thus visual cues may be easily utilized in the analysis of their behavior and brain function. Visual cues, including the sight of conspecifics, have been employed to induce social behavior in zebrafish. However, the method of presentation of these cues and the question of whether computer animated images versus live stimulus fish have differential effects have not been systematically analyzed. Here, we compare the effects of five stimulus presentation types: live conspecifics in the experimental tank or outside the tank, playback of video-recorded live conspecifics, computer animated images of conspecifics presented by two software applications, the previously employed General Fish Animator, and a new application Zebrafish Presenter. We report that all stimuli were equally effective and induced a robust social response (shoaling) manifesting as reduced distance between stimulus and experimental fish. We conclude that presentation of live stimulus fish, or 3D images, is not required and 2D computer animated images are sufficient to induce robust and consistent social behavioral responses in zebrafish. PMID:24575942

Induction of social behavior in zebrafish: live versus computer animated fish as stimuli.

PubMed

Qin, Meiying; Wong, Albert; Seguin, Diane; Gerlai, Robert

2014-06-01

The zebrafish offers an excellent compromise between system complexity and practical simplicity and has been suggested as a translational research tool for the analysis of human brain disorders associated with abnormalities of social behavior. Unlike laboratory rodents zebrafish are diurnal, thus visual cues may be easily utilized in the analysis of their behavior and brain function. Visual cues, including the sight of conspecifics, have been employed to induce social behavior in zebrafish. However, the method of presentation of these cues and the question of whether computer animated images versus live stimulus fish have differential effects have not been systematically analyzed. Here, we compare the effects of five stimulus presentation types: live conspecifics in the experimental tank or outside the tank, playback of video-recorded live conspecifics, computer animated images of conspecifics presented by two software applications, the previously employed General Fish Animator, and a new application Zebrafish Presenter. We report that all stimuli were equally effective and induced a robust social response (shoaling) manifesting as reduced distance between stimulus and experimental fish. We conclude that presentation of live stimulus fish, or 3D images, is not required and 2D computer animated images are sufficient to induce robust and consistent social behavioral responses in zebrafish.

Fractal analysis of the susceptibility weighted imaging patterns in malignant brain tumors during antiangiogenic treatment: technical report on four cases serially imaged by 7 T magnetic resonance during a period of four weeks.

PubMed

Di Ieva, Antonio; Matula, Christian; Grizzi, Fabio; Grabner, Günther; Trattnig, Siegfried; Tschabitscher, Manfred

2012-01-01

The need for new and objective indexes for the neuroradiologic follow-up of brain tumors and for monitoring the effects of antiangiogenic strategies in vivo led us to perform a technical study on four patients who received computerized analysis of tumor-associated vasculature with ultra-high-field (7 T) magnetic resonance imaging (MRI). The image analysis involved the application of susceptibility weighted imaging (SWI) to evaluate vascular structures. Four patients affected by recurrent malignant brain tumors were enrolled in the present study. After the first 7-T SWI MRI procedure, the patients underwent antiangiogenic treatment with bevacizumab. The imaging was repeated every 2 weeks for a period of 4 weeks. The SWI patterns visualized in the three MRI temporal sequences were analyzed by means of a computer-aided fractal-based method to objectively quantify their geometric complexity. In two clinically deteriorating patients we found an increase of the geometric complexity of the space-filling properties of the SWI patterns over time despite the antiangiogenic treatment. In one patient, who showed improvement with the therapy, the fractal dimension of the intratumoral structure decreased, whereas in the fourth patient, no differences were found. The qualitative changes of the intratumoral SWI patterns during a period of 4 weeks were quantified with the fractal dimension. Because SWI patterns are also related to the presence of vascular structures, the quantification of their space-filling properties with fractal dimension seemed to be a valid tool for the in vivo neuroradiologic follow-up of brain tumors. Copyright © 2012 Elsevier Inc. All rights reserved.

Research Review: Emanuel Miller Memorial Lecture 2012 – Neuroscientific studies of intervention for language impairment in children: interpretive and methodological problems

PubMed Central

Bishop, D V M

2013-01-01

Background Our ability to look at structure and function of a living brain has increased exponentially since the early 1970s. Many studies of developmental disorders now routinely include a brain imaging or electrophysiological component. Amid current enthusiasm for applications of neuroscience to educational interventions, we need to pause to consider what neuroimaging data can tell us. Images of brain activity are seductive, and have been used to give credibility to commercial interventions, yet we have only a limited idea of what the brain bases of language disorders are, let alone how to alter them. Scope and findings A review of six studies of neuroimaging correlates of language intervention found recurring methodological problems: lack of an adequate control group, inadequate power, incomplete reporting of data, no correction for multiple comparisons, data dredging and failure to analyse treatment effects appropriately. In addition, there is a tendency to regard neuroimaging data as more meaningful than behavioural data, even though it is behaviour that interventions aim to alter. Conclusion In our current state of knowledge, it would be better to spend research funds doing well-designed trials of behavioural treatment to establish which methods are effective, rather than rushing headlong into functional imaging studies of unproven treatments. PMID:23278309

Large-scale Scanning Transmission Electron Microscopy (Nanotomy) of Healthy and Injured Zebrafish Brain.

PubMed

Kuipers, Jeroen; Kalicharan, Ruby D; Wolters, Anouk H G; van Ham, Tjakko J; Giepmans, Ben N G

2016-05-25

Large-scale 2D electron microscopy (EM), or nanotomy, is the tissue-wide application of nanoscale resolution electron microscopy. Others and we previously applied large scale EM to human skin pancreatic islets, tissue culture and whole zebrafish larvae(1-7). Here we describe a universally applicable method for tissue-scale scanning EM for unbiased detection of sub-cellular and molecular features. Nanotomy was applied to investigate the healthy and a neurodegenerative zebrafish brain. Our method is based on standardized EM sample preparation protocols: Fixation with glutaraldehyde and osmium, followed by epoxy-resin embedding, ultrathin sectioning and mounting of ultrathin-sections on one-hole grids, followed by post staining with uranyl and lead. Large-scale 2D EM mosaic images are acquired using a scanning EM connected to an external large area scan generator using scanning transmission EM (STEM). Large scale EM images are typically ~ 5 - 50 G pixels in size, and best viewed using zoomable HTML files, which can be opened in any web browser, similar to online geographical HTML maps. This method can be applied to (human) tissue, cross sections of whole animals as well as tissue culture(1-5). Here, zebrafish brains were analyzed in a non-invasive neuronal ablation model. We visualize within a single dataset tissue, cellular and subcellular changes which can be quantified in various cell types including neurons and microglia, the brain's macrophages. In addition, nanotomy facilitates the correlation of EM with light microscopy (CLEM)(8) on the same tissue, as large surface areas previously imaged using fluorescent microscopy, can subsequently be subjected to large area EM, resulting in the nano-anatomy (nanotomy) of tissues. In all, nanotomy allows unbiased detection of features at EM level in a tissue-wide quantifiable manner.

Large-scale Scanning Transmission Electron Microscopy (Nanotomy) of Healthy and Injured Zebrafish Brain

PubMed Central

Kuipers, Jeroen; Kalicharan, Ruby D.; Wolters, Anouk H. G.

2016-01-01

Large-scale 2D electron microscopy (EM), or nanotomy, is the tissue-wide application of nanoscale resolution electron microscopy. Others and we previously applied large scale EM to human skin pancreatic islets, tissue culture and whole zebrafish larvae1-7. Here we describe a universally applicable method for tissue-scale scanning EM for unbiased detection of sub-cellular and molecular features. Nanotomy was applied to investigate the healthy and a neurodegenerative zebrafish brain. Our method is based on standardized EM sample preparation protocols: Fixation with glutaraldehyde and osmium, followed by epoxy-resin embedding, ultrathin sectioning and mounting of ultrathin-sections on one-hole grids, followed by post staining with uranyl and lead. Large-scale 2D EM mosaic images are acquired using a scanning EM connected to an external large area scan generator using scanning transmission EM (STEM). Large scale EM images are typically ~ 5 - 50 G pixels in size, and best viewed using zoomable HTML files, which can be opened in any web browser, similar to online geographical HTML maps. This method can be applied to (human) tissue, cross sections of whole animals as well as tissue culture1-5. Here, zebrafish brains were analyzed in a non-invasive neuronal ablation model. We visualize within a single dataset tissue, cellular and subcellular changes which can be quantified in various cell types including neurons and microglia, the brain's macrophages. In addition, nanotomy facilitates the correlation of EM with light microscopy (CLEM)8 on the same tissue, as large surface areas previously imaged using fluorescent microscopy, can subsequently be subjected to large area EM, resulting in the nano-anatomy (nanotomy) of tissues. In all, nanotomy allows unbiased detection of features at EM level in a tissue-wide quantifiable manner. PMID:27285162

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.

PubMed

Tonutti, Michele; Gras, Gauthier; Yang, Guang-Zhong

2017-07-01

Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models. A brain tumour is used as the subject of the deformation model. Load-driven FEM simulations are performed on a tetrahedral brain mesh afflicted by a tumour. Forces of varying magnitudes, positions, and inclination angles are applied onto the brain's surface. Two machine learning algorithms-artificial neural networks (ANNs) and support vector regression (SVR)-are employed to derive a model that can predict the resulting deformation for each node in the tumour's mesh. The tumour deformation can be predicted in real time given relevant information about the geometry of the anatomy and the load, all of which can be measured instantly during a surgical operation. The models can predict the position of the nodes with errors below 0.3mm, beyond the general threshold of surgical accuracy and suitable for high fidelity AR systems. The SVR models perform better than the ANN's, with positional errors for SVR models reaching under 0.2mm. The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantification of changes in language-related brain areas in autism spectrum disorders using large-scale network analysis.

PubMed

Goch, Caspar J; Stieltjes, Bram; Henze, Romy; Hering, Jan; Poustka, Luise; Meinzer, Hans-Peter; Maier-Hein, Klaus H

2014-05-01

Diagnosis of autism spectrum disorders (ASD) is difficult, as symptoms vary greatly and are difficult to quantify objectively. Recent work has focused on the assessment of non-invasive diffusion tensor imaging-based biomarkers that reflect the microstructural characteristics of neuronal pathways in the brain. While tractography-based approaches typically analyze specific structures of interest, a graph-based large-scale network analysis of the connectome can yield comprehensive measures of larger-scale architectural patterns in the brain. Commonly applied global network indices, however, do not provide any specificity with respect to functional areas or anatomical structures. Aim of this work was to assess the concept of network centrality as a tool to perform locally specific analysis without disregarding the global network architecture and compare it to other popular network indices. We create connectome networks from fiber tractographies and parcellations of the human brain and compute global network indices as well as local indices for Wernicke's Area, Broca's Area and the Motor Cortex. Our approach was evaluated on 18 children suffering from ASD and 18 typically developed controls using magnetic resonance imaging-based cortical parcellations in combination with diffusion tensor imaging tractography. We show that the network centrality of Wernicke's area is significantly (p<0.001) reduced in ASD, while the motor cortex, which was used as a control region, did not show significant alterations. This could reflect the reduced capacity for comprehension of language in ASD. The betweenness centrality could potentially be an important metric in the development of future diagnostic tools in the clinical context of ASD diagnosis. Our results further demonstrate the applicability of large-scale network analysis tools in the domain of region-specific analysis with a potential application in many different psychological disorders.

CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE

PubMed Central

Chen, Ting; Rangarajan, Anand; Vemuri, Baba C.

2010-01-01

This paper presents a novel classification via aggregated regression algorithm – dubbed CAVIAR – and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature – the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain – and this allows us to automatically discriminate between various classes within OASIS [1] using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS. PMID:21151847

CAVIAR: CLASSIFICATION VIA AGGREGATED REGRESSION AND ITS APPLICATION IN CLASSIFYING OASIS BRAIN DATABASE.

PubMed

Chen, Ting; Rangarajan, Anand; Vemuri, Baba C

2010-04-14

This paper presents a novel classification via aggregated regression algorithm - dubbed CAVIAR - and its application to the OASIS MRI brain image database. The CAVIAR algorithm simultaneously combines a set of weak learners based on the assumption that the weight combination for the final strong hypothesis in CAVIAR depends on both the weak learners and the training data. A regularization scheme using the nearest neighbor method is imposed in the testing stage to avoid overfitting. A closed form solution to the cost function is derived for this algorithm. We use a novel feature - the histogram of the deformation field between the MRI brain scan and the atlas which captures the structural changes in the scan with respect to the atlas brain - and this allows us to automatically discriminate between various classes within OASIS [1] using CAVIAR. We empirically show that CAVIAR significantly increases the performance of the weak classifiers by showcasing the performance of our technique on OASIS.

NiftyNet: a deep-learning platform for medical imaging.

PubMed

Gibson, Eli; Li, Wenqi; Sudre, Carole; Fidon, Lucas; Shakir, Dzhoshkun I; Wang, Guotai; Eaton-Rosen, Zach; Gray, Robert; Doel, Tom; Hu, Yipeng; Whyntie, Tom; Nachev, Parashkev; Modat, Marc; Barratt, Dean C; Ourselin, Sébastien; Cardoso, M Jorge; Vercauteren, Tom

2018-05-01

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new applications. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Novel active contour model based on multi-variate local Gaussian distribution for local segmentation of MR brain images

NASA Astrophysics Data System (ADS)

Zheng, Qiang; Li, Honglun; Fan, Baode; Wu, Shuanhu; Xu, Jindong

2017-12-01

Active contour model (ACM) has been one of the most widely utilized methods in magnetic resonance (MR) brain image segmentation because of its ability of capturing topology changes. However, most of the existing ACMs only consider single-slice information in MR brain image data, i.e., the information used in ACMs based segmentation method is extracted only from one slice of MR brain image, which cannot take full advantage of the adjacent slice images' information, and cannot satisfy the local segmentation of MR brain images. In this paper, a novel ACM is proposed to solve the problem discussed above, which is based on multi-variate local Gaussian distribution and combines the adjacent slice images' information in MR brain image data to satisfy segmentation. The segmentation is finally achieved through maximizing the likelihood estimation. Experiments demonstrate the advantages of the proposed ACM over the single-slice ACM in local segmentation of MR brain image series.

Application of optical coherence tomography for in vivo monitoring of the meningeal lymphatic vessels during opening of blood-brain barrier: mechanisms of brain clearing

NASA Astrophysics Data System (ADS)

Semyachkina-Glushkovskaya, Oxana; Abdurashitov, Arkady; Dubrovsky, Alexander; Bragin, Denis; Bragina, Olga; Shushunova, Nataliya; Maslyakova, Galina; Navolokin, Nikita; Bucharskaya, Alla; Tuchin, Valery; Kurths, Juergen; Shirokov, Alexander

2017-12-01

The meningeal lymphatic vessels were discovered 2 years ago as the drainage system involved in the mechanisms underlying the clearance of waste products from the brain. The blood-brain barrier (BBB) is a gatekeeper that strongly controls the movement of different molecules from the blood into the brain. We know the scenarios during the opening of the BBB, but there is extremely limited information on how the brain clears the substances that cross the BBB. Here, using the model of sound-induced opening of the BBB, we clearly show how the brain clears dextran after it crosses the BBB via the meningeal lymphatic vessels. We first demonstrate successful application of optical coherence tomography (OCT) for imaging of the lymphatic vessels in the meninges after opening of the BBB, which might be a new useful strategy for noninvasive analysis of lymphatic drainage in daily clinical practice. Also, we give information about the depth and size of the meningeal lymphatic vessels in mice. These new fundamental data with the applied focus on the OCT shed light on the mechanisms of brain clearance and the role of lymphatic drainage in these processes that could serve as an informative platform for a development of therapy and diagnostics of diseases associated with injuries of the BBB such as stroke, brain trauma, glioma, depression, or Alzheimer disease.

A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

PubMed Central

Adali, Tülay; Yu, Qingbao; Calhoun, Vince D.

2011-01-01

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data. PMID:22108139

Canine neuroanatomy: Development of a 3D reconstruction and interactive application for undergraduate veterinary education

PubMed Central

Raffan, Hazel; Guevar, Julien; Poyade, Matthieu; Rea, Paul M.

2017-01-01

Current methods used to communicate and present the complex arrangement of vasculature related to the brain and spinal cord is limited in undergraduate veterinary neuroanatomy training. Traditionally it is taught with 2-dimensional (2D) diagrams, photographs and medical imaging scans which show a fixed viewpoint. 2D representations of 3-dimensional (3D) objects however lead to loss of spatial information, which can present problems when translating this to the patient. Computer-assisted learning packages with interactive 3D anatomical models have become established in medical training, yet equivalent resources are scarce in veterinary education. For this reason, we set out to develop a workflow methodology creating an interactive model depicting the vasculature of the canine brain that could be used in undergraduate education. Using MR images of a dog and several commonly available software programs, we set out to show how combining image editing, segmentation and surface generation, 3D modeling and texturing can result in the creation of a fully interactive application for veterinary training. In addition to clearly identifying a workflow methodology for the creation of this dataset, we have also demonstrated how an interactive tutorial and self-assessment tool can be incorporated into this. In conclusion, we present a workflow which has been successful in developing a 3D reconstruction of the canine brain and associated vasculature through segmentation, surface generation and post-processing of readily available medical imaging data. The reconstructed model was implemented into an interactive application for veterinary education that has been designed to target the problems associated with learning neuroanatomy, primarily the inability to visualise complex spatial arrangements from 2D resources. The lack of similar resources in this field suggests this workflow is original within a veterinary context. There is great potential to explore this method, and introduce a new dimension into veterinary education and training. PMID:28192461

Canine neuroanatomy: Development of a 3D reconstruction and interactive application for undergraduate veterinary education.

PubMed

Raffan, Hazel; Guevar, Julien; Poyade, Matthieu; Rea, Paul M

2017-01-01

Current methods used to communicate and present the complex arrangement of vasculature related to the brain and spinal cord is limited in undergraduate veterinary neuroanatomy training. Traditionally it is taught with 2-dimensional (2D) diagrams, photographs and medical imaging scans which show a fixed viewpoint. 2D representations of 3-dimensional (3D) objects however lead to loss of spatial information, which can present problems when translating this to the patient. Computer-assisted learning packages with interactive 3D anatomical models have become established in medical training, yet equivalent resources are scarce in veterinary education. For this reason, we set out to develop a workflow methodology creating an interactive model depicting the vasculature of the canine brain that could be used in undergraduate education. Using MR images of a dog and several commonly available software programs, we set out to show how combining image editing, segmentation and surface generation, 3D modeling and texturing can result in the creation of a fully interactive application for veterinary training. In addition to clearly identifying a workflow methodology for the creation of this dataset, we have also demonstrated how an interactive tutorial and self-assessment tool can be incorporated into this. In conclusion, we present a workflow which has been successful in developing a 3D reconstruction of the canine brain and associated vasculature through segmentation, surface generation and post-processing of readily available medical imaging data. The reconstructed model was implemented into an interactive application for veterinary education that has been designed to target the problems associated with learning neuroanatomy, primarily the inability to visualise complex spatial arrangements from 2D resources. The lack of similar resources in this field suggests this workflow is original within a veterinary context. There is great potential to explore this method, and introduce a new dimension into veterinary education and training.

Parameter estimation of brain tumors using intraoperative thermal imaging based on artificial tactile sensing in conjunction with artificial neural network

NASA Astrophysics Data System (ADS)

Sadeghi-Goughari, M.; Mojra, A.; Sadeghi, S.

2016-02-01

Intraoperative Thermal Imaging (ITI) is a new minimally invasive diagnosis technique that can potentially locate margins of brain tumor in order to achieve maximum tumor resection with least morbidity. This study introduces a new approach to ITI based on artificial tactile sensing (ATS) technology in conjunction with artificial neural networks (ANN) and feasibility and applicability of this method in diagnosis and localization of brain tumors is investigated. In order to analyze validity and reliability of the proposed method, two simulations were performed. (i) An in vitro experimental setup was designed and fabricated using a resistance heater embedded in agar tissue phantom in order to simulate heat generation by a tumor in the brain tissue; and (ii) A case report patient with parafalcine meningioma was presented to simulate ITI in the neurosurgical procedure. In the case report, both brain and tumor geometries were constructed from MRI data and tumor temperature and depth of location were estimated. For experimental tests, a novel assisted surgery robot was developed to palpate the tissue phantom surface to measure temperature variations and ANN was trained to estimate the simulated tumor’s power and depth. Results affirm that ITI based ATS is a non-invasive method which can be useful to detect, localize and characterize brain tumors.

Automated segmentation of three-dimensional MR brain images

NASA Astrophysics Data System (ADS)

Park, Jonggeun; Baek, Byungjun; Ahn, Choong-Il; Ku, Kyo Bum; Jeong, Dong Kyun; Lee, Chulhee

2006-03-01

Brain segmentation is a challenging problem due to the complexity of the brain. In this paper, we propose an automated brain segmentation method for 3D magnetic resonance (MR) brain images which are represented as a sequence of 2D brain images. The proposed method consists of three steps: pre-processing, removal of non-brain regions (e.g., the skull, meninges, other organs, etc), and spinal cord restoration. In pre-processing, we perform adaptive thresholding which takes into account variable intensities of MR brain images corresponding to various image acquisition conditions. In segmentation process, we iteratively apply 2D morphological operations and masking for the sequences of 2D sagittal, coronal, and axial planes in order to remove non-brain tissues. Next, final 3D brain regions are obtained by applying OR operation for segmentation results of three planes. Finally we reconstruct the spinal cord truncated during the previous processes. Experiments are performed with fifteen 3D MR brain image sets with 8-bit gray-scale. Experiment results show the proposed algorithm is fast, and provides robust and satisfactory results.

Optical monitoring of shock wave-induced spreading depolarization and concomitant hypoxemia in rat brain

NASA Astrophysics Data System (ADS)

Okuda, Wataru; Kawauchi, Satoko; Ashida, Hiroshi; Sato, Shunichi; Nishidate, Izumi

2014-03-01

Blast-induced traumatic brain injury is a growing concern, but its underlying pathophysiology and mechanism are still unknown. Thus, study using an animal model is needed. We have been proposing the use of a laser-induced shock wave (LISW), whose energy is highly controllable and reproducible, to mimic blast-related injury. We previously observed the occurrence of spreading depolarization (SD) and prolonged hypoxemia in the rat brain exposed to an LISW. However, the relationship between these two events is unclear. In this study, we investigated the spatiotemporal characteristics of hypoxemia and SD to examine their correlation, for which multichannel fiber measurement and multispectral imaging of the diffuse reflectance were performed for the rat brain exposed to an LISW. We also quantified tissue oxygen saturation (StO2) in the hypoxemic phase, which is associated with possible neuronal cell death, based on an inverse Monte Carlo simulation. Fiber measurement showed that the region of hypoxemia was expanding from the site of LISW application to the distant region over the brain; the speed of expansion was similar to that of the propagation speed of SD. Simulation showed that oxygen saturation was decreased by ~40%. Multispectral imaging showed that after LISW application, a vasodilatation occurred for ~1 min, which was followed by a long-lasting vasoconstriction. In the phase of vasoconstriction, StO2 declined all over the field of view. These results indicate a strong correlation between SD and hypoxemia; the estimated StO2 seems to be low enough to induce neuronal cell death.

32-channel 3 Tesla receive-only phased-array head coil with soccer-ball element geometry.

PubMed

Wiggins, G C; Triantafyllou, C; Potthast, A; Reykowski, A; Nittka, M; Wald, L L

2006-07-01

A 32-channel 3T receive-only phased-array head coil was developed for human brain imaging. The helmet-shaped array was designed to closely fit the head with individual overlapping circular elements arranged in patterns of hexagonal and pentagonal symmetry similar to that of a soccer ball. The signal-to-noise ratio (SNR) and noise amplification (g-factor) in accelerated imaging applications were quantitatively evaluated in phantom and human images and compared with commercially available head coils. The 32-channel coil showed SNR gains of up to 3.5-fold in the cortex and 1.4-fold in the corpus callosum compared to a (larger) commercial eight-channel head coil. The experimentally measured g-factor performance of the helmet array showed significant improvement compared to the eight-channel array (peak g-factor 59% and 26% of the eight-channel values for four- and fivefold acceleration). The performance of the arrays is demonstrated in high-resolution and highly accelerated brain images. Copyright (c) 2006 Wiley-Liss, Inc.

Signal detection using support vector machines in the presence of ultrasonic speckle

NASA Astrophysics Data System (ADS)

Kotropoulos, Constantine L.; Pitas, Ioannis

2002-04-01

Support Vector Machines are a general algorithm based on guaranteed risk bounds of statistical learning theory. They have found numerous applications, such as in classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. In this paper we propose the use of support vector machines to segment lesions in ultrasound images and we assess thoroughly their lesion detection ability. We demonstrate that trained support vector machines with a Radial Basis Function kernel segment satisfactorily (unseen) ultrasound B-mode images as well as clinical ultrasonic images.

Resting-state functional brain connectivity: lessons from functional near-infrared spectroscopy.

PubMed

Niu, Haijing; He, Yong

2014-04-01

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain's low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Toward the holistic, reference, and extendable atlas of the human brain, head, and neck.

PubMed

Nowinski, Wieslaw L

2015-06-01

Despite numerous efforts, a fairly complete (holistic) anatomical model of the whole, normal, adult human brain, which is required as the reference in brain studies and clinical applications, has not yet been constructed. Our ultimate objective is to build this kind of atlas from advanced in vivo imaging. This work presents the taxonomy of our currently developed brain atlases and addresses the design, content, functionality, and current results in the holistic atlas development as well as atlas usefulness and future directions. We have developed to date 35 commercial brain atlases (along with numerous research prototypes), licensed to 63 companies and institutions, and made available to medical societies, organizations, medical schools, and individuals. These atlases have been applied in education, research, and clinical applications. Hundreds of thousands of patients have been treated by using our atlases. Based on this experience, the first version of the holistic and reference atlas of the brain, head, and neck has been developed and made available. The atlas has been created from multispectral 3 and 7 Tesla and high-resolution CT in vivo scans. It is fully 3D, scalable, interactive, and highly detailed with about 3,000 labeled components. This atlas forms a foundation for the development of a multi-level molecular, cellular, anatomical, physiological, and behavioral brain atlas platform.

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

NASA Astrophysics Data System (ADS)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Laser induced thermal therapy (LITT) for pediatric brain tumors: case-based review

PubMed Central

Riordan, Margaret

2014-01-01

Integration of Laser induced thermal therapy (LITT) to magnetic resonance imaging (MRI) have created new options for treating surgically challenging tumors in locations that would otherwise have represented an intrinsic comorbidity by the approach itself. As new applications and variations of the use are discussed, we present a case-based review of the history, development, and subsequent updates of minimally invasive MRI-guided laser interstitial thermal therapy (MRgLITT) ablation in pediatric brain tumors. PMID:26835340

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.

Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments.

PubMed

Balardin, Joana B; Zimeo Morais, Guilherme A; Furucho, Rogério A; Trambaiolli, Lucas; Vanzella, Patricia; Biazoli, Claudinei; Sato, João R

2017-01-01

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e., table tennis), playing a musical instrument (i.e., piano and violin) alone or in duo and performing daily activities for many hours (i.e., continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain hemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience.

Imaging Brain Function with Functional Near-Infrared Spectroscopy in Unconstrained Environments

PubMed Central

Balardin, Joana B.; Zimeo Morais, Guilherme A.; Furucho, Rogério A.; Trambaiolli, Lucas; Vanzella, Patricia; Biazoli, Claudinei; Sato, João R.

2017-01-01

Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e., table tennis), playing a musical instrument (i.e., piano and violin) alone or in duo and performing daily activities for many hours (i.e., continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain hemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience. PMID:28567011

A digital interactive human brain atlas based on Chinese visible human datasets for anatomy teaching.

PubMed

Li, Qiyu; Ran, Xu; Zhang, Shaoxiang; Tan, Liwen; Qiu, Mingguo

2014-01-01

As we know, the human brain is one of the most complicated organs in the human body, which is the key and difficult point in neuroanatomy and sectional anatomy teaching. With the rapid development and extensive application of imaging technology in clinical diagnosis, doctors are facing higher and higher requirement on their anatomy knowledge. Thus, to cultivate medical students to meet the needs of medical development today and to improve their ability to read and understand radiographic images have become urgent challenges for the medical teachers. In this context, we developed a digital interactive human brain atlas based on the Chinese visible human datasets for anatomy teaching (available for free download from http://www.chinesevisiblehuman.com/down/DHBA.rar). The atlas simultaneously provides views in all 3 primary planes of section. The main structures of the human brain have been anatomically labeled in all 3 views. It is potentially useful for anatomy browsing, user self-testing, and automatic student assessment. In a word, it is interactive, 3D, user friendly, and free of charge, which can provide a new, intuitive means for anatomy teaching.

Spatio-Temporal Video Segmentation with Shape Growth or Shrinkage Constraint

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Charpiat, Guillaume; Brucker, Ludovic; Menze, Bjoern H.

2014-01-01

We propose a new method for joint segmentation of monotonously growing or shrinking shapes in a time sequence of noisy images. The task of segmenting the image time series is expressed as an optimization problem using the spatio-temporal graph of pixels, in which we are able to impose the constraint of shape growth or of shrinkage by introducing monodirectional infinite links connecting pixels at the same spatial locations in successive image frames. The globally optimal solution is computed with a graph cut. The performance of the proposed method is validated on three applications: segmentation of melting sea ice floes and of growing burned areas from time series of 2D satellite images, and segmentation of a growing brain tumor from sequences of 3D medical scans. In the latter application, we impose an additional intersequences inclusion constraint by adding directed infinite links between pixels of dependent image structures.

Multivariate statistics of the Jacobian matrices in tensor based morphometry and their application to HIV/AIDS.

PubMed

Lepore, Natasha; Brun, Caroline A; Chiang, Ming-Chang; Chou, Yi-Yu; Dutton, Rebecca A; Hayashi, Kiralee M; Lopez, Oscar L; Aizenstein, Howard J; Toga, Arthur W; Becker, James T; Thompson, Paul M

2006-01-01

Tensor-based morphometry (TBM) is widely used in computational anatomy as a means to understand shape variation between structural brain images. A 3D nonlinear registration technique is typically used to align all brain images to a common neuroanatomical template, and the deformation fields are analyzed statistically to identify group differences in anatomy. However, the differences are usually computed solely from the determinants of the Jacobian matrices that are associated with the deformation fields computed by the registration procedure. Thus, much of the information contained within those matrices gets thrown out in the process. Only the magnitude of the expansions or contractions is examined, while the anisotropy and directional components of the changes are ignored. Here we remedy this problem by computing multivariate shape change statistics using the strain matrices. As the latter do not form a vector space, means and covariances are computed on the manifold of positive-definite matrices to which they belong. We study the brain morphology of 26 HIV/AIDS patients and 14 matched healthy control subjects using our method. The images are registered using a high-dimensional 3D fluid registration algorithm, which optimizes the Jensen-Rényi divergence, an information-theoretic measure of image correspondence. The anisotropy of the deformation is then computed. We apply a manifold version of Hotelling's T2 test to the strain matrices. Our results complement those found from the determinants of the Jacobians alone and provide greater power in detecting group differences in brain structure.

Cerebral White Matter Integrity and Cognitive Aging: Contributions from Diffusion Tensor Imaging

PubMed Central

Madden, David J.; Bennett, Ilana J.; Song, Allen W.

2009-01-01

The integrity of cerebral white matter is critical for efficient cognitive functioning, but little is known regarding the role of white matter integrity in age-related differences in cognition. Diffusion tensor imaging (DTI) measures the directional displacement of molecular water and as a result can characterize the properties of white matter that combine to restrict diffusivity in a spatially coherent manner. This review considers DTI studies of aging and their implications for understanding adult age differences in cognitive performance. Decline in white matter integrity contributes to a disconnection among distributed neural systems, with a consistent effect on perceptual speed and executive functioning. The relation between white matter integrity and cognition varies across brain regions, with some evidence suggesting that age-related effects exhibit an anterior-posterior gradient. With continued improvements in spatial resolution and integration with functional brain imaging, DTI holds considerable promise, both for theories of cognitive aging and for translational application. PMID:19705281

Prenatal diagnosis of holoprosencephaly.

PubMed

Kousa, Youssef A; du Plessis, Adré J; Vezina, Gilbert

2018-05-17

Holoprosencephaly is a spectrum of congenital defects of forebrain development characterized by incomplete separation of the cerebral hemispheres. In vivo diagnosis can be established with prenatal brain imaging and disease severity correlates with extent of abnormally developed brain tissue. Advances in magnetic resonance imaging (MRI) over the past 25 years and their application to the fetus have enabled diagnosis of holoprosencephaly in utero. Here, we report on the prenatal diagnosis of holoprosencephaly using MRI as part of a diagnostic and management evaluation at a tertiary and quaternary referral center. Using an advanced MRI protocol and a 1.5-Tesla magnet, we show radiographic data diagnostic for the holoprosencephaly spectrum, including alobar, semilobar, lobar, middle interhemispheric, and septopreoptic variant. Accurate prenatal evaluation is important because the severity of imaging findings correlates with postnatal morbidity and mortality in holoprosencephaly. Therefore, this work has implications for the evaluation, diagnosis, management, and genetic counseling that families can receive during a pregnancy. © 2018 Wiley Periodicals, Inc.

Postdoctoral Fellow | Center for Cancer Research

Cancer.gov

The Neuro-Oncology Branch (NOB), Center for Cancer Research (CCR), National Cancer Institute (NCI) of the National Institutes of Health (NIH) is seeking outstanding postdoctoral candidates interested in studying the metabolic changes in brain tumors such as glioblastoma multiforme (GBMs).  NOB’s Metabolomics program is interested in revealing the metabolic alterations of isocitrate dehydrogenase (IDH1)-mutated GBMs and in exploiting these deregulations for therapeutic applications.  A combination of methods such as molecular biology, animal models, as well as in vitro and in vivo metabolomics using Raman Imaging Microscopy, Nuclear Magnetic Resonance spectroscopy (NMR), Mass Spectrometry (MS) and Magnetic Resonance Imaging (MRI) techniques are employed.  The position will specifically focus on molecular biology and Raman Imaging Microscopy, which includes work in Western Blotting, mammalian cell culture and other common biomedical techniques used in cancer bio  logy labs such as handling tissue samples, preparing tissue slides, staining, and extracting proteins from brain tissue.

Extending the seductive allure of neuroscience explanations effect to popular articles about educational topics.

PubMed

Im, Soo-Hyun; Varma, Keisha; Varma, Sashank

2017-12-01

The seductive allure of neuroscience explanations (SANE) is the finding that people overweight psychological arguments when framed in terms of neuroscience findings. This study extended this finding to arguments concerning the application of psychological findings to educational topics. Participants (n = 320) were recruited from the general public, specifically among English-speaking Amazon Mechanical Turk workers residing in the United States. We developed eight articles that orthogonally varied two processes (learning vs. development) with two disciplines (cognitive vs. affective psychology). We increased neuroscience framing across four levels: psychological finding alone, with an extraneous neuroscience finding (verbal), with an extraneous neuroscience finding (verbal) and graph, and with an extraneous neuroscience finding (verbal) and brain image. Participants were randomly assigned to one level of neuroscience framing and rated the credibility of each article's argument. Seductive allure of neuroscience explanations effects were not ubiquitous. Extraneous verbal neuroscience framings, either alone or accompanied by graphs, did not influence the credibility of the application of psychological findings to educational topics. However, there was a SANE effect when educational articles were accompanied by both extraneous verbal neuroscience findings and brain images. This effect persisted even after controlling for individual differences in familiarity with education, attitude towards psychology, and knowledge of neuroscience. The results suggest that there is a SANE effect for articles about educational topics among the general public when they are accompanied by both extraneous verbal neuroscience findings and brain images. © 2017 The British Psychological Society.

Multiphoton tomography of the human eye

NASA Astrophysics Data System (ADS)

König, Karsten; Batista, Ana; Hager, Tobias; Seitz, Berthold

2017-02-01

Multiphoton tomography (MPT) is a novel label-free clinical imaging method for non-invasive tissue imaging with high spatial (300 nm) and temporal (100 ps) resolutions. In vivo optical histology can be realized due to the nonlinear excitation of endogenous fluorophores and second-harmonic generation (SHG) of collagen. Furthermore, optical metabolic imaging (OMI) is performed by two-photon autofluorescence lifetime imaging (FLIM). So far, applications of the multiphoton tomographs DermaInspect and MPTflex were limited to dermatology. Novel applications include intraoperative brain tumor imaging as well as cornea imaging. In this work we describe two-photon imaging of ex vivo human corneas unsuitable for transplantation. Furthermore, the cross-linking (CXL) process of corneal collagen based on UVA exposure and 0.1 % riboflavin was studied. The pharmacokinetics of the photosensitizer could be detected with high spatial resolution. Interestingly, an increase in the stromal autofluorescence intensity and modifications of the autofluorescence lifetimes were observed in the human corneal samples within a few days following CXL.

Hierarchical brain tissue segmentation and its application in multiple sclerosis and Alzheimer's disease

NASA Astrophysics Data System (ADS)

Lei, Tianhu; Udupa, Jayaram K.; Moonis, Gul; Schwartz, Eric; Balcer, Laura

2005-04-01

Based on Fuzzy Connectedness (FC) object delineation principles and algorithms, a hierarchical brain tissue segmentation technique has been developed for MR images. After MR image background intensity inhomogeneity correction and intensity standardization, three FC objects for cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) are generated via FC object delineation, and an intracranial (IC) mask is created via morphological operations. Then, the IC mask is decomposed into parenchymal (BP) and CSF masks, while the BP mask is separated into WM and GM masks. WM mask is further divided into pure and dirty white matter masks (PWM and DWM). In Multiple Sclerosis studies, a severe white matter lesion (LS) mask is defined from DWM mask. Based on the segmented brain tissue images, a histogram-based method has been developed to find disease-specific, image-based quantitative markers for characterizing the macromolecular manifestation of the two diseases. These same procedures have been applied to 65 MS (46 patients and 19 normal subjects) and 25 AD (15 patients and 10 normal subjects) data sets, each of which consists of FSE PD- and T2-weighted MR images. Histograms representing standardized PD and T2 intensity distributions and their numerical parameters provide an effective means for characterizing the two diseases. The procedures are systematic, nearly automated, robust, and the results are reproducible.

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.

The Architecture of an Automatic eHealth Platform With Mobile Client for Cerebrovascular Disease Detection

PubMed Central

Wang, Xingce; Bie, Rongfang; Wu, Zhongke; Zhou, Mingquan; Cao, Rongfei; Xie, Lizhi; Zhang, Dong

2013-01-01

Background In recent years, cerebrovascular disease has been the leading cause of death and adult disability in the world. This study describes an efficient approach to detect cerebrovascular disease. Objective In order to improve cerebrovascular treatment, prevention, and care, an automatic cerebrovascular disease detection eHealth platform is designed and studied. Methods We designed an automatic eHealth platform for cerebrovascular disease detection with a four-level architecture: object control layer, data transmission layer, service supporting layer, and application service layer. The platform has eight main functions: cerebrovascular database management, preprocessing of cerebral image data, image viewing and adjustment model, image cropping compression and measurement, cerebrovascular segmentation, 3-dimensional cerebrovascular reconstruction, cerebrovascular rendering, cerebrovascular virtual endoscope, and automatic detection. Several key technologies were employed for the implementation of the platform. The anisotropic diffusion model was used to reduce the noise. Statistics segmentation with Gaussian-Markov random field model (G-MRF) and Stochastic Estimation Maximization (SEM) parameter estimation method were used to realize the cerebrovascular segmentation. Ball B-Spline curve was proposed to model the cerebral blood vessels. Compute unified device architecture (CUDA) based on ray-casting volume rendering presented by curvature enhancement and boundary enhancement were used to realize the volume rendering model. We implemented the platform with a network client and mobile phone client to fit different users. Results The implemented platform is running on a common personal computer. Experiments on 32 patients’ brain computed tomography data or brain magnetic resonance imaging data stored in the system verified the feasibility and validity of each model we proposed. The platform is partly used in the cranial nerve surgery of the First Hospital Affiliated to the General Hospital of People's Liberation Army and radiology of Beijing Navy General Hospital. At the same time it also gets some applications in medical imaging specialty teaching of Tianjin Medical University. The application results have also been validated by our neurosurgeon and radiologist. Conclusions The platform appears beneficial in diagnosis of the cerebrovascular disease. The long-term benefits and additional applications of this technology warrant further study. The research built a diagnosis and treatment platform of the human tissue with complex geometry and topology such as brain vessel based on the Internet of things. PMID:25098861

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning.

PubMed

Formisano, Elia; De Martino, Federico; Valente, Giancarlo

2008-09-01

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.

Cognitive neuroscience: the troubled marriage of cognitive science and neuroscience.

PubMed

Cooper, Richard P; Shallice, Tim

2010-07-01

We discuss the development of cognitive neuroscience in terms of the tension between the greater sophistication in cognitive concepts and methods of the cognitive sciences and the increasing power of more standard biological approaches to understanding brain structure and function. There have been major technological developments in brain imaging and advances in simulation, but there have also been shifts in emphasis, with topics such as thinking, consciousness, and social cognition becoming fashionable within the brain sciences. The discipline has great promise in terms of applications to mental health and education, provided it does not abandon the cognitive perspective and succumb to reductionism. Copyright © 2010 Cognitive Science Society, Inc.

A compact light-sheet microscope for the study of the mammalian central nervous system

PubMed Central

Yang, Zhengyi; Haslehurst, Peter; Scott, Suzanne; Emptage, Nigel; Dholakia, Kishan

2016-01-01

Investigation of the transient processes integral to neuronal function demands rapid and high-resolution imaging techniques over a large field of view, which cannot be achieved with conventional scanning microscopes. Here we describe a compact light sheet fluorescence microscope, featuring a 45° inverted geometry and an integrated photolysis laser, that is optimized for applications in neuroscience, in particular fast imaging of sub-neuronal structures in mammalian brain slices. We demonstrate the utility of this design for three-dimensional morphological reconstruction, activation of a single synapse with localized photolysis, and fast imaging of neuronal Ca2+ signalling across a large field of view. The developed system opens up a host of novel applications for the neuroscience community. PMID:27215692

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

Sui, Jing; Adali, Tülay; Li, Yi-Ou; Yang, Honghui; Calhoun, Vince D.

2010-03-01

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.