Over the past decade, the PI and colleagues have developed algorithms and software for the segmentation, registration, and analysis of structural MRI and related image data. These software tools have been integrated into a publicly released package, named Brain Suite that has been used in numerous neuroimaging studies. Our approach has emphasized the development of separate, validated modules addressing different aspects of the image analysis problem, which are then integrated through an interactive interface to allow fast automated or semi-automated processing and image visualization. This application proposes the continued development and support of these tools under the Continued Development and Maintenance of Software (R01) program. We will improve our software and its utility through enhancements to its functionality, better user and developer documentation, better user training and support, improved interoperability, specific enhancements to support data from subjects with epilepsy, and routine evaluation of the performance of the tools. The Brain Suite software will be distributed under an open-source software license.
Aim 1 : We will develop the Brain Suite software package to provide advanced automated and interactive software for the segmentation and registration of brain MRI. Specifically, we focus on: (a) software for segmenting individual subject MRI to identify structures, e.g., skull and scalp models, inner and outer cortical surface mesh models, within human brain MRI; (b) software for performing spatial alignment of brain structures across subjects using surface-based and joint surface/volume methods; (c) analysis software for performing comparisons of brain data extracted and mapped into common spaces using the tools in (a) and (b); (d) a cross-platform graphical user interface providing an easy-to-use, interactive framework in which to apply the segmentation, registration, and analysis tools, as well as to perform interactive delineation and visualization of data. All software development will be performed adhering to sound software engineering practices (coding style, documentation, version control).
Aim 2 : We will develop the resources and capabilities to enhance the utility of our software for a broad range of users from the neuroimaging and clinical communities. This will be achieve through: online documentation, support forums, and training videos and courses; quality assurance testing to detect potential failures during automated processing; and enhanced interoperability with other software packages used by the neuroimaging community.
Aim 3 : We will enhance the Brain Suite tools to provide improved functionality for processing data with clinical abnormalities, in particular, data from people affected by epilepsy.
Aim 4 : We will adopt several procedures for routinely evaluating the quality of the results our software produces and the software's impact in the clinical setting and neuroimaging community.

Public Health Relevance

This project proposes to develop a powerful suite of open source integrated software tools that will allow a high degree of automated analysis of 3D images of the human brain, with optional user interaction where necessary, to automatically identify neuroanatomical structures including the cerebral cortex and subcortical structures. The software package will also provide capabilities to align images from multiple subjects into a common space in which intersubject comparison studies may be performed. The performance of the software will be evaluated using standard databases of hand labeled brains as well as clinical data from patients with epilepsy.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Gnadt, James W
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University of California Los Angeles
Schools of Medicine
Los Angeles
United States
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Shattuck, David W (2018) Multiuser virtual reality environment for visualising neuroimaging data. Healthc Technol Lett 5:183-188
Coletti, Amanda M; Singh, Deepinder; Kumar, Saurabh et al. (2018) Characterization of the ventricular-subventricular stem cell niche during human brain development. Development 145:
Phillips, Owen Robert; Joshi, Shantanu H; Narr, Katherine L et al. (2018) Superficial white matter damage in anti-NMDA receptor encephalitis. J Neurol Neurosurg Psychiatry 89:518-525
Lobos, Rodrigo A; Kim, Tae Hyung; Hoge, W Scott et al. (2018) Navigator-Free EPI Ghost Correction With Structured Low-Rank Matrix Models: New Theory and Methods. IEEE Trans Med Imaging 37:2390-2402
Miao, Xin; Choi, Soyoung; Tamrazi, Benita et al. (2018) Increased brain iron deposition in patients with sickle cell disease: an MRI quantitative susceptibility mapping study. Blood 132:1618-1621
Kim, Tae Hyung; Bilgic, Berkin; Polak, Daniel et al. (2018) Wave-LORAKS: Combining wave encoding with structured low-rank matrix modeling for more highly accelerated 3D imaging. Magn Reson Med :
Varadarajan, Divya; Haldar, Justin P (2017) A theoretical signal processing framework for linear diffusion MRI: Implications for parameter estimation and experiment design. Neuroimage 161:206-218
Kim, Tae Hyung; Setsompop, Kawin; Haldar, Justin P (2017) LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration. Magn Reson Med 77:1021-1035
Thomason, Moriah E; Marusak, Hilary A (2017) Toward understanding the impact of trauma on the early developing human brain. Neuroscience 342:55-67
Awate, Suyash P; Leahy, Richard M; Joshi, Anand A (2017) Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex. Inf Process Med Imaging 10265:28-40

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