Over the past 16 years, we have developed a collection of algorithms and software for the segmentation, registration, labeling, and analysis of structural and diffusion MRI, integrated into the open source package BrainSuite (http://brainsuite.org). Our approach emphasizes the development of separate, validated modules addressing each aspect of the image analysis problem, which are then integrated through an interactive interface, to provide fast automated or semi-automated processing and image visualization. Command line tools using the same functions are also provided for large scale processing. The software runs on and is consistent across Mac, Windows, and Linux platforms. This renewal application builds on these tools with a continued emphasis on the ability to process large (now multimodal) data sets while simultaneously retaining the ability to rapidly visualize, review, and where necessary modify intermediate results to optimize the fidelity of each stage of processing. The renewal emphasizes development of new tools for coregistration of multimodal data, modeling and analysis of diffusion data, and quantitative analysis of functional and structural connectivity. The project has five specific aims.
Aim 1 will develop advanced methods for intersubject anatomical, diffusion, and functional MRI analysis that account for individual structural and functional differences. This will improve upon existing methods that rely solely on structural (T1-weighed) images to define homologies between subjects.
Aim 2 will develop tools for intrasubject coregistration of multimodal imaging data that explicitly account for and estimate resolution differences between modalities. In combination with the intersubject methods in Aim 1, this will facilitate group pointwise and regional statistical multimodal analysis.
Aim 3 will develop tools to analyze diffusion data characterized by flexible sampling schemes and multiple b-values, addressing the limited ability of current tools to model data produced by increasingly widely used modern acquisition schemes such as those required by the Human Connectome Project and related NIH projects.
Aim 4 will expand the BrainSuite Statistics toolbox, which uses Python and R to provide an extensible statistical framework for analyzing data;
this aim will also facilitate the use of BrainSuite as part of larger image analysis pipelines by continuing to support standard formats and developing our new tools as modular command line programs. Distributions will be compatible with Nipype and NITRC-CE.
Under Aim 5, we will continue software development employing standard best practices. We will develop web-based interfaces for rapidly visualizing and evaluating results from large, multisubject studies. User support will be provided through online forums, tutorials, videos, documentation, and hands-on training. New analysis methods developed in the above aims will be validated through simulation and evaluation on existing in vivo imaging data.
BrainSuite (http://brainsuite.org) is a collection of algorithms and software for the analysis of magnetic resonance images of the brain. The software includes a graphical interface, which enables users to interact visually with the image data, as well as command-line functions to allow automated analysis of data from large numbers of subject. This project builds on the current version of the software to allow researchers to explore individual and group differences in brain anatomy, function, and connectivity, with the broad goal of improving our understanding and optimizing treatment of neurological disease.
|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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