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.
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.
|Chong, M; Bhushan, C; Joshi, A A et al. (2017) Individual parcellation of resting fMRI with a group functional connectivity prior. Neuroimage 156:87-100|
|Thomason, Moriah E; Marusak, Hilary A (2017) Toward understanding the impact of trauma on the early developing human brain. Neuroscience 342:55-67|
|Choi, Soyoung; Bush, Adam M; Borzage, Matthew T et al. (2017) Hemoglobin and mean platelet volume predicts diffuse T1-MRI white matter volume decrease in sickle cell disease patients. Neuroimage Clin 15:239-246|
|Joshi, Anand A; Chong, Minqi; Leahy, Richard M (2017) BrainSync: An Orthogonal Transformation for Synchronization of fMRI Data Across Subjects. Med Image Comput Comput Assist Interv 10433:486-494|
|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|
|Wang, Z I; Krishnan, B; Shattuck, D W et al. (2016) Automated MRI Volumetric Analysis in Patients with Rasmussen Syndrome. AJNR Am J Neuroradiol 37:2348-2355|
|Wang, Z Irene; Suwanpakdee, P; Jones, S E et al. (2016) Re-review of MRI with post-processing in nonlesional patients in whom epilepsy surgery has failed. J Neurol 263:1736-45|
|Haldar, Justin P; Zhuo, Jingwei (2016) P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data. Magn Reson Med 75:1499-514|
|Phillips, Owen R; Joshi, Shantanu H; Piras, Fabrizio et al. (2016) The superficial white matter in Alzheimer's disease. Hum Brain Mapp 37:1321-34|
|Phillips, Owen R; Joshi, Shantanu H; Squitieri, Ferdinando et al. (2016) Major Superficial White Matter Abnormalities in Huntington's Disease. Front Neurosci 10:197|
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