The purpose of this grant is to support continued development and maintenance of MriStudio software developed in Johns Hopkins University. MriStudio is comprehensive software for MR image processing and analysis with emphasis on white matter anatomy. MriStudio consists of three modules, DtiStudio, DiffeoMap, and RoiEditor. DtiStudio was introduced in 2001 and remains one of the most widely used programs to process diffusion tensor imaging (DTI) data. DiffeoMap and RoiEditor were introduced in 2007, which provides a very unique environment to perform a cutting-edge image transformation and atlas-based automated image segmentation. What is especially unique about DiffeoMap is, because our advanced brain mapping algorithms are highly CPU and memory intensive, it adopts Cloud-type architecture, through which users can have access to our supercomputation resource. Currently, there are more than 6,500 registered uses. In this application, we propose to extend this service to the community through the following aims;
Aim 1 : Continued user support through training and dissemination Currently, two major channels of training and dissemination are through web-based resources (manuals and videos) and hand-on monthly 2-day tutorials. As the functionalities of MriStudio expand, we will continuously update the online materials and tutorials.
Aim 2 : Extension of the functionality Aim 2-1: Advanced diffusion MRI analysis package: Through the collaboration with Dr. Tournier, spherical harmonic decomvolution algorithm will be implemented.
Aim 2 -2: Automated and probabilistic tractography: We will incorporate a probabilistic tractography method based on dynamic programing and automate the ROI definition process.
Aim 2 -3: Quality control reporting: We will deploy a comprehensive and quantitative quality control reporting system, which is extremely important for automated analysis of large-scale studies.
Aim 3 : Cross-platform extension by adopting web-based interface We will develop web-based Cloud computation service, which will eliminate the platform-dependence.
Aim 4 : Completion of XNAT-based solution We will develop a server-based automated analysis pipeline that is linked to a research image database system, called XNAT.
Aim 5 : Deployment of multi-atlas-based brain segmentation algorithm We will deploy our multi-atlas technology in our server and make them available for testing to users through the Cloud computation system.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
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Biodata Management and Analysis Study Section (BDMA)
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Liu, Yuan
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Johns Hopkins University
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