The BRAINS program has been developed over the past fifteen years to study psychiatric and neurological disorders and has been downloaded by over 350 sites worldwide. It provides extensive tools for the analysis of brain morphology including spatial normalization, image co-registration, tissue classification, automated structure identification, surface generation, diffusion tensor analysis, and chemical shift imaging partial volume correction. Currently, the BRAINS software is the primary image processing package for 37 active grants and pending grants at The University of Iowa and 46 at outside sites. The software contains a reliable and valid image processing pipeline for analyzing brain morphology. In this proposal we will fully automate this pipeline allowing for large scale imaging studies to be rapidly analyzed. This work will build upon the integration of the ITK toolkit into the BRAINS software. We are proposing to streamline maintenance, enhance development, and make BRAINS available to a wider audience of users. The maintenance and code hardening tasks will leverage several open source tools (CMake and DART) that have been developed as part of the ITK project. This will allow the code review process to be open to users of the program. The current BRAINS user interface will be replaced with FLTK providing a native user interface across Windows, Macintosh and Unix/Linux based platforms. To evaluate BRAINS and several other neuroimaging software applications a unique multi-center dataset that has been collected by the MIND Institute will be used to measure intra- and inter-site reliability as well as validity of these tools. ? ? ?
| Ghayoor, Ali; Vaidya, Jatin G; Johnson, Hans J (2018) Robust automated constellation-based landmark detection in human brain imaging. Neuroimage 170:471-481 |
| Hong, Sungmin; Fishbaugh, James; Rezanejad, Morteza et al. (2017) Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis. Proc SPIE Int Soc Opt Eng 10133: |
| Long, Jeffrey D; Langbehn, Douglas R; Tabrizi, Sarah J et al. (2017) Validation of a prognostic index for Huntington's disease. Mov Disord 32:256-263 |
| Gerig, Guido; Fishbaugh, James; Sadeghi, Neda (2016) Longitudinal modeling of appearance and shape and its potential for clinical use. Med Image Anal 33:114-121 |
| Matsui, Joy T; Vaidya, Jatin G; Wassermann, Demian et al. (2015) Prefrontal cortex white matter tracts in prodromal Huntington disease. Hum Brain Mapp 36:3717-32 |
| Wassef, Shafik N; Wemmie, John; Johnson, Casey P et al. (2015) T1? imaging in premanifest Huntington disease reveals changes associated with disease progression. Mov Disord 30:1107-14 |
| Matsui, Joy T; Vaidya, Jatin G; Johnson, Hans J et al. (2014) Diffusion weighted imaging of prefrontal cortex in prodromal Huntington's disease. Hum Brain Mapp 35:1562-73 |
| Muralidharan, Prasanna; Fishbaugh, James; Johnson, Hans J et al. (2014) Diffeomorphic shape trajectories for improved longitudinal segmentation and statistics. Med Image Comput Comput Assist Interv 17:49-56 |
| Younes, Laurent; Ratnanather, J Tilak; Brown, Timothy et al. (2014) Regionally selective atrophy of subcortical structures in prodromal HD as revealed by statistical shape analysis. Hum Brain Mapp 35:792-809 |
| Paulsen, Jane S; Long, Jeffrey D; Ross, Christopher A et al. (2014) Prediction of manifest Huntington's disease with clinical and imaging measures: a prospective observational study. Lancet Neurol 13:1193-201 |
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