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Abstract #3362

Investigating microstructural signatures for low-grade gliomas using Linear Multi-scale Modeling of diffusion MRI data

Barbara D. Wichtmann1,2, Aapo Nummenmaa1, Qiuyun Fan1, Thomas Witzel1, Elizabeth R. Gerstner3, Alexandra J. Golby4,5, Sandro Santagata6, Bruce R. Rosen1, Lothar Schad2, Lawrence L. Wald1,7, and Susie Y. Huang1,7

1A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States, 2Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 3Department of Neurology, Center for Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 4Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, 5Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, 6Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, 7Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Linear Multi-scale Modeling (LMM) of diffusion MRI data is a recently developed DWI analysis technique for separating orientation distributions of restricted and hindered diffusion water compartments over a range of length scales, thereby allowing more detailed characterization of tissue microstructure. Here, we apply the LMM framework to characterize a low-grade oligodendroglioma prior to resection. We use the distinct microstructural signature of the tumor to delineate tumor extent and use results from pathology and numerical simulations to refine our understanding of the tumor microstructure.

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