Abstract #3362

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

Barbara D. Wichtmann^1,2, Aapo Nummenmaa¹, Qiuyun Fan¹, Thomas Witzel¹, Elizabeth R. Gerstner³, Alexandra J. Golby^4,5, Sandro Santagata⁶, Bruce R. Rosen¹, Lothar Schad², Lawrence L. Wald^1,7, and Susie Y. Huang^1,7

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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