Abstract #3427

Deep Learner estimated isotropic volume fraction enables reliable single-shell NODDI reconstruction

Abrar Faiyaz¹, Marvin M Doyley^1,2,3, Giovanni Schifitto^2,4, Jianhui Zhong^2,3,5, and Md Nasir Uddin⁴

¹Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States, ²Department of Imaging Sciences, University of Rochester, Rochester, NY, United States, ³Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States, ⁴Department of Neurology, University of Rochester, Rochester, NY, United States, ⁵Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States

The study explores the possibility to create neurite orientation dispersion and density imaging (NODDI) parameter maps with single-shell diffusion MRI data, using isotropic volume fraction (f_ISO) as prior. Proposed dictionary based deep learning prior NODDI (DLpN) framework leverages f_ISO estimation with a diffusion imaging scalar and T2 weighted non-diffusion signal. f_ISO estimation was validated in simulation and in-vivo. DLpN derived NDI and ODI maps for single-shell protocols are comparable with original multi-shell NODDI (error <5%) and may allow NODDI evaluation of retrospective brain studies on single-shell diffusion MRI data by multi-shell scanning of two subjects for DictNet f_ISO training.

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