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Abstract #3449

Adversarial Non-local Multi-modality MRI Aggregation for Directional DWI Synthesis

Xiaofeng Liu1, Fangxu Xing1, Van Jay Wedeen1, Georges El Fakhri1, and Jonghye Woo1
1Dept. of Radiology, MGH and Harvard Medical School, Boston, MA, United States


Diffusion MRI is sensitive to subject motion, and with prolonged acquisition time, it suffers from motion corruption and artifacts. To address this, we present an adversarial non-local network-based multi-modality MRI fusion framework for directional DWI synthesis. Our framework is based on a generative model conditioned on a specified b-vector sampled in q-space, where it efficiently fuses information from multiple structural MRIs, including T1- and T2-weighted MRI, and B0 image, with an adaptive attention scheme. Experimental results, using a total of ten q-ball data, show its potential to synthesize high-fidelity DWIs at arbitrary q-space coordinates and facilitate quantification of diffusion parameters.

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