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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