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Abstract #3253

Deep Image Synthesis for Extraction of Vascular and Gray Matter Metrics  

Farnaz Orooji1, Xinyang Wang1, Mohammed Ayoub Alaoui Mhamdi1, and Russell Butler1
1Computer Science, Bishop's University, Sherbrooke, QC, Canada

One of the main strengths of MRI is the wide range of soft tissue contrasts which can be obtained using different sequence parameters. T1-weighted-images provide exquisite contrast between gray/white matter but fail to capture arterial vessels. Here we experiment with deep-learning for image synthesis, to synthesize a time-of-flight-(TOF) angiogram based on T1 contrast image. We then compare arterial diameters from synthesized TOF with ground-truth-TOF diameters. We also synthesize a T1 from a T2, and compare cortical metrics such as thickness, curvature etc. We show that it is possible to obtain vessel diameters from T1, and cortical thick/vol/curv measures from T2.

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