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Abstract #3619

Deep Simultaneous Optimization of Sampling and Reconstruction for Multi-contrast MRI

Xinwen Liu1, Jing Wang1,2, Fangfang Tang1, Shekhar S. Chandra1, Feng Liu1, and Stuart Crozier1
1School of Information Technology and Electrical Engineering, the University of Queensland, Brisbane, Australia, 2School of Information and Communication Technology, Griffith University, Brisbane, Australia

MRI images of the same subject in different contrasts contain shared information, such as the anatomical structure. Utilizing the redundant information amongst the contrasts to sub-sample and faithfully reconstruct multi-contrast images could greatly accelerate the imaging speed, improve image quality and shorten scanning protocols. We propose an algorithm that generates the optimized sampling pattern and reconstruction scheme of one contrast (e.g. T2-weighted image) when images with different contrast (e.g. T1-weighted image) have been acquired. The proposed algorithm achieves increased PSNR and SSIM with the resulting optimal sampling pattern compared to other acquisition patterns and single contrast methods.

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