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

• URL: http://arxiv.org/abs/2103.16744v1
• Date: Wed, 31 Mar 2021 00:45:58 GMT
• Title: Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
• Authors: Xinwen Liu, Jing Wang, Fangfang Tang, Shekhar S. Chandra, Feng Liu, and Stuart Crozier
• Abstract summary: We propose an algorithm that generates the optimised sampling pattern and reconstruction scheme of one contrast. The proposed algorithm achieves increased PSNR and SSIM with the resulting optimal sampling pattern.
• Score: 6.981200113164422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 optimised 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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