Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
This work addresses the need for faster and higher-quality MRI scans in medical imaging, though it appears incremental as it builds on prior multi-contrast optimization approaches.
The paper tackles the problem of accelerating multi-contrast MRI by optimizing both sampling patterns and reconstruction schemes, leveraging shared anatomical information across contrasts to improve efficiency. It reports increased PSNR and SSIM with the optimized sampling pattern compared to existing methods.
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.