Deep Simultaneous Optimisation of Sampling and Reconstruction for Multi-contrast MRI
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 31 Mar 2021.
Builds on
4 of 8 references.
References · 4
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
- 2019Learning-based Optimization of the Under-sampling Pattern in MRIcited by 32
- 2019Deep-learning-based Optimization of the Under-sampling Pattern in MRIcited by 2
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Citing papers per year: 2021: 1.
1 paper.