Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting
Abstract
We propose an unsupervised convolutional neural network (CNN) for relaxation parameter estimation. This network incorporates signal relaxation and Bloch simulations while taking advantage of residual learning and spatial relations across neighboring voxels. Quantification accuracy and robustness to noise is shown to be significantly improved compared to standard parameter estimation methods in numerical simulations and in vivo data for multi-echo T2 and T2* mapping. The combination of the proposed network with subspace modeling and MR fingerprinting (MRF) from highly undersampled data permits high quality T1 and T2 mapping.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2021
- DOI:
- 10.48550/arXiv.2112.03815
- arXiv:
- arXiv:2112.03815
- Bibcode:
- 2021arXiv211203815G
- Keywords:
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- Electrical Engineering and Systems Science - Image and Video Processing;
- Computer Science - Machine Learning;
- Physics - Medical Physics
- E-Print:
- 7 pages, 5 figures, submitted to International Society for Magnetic Resonance in Medicine 2022