Keywords: Sparse & Low-Rank Models, Multi-Contrast, low-rank, Brain imaging, Quantitative MRI, MR fingerprinting
Motivation: 3D MR fingerprinting (MRF) and MPnRAGE acquire a large number (400-1000+) of non-steady state images with different encodings to estimate quantitative relaxometry parameters. The large number of volumetric images presents serious computational and memory issues for many advanced image reconstruction techniques, especially those utilizing deep learning.
Goal(s): This work develops a memory efficient, pretrained deep factor model (DFM) for high quality, high temporal images.
Approach: We apply a progressive training and pre-training strategy to accelerate the convergence for a self-supervised DFM.
Results: DFM recovers 384 3D brain images (1mm isotropic resolution) from a 2.3 minutes MPnRAGE scan within 30 minutes of reconstruction time.
Impact: The proposed pretrained deep factor model enables fast MRF reconstruction from accelerated acquisition in a 3D+time setting. It has the potential to significantly shorten the acquisition time for quantitative MRI, while providing high quality weighted MRI results.
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