LGGS: Longitudinal MRI Reconstruction via Guided Group Sparsity
Serial MRI is routinely performed in current clinical practice for disease surveillance and applications such as MR-guided radiotherapy. However, long MRI scan times increase costs and limit patient access, making accelerated image reconstruction of broad clinical importance. Previous scans of the same patient contain image information highly correlated with the current exam, yet existing reconstruction methods rarely exploit this information. Major challenges in the effective use of prior scans include inter-visit misregistration, pathological changes, and image phase variation. In this work, we propose Longitudinal Guided Group Sparsity (LGGS), an interpretable deep-learning framework f...
| Authors | Nikola Janjušević, Hersh Chandarana, Yao Wang et al. |
|---|---|
| Primary category | eess.IV |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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