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arXiv Computer Vision By Nikola Janju\v{s}evi\'{c}, Hersh Chandarana, Yao Wang, Li Feng

LGGS: Longitudinal MRI Reconstruction via Guided Group Sparsity

Read the original on arXiv Computer Vision →

The paper introduces LGGS, a deep‑learning framework that uses prior longitudinal MRI scans to guide accelerated image reconstruction through a learned, phase‑invariant circulant attention mechanism. This approach incorporates group sparsity principles to handle misregistration and phase variation, achieving state‑of‑the‑art performance on longitudinal T1‑weighted brain MRI data with fewer parameters than existing methods. The authors also show that even accelerated prior images can serve as effective guides with minimal quality loss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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