Self-Auditing Residual Drifting for Pathology-Preserving Accelerated Knee MRI
Accelerated magnetic resonance imaging reduces acquisition time, but reconstruction from undersampled k-space can blur diagnostically relevant structures or introduce failures that are not captured by global image metrics. We propose SA-RDM-DC, a Self-Auditing Residual generative Drifting Model with Data Consistency for accelerated knee MRI. The method adapts the newly proposed generative drifting paradigm to accelerated MRI by training a physics-conditioned drift field from the zero-filled reconstruction toward the fully sampled residual correction. It predicts image- and missing-k-space residual corrections, enforces data consistency with acquired k-space, uses frequency-aware and resid...
| Authors | Qing Lyu, Jianxu Wang, Mohammad Kawas et al. |
|---|---|
| Primary category | eess.IV |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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