SDUM: A Scalable Deep Unrolled Model for Universal MRI Reconstruction

arXiv:2512.17137v2 Announce Type: replace-cross
Abstract: Clinical MRI encompasses diverse imaging protocols–spanning anatomical targets (cardiac, brain, knee), contrasts (T1, T2, mapping), sampling patterns (Cartesian, radial, spiral, kt-space), and acceleration factors–yet current deep learning reconstructions are typically protocol-specific, hindering generalization and deployment. We introduce Scalable Deep Unrolled Model (SDUM), a universal framework combining a Restormer-based reconstructor, a learned coil sensitivity map estimator (CSME), sampling-aware weighted data consistency (SWDC), universal conditioning (UC) on cascade index and protocol metadata, and progressive cascade expansion training. SDUM exhibits foundation-model-like scaling behavior: reconstruction quality follows PSNR $sim$ log(parameters) with correlation $r=0.986$ ($R^2=0.973$) up to 18 cascades, demonstrating predictable performance gains with model depth. A single SDUM trained on heterogeneous data achieves state-of-the-art results across all four CMRxRecon2025 challenge tracks–multi-center, multi-disease, 5T, and pediatric–without task-specific fine-tuning, surpassing specialized baselines by up to $+1.0$~dB. On CMRxRecon2024, SDUM outperforms the winning method PromptMR+ by $+0.55$~dB; on fastMRI brain, it exceeds PC-RNN by $+1.8$~dB. Ablations validate each component: SWDC $+0.43$~dB over standard DC, per-cascade CSME $+0.51$~dB, UC $+0.38$~dB. These results establish SDUM as a practical path toward universal, scalable MRI reconstruction.