SDUM: A Scalable Deep Unrolled Model for Universal MRI Reconstruction

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 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.

Reproductions