SDUM: A Scalable Deep Unrolled Model for Universal Cardiac MRI Reconstruction

Clinical cardiac MRI spans diverse contrasts, sampling tra-jectories, accelerations, scanners, and patient populations, yet many de-terministic deep-learning reconstruction methods remain protocol spe-cific. We present the Scalable Deep Unrolled Model (SDUM), which in-tegrates a Restormer-based unrolled reconstructor, per-cascade coil sen-sitivity estimation, sampling aware weighted data consistency, and uni-versal conditioning on cascade index and acquisition metadata. A sin-gle SDUM model achieves state-of-the-art performance across all CM-RxRecon2025 tracks without task-specific fine-tuning and outperformsPromptMR+ on CMRxRecon2024 by +0.55 dB. Scaling experimentsshow near-logarithmic gains with depth up to 18 cascades (r=0.986,R2 =0.973) and continued but diminishing gains from data scaling (32.72 dBat 40% to 33.18 dB at 100%). SDUM also generalizes in a zero-shot set-ting to unseen in-house chemical exchange saturation transfer (CEST)MRI (43.57 dB PSNR, 0.9769 SSIM). When trained separately on fastMRIbrain, SDUM surpasses PC-RNN by +1.8 dB. These results supportSDUM as a scalable framework for robust MRI reconstruction acrossheterogeneous acquisition settings beyond cardiac MRI.Code: https://github.com/NVIDIA-Medtech/NV-Raw2insights-MRIModel: https://huggingface.co/nvidia/NV-Raw2insights-MRI