Transferable Variational Feedback Network for Vendor Generalization in Accelerated MRI

Magnetic Resonance Imaging (MRI) is a widely used diagnostic tool in medicine. The long acquisition time of MRI remains to be a practical concern, leading to suboptimal patient experiences. Existing deep learning models for fast MRI acquisition struggle to handle the problem of data heterogeneity due to scanners from different vendors. This study explores the transfer learning capabilities of variational deep learning architectures to address this problem. Using standard ACR protocols, we acquired 135 ACR phantom samples from GE and Siemens 3.0T MR scanners and conducted comprehensive experiments to compare the reconstruction quality of the images produced by different models. Our experiments identified vendor differences as a major challenge in the generalization of accelerated MRI. We propose a feature refinement-based transfer learning method that outperforms the baseline networks by \(\sim \) 2.0 dB (PSNR), 1.8% (SSIM) for GE, and \(\sim \) 3.0 dB (PSNR), 0.8% (SSIM) for SIEMENS. Furthermore, we used experience replay to address the problem of catastrophic forgetting. We established it as a robust baseline through experiments with strong results (PSNR and SSIM performance drop reduced by 25.55% and 9.5%, respectively).