Magnetic Resonance Imaging(MRI)serves as a crucial diagnostic tool in medical practice,yet its lengthy acquisition times pose challenges for patient comfort and clinical efficiency.Current deep learning approaches for...Magnetic Resonance Imaging(MRI)serves as a crucial diagnostic tool in medical practice,yet its lengthy acquisition times pose challenges for patient comfort and clinical efficiency.Current deep learning approaches for accelerating MRI acquisition face difficulties in managing data variability caused by different scanner vendors or imaging protocols.This research investigates the use of transfer learning in variational deep learning models to enhance generalization capabilities.We collect 135 ACR phantom samples from 3.0T GE and SIEMENS MRI scanners,following standard ACR guidelines,to study vendor-specific generalization.Additionally,the fastMRI brain dataset,a recognized benchmark for MRI acceleration,is utilized to evaluate performance across diverse acquisition sequences.Through comprehensive testing,we identify vendor and sequence inconsistencies as key hurdles for accelerated MRI generalization.To overcome these challenges,we introduce a feature refinement-based transfer learning method,achieving significant gains over baseline models in both vendor and sequence generalization tasks.Moreover,we incorporate experience replay to mitigate catastrophic forgetting,resulting in notable performance stability.For vendor generalization,our approach reduces Peak Signal Noise-to-Ratio(PSNR)and Structural SIMilarity(SSIM)degradation by 25.55%and 9.5%,respectively.Similarly,for sequence transfer,forgetting is reduced by 3.5%(PSNR)and 2%(SSIM),establishing a robust framework with substantial improvements.展开更多
基金supported by the following grants:the National Institutes of Health(Nos.RF1AG073424 and P30AG072980)the Arizona Department of Health Services(No.CTR057001).
文摘Magnetic Resonance Imaging(MRI)serves as a crucial diagnostic tool in medical practice,yet its lengthy acquisition times pose challenges for patient comfort and clinical efficiency.Current deep learning approaches for accelerating MRI acquisition face difficulties in managing data variability caused by different scanner vendors or imaging protocols.This research investigates the use of transfer learning in variational deep learning models to enhance generalization capabilities.We collect 135 ACR phantom samples from 3.0T GE and SIEMENS MRI scanners,following standard ACR guidelines,to study vendor-specific generalization.Additionally,the fastMRI brain dataset,a recognized benchmark for MRI acceleration,is utilized to evaluate performance across diverse acquisition sequences.Through comprehensive testing,we identify vendor and sequence inconsistencies as key hurdles for accelerated MRI generalization.To overcome these challenges,we introduce a feature refinement-based transfer learning method,achieving significant gains over baseline models in both vendor and sequence generalization tasks.Moreover,we incorporate experience replay to mitigate catastrophic forgetting,resulting in notable performance stability.For vendor generalization,our approach reduces Peak Signal Noise-to-Ratio(PSNR)and Structural SIMilarity(SSIM)degradation by 25.55%and 9.5%,respectively.Similarly,for sequence transfer,forgetting is reduced by 3.5%(PSNR)and 2%(SSIM),establishing a robust framework with substantial improvements.
