Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction
This work presents a novel zero-shot learning method for undersampled magnetic resonance imaging (MRI) reconstruction. The proposed method utilizes a plug-and-play approach, wherein the denoiser neural network, serving as the image prior, is trained solely with the single acquired undersampled k-spa...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10933922/ |
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| author | Tae Hyung Kim |
| author_facet | Tae Hyung Kim |
| author_sort | Tae Hyung Kim |
| collection | DOAJ |
| description | This work presents a novel zero-shot learning method for undersampled magnetic resonance imaging (MRI) reconstruction. The proposed method utilizes a plug-and-play approach, wherein the denoiser neural network, serving as the image prior, is trained solely with the single acquired undersampled k-space data. Specifically, the training of the denoiser employs the Noise2Noise and Self2Self frameworks; the acquired k-space is divided into two disjoint subsets using complementary subsampling masks, which are then reconstructed to serve as the training data and target labels, respectively. We provide theoretical justification for the proposed method, demonstrating the feasibility of training an effective denoiser without the need for extra training data or fully sampled ground truths. The trained denoiser is subsequently utilized as a plug-and-play denoiser for undersampled MRI reconstruction. We have evaluated and compared the performance of our method with several other reconstruction techniques, thereby demonstrating the advantages of the proposed approach. |
| format | Article |
| id | doaj-art-908982b432b140de915bbdd39e5d6098 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-908982b432b140de915bbdd39e5d60982025-08-20T03:42:15ZengIEEEIEEE Access2169-35362025-01-0113505905060210.1109/ACCESS.2025.355285310933922Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging ReconstructionTae Hyung Kim0https://orcid.org/0000-0001-5881-7265Department of Computer Engineering, Hongik University, Seoul, Republic of KoreaThis work presents a novel zero-shot learning method for undersampled magnetic resonance imaging (MRI) reconstruction. The proposed method utilizes a plug-and-play approach, wherein the denoiser neural network, serving as the image prior, is trained solely with the single acquired undersampled k-space data. Specifically, the training of the denoiser employs the Noise2Noise and Self2Self frameworks; the acquired k-space is divided into two disjoint subsets using complementary subsampling masks, which are then reconstructed to serve as the training data and target labels, respectively. We provide theoretical justification for the proposed method, demonstrating the feasibility of training an effective denoiser without the need for extra training data or fully sampled ground truths. The trained denoiser is subsequently utilized as a plug-and-play denoiser for undersampled MRI reconstruction. We have evaluated and compared the performance of our method with several other reconstruction techniques, thereby demonstrating the advantages of the proposed approach.https://ieeexplore.ieee.org/document/10933922/Magnetic resonance imaginginverse problemsimage reconstructiondenoisingplug-and-play methods |
| spellingShingle | Tae Hyung Kim Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction IEEE Access Magnetic resonance imaging inverse problems image reconstruction denoising plug-and-play methods |
| title | Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction |
| title_full | Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction |
| title_fullStr | Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction |
| title_full_unstemmed | Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction |
| title_short | Zero-Shot Recon2Recon: Data-Free Unsupervised Denoiser Learning for Plug-and-Play Magnetic Resonance Imaging Reconstruction |
| title_sort | zero shot recon2recon data free unsupervised denoiser learning for plug and play magnetic resonance imaging reconstruction |
| topic | Magnetic resonance imaging inverse problems image reconstruction denoising plug-and-play methods |
| url | https://ieeexplore.ieee.org/document/10933922/ |
| work_keys_str_mv | AT taehyungkim zeroshotrecon2recondatafreeunsuperviseddenoiserlearningforplugandplaymagneticresonanceimagingreconstruction |