Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911171177611264 |
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| author | Liu, Tingjun Park, Chicago Y. Hu, Yuyang An, Hongyu Kamilov, Ulugbek S. |
| author_facet | Liu, Tingjun Park, Chicago Y. Hu, Yuyang An, Hongyu Kamilov, Ulugbek S. |
| contents | Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction by combining diffusion priors with physical measurement models. However, they typically rely on pre-calibrated coil sensitivity maps (CSMs) and ground truth images, making them often impractical: CSMs are difficult to estimate accurately under heavy undersampling and ground-truth images are often unavailable. We propose Calibration-free Measurement Score-based diffusion Model (C-MSM), a new method that eliminates these dependencies by jointly performing automatic CSM estimation and self-supervised learning of measurement scores directly from k-space data. C-MSM reconstructs images by approximating the full posterior distribution through stochastic sampling over partial measurement posterior scores, while simultaneously estimating CSMs. Experiments on the multi-coil brain fastMRI dataset show that C-MSM achieves reconstruction performance close to DIS with clean diffusion priors -- even without access to clean training data and pre-calibrated CSMs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_18402 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation Liu, Tingjun Park, Chicago Y. Hu, Yuyang An, Hongyu Kamilov, Ulugbek S. Image and Video Processing Machine Learning Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction by combining diffusion priors with physical measurement models. However, they typically rely on pre-calibrated coil sensitivity maps (CSMs) and ground truth images, making them often impractical: CSMs are difficult to estimate accurately under heavy undersampling and ground-truth images are often unavailable. We propose Calibration-free Measurement Score-based diffusion Model (C-MSM), a new method that eliminates these dependencies by jointly performing automatic CSM estimation and self-supervised learning of measurement scores directly from k-space data. C-MSM reconstructs images by approximating the full posterior distribution through stochastic sampling over partial measurement posterior scores, while simultaneously estimating CSMs. Experiments on the multi-coil brain fastMRI dataset show that C-MSM achieves reconstruction performance close to DIS with clean diffusion priors -- even without access to clean training data and pre-calibrated CSMs. |
| title | Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2509.18402 |