Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation

• URL: http://arxiv.org/abs/2509.18402v1
• Date: Mon, 22 Sep 2025 20:33:41 GMT
• Title: Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation
• Abstract summary: Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction.<n>They typically rely on pre-calibrated coil sensitivity maps (CSMs) and ground truth images, making them often impractical.<n>We propose a new method that eliminates these dependencies by jointly performing automatic estimation and self-supervised learning of measurement scores.
• Score: 10.013774165445385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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