PnP-CoSMo: A Multi-Contrast MRI Reconstruction Framework based on Content/Style Modeling [R]

Reddit r/MachineLearning 07/16/26, 01:10 PM Papers

Summary

PnP-CoSMo is a plug-and-play framework for multi-contrast MRI reconstruction that learns content/style models from image data, enabling reconstruction without raw k-space training data. It is generalizable across contrasts and forward operators.

What is the shared structural essence that underlies a pair of MRI contrast spaces? Explicitly modeling this contrast-invariant latent “content” unlocks a powerful multi-contrast reconstruction algorithm that is competitive with state-of-the-art unrolled networks while: PnP-CoSMo: A plug-and-play framework for multi-contrast MRI reconstruction based on content/style modeling. The first stage learns the content/style model from purely image-domain data. The second stage freezes this model and applies it as a powerful prior in iterative reconstruction. Requiring no raw k-space training data (which is a serious data bottleneck in the ML-based MRI world), Being generalizable across different MR contrasts and forward operators by design, and Offering a built-in explanatory framework. In our paper now published in Medical Image Analysis, we introduce PnP-CoSMo. Read the substack article here (with links to the MedIA paper and code): https://cnmyro.substack.com/p/pnp-cosmo-a-plug-and-play-method

Original Article

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