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

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.

1. Requiring no raw k-space training data (which is a serious data bottleneck in the ML-based MRI world),
2. Being generalizable across different MR contrasts and forward operators by design, and
3. 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