A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling

원문 초록

Since multiple MRI contrasts of the same anatomy contain redundant information, one contrast can guide the reconstruction of an undersampled subsequent contrast. To this end, several end-to-end learning-based guided reconstruction methods have been proposed. However, a key challenge is the requirement of large paired training datasets comprising raw data and aligned reference images. We propose a modular two-stage approach addressing this issue, additionally providing an explanatory framework for the multi-contrast problem based on the shared and non-shared generative factors underlying two given contrasts. A content/style model of two-contrast image data is learned from a largely unpaired image-domain dataset and is subsequently applied as a plug-and-play operator in iterative reconstruction. The disentanglement of content and style allows explicit representation of contrast-independent and contrast-specific factors. Consequently, incorporating prior information into the reconstruction reduces to a simple replacement of the aliased content of the reconstruction iterate with high-quality content derived from the reference scan. Combining this component with a data consistency step and introducing a general corrective process for the content yields an iterative scheme. We name this novel approach PnP-CoSMo. Various aspects like interpretability and convergence are explored via simulations. Furthermore, its practicality is demonstrated on the NYU fastMRI DICOM dataset, showing improved generalizability compared to end-to-end methods, and on two in-house multi-coil raw datasets, offering up to 32.6% more acceleration over learning-based non-guided reconstruction for a given SSIM. In a small radiological task, PnP-CoSMo allowed 33.3% more acceleration over clinical reconstruction at diagnostic quality.