A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling
Since multiple MRI contrasts of the same anatomy contain redundant information, one contrast can be used as a prior for guiding the reconstruction of an undersampled subsequent contrast. To this end, several 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 for guided reconstruction addressing this issue, which additionally provides an explanatory framework for the multi-contrast problem in terms of 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. Based on this, incorporating prior information into the reconstruction reduces to simply replacing the aliased content of the image estimate 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-MUNIT. Various aspects like interpretability and convergence are explored via simulations. Furthermore, its practicality is demonstrated on the NYU fastMRI DICOM dataset and two in-house multi-coil raw datasets, obtaining up to 32.6% more acceleration over learning-based non-guided reconstruction for a given SSIM. In a radiological task, PnP-MUNIT allowed 33.3% more acceleration over clinical reconstruction at diagnostic quality.
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fastMRI
