Contrast Representation Learning from Imaging Parameters for Magnetic Resonance Image Synthesis

Magnetic Resonance Imaging (MRI) is a widely used non-invasive medical imaging technique that provides excellent contrast for soft tissues, making it invaluable for diagnosis and intervention. Acquiring multiple contrast images is often desirable for comprehensive evaluation and precise disease diagnosis. However, due to technical limitations, patient-related issues, and medical conditions, obtaining all desired MRI contrasts is not always feasible. Cross-contrast MRI synthesis can potentially address this challenge by generating target contrasts based on existing source contrasts. In this work, we propose Contrast Representation Learning (CRL), which explores the changes in MRI contrast by modifying MR sequences. Unlike generative models that treat image generation as an end-to-end cross-domain mapping, CRL aims to uncover the complex relationships between contrasts by embracing the interplay of imaging parameters within this space. By doing so, CRL enhances the fidelity and realism of synthesized MR images, providing a more accurate representation of intricate details. Experimental results on the Fast Spin Echo (FSE) sequence demonstrate the promising performance and generalization capability of CRL, even with limited training data. Moreover, CRL introduces a perspective of considering imaging parameters as implicit coordinates, shedding light on the underlying structure governing contrast variation in MR images. Our code is available at https://github.com/xionghonglin/CRL_MICCAI_2024 .