Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, GAN, Style Transfer
Motivation: Acquiring multi-contrast MR images with different contrasts enhances diagnostic accuracy, but the long scan time often limits their acquisition.
Goal(s): This study aims to synthesize multi-contrast images from a single MR image, guided by sequence parameters, without distortions.
Approach: The network based on style transfer disentangles content and style within a single MR image and manipulates only the style by MR sequence parameters.
Results: Experimental results demonstrate our method enables not only contrast conversion within the same MR sequence but also conversion across different sequences.
Impact: This method adjusts the contrast of MR images based on MR sequence parameters without requiring additional scans. This approach has the potential to reduce the scan time needed to acquire multi-contrast MR images.
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