Unlocking Data-Consistent Synthesis of Clinical Contrasts from Magnetic Resonance Fingerprinting with Semi-Supervised Learning

Motivation: We aim to introduce an end-to-end, ultra-fast acquisition and synthesis protocol for contrast-weighted image generation from magnetic resonance fingerprinting. Goal(s): Our objective is to facilitate data compilation from large patient cohorts to enhance generalizability and accuracy of synthesis. Approach: We leverage a semi-supervised framework to enable model training using highly-accelerated ground-truth data of the target contrasts, and introduce data-consistent synthesis in inference by performing subject-specific fine-tuning and validation. Results: Our experiments indicate that the proposed method enables high-quality synthesis using network models trained on prospectively undersampled data of the contrast-weighted images. We show that data-consistent synthesis helps improve synthesis quality and mitigate hallucinations. Impact: Conventional synthesis models for MRF perform a single-shot inference and are prone to hallucinations and inaccuracy. We introduce semi-supervised learning that enables data-consistency in inference, by merely getting an additional, ultra-fast 1-2min data of the target contrasts for subject-specific fine-tuning.