Abstract #3562

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

Mahmut Yurt¹, Zihan Zhou², Congyu Liao³, Cagan Alkan¹, Xiaozhi Cao³, Nan Wang³, Julio Oscanoa⁴, Sophie Schauman⁵, Mengze Gao⁶, Zhitao Li⁷, Tolga Cukur⁸, Bruno Soares³, Ali Syed³, Shreyas Vasanawala³, John Pauly¹, and Kawin Setsompop³

¹Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ²Stanford University, Stanford, CA, United States, ³Department of Radiology, Stanford University, Stanford, CA, United States, ⁴Department of Bioengineering, Stanford University, Stanford, CA, United States, ⁵Karolinska Institutet, Solna, Sweden, ⁶Department of Biomedical Physics, Stanford University, Stanford, CA, United States, ⁷Department of Radiology, Northwestern University, Chicago, IL, United States, ⁸Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey

Synopsis

Keywords: MR Fingerprinting, MR Fingerprinting

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.

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