Semi-Supervision for Clinical Contrast-Weighted Image Synthesis from Magnetic Resonance Fingerprinting

Previous works have introduced deep models to synthesize clinical contrast-weighted images from magnetic resonance fingerprinting (MRF). Although these models achieve high synthesis accuracy, they demand full-supervision from fully-sampled training data of clinical contrasts which might become difficult to acquire across diverse sets due to scan costs. To eliminate undesirable reliance on full-supervision, we introduce a semi-supervised model, ssMRF, that allows training using accelerated references. ssMRF introduces a semi-supervised loss function based only on collected k-space samples of clinical contrasts, and further leverages complementary Poisson disc masks, via a multi-task learning protocol to synergistically synthesize multiple contrasts.