Meeting Banner
Abstract #1241

A Semi-Supervised Learning Framework for Jointly Accelerated Multi-Contrast MRI Synthesis without Fully-Sampled Ground-Truths

Mahmut Yurt1,2, Salman Ul Hassan Dar1,2, Berk Tinaz1,2,3, Muzaffer Ozbey1,2, Yilmaz Korkmaz1,2, and Tolga Çukur1,2,4
1Department of Electrical and Electronics Engineering, Bilkent University, Ankara, Turkey, 2National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey, 3Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States, 4Neuroscience Program, Aysel Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey

Current approaches for synthetic multi-contrast MRI involve deep networks trained to synthesize target-contrast images from source-contrast images in fully-supervised protocols. Yet, their performance is undesirably circumscribed to training sets of costly fully-sampled source-target images. For practically advanced multi-contrast MRI synthesis accelerated across the k-space and contrast sets, we propose a semi-supervised generative model that can be trained to synthesize fully-sampled images using only undersampled ground-truths by introducing a selective loss function expressed only on the acquired k-space coefficients randomized across training subjects. Demonstrations on multi-contrast brain images indicate that the proposed model maintains equivalent performance to the gold-standard fully-supervised model.

This abstract and the presentation materials are available to members only; a login is required.

Join Here