Meeting Banner
Abstract #3539

A Multi-Stream GAN Approach for Multi-Contrast MRI Synthesis

Mahmut Yurt1,2, Salman Ul Hassan Dar1,2, Aykut Erdem3, Erkut Erdem3, 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 Computer Engineering, Hacettepe University, Ankara, Turkey, 4Neuroscience Program, Sabuncu Brain Research Center, Bilkent University, Ankara, Turkey

For synthesis of a single target contrast within a multi-contrast MRI protocol, current approaches perform either one-to-one or many-to-one mapping. One-to-one methods take as input a single source contrast and learn representations sensitive to unique features of the given source. Meanwhile, many-to-one methods take as input multiple source contrasts and learn joint representations sensitive to shared features across sources. For enhanced synthesis, we propose a novel multi-stream generative adversarial network model that adaptively integrates information across the sources via multiple one-to-one streams and a many-to-one stream. Demonstrations on neuroimaging datasets indicate superior performance of the proposed method against state-of-the-art methods.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here