Abstract #2427

Multi-contrast CS reconstruction using data-driven and model-based deep neural networks

Tomoki Miyasaka¹, Satoshi Funayama², Daiki Tamada², Utaroh Motosugi³, Hiroyuki Morisaka², Hiroshi Onishi², and Yasuhiko Terada¹

¹Institute of Applied Physics, University of Tsukuba, Tsukuba, Japan, ²Department of Radiology, University of Yamanashi, Chuo, Japan, ³Department of Radiology, Kofu-Kyoritsu Hospital, Kofu, Japan

The use of deep learning (DL) for compressed sensing (CS) have recently received increased attention. Generally, DL-CS uses single-contrast CS reconstruction (SCCS) where the single-contrast image is used as the network input. However, in clinical routine examinations, different contrast images are acquired in the same session, and CS reconstruction using multi-contrast images as the input (MCCS) has the potential to show better performance. Here, we applied DL-MCCS to brain MRI images acquired during routine examinations. We trained data-driven and model-based networks, and showed that for both cases, MCCS outperformed SCCS.

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