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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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