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
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
We validated the usefulness of deep learning
based multi-contrast compressed sensing (MCCS) reconstruction, in which
multiple contrast images are used for compressed sensing (CS) reconstruction. Applied
to data-driven and model-based networks, MCCS outperformed single-contrast CS.
Fig. 2 Examples
of SCCS and MCCS results for AF = 2.
(a)
Unet-DC. (b) MoDL. The number of epochs in the training were 1 for SCCS and 5 for MCCS.
Fig.
1 Experimental
condition. (a) Acquisition
parameters. (b, c) Input/output images for (b) SCCS and (c) MCCS. US-FLAIR:
undersampled FLAIR, US-T1WI: undersampled T1-weighted image.