Hitoshi Kubo1, Yuya Abe2, Tomoya Yokokawa2, Seira Yokoyama2, and Koji Hoshi2
1Fukushima Medical University, Fukushima, Japan, 2Hoshi General Hospital, Koriyama, Japan
1Fukushima Medical University, Fukushima, Japan, 2Hoshi General Hospital, Koriyama, Japan
SNRs were increased higher
significantly by DLR in all SNR ranges. Increasing ratio of SNR was varied by
means of parameter settings. Combination of the DLR parameters affected varies
to SNR, SSIM, and spatial resolution of the images.
Signal-to-Noise ratios on the images of 2 mm, 4 mm, and 8 mm of slice
thickness with various parameters of Deep Learning Reconstruction (DLR) were
shown. DLR had a powerful performance to increase SNR in all SNR ranges.
Structural similarity (SSIM) of
the images were shown. Decreasing of SSIM higher in thinner slice images is
compared to that in thicker slice images. Edge enhancement recovered SSIM
higher, especially in thinner slice images.