Not applicable, Observational, Artifacts, Computer Applications-General, CT, eHealth, Computer applications, Artificial Intelligence, Artificial Intelligence and Machine Learning
M. Patwari1, R. Gutjahr2, R. Raupach3, A. Maier1; 1Erlangen/DE, 2Nürnberg, Deutschland/DE, 3Forchheim/DE
The use of a perceptual loss function improved both feature preservation and denoising performance in 2D denoising networks (SSIM = 0.7067, PSNR = 44.5189) compared to mean squared error denoising (SSIM = 0.7065, PSNR = 44.4888).
Perceptual loss functions improved both feature preservation and denoising performance in 3D denoising networks (SSIM = 0.6944, PSNR = 45.9397) compared to mean squared error denoising (SSIM = 0.6871, PSNR = 45.6718).
Additionally, 3D networks were better at noise removal compared to 2D networks. However, the 2D networks improved feature preservation in comparison with 3D networks.
|2D Denoising Network with MSE loss only
||44.49 ± 2.378
||0.71 ± 0.034
|2D Denoising Network with Perceptual Loss
||44.51 ± 2.301
||0.71 ± 0.039
|3D Denoising Network with MSE loss only
||45.67 ± 2.704
||0.69 ± 0.034
|3D Denoising Network with Perceptual Loss
||45.94 ± 2.605
||0.69 ± 0.029