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Table 3 Analysis of the u-net performance

From: Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset

Abbreviation Synthetic versus noisy projections Synthetic versus noise-free projections
SSIM NRMSE SSIM NRMSE
U1 0.979 (0.025) 2.45% (1.18%) 0.995 (0.011) 1.19% (0.75%)
U2 0.979 (0.026) 2.44% (1.19%) 0.995 (0.011) 1.19% (0.79%)
U3 0.973 (0.033) 2.79% (1.29%) 0.989 (0.022) 1.85% (1.17%)
U4 0.978 (0.024) 2.52% (1.25%) 0.994 (0.007) 1.29% (0.75%)
U5 0.978 (0.024) 2.53% (1.20%) 0.994(0.008) 1.32% (0.68%)
U6 0.978 (0.025) 2.56% (1.53%) 0.994 (0.009) 1.37% (0.75%)
U7 0.982 (0.018) 2.24% (1.10%) 0.997 (0.006) 1.04% (0.61%)
  1. Mean SSIM and NRMSE values between synthetic projections and noisy/noise-free projections, respectively. Data in parentheses are standard deviations