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This study aims to develop and evaluate an innovative simulation algorithmfor generating thick-slice CT images that closely resemble actual images in theAAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method wasevaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error(RMSE) metrics, with the hypothesis that our simulation would produce imagesmore congruent with their real counterparts. Our proposed method demonstratedsubstantial enhancements in terms of both PSNR and RMSE over other simulationmethods. The highest PSNR values were obtained with the proposed method,yielding 49.7369 $\pm$ 2.5223 and 48.5801 $\pm$ 7.3271 for D45 and B30reconstruction kernels, respectively. The proposed method also registered thelowest RMSE with values of 0.0068 $\pm$ 0.0020 and 0.0108 $\pm$ 0.0099 for D45and B30, respectively, indicating a distribution more closely aligned with theauthentic thick-slice image. Further validation of the proposed simulationalgorithm was conducted using the TCIA LDCT-and-Projection-data dataset. Thegenerated images were then leveraged to train four distinct super-resolution(SR) models, which were subsequently evaluated using the real thick-sliceimages from the 2016 Low Dose CT Grand Challenge dataset. When trained withdata produced by our novel algorithm, all four SR models exhibited enhancedperformance.

Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation