Enhancing Super-Resolution Networks through Realistic Thick-Slice CT
  Simulation
Zeyu Tang Xiaodan Xing Guang Yang
This study aims to develop and evaluate an innovative simulation algorithm
for generating thick-slice CT images that closely resemble actual images in the
AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was
evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error
(RMSE) metrics, with the hypothesis that our simulation would produce images
more congruent with their real counterparts. Our proposed method demonstrated
substantial enhancements in terms of both PSNR and RMSE over other simulation
methods. 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 B30
reconstruction kernels, respectively. The proposed method also registered the
lowest RMSE with values of 0.0068 $\pm$ 0.0020 and 0.0108 $\pm$ 0.0099 for D45
and B30, respectively, indicating a distribution more closely aligned with the
authentic thick-slice image. Further validation of the proposed simulation
algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The
generated images were then leveraged to train four distinct super-resolution
(SR) models, which were subsequently evaluated using the real thick-slice
images from the 2016 Low Dose CT Grand Challenge dataset. When trained with
data produced by our novel algorithm, all four SR models exhibited enhanced
performance.