Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images

Objective To investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images. Materials and methods A retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m 2 ) obtained with a GE Revolution CT (70 kVp tube voltage; adaptive statistical iterative reconstruction-Veo-filtered back projection, 50% blending) and designated group A. Group B images were then obtained by applying PS to group A image datasets. Subjective image quality was evaluated by two radiologists with a 5-point scoring system; the scores of the groups were compared. Image signal was assessed using CT values of the urinary bladder. CT and standard deviation (SD) values of the gluteus maximus were measured, and SD values of the gluteus maximus were used to represent image noise. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the bladder were calculated. Image noise, SNR, and CNR of two groups were compared using paired t-tests. Results The subjective visual image quality scores of groups A and B, respectively, were 3.11 ± 0.30 vs. 3.82 ± 0.57; image noise was 15.79 ± 2.05 Hounsfield units (HU) vs. 11.06 ± 2.22 HU; SNRs of bladder were 0.50 ± 0.23 vs. 0.79 ± 0.39; and CNRs of bladder were 3.72 ± 0.85 vs. 5.14 ± 1.27. Group B showed better subjective image quality, lower image noise, and improved SNR and CNR, compared to group A; these differences were statistically significant ( P