PixelShine Algorithm in Enhancing the Quality of Reconstructed Abdominal Arterial Phase CT Image / 中国医学影像学杂志

ABSTRACT

Purpose To explore the feasibility of denoising algorithm-PixelShine algorithm based on deep learning to enhance the quality of abdominal arterial phase CT images rebuilt by 70 kVp combined with adaptive statistical iterative reconstruction-Veo (ASiR-V). Materials and Methods Abdominal arterial phase images of 33 patients [body mass index (BMI) BMI≤20 kg/m2] scanned by GE Revolution CT were retrospectively analyzed (group A) using 70 kVp tube voltage and 50% ASiR-V technique. PixelShine algorithm B2 mode was applied to post-process group A images to obtain PixelShine image (group B). Two observers rated the image quality of the two groups via a 5-point rating system. The consistency of the rating was analyzed. The difference in ratings, noise, virtual signal-to-noise ratio (SNR) of liver and pancreas and contrast noise ratio (CNR) were compared between the two groups of images. Results The image quality rating of group A and B were(3.12±0.33) scores and(3.97±0.53) scores respectively,noise value(14.50±1.42)HU vs(10.05±1.80)HU, liver virtual SNR 4.51±0.53 vs 6.78.±1.27,liver virtual CNR 0.89±0.55 vs 1.42±0.81,pancreatic virtual SNR 9.51±1.69 vs 13.87±3.26, and pancreatic virtual CNR 5.83±1.66 vs 8.48±2.46.The quality rating of images,liver and pancreas virtual SNR,CNR in group B were all higher than those in group A, and the image noise of group B decreased about 31% compared with that of group A, the difference was statistically significant (P<0.05). Conclusion Post-processing with PixelShine algorithm can improve the image quality of 70 kVp abdominal arterial phase, significantly reduce image noise, and increase image SNR and CNR.