Congress:
ECR25
Poster Number:
C-17860
Type:
Poster: EPOS Radiologist (scientific)
Authorblock:
X. Zhang1, G. Zhang1, H. Sun1, Z. Jin1, J. Yan1, M. Xu1, L. Xu2, J. Zhang1, X. Bai1; 1Beijing/CN, 2Hangzhou/CN
Disclosures:
Xiaoxiao Zhang:
Nothing to disclose
Gumuyang Zhang:
Nothing to disclose
Hao Sun:
Nothing to disclose
Zhengyu Jin:
Nothing to disclose
Jing Yan:
Nothing to disclose
Min Xu:
Nothing to disclose
Lili Xu:
Nothing to disclose
Jiahui Zhang:
Nothing to disclose
Xin Bai:
Nothing to disclose
Keywords:
Abdomen, Kidney, Urinary Tract / Bladder, CT, Technology assessment, Calcifications / Calculi
- Alatab S, Pourmand G, El Howairis MF et al (2016) National profles of urinary calculi: a comparison between developing and developed worlds.
- Iran J Kidney Dis 10(2):51–61Moe OW (2006) Kidney stones: pathophysiology and medical management. Lancet 367(9507):333–344
- Nakamura Y, Higaki T, Tatsugami F et al (2020) Possibility of deep learning in medical imaging focusing improvement of computed tomography image quality. J Comput Assist Tomogr 44(2):161–167
- Higaki T, Nakamura Y, Zhou J et al (2020) Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 27(1):82–87
- Van Stiphout JA, Driessen J, Koetzier LR et al (2022) The efect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis. Eur Radiol 32(5):2921–2929