Improving image quality of triple-low-protocol renal artery CT angiography with deep-learning image reconstruction: a comparative study with standard-dose single-energy and dual-energy CT with adaptive statistical iterative reconstruction.

Abstract

Authors

• Z Meng
  Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
• Y Guo
  Pingan Technology (Shenzhen) Co., Ltd., Institute for Smart Health, Intelligent Medical Image Analysis, Shenzhen 518046, China.
• S Deng
  Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
• Q Xiang
  Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China.
• J Cao
• Y Zhang
  University Technology Sydney, 15 Broadway, Ultimo, NSW Australia.
• K Zhang
  Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, LA, 70125, USA.
• K Ma
  Department of Pathology, School of Basic Medical Science, Central South University, Changsha, 410013, Hunan, China.
• S Xie
  Zhongshan Ophthalmic Centre, Sun Yat-sen University, State Key Laboratory of Ophthalmology, Guangzhou 510060, China.
• Z Kang
  Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Tianhe District, Tianhe Road, 600, Guangzhou, 510620, China. Electronic address: kzkz_kzkz@126.com.

Keywords

External Resources