Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction

Xu Lin; Yankun Gao; Chao Zhu; Jian Song; Ling Liu; Jianying Li; Xingwang Wu

doi:10.1007/s00261-024-04221-y

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction

Abdom Radiol (NY). 2024 Mar 14. doi: 10.1007/s00261-024-04221-y. Online ahead of print.

Authors

Xu Lin^#¹, Yankun Gao^#¹, Chao Zhu¹, Jian Song¹, Ling Liu², Jianying Li², Xingwang Wu³

Affiliations

¹ Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
² CT Research Center, GE Healthcare China, Shanghai, 210000, China.
³ Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China. duobi2004@126.com.

^# Contributed equally.

PMID: 38480547
DOI: 10.1007/s00261-024-04221-y

Abstract

Objective: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images.

Methods: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases.

Results: Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05].

Conclusions: DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

Keywords: Computed tomography enterography; Deep-learning image reconstruction; Image quality; Image reconstruction; Radiation dose.