Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis

J Ren; J Zhao; Y Wang; M Xu; X-Y Liu; Z-Y Jin; Y-L He; Y Li; H-D Xue

doi:10.1016/j.crad.2023.07.016

Value of deep-learning image reconstruction at submillisievert CT for evaluation of the female pelvis

Clin Radiol. 2023 Nov;78(11):e881-e888. doi: 10.1016/j.crad.2023.07.016. Epub 2023 Aug 23.

Authors

J Ren¹, J Zhao¹, Y Wang¹, M Xu², X-Y Liu¹, Z-Y Jin¹, Y-L He³, Y Li⁴, H-D Xue⁵

Affiliations

¹ Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China.
² Cannon Medical System, Beijing, PR China.
³ Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China. Electronic address: heyonglan@pumch.cn.
⁴ Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic Diseases, Beijing, PR China. Electronic address: liyuan10833@pumch.cn.
⁵ Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, PR China. Electronic address: bjdanna95@hotmail.com.

PMID: 37620170
DOI: 10.1016/j.crad.2023.07.016

Abstract

Aim: To assess the value of deep-learning reconstruction (DLR) at submillisievert computed tomography (CT) for the evaluation of the female pelvis, with standard dose (SD) hybrid iterative reconstruction (IR) images as reference.

Materials and methods: The present study enrolled 50 female patients consecutively who underwent contrast-enhanced abdominopelvic CT for clinically indicated reasons. Submillisievert pelvic images were acquired using a noise index of 15 for low-dose (LD) scans, which were reconstructed with DLR (body and body sharp), hybrid-IR, and model-based IR (MBIR). Additionally, SD scans were reconstructed with a noise index of 7.5 using hybrid-IR. Radiation dose, quantitative image quality, overall image quality, image appearance using a five-point Likert scale (1-5: worst to best), and lesion evaluation in both SD and LD images were analysed and compared.

Results: The submillisievert pelvic CT examinations showed a 61.09 ± 4.13% reduction in the CT dose index volume compared to SD examinations. Among the LD images, DLR (body sharp) had the highest quantitative quality, followed by DLR (body), MBIR, and hybrid-IR. LD DLR (body) had overall image quality comparable to the reference (p=0.084) and favourable image appearance (p=0.209). In total, 40 pelvic lesions were detected in both SD and LD images. LD DLR (body and body sharp) exhibited similar diagnostic confidence (p=0.317 and 0.096) compared with SD hybrid-IR.

Conclusion: DLR algorithms, providing comparable image quality and diagnostic confidence, are feasible in submillisievert abdominopelvic CT. The DLR (body) algorithm with favourable image appearance is recommended in clinical settings.