Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose

Kun Zhang; Xiang Shi; Shuang-Shuang Xie; Ji-Hang Sun; Zhuo-Heng Liu; Shuai Zhang; Jia-Yang Song; Wen Shen

doi:10.21037/qims-21-936

Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose

Quant Imaging Med Surg. 2022 Jun;12(6):3238-3250. doi: 10.21037/qims-21-936.

Authors

Kun Zhang¹, Xiang Shi², Shuang-Shuang Xie¹, Ji-Hang Sun³, Zhuo-Heng Liu⁴, Shuai Zhang⁴, Jia-Yang Song⁴, Wen Shen¹

Affiliations

¹ Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, School of Medicine, Nankai University, Tianjin, China.
² First Central Clinical College, Tianjin Medical University, Tianjin, China.
³ Imaging Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
⁴ GE Healthcare, Computed Tomography Research Center, Beijing, China.

Abstract

Background: Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population.

Methods: A pediatric phantom was used for the pilot study, and 20 children were recruited for clinical verification. The preset scan parameter noise index (NI) was 5, 8, 11, 13, 15, and 18 for the phantom study, and 8 and 13 for the clinical pediatric study. We reconstructed CT images with ASiR-V 30%, ASiR-V 70%, DLIR-M (medium) and DLIR-H (high). The regions of interest (ROI) were marked on the organs of the abdomen (liver, kidney, and subcutaneous fat) and the chest (lung, mediastinum, and spine). The CT dose index volume (CTDI_vol), CT value, image noise (N), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated. The subjective image quality was assessed by 3 radiologists blindly using a 5-point scale. The dose reduction efficiency of DLIR was estimated.

Results: In the phantom study, the interobserver assessment of the data measurement demonstrated good agreement [intraclass correlation coefficient (ICC) =0.814 for abdomen, 0.801 for chest]. Within the same dose level, the N, SNR, and CNR were statistically different among reconstructions, while the CT value remained the same. The N increased and SNR decreased as the radiation dose decreased. The DLIR-H performed better than ASiR-V when the radiation dose was reduced, without sacrificing image quality. In the patient study, the interobserver assessment of the data measurement demonstrated good agreement (ICC =0.774 for abdomen, 0.751 for chest). DLIR-H had the highest subjective and objective scores in the abdomen.

Conclusions: Application of DLIR could help to reduce radiation dose without sacrificing the image quality of pediatric CT scans. Further clinical validation is required.

Keywords: Pediatrics; deep learning image reconstruction (DLIR); radiation dosage; radiologic phantom.