Application of deep learning image reconstruction algorithm to improve image quality in CT angiography of children with Takayasu arteritis

Article type: Research Article

Authors: Sun, Jihang^{a; 1} | Li, Haoyan^{a; 1} | Li, Haiyun^b | Li, Michelle^c | Gao, Yingzi^a | Zhou, Zuofu^{d; 2; *} | Peng, Yun^{a; 2; *}

Affiliations: [a] Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, China | [b] School of Biomedical Engineering, Capital Medical University, Fengtai District, Beijing, China | [c] Department of Human Biology, Stanford University, Stanford, CA, USA | [d] Department of Radiology, Fujian Provincial Maternity and Children’s Hospital, Affiliated Hospital of Fujian Medical University, Gulou District, Fujian, China

Correspondence: [*] Corresponding authors: Yun Peng, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, 56 Nanlishi Road, Xicheng District, Beijing 100045, China. Tel.: +86 010 59617038; E-mail: [email protected]. Zuofu Zhou, Department of Radiology, Fujian Provincial Maternity and Children’s Hospital, Affiliated Hospital of Fujian Medical University, 18 Daoshan Road, Gulou District, Fujian 350000, China. Tel.: +86 13328659789; E-mail: [email protected].

Note: [1] Dr. Jihang Sun and Dr. Haoyan Li have made the equal contribution as the First author in this study.

Note: [2] Dr. Yun Peng, and Dr. Zuofu Zhou have made the equal contribution as the corresponding author in this study.

Abstract: BACKGROUND:The inflammatory indexes of children with Takayasu arteritis (TAK) usually tend to be normal immediately after treatment, therefore, CT angiography (CTA) has become an important method to evaluate the status of TAK and sometime is even more sensitive than laboratory test results. OBJECTIVE:To evaluate image quality improvement in CTA of children diagnosed with TAK using a deep learning image reconstruction (DLIR) in comparison to other image reconstruction algorithms. METHODS:hirty-two TAK patients (9.14±4.51 years old) underwent neck, chest and abdominal CTA using 100 kVp were enrolled. Images were reconstructed at 0.625 mm slice thickness using Filtered Back-Projection (FBP), 50%adaptive statistical iterative reconstruction-V (ASIR-V), 100%ASIR-V and DLIR with high setting (DLIR-H). CT number and standard deviation (SD) of the descending aorta and back muscle were measured and contrast-to-noise ratio (CNR) for aorta was calculated. The vessel visualization, overall image noise and diagnostic confidence were evaluated using a 5-point scale (5, excellent; 3, acceptable) by 2 observers. RESULTS:There was no significant difference in CT number across images reconstructed using different algorithms. Image noise values (in HU) were 31.36±6.01, 24.96±4.69, 18.46±3.91 and 15.58±3.65, and CNR values for aorta were 11.93±2.12, 15.66±2.37, 22.54±3.34 and 24.02±4.55 using FBP, 50%ASIR-V, 100%ASIR-V and DLIR-H, respectively. The 100%ASIR-V and DLIR-H images had similar noise and CNR (all P > 0.05), and both had lower noise and higher CNR than FBP and 50%ASIR-V images (all P < 0.05). The subjective evaluation suggested that all images were diagnostic for large arteries, however, only 50%ASIR-V and DLIR-H met the diagnostic requirement for small arteries (3.03±0.18 and 3.53±0.51). CONCLUSION:DLIR-H improves CTA image quality and diagnostic confidence for TAK patients compared with 50%ASIR-V, and best balances image noise and spatial resolution compared with 100%ASIR-V.

Keywords: Computed Tomography Angiography (CTA), takayasu arteritis, pediatrics, deep learning image reconstruction

DOI: 10.3233/XST-211033

Journal: Journal of X-Ray Science and Technology, vol. 30, no. 1, pp. 177-184, 2022