Impact of CT reconstruction algorithms on pericoronary and epicardial adipose tissue attenuation

Huawei Xiao; Xiangquan Wang; Panfeng Yang; Ling Wang; Jiada Xi; Jian Xu

doi:10.1016/j.ejrad.2025.112132

Impact of CT reconstruction algorithms on pericoronary and epicardial adipose tissue attenuation

Eur J Radiol. 2025 Apr 23:188:112132. doi: 10.1016/j.ejrad.2025.112132. Online ahead of print.

Authors

Huawei Xiao¹, Xiangquan Wang², Panfeng Yang¹, Ling Wang¹, Jiada Xi¹, Jian Xu³

Affiliations

¹ Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China.
² Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China; Zhejiang Chinese Medical University, China.
³ Heart Center, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, 310014 Hangzhou, China. Electronic address: hzxj_610@163.com.

PMID: 40344712
DOI: 10.1016/j.ejrad.2025.112132

Abstract

Objective: This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adipose tissue (EAT). Furthermore, we propose to explore the feasibility of correcting the effects through fat threshold adjustment.

Methods: A retrospective analysis was conducted on the imaging data of 134 patients who underwent coronary CT angiography (CCTA) between December 2023 and January 2024. These data were reconstructed into seven datasets using filtered back projection (FBP), ASIR-V at three different intensities (ASIR-V 30%, ASIR-V 50%, ASIR-V 70%), and DLIR at three different intensities (DLIR-L, DLIR-M, DLIR-H). Repeated-measures ANOVA was used to compare differences in fat, PCAT and EAT attenuation values among the reconstruction algorithms, and Bland-Altman plots were used to analyze the agreement between ASIR-V or DLIR and FBP algorithms in PCAT attenuation values.

Results: Compared to FBP, ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, DLIR-M, and DLIR-H significantly increased fat attenuation values (-103.91 ± 12.99 HU, -102.53 ± 12.68 HU, -101.14 ± 12.78 HU, -101.81 ± 12.41 HU, -100.87 ± 12.25 HU, -99.08 ± 12.00 HU vs. -105.95 ± 13.01 HU, all p < 0.001). When the fat threshold was set at -190 to -30 HU, ASIR-V and DLIR algorithms significantly increased PCAT and EAT attenuation values compared to FBP algorithm (all p < 0.05), with these values increasing as the reconstruction intensity level increased. After correction with a fat threshold of -200 to -35 HU for ASIR-V 30 %, -200 to -40 HU for ASIR-V 50 % and DLIR-L, and -200 to -45 HU for ASIR-V 70 %, DLIR-M, and DLIR-H, the mean differences in PCAT attenuation values between ASIR-V or DLIR and FBP algorithms decreased (-0.03 to 1.68 HU vs. 2.35 to 8.69 HU), and no significant difference was found in PCAT attenuation values between FBP and ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, and DLIR-M (all p > 0.05).

Conclusion: Compared to the FBP algorithm, ASIR-V and DLIR algorithms increase PCAT and EAT attenuation values. Adjusting the fat threshold can mitigate the impact of ASIR-V and DLIR algorithms on PCAT attenuation values.

Keywords: Coronary CT angiography; Deep learning reconstruction; Epicardial adipose tissue; Pericoronary adipose tissue.