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

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.