Cardiac-Specific AI Reconstruction Versus Hybrid Iterative Reconstruction for Emergency Coronary CT Angiography: A Paired Single-Center Study Including Triple-Rule-Out CT
- 1Department of Radiology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, 20 Ilsan-ro, Wonju 26426, Republic of Korea
- 2United Imaging Healthcare North America, Houston, TX 77054, USA
Abstract
Background/Objectives: Optimizing coronary image quality is important in emergency computed tomography (CT), including coronary CT angiography (CCTA) and triple-rule-out CT angiography (TRO-CTA). We compared objective and subjective image quality between cardiac-specific artificial intelligence (AI) reconstruction and hybrid iterative reconstruction (HIR) using paired images from the same acquisition. Methods: This retrospective single-center study included 66 of 75 consecutively assessed emergency examinations: 28 standard CCTA and 38 TRO-CTA examinations acquired without pharmacologic heart-rate control. Both protocols used identical cardiac acquisition settings. AI and HIR images were reconstructed from the same acquisition with matched cardiac phases and motion-correction settings. Objective measurements were obtained at nine coronary locations, and two readers independently assessed overall image quality and perceived vessel-edge sharpness in unmasked paired comparisons. Results: AI reduced coronary noise from 45.8 ± 8.5 to 24.2 ± 7.5 Hounsfield units (HU; −47.3%), increased signal-to-noise ratio (SNR) from 9.0 ± 2.4 to 20.6 ± 5.6 (+129.6%), and increased contrast-to-noise ratio (CNR) from 14.8 ± 4.2 to 45.8 ± 16.4 (+209.7%; all Holm-adjusted p < 0.001). Readers 1 and 2 preferred AI for overall quality in 65/66 and 63/66 examinations and for sharpness in 64/66 and 63/66, respectively. Conclusions: Cardiac-specific AI reconstruction reduced image noise and increased SNR and CNR in emergency CCTA, including TRO-CTA, with readers generally favoring AI images for perceived quality and sharpness. Further studies should evaluate whether these image-quality improvements translate into better diagnostic performance.
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BibTeX
@article{ko2026cardiacspecific,
title = {{Cardiac-Specific AI Reconstruction Versus Hybrid Iterative Reconstruction for Emergency Coronary CT Angiography: A Paired Single-Center Study Including Triple-Rule-Out CT}},
author = {Sung Min Ko and Hyunjung Kim and Sung-Jin Cha and Yongfeng Gao},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207940},
url = {https://www.mdpi.com/2077-0383/15/20/7940},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Cardiac-Specific AI Reconstruction Versus Hybrid Iterative Reconstruction for Emergency Coronary CT Angiography: A Paired Single-Center Study Including Triple-Rule-Out CT
AU - Sung Min Ko
AU - Hyunjung Kim
AU - Sung-Jin Cha
AU - Yongfeng Gao
PY - 2026
DA - 2026/10/01
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207940
UR - https://www.mdpi.com/2077-0383/15/20/7940
AB - Background/Objectives: Optimizing coronary image quality is important in emergency computed tomography (CT), including coronary CT angiography (CCTA) and triple-rule-out CT angiography (TRO-CTA). We compared objective and subjective image quality between cardiac-specific artificial intelligence (AI) reconstruction and hybrid iterative reconstruction (HIR) using paired images from the same acquisition. Methods: This retrospective single-center study included 66 of 75 consecutively assessed emergency examinations: 28 standard CCTA and 38 TRO-CTA examinations acquired without pharmacologic heart-rate control. Both protocols used identical cardiac acquisition settings. AI and HIR images were reconstructed from the same acquisition with matched cardiac phases and motion-correction settings. Objective measurements were obtained at nine coronary locations, and two readers independently assessed overall image quality and perceived vessel-edge sharpness in unmasked paired comparisons. Results: AI reduced coronary noise from 45.8 ± 8.5 to 24.2 ± 7.5 Hounsfield units (HU; −47.3%), increased signal-to-noise ratio (SNR) from 9.0 ± 2.4 to 20.6 ± 5.6 (+129.6%), and increased contrast-to-noise ratio (CNR) from 14.8 ± 4.2 to 45.8 ± 16.4 (+209.7%; all Holm-adjusted p < 0.001). Readers 1 and 2 preferred AI for overall quality in 65/66 and 63/66 examinations and for sharpness in 64/66 and 63/66, respectively. Conclusions: Cardiac-specific AI reconstruction reduced image noise and increased SNR and CNR in emergency CCTA, including TRO-CTA, with readers generally favoring AI images for perceived quality and sharpness. Further studies should evaluate whether these image-quality improvements translate into better diagnostic performance.
SN - 2077-0383
ER -