Impact of the ADMIRE reconstruction algorithm combined with the Sa36 kernel on quantitative measurement of coronary artery calcification in AI: a single-arm prospective study.

Polish journal of radiology Pub Date : 2025-07-14 eCollection Date: 2025-01-01 DOI:10.5114/pjr/205465
Huayang Du, Quanyu He, Jia Ren, Nan Jiang, Yanling Wang, Guisong Yang, Fei Han, Huahu Xu
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Abstract

Purpose: Accurate quantification of coronary artery calcium (CAC) via computed tomography (CT) imaging is essential for effective cardiovascular risk assessment. This study investigates the impact of different iteration levels in the advanced model-based iterative reconstruction (ADMIRE) algorithm on artificial intelligence-driven CAC quantification and subsequent risk stratification, with filtered back projection (FBP) serving as the reference.

Material and methods: For 254 patients undergoing coronary CT angiography (120 kVp, automated tube current), raw data were reconstructed using FBP and ADMIRE levels 1-5 (kernel Sa36, 3.0 mm slice thickness, 1.5 mm spacing). AI-derived CAC parameters (volume, mass, Agatston score) and risk stratification were compared across reconstruction groups. Statistical analysis employed the Friedman test, one-way analysis of variance, and c2 test.

Results: Compared to FBP, ADMIRE 1-5 reduced image noise by 9.70% to 49.76% (noise: 14.95 ± 2.26 HU vs. 7.55 ± 1.40 HU, F = 455.105, p < 0.001). Maximum CAC CT values progressively decreased with higher ADMIRE levels (FBP: 458.50 [306.00-645.00] HU vs. ADMIRE 5: 432.50 [281.75-620.75] HU; χ2 = 455.105, p < 0.001). CAC volume, mass, and Agatston scores declined significantly (p < 0.001 for all): volume decreased by 8.56-32.55% (FBP: 47.56 ± 5.93 mm3 vs. ADMIRE 5: 21.77 ± 3.46 mm3; F = 32.310); mass decreased by 8.73-32.57% (F = 29.477); and Agatston scores decreased by 8.77-33.13% (F = 31.104). Risk stratification shifted in 24/161 patients (14.91%) with detectable CAC. The effective radiation dose was 0.61 ± 0.18 mSv.

Conclusions: ADMIRE reconstruction reduces image noise but progressively lowers CAC quantification (volume, mass, Agatston score) and maximum CT values, leading to underestimation of cardiovascular risk in a subset of patients. Caution is warranted when applying ADMIRE iterative reconstruction for CAC scoring.

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结合Sa36核的佩服重建算法对人工智能冠状动脉钙化定量测量的影响:单臂前瞻性研究
目的:通过计算机断层扫描(CT)准确定量冠状动脉钙(CAC)对有效的心血管风险评估至关重要。本研究以滤波后投影(filter back projection, FBP)为参考,研究了基于先进模型的迭代重建(advanced model-based iterative reconstruction,钦佩)算法中不同迭代级别对人工智能驱动的CAC量化及后续风险分层的影响。材料和方法:对254例接受冠状动脉CT血管造影(120 kVp,自动管电流)的患者,使用FBP和1-5级(核Sa36, 3.0 mm切片厚度,1.5 mm间距)重建原始数据。人工智能衍生的CAC参数(体积、质量、Agatston评分)和风险分层在重建组之间进行比较。统计分析采用Friedman检验、单因素方差分析和c2检验。结果:与FBP相比,佩服1-5将图像噪声降低了9.70% ~ 49.76%(噪声:14.95±2.26 HU vs 7.55±1.40 HU, F = 455.105, p < 0.001)。CAC CT最大值随着敬仰水平的升高而逐渐降低(FBP: 458.50 [306.00-645.00] HU vs.敬仰5:432.50 [281.75-620.75]HU; χ2 = 455.105, p < 0.001)。CAC体积、质量和Agatston评分均显著下降(p < 0.001):体积下降8.56-32.55% (FBP: 47.56±5.93 mm3 vs.钦佩5:21.77±3.46 mm3, F = 32.310);质量降低8.73 ~ 32.57% (F = 29.477);Agatston评分下降8.77% ~ 33.13% (F = 31.104)。在24/161例(14.91%)可检测到CAC的患者中,风险分层发生了变化。有效辐射剂量为0.61±0.18 mSv。结论:钦佩重建降低了图像噪声,但逐渐降低了CAC量化(体积、质量、Agatston评分)和最大CT值,导致对一部分患者心血管风险的低估。在应用钦佩迭代重建进行CAC评分时,需要谨慎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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