TECHNOLOGY AND CONTRAST MEDIA / ORIGINAL PAPER
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
1
Department of Radiology, Key Laboratory of Birth Defects and Related Diseases of Women and Children of Ministry of Education, West China Second University Hospital, Sichuan University, Sichuan, China
2
Department of Radiology, Mianyang Hospital of Traditional Chinese Medicine, Sichuan, China
3
Department of Radiology, Ulaanchabu City Center Hospital, Inner Mongolia, China
4
Shanghai Lianyin Intelligent Medical Technology Co. Shanghai, Shanghai, China
Submission date: 2025-02-18
Acceptance date: 2025-05-22
Publication date: 2025-07-14
Corresponding author
Pol J Radiol, 2025; 90: 356-366
KEYWORDS
coronary arterycoronary artery calciumiterative reconstructioncomputed tomographyadvanced modelled iterative reconstruction
TOPICS
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; x2 = 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.
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; x2 = 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.
REFERENCES (34)
1.
Yao H, Feng G, Liu Y, Chen Y, Shao C, Wang Z. Coronary artery calcification burden, atherogenic index of plasma, and risk of adverse cardiovascular events in the general population: evidence from a mediation analysis. Lipids Health Dis 2024; 23: 258. DOI: 10.1186/s12944-024-02255-1.
2.
Vatsa N, Faaborg-Andersen C, Dong T, Blaha MJ, Shaw LJ, Quintana RA.Coronary atherosclerotic plaque burden assessment by computed tomography and its clinical implications. Circ Cardiovasc Imaging 2024; 17: e016443. DOI: 10.1161/CIRCIMAGING.123.016443.
3.
Kim KP, Einstein AJ, Berrington de González A. Coronary artery calcification screening: estimated radiation dose and cancer risk. Arch Intern Med 2009; 169: 1188-1194.
4.
Gräni C, Vontobel J, Benz DC, Bacanovic S, Giannopoulos AA, Messerli M, et al. Ultra-low-dose coronary artery calcium scoring using novel scoring thresholds for low tube voltage protocols-a pilot study. Eur Heart J Cardiovasc Imaging 2018; 19: 1362-1371.
5.
Bechtiger FA, Grossmann M, Bakula A, Patriki D, von Felten E, Fuchs TA, et al. Risk stratification using coronary artery calcium scoring based on low tube voltage computed tomography. Int J Cardiovasc Imaging 2022; 38: 2227-2234.
6.
Dey D, Nakazato R, Pimentel R, Paz W, Hayes SW, Friedman JD, et al. Low radiation coronary calcium scoring by dual-source CT with tube current optimization based on patient body size. J Cardiovasc Comput Tomogr 2012; 6: 113-120.
7.
Wang W, Zhao YE, Qi L, Zhou CS, Lu MJ, Yang JX, et al. Sinogram-affirmed iterative reconstruction negatively impacts the risk category based on Agatston score: a study combining coronary calcium score measurement and coronary CT angiography. Biomed Res Int 2020; 13: 6909130. DOI: 10.1155/2020/6909130.
8.
Kang HW, Ahn WJ, Jeong JH, Suh YJ, Yang DH, Choi H, et al. Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness. Eur Radiol 2023; 33: 1973-1981.
9.
Zook S, Tayal B, Kragholm K, et al. Intraindividual comparison of dose reduction and coronary calcium scoring accuracy using kilovolt-independent and tin filtration CT protocols. Radiol Cardiothorac Imaging 2024; 6: e230246. DOI: 10.1148/ryct.230246.
10.
Marwan M, Mettin C, Pflederer T, Seltmann M, Schuhbäck A, Muschiol G, et al. Very low-dose coronary artery calcium scanning with high-pitch spiral acquisition mode: comparison between 120-kV and 100-kV tube voltage protocols. J Cardiovasc Comput Tomogr 2013; 7: 32-38.
11.
Gebhard C, Fiechter M, Fuchs TA, Ghadri JR, Herzog BA, Kuhn F, et al. Coronary artery calcium scoring: Influence of adaptive statistical iterative reconstruction using 64-MDCT. Int J Cardiol 2013; 167: 2932-2937.
12.
An S, Fan R, Zhao B, Yi Q, Yao S, Shi X, et al. Evaluating coronary artery calcification with low-dose chest CT reconstructed by different kernels. Clin Imaging 2022; 83: 166-171.
13.
Messerli M, Rengier F, Desbiolles L, Ehl NF, Bauer RW, Leschka S, et al. Impact of advanced modeled iterative reconstruction on coronary artery calcium quantification. Acad Radiol 2016; 23: 1506-1512.
14.
Tesche C, De Cecco CN, Schoepf UJ, Duguay TM, Albrecht MH, Caruso D, et al. Iterative beam-hardening correction with advanced modeled iterative reconstruction in low voltage CT coronary calcium scoring with tin filtration: Impact on coronary artery calcium quantification and image quality. J Cardiovasc Comput Tomogr 2017; 11: 354-359.
15.
Kurata A, Dharampal A, Dedic A, de Feyter PJ, Krestin GP, Dijkshoorn ML, Nieman K. Impact of iterative reconstruction on CT coronary calcium quantification. Eur Radiol 2013; 23: 3246-3252.
16.
Deak PD, Smal Y, Kalender WA. Multisection CT protocols: sex- and age-specific conversion factors used to determine effective dose from dose-length product. Radiology 2010; 257: 158-166.
17.
Wongyikul P, Tantraworasin A, Suwannasom P, Srisuwan T, Wannasopha Y, Phinyo P. Prediction model for recommending coronary artery calcium score screening (CAC-prob) in cardiology outpatient units: a development study. PLoS One 2024; 19: e0308890. DOI: 10.1371/journal.pone.0308890.
18.
Schindler A, Vliegenthart R, Schoepf UJ, Blanke P, Ebersberger U, Cho YJ, et al. Iterative image reconstruction techniques for CT coronary artery calcium quantification: comparison with traditional filtered back projection in vitro and in vivo. Radiology 2014; 270: 387-393.
19.
Xu X, Sui X, Song L, Huang Y, Ge Y, Jin Z, Song W. Feasibility of low-dose CT with spectral shaping and third-generation iterative reconstruction in evaluating interstitial lung diseases associated with connective tissue disease: an intra-individual comparison study. Eur Radiol 2019; 29: 4529-4537.
20.
Renker M, Nance JW, Schoepf UJ, O’Brien TX, Zwerner PL, Meyer M, et al. Evaluation of heavily calcified vessels with coronary CT angiography: comparison of iterative and filtered back projection image reconstruction. Radiology 2011; 260: 390-399.
21.
Kang DK. Assessment of coronary stenosis using coronary CT angiography in patients with high calcium scores: current limitations and future perspectives. J Korean Soc Radiol 2024; 85: 270-296 [Article in Korean].
22.
Vingiani V, Abadia AF, Schoepf UJ, Fischer AM, Varga-Szemes A, Sahbaee P, et al. Low-kV coronary artery calcium scoring with tin filtration using a kV-independent reconstruction algorithm. J Cardiovasc Comput Tomogr 2020; 14: 246-250.
23.
Frey SM, Huré G, Leibfarth JP, Thommen K, Amrein M, Rumora K, et al. Diagnostic utility of coronary artery calcium score percentiles and categories to exclude abnormal scans and relevant ischemia in rubidium positron emission tomography. Front Cardiovasc Med 2024; 11: 1467916. DOI: 10.3389/fcvm.2024.1467916.
24.
Sulaiman N, Soon J, Park JK, Naoum C, Kueh SH, Blanke P, et al. Comparison of low-dose coronary artery calcium scoring using low tube current technique and hybrid iterative reconstruction vs. filtered back projection. Clin Imaging 2017; 43: 19-23.
25.
Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, et al. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 2018; 16: 412-441.
26.
Jia CF, Zhong J, Meng XY, Sun XX, Yang ZQ, Zou YJ, et al. Image quality and diagnostic value of ultra low-voltage, ultra low-contrast coronary CT angiography. Eur Radiol 2019; 29: 3678-3685.
27.
Fink N, Zsarnoczay E, Schoepf UJ, O’Doherty J, Griffith JP 3rd, Pinos D, et al. Radiation dose reduction for coronary artery calcium scoring using a virtual noniodine algorithm on photon-counting detector computed-tomography phantom data. Diagnostics (Basel) 2023; 13: 1540. DOI: 10.3390/diagnostics13091540.
28.
Madaj P, Li D, Nakanishi R, Andreini D, Pontone G, Conte E, et al. Radiation doses in patients undergoing computed tomographic coronary artery calcium evaluation with a 64-slice scanner versus a 256-slice scanner. Tex Heart Inst J 2022; 49: e186793. DOI: 10.14503/THIJ-18-6793.
29.
Vonder M, van der Werf NR, Leiner T, Greuter MJW, Fleischmann D, Vliegenthart R, et al. The impact of dose reduction on the quantification of coronary artery calcifications and risk categorization: a systematic review. J Cardiovasc Comput Tomogr 2018; 12: 352-363.
30.
Voros S, Rivera JJ, Berman DS, Blankstein R, Budoff MJ, Cury RC, et al. Guideline for minimizing radiation exposure during acquisition of coronary artery calcium scans with the use of multidetector computed tomography: a report by the Society for Atherosclerosis Imaging and Prevention Tomographic Imaging and Prevention Councils in collaboration with the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr 2011; 5: 75-83.
31.
Tang YC, Liu YC, Hsu MY, Tsai HY, Chen CM. Adaptive iterative dose reduction 3D integrated with automatic tube current modulation for CT coronary artery calcium quantification: comparison to traditional filtered back projection in an anthropomorphic phantom and patients. Acad Radiol 2018; 25: 1010-1017.
32.
Apfaltrer G, Albrecht MH, Schoepf UJ, Duguay TM, De Cecco CN, Nance JW, et al. High-pitch low-voltage CT coronary artery calcium scoring with tin filtration: accuracy and radiation dose reduction. Eur Radiol 2018; 28: 3097-3104.
33.
Dane B, O’Donnell T, Liu S, Vega E, Mohammed S, Singh V, et al. Radiation dose reduction, improved isocenter accuracy and CT scan time savings with automatic patient positioning by a 3D camera. Eur J Radiol 2021; 136: 109537. DOI: 10.1016/j.ejrad.2021.109537.
34.
Saltybaeva N, Schmidt B, Wimmer A, Flohr T, Alkadhi H. Precise and automatic patient positioning in computed tomography: avatar modeling of the patient surface using a 3-dimensional camera. Invest Radiol 2018; 53: 641-646.
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