Impact of Slice Thickness on CACS Calculation with PureCalcium Algorithm in Photon-Counting CT

Impact of Slice Thickness on CACS Calculation with PureCalcium Algorithm in Photon-Counting CT | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Slice Thickness on CACS Calculation with PureCalcium Algorithm in Photon-Counting CT Qiuju Hu, Huixin Zhang, Bangjun Guo, Dongsheng Jin, Meirong Sun, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6843811/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted 13 You are reading this latest preprint version Abstract Background : This study aims to investigate the feasibility of coronary artery calcium scoring (CACS) calculating from PureCalcium virtual non-iodine algorithm on photon-counting detector CT (PCD-CT) and the potential impact of different section thickness, level of virtual monoenergetic images (VMIs), and quantum iterative reconstruction (QIR) on the accuracy of CACS quantification. Materials and Methods : A total of 123 patients who underwent coronary CT angiography on PCD-CT with a separate true non-contrast CACS (CACS TNC ) scan were prospectively included. Agatston scores were calculated from the PureCalcium algorithm (CACS PC ) using a section thickness of 3mm or 1.5mm, different VMI (55–75 kilo-electron volt (keV)) and QIR (strength 1,4) levels, respectively. CACS TNC at 70 keV and QIR 2 were used as reference standards. Differences in CACS of different reconstructions section thicknesses, various keV levels, and QIR strength were compared using the Wilcoxon rank sum test with Bonferroni correction. The intraclass correlation coefficients (ICCs) and Bland-Altman analysis were conducted to assessed the agreement. The agreement of plaque burden groups (based on CACS) at different reconstruction parameters was evaluated using weighted Cohen kappa. Results : At all investigated section thickness, VMI, and QIR levels, the CACS PC were strongly correlated with CACS TNC (ICC: 0.94–0.98, P < 0.001 for all). There were no statistical differences in CACS between CACS PC at 3mm section thickness, 60/65 keV (QIR1/4), and at 1.5 mm section thickness with 55 keV (QIR1/4), compared with CACS TNC . The smallest CACS bias was observed at a 1.5 mm section thickness, 55 keV, QIR 1, with mean bias of 2.4; LoA (IQR: −182.7, 187.4). CACS PC correctly identified 105 of 123 participants (85.4%) into the corresponding plaque burden group using CACS TNC as the referent standard (excellent agreement, κ = 0.904). Conclusion: CACS derived from the PureCalcium algorithm with optimized reconstruction parameters shows excellent correlation with true non-contrast scans derived values. Thus, it is may possible to use the PureCalcium virtual non-iodine algorithm to replace the true non-contrast scans for CACS quantification, without additional radiation dose exposure. Coronary artery calcium scoring spectral imaging virtual non-iodine algorithm coronary artery disease photon counting CT Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Coronary artery calcium score (CACS), which is obtained from non-contrast CT scan, has been demonstrated as an independent prognostic predictor for all-cause mortality in asymptomatic individuals [1] . The progression of coronary calcification remained as an independent predictor for all-cause mortality after adjusting for baseline CACS, interval time of scan, and clinical risk factors [2] . Nowadays, CACS is recommended at a class IIa level of the 2019 AHA guidelines for patients with borderline/intermediate risk and is helpful in guiding clinical management and treatment [3] . In routine clinical examinations, a separate non-contrast CT scan before coronary CT angiography (CCTA), was conducted to quantify the CACS, which may increase the examination time and lead to extra radiation dose. With the advent of dual-energy and spectral imaging techniques, several studies [4-6] have explored the assessment of the CACS on virtual non-contrast (VNC) images. These studies using dual-energy techniques, and have demonstrated high correlations but underestimated CACS compared to the true non-contrast (TNC) images. With the advent of photon-counting detector CT (PCD-CT), it has been shown to have higher spatial resolution and reduced radiation dose. Additionally, PCD-CT data contain differentiated spectral information, which can be used for multi-material decomposition [7] . In this context, a novel virtual noniodine (VNI) algorithm has been developed to create PureCalcium reconstructions on PCD-CT data. Previous studies have demonstrated that the PureCalcium algorithm achieves significantly higher accuracy in CACS quantification than VNC reconstructions [8] . Of noted, VNI reconstructions still underestimate CACS compared with TNC images. The impact of automatic generation of virtual monoenergetic images (VMIs) at different levels of kilo-electron volt (keV), quantum iterative reconstruction (QIR) at various intensity levels, and other section thickness on the accuracy of CACS quantification has not been systematically investigated [9,10] . In this study, we aimed to assess the impact of different VMIs, QIR levels, and section thickness on the accuracy of CACS quantification on the PureCalcium algorithm on PCD-CT. Materials and Methods Study patients A total of 123 patients who underwent separate TNC and contrast-enhanced photon-counting CCTA due to suspected coronary artery disease between January 2024 and April 2024 were prospectively enrolled. Inclusion criteria included subjects older than 18 who underwent CCTA examinations by standard spatial resolution scanning. Exclusion criteria were as follows: (1) patients with CACS = 0; (2) patients who had a history of coronary revascularization (percutaneous coronary intervention or coronary artery bypass graft). This study was approved by our ethics committee (YJ-2024-044-1), and written informed consent was obtained. Data Acquisition All patients were scanned using a PCD-CT (Naeotom Alpha, Siemens Healthineers). TNC scans were performed using the prospective ECG-gated sequential technique, the tube voltage was set at 120 keV, and the tube current-time product was set to an IQ level of 19. CCTA scans were selected as prospective and retrospective based on the participants’ heart rate and rhythm. The tube voltage was set at 120 keV, and the tube current-time product was set to an IQ level of 64. The contrast materials were applied using bolus tracking with an enhancement threshold of 100 HU in the descending aorta and a time delay of 6 seconds. A total of 45 to 60 mL of an iodinated contrast agent (350 mg iohexol/mL, Omnipaque; GE HealthCare) was injected at a flow rate of 4.5 mL/sec. Subsequently, 40 ml of saline was injected with the same rate. If not clinically contraindicated, 0.4 mg of nitroglycerin was sublingually administered approximately 5 minutes before the scan [11] . Image Reconstruction TNC images were reconstructed using a section thickness of 3.0 mm with an increment of 1.5 mm at 70 keV, QIR 2, and a recommended kernel Qr36 (given by the factory protocol). The standard-resolution CCTA images were post-processed using a novel VNI algorithm (PureCalcium, Siemens Healthineers) with Qr36 kernel. For all of the patient data, VMIs were reconstructed at 5-keV intervals from 55 to 75 keV with QIR level 1 or 4 for each keV level and section thickness of 3.0 mm with an increment of 1.5 mm or section thickness of 1.5 mm with an increment of 1.0 mm. Calcium Scoring Analysis The TNC and PureCalcium datasets were evaluated on a semi-automated workflow (CT CaScoring, syngo.via VB60; Siemens Healthineers). Calcified lesion attenuation exceeding 130 HU and a minimum area of 0.5 mm 2 was automatically identified as calcium. Manual adjustments were made as necessary. The calcium scoring calculation was performed by two radiologists (H.X.Z. and Y.E.Z., with >10 years of experience in cardiovascular imaging) on anonymized data sets provided on a syngo.via server. They were blinded to clinical information and independently assessed all data. The CACS on PureCalcium algorithm (CACS PC ) with standard reconstructions (at 55 keV, QIR 1 and 60 keV, QIR 4, section thickness 3.0 mm) and TNC (CACS TNC ) (at 70 keV, QIR 2) [ 9 ] were compared to identify those patients of CACS TNC > 0 but not visible in the standard reconstruction. The smallest basis of CACS determined the combination of optimal reconstruction parameters for calcium detection compared to the true non-contrast scan. The burden of calcium plaque in each patient was classified according to the Coronary Artery Disease Reporting System (P0 = score of 0 [none]; P1 = 1–100 [mild]; P2 = 101–300 [moderate]; P3 = 301–999 [severe]; P4>=1000 [extensive]) [ 11 ] . Statistical Analysis Continuous data were described as mean ± SD, while categorical data were expressed as frequencies and proportions. The Kolmogorov-Smirnov test was used to test the continuous data for normality. Spearman's correlation coefficient (r) was used to assess the correlation between CACS TNC and CACS PC . The differences in calculated CACS with different reconstruction methods were compared with the referent standard (CACS TNC ) using the Friedman test with Bonferroni correction. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used for agreement assessment. The agreement of plaque burden classification between CACS TNC and CACS PC was analyzed using Cohen's kappa (κ) statistics. Statistical analysis was performed using SPSS 27 (IBM Corporation, Armonk, NY) and MedCalc 22.0 (MedCalc, Ostend, Belgium). Results Patient Characteristics A total of 123 patients, including 78 males, were enrolled in this study. The mean age was 69.9 ± 9.5 years, with a range of 45–89 years. Table 1 summarizes the demographic characteristics of the included patients in detailed. Comparison of CACS TNC and CACS PC using different reconstruction parameters The median value of CACS TNC was 109.7 (IQR: 36.9–496.9). With the reconstruction parameters of 3 mm section thickness and an increment of 1.5 mm, the value of median CACS PC ranged from 83.2 (IQR: 5.5–399.7; 75 keV, QIR 4) to 132.5 (IQR: 20.2–570.1; 55keV, QIR 1) depending on different level of VMI and QIR. For the reconstruction parameters of 1.5 mm section thickness and an increment of 1 mm, the median value of CACS PC ranged from 76.9 (IQR: 10.8–364.5; 75 keV, QIR 4) to 107.4 (IQR: 17.0–474.6; 55kV, QIR 1). Regardless of whether the reconstruction section thickness is 3 mm or 1.5 mm, CACS PC strongly correlates with CACS TNC (r = 0.93 and r = 0.96, respectively, P < 0.001) and shows excellent agreement (ICC between 0.97 and 0.98 for all) for different keV and QIR levels. No statistical differences were observed in CACS PC at 3mm section thickness, 55 keV (QIR4), 60/65 keV (QIR1/4), and at 1.5 mm section thickness with 55 keV (QIR1/4), 60 keV (QIR1) compared with CACS TNC (Figure 1). Detailed results of correlation and agreement analysis are illustrated in Table 2. The smallest mean bias was obtained at 1.5mm section thickness, 55 keV with QIR 1 (CACS: 107.5 [IQR: 18.9–480.4] compared to CACS TNC : 109.7 [IQR: 36.9–496.9] mean bias, 2.3; LoA, (−182.7/187.4)) (Figure 2). Regardless of section thickness and QIR level, CACS PC values were gradually decreased with increased keV values (P < 0.001 for all) (Figure 1). Agreement between the 2 readers was excellent for both CACS PC (ICC = 0.99 for all) and CACS TNC (ICC = 0.98 for all). Comparison of standard PureCalcium and optimized PureCalcium algorithms for tiny calcium detection Of the 123 participants included, 21 (17.7%) participants had CACS TNC >0 but were not detected on standard PureCalcium parameters reconstructions. The median CACS TNC was 12.4 (IQR: 6.4–19.35). Of those, 13 (61.9%) participants were detected using optimized PureCalcium reconstructions with a section thickness of 1.5 mm and an increment of 1.0 mm, 55 keV, and QIR 1. Figure 3 shows representative cases of CACS TNC and section thickness 3.0 mm standard CACS PC compared with section thickness 1.5mm CACS PC using the optimized reconstruction. Impact of Differences in CACS on Plaque Burden Classification In the P classification of the Coronary Artery Disease Reporting System, 56, 26, 32, and 9 patients were classified as P1, P2, P3, and P4, respectively, using CACS TNC as the reference standard. No statistical difference was observed in P classification overall between CACS TNC and CACS PC images (P = 0.06). Of note, 18 patients had different P classification results between CACS PC and CACS TNC . On PureCalcium images, 8 patients were misdiagnosed as P0 instead of P1 on TNC images. In these patients, the median CACS PC was 3.95 (range: 0.3 to 10.2). Additionally, 3 patients were misclassified as P1 instead of P2, and 1 was misclassified as P3 instead of P2. P2 and P4 were misdiagnosed instead of P3 in 1 patient and 4 patients, respectively. Furthermore, 1 patient was misclassified as P3 instead of P4 (Table 3,4) (Fig.4). The categorical agreement of plaque classification between CACS TNC and CACS PC was excellent (κ = 0.87). Discussion Our study aimed to evaluate the feasibility of CACS quantification based on the PureCalcium algorithm from contrast-enhanced CCTA and the effects of different section thickness, VMI, and QIR levels during imaging reconstruction on the accuracy of CACS calculation. The findings of our study are as follows: First, the value of CACS PC showed excellent correlation and agreement compared to CASC TNC . Second, the value of CACS PC at a reconstruction parameter of a section thickness of 1.5 mm, 55 kV (QIR 1), is closest to CACS TNC . In this study, only QIR1 and QIR4 were used for imaging reconstruction [ 6 ] , consistent with previous studies that used different imaging techniques to evaluate the accuracy and consistency of calcium imaging. Compared with the energy integral detector CT (EID-CT), PCD-CT has higher spatial resolution and can improve dose efficiency. PCD-CT can utilize higher spectral separation technology for multi-substance separation and generate advanced spectral post-processed images, such as pure calcium images, which can significantly improve the accuracy of CACS calculation both in vitro and in vivo studies [ 9,12-13 ] . However, standard PureCalcium reconstruction is unable to detect very low-density calcifications accurately. This may be attributed to the following reasons: the limited detectability of low-density calcifications and partial volume effects due to the section thickness of 3 mm [ 14-17 ] . Vliegenthart et al. showed that the calcium volumes were higher in 1.5 mm slices than in 3 mm in vitro and in vivo [ 18 ] . Georg et al. observed that a thinner section was associated with increased CACS values [ 19 ] . Our study demonstrated that the 1.5 mm sections can detect more tiny calcification, which can guide the use of this reconstruction parameter to detect small and low-density plaques [ 18 ] . According to CACS PC , the agreement on risk categorization was acceptable. In this study, 85% of patients' CACS PC burdens were correctly categorized using CACS TNC as the reference. It is essential for risk stratification and management recommendations. Although most CACS PC misdiagnoses occur at P1, which may result in false-negative CACS, our study partially addressed this issue by employing 1.5 mm sections. So, CACS calculation on the PureCalcium algorithm with a reconstruction section of 1.5mm may be a more accurate way for CACS quantification. Although this study has demonstrated the excellent correlation between CACS PC and CACS TNC and showed improved identification of tiny coronary calcifications by using 1.5 mm section thickness images, several limitations should be noted. Firstly, there were no detailed analytical results based on different QIR levels; only QIR1 and QIR4 were used in this study. The CACS calculated from other levels of QIRs should be further investigated. Secondly, the quantification of CACS can be affected by various factors; only VIMs, iterative reconstruction levels, and slice parameters were investigated; other parameters, such as different densities of calcification in terms of position, breath-hold depth, and heart rate, require further investigation. Thirdly, a relatively small number of participants were included for analysis; the consistency between CACS PC and CACS TNC in larger-scale populations with different severities of calcification should be further investigated. Fourthly, this study is a single center and only involved one CT vendor; the results should be interpreted carefully in other CT vendors with different virtual noniodine algorithms. In conclusion, the CACS calculated from the PureCalcium algorithm showed excellent correlations with true non-contrast images. The section thickness, VMI, and QIR levels can affect the results of CACS. Compared to CACS TNC , the lower CACS PC can be mitigated by using thinner sections and lower keV reconstructions. This method facilitates accurate PureCalcium-based assessment of CACS in clinical practice and potentially eliminates the need for separate non-enhanced scans, thereby reducing radiation dose. Abbreviations CACS, coronary artery calcium scoring PCD-CT, photon-counting detector CT VMIs, virtual monoenergetic images QIR, quantum iterative reconstruction VNC, virtual non-contrast TNC, true non-contrast CCTA, coronary CT angiography ICC, Intraclass correlation coefficient Declarations Acknowledgements ： Not applicable. Ethics approval ： This study was approved by the Ethics Committee of Geriatric Hospital of Nanjing Medical University(YJ-2024-044-1) in accordance with the Declaration of Helsinki Consent to participate ： Not applicable Consent for publication ： Not applicable Availability of data and materials ： The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request Competing interests ：The authors declare that they have no competing interests Funding ： This research received no external funding Authors' contributions ： Qiuju hu: Writing original draft, Conceptualization, Project administration, Writing - review & editing. Huixin zhang: Data curation, Formal analysis, Bangju guo: Conceptualization, review & editing. Dongsheng Jin: Resource, Meirong Sun:Data curation Jiliang Jilang Chen: Methodology Song Luo:Investigation, Resources, Software,Writing - review & editing. Yane Zhao:Formal analysis,Methodology,Writing - review & editing Guang-ming Lu:Investigation, Resource Acknowledgements ：Not applicable References Parsa S, Saleh A, Raygor V, et al. Measurement and application of incidentally detected coronary calcium: JACC Review Topic of the Week. J Am Coll Cardiol, 2024, 83(16):1557-1567. https://doi.org/10.1016/j.jacc.2024.01.039 Golub IS, Termeie OG, Kristo S, et al. Major global coronary artery calcium guidelines. JACC Cardiovasc Imaging, 2023, 16(1):98-117. https://doi.org/10.1016/j.jcmg.2022.06.018 Arnett DK, Blumenthal RS, Albert MA, et al. (2019) 2019 ACC/ AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines. Circulation, 2019,140:e596-e646. https://doi.org/10.1161/CIR.0000000000000678. Gassert FG, Schacky CE, Müller-Leisse C, et al. 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Characteristic Value Male/Female 78/45 Age, years 69.9 ± 9.5 (45-89) Heart rate during CCTA, bpm 68.0 ± 17.3 (58-123) Body mass index, kg/m 2 25.1 ± 2.4 Total CACS 109.7 (36.95/ 485.95) Risk factors, n (%) Hypertension 96 (78) Hyperlipidemia 77 (63) Diabetes mellitus 41 (33) Smoking 76 (62) Family history of coronary artery disease 32 (42) Values are mean ± SD, median (interquartile range), or n (%). CCTA = coronary CT angiography; CACS = coronary artery calcium scoring Table 2 . Comparison of CACS TNC and CACS PC with different section thicknesses, VMI, and QIR Reconstruction plans Agatston Score (IQR) Friedman P* r ICC Bias LoA Reconstruction methods section thickness /increment（mm/mm） VMI (keV) QIR TNC 3/1.5 70 2 109.7 (36.87,496.92) - - - - - PureCalcium 3/1.5 55 1 132.5 (20.25,570.15) 0.002 0.96 0.97 48.63 -206.86/304.15 4 126.4 (18.87,552.82) ＞0.99 0.95 0.98 43.77 -197.32/284.87 60 1 121.6 (17.12,513.02) ＞0.99 0.96 0.98 19.89 -185.98/225.77 4 112.5 (11.37,502.67) ＞0.99 0.95 0.98 15.21 -180.13/210.57 65 1 108.4 (13.47,473.87) ＞0.99 0.95 0.98 -5.56 -187.44/176.31 4 94.1 (7.47,470.07) 0.025 0.93 0.98 -13.78 -203.21/175.65 70 1 100.2 (11.72,438.65) ＜0.001 0.95 0.98 -28.07 -214.63/158.48 4 89.1 (5.50,438.20) ＜0.001 0.94 0.98 -34.86 -228.95/159.23 75 1 89.7 (9.65, 408.85) ＜0.001 0.95 0.98 -47.60 -256.27/161.07 4 83.2 (5.52,399.70) ＜0.001 0.94 0.98 -54.33 -271.89/163.23 1.5/1.0 55 1 107.5 (18.90,480.37) ＞0.99 0.96 0.98 2.35 -182.68/187.39 4 107.4 (17.02, 474.55) ＞0.99 0.95 0.98 -7.31 -186.34/171.71 60 1 99.8 (17.12,452.80) ＞0.99 0.96 0.98 -18.45 -190.94/154.04 4 99.4 (15.90,438.70) 0.001 0.95 0.98 -27.86 -213.66/157.93 65 1 93.1 (15.05,429.60) 0.001 0.96 0.98 -36.89 -226.26/152.48 4 90.1 (14.72,412.90) 0.001 0.95 0.98 -46.17 -248.44/156.10 70 1 89.3 (63.16,441.24) ＜0.001 0.96 0.98 -52.38 -264.78/160.00 4 80.7 (13.55,381.72) ＜0.001 0.95 0.98 -62.29 -287.98/163.39 75 1 86.3 (11.92,389.10) ＜0.001 0.95 0.98 -64.67 -297.70/168.34 4 ＜0.001 ＜0.001 0.96 0.98 -75.24 -326.30/175.82 Values are median (interquartile range) TNC indicates true noncontrast; VMI,virtual monoenergetic images; QIR, quantum iterative reconstruction; ICC, intraclass correlation; LoA, limits of agreement Table 3 . CACS Distribution in TNC and PureCalcium Reconstruction Scan Type and CACS CACS burden PureCalcium images TNC images 0 8 (6) 0 1-100 51 (41) 56 (46) 101-300 23 (19) 26 (21) 301-999 29 (24) 32 (26) >1000 12 (10) 9 (7) Data are numbers of patients, with percentages in parentheses Table 4 . CAD-RADS P Classification Based on CACS TNC Images or CACS PC Images CAD-RADS P Classification TNC P1 TNC P2 TNC P3 TNC P4 Total PureCalcium P0 8 0 0 0 8 PureCalcium P1 48 3 0 0 51 PureCalcium P2 0 22 1 0 23 PureCalcium P3 0 1 27 1 29 PureCalcium P4 0 0 4 8 12 Total 56 26 32 9 123 CAD-RADS = Coronary Artery Disease Reporting and Data System Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Jan, 2026 Read the published version in BMC Medical Imaging → Version 1 posted Editorial decision: Revision requested 16 Nov, 2025 Reviews received at journal 14 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviews received at journal 18 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers invited by journal 12 Sep, 2025 Editor invited by journal 15 Jul, 2025 Editor assigned by journal 19 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 07 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6843811","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516934221,"identity":"a73f7fa9-3d59-414f-8bf7-04683a4c7fef","order_by":0,"name":"Qiuju Hu","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University,Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Qiuju","middleName":"","lastName":"Hu","suffix":""},{"id":516934223,"identity":"2633b151-fb4c-4a90-899b-7a698a3df0b3","order_by":1,"name":"Huixin Zhang","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University,Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Huixin","middleName":"","lastName":"Zhang","suffix":""},{"id":516934225,"identity":"e2c56b1c-2e34-4aff-ba60-3106828a8c8a","order_by":2,"name":"Bangjun Guo","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Bangjun","middleName":"","lastName":"Guo","suffix":""},{"id":516934227,"identity":"260ba089-6d72-4b81-a8aa-156687e53b53","order_by":3,"name":"Dongsheng Jin","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University,Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Jin","suffix":""},{"id":516934232,"identity":"f9d636bc-3ff8-4514-8486-26614d1d39df","order_by":4,"name":"Meirong Sun","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University,Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Meirong","middleName":"","lastName":"Sun","suffix":""},{"id":516934233,"identity":"482e18d5-e422-49b9-a0bb-081c416e1c84","order_by":5,"name":"Jiliang Chen","email":"","orcid":"","institution":"CT Collaboration, Siemens Healthineers","correspondingAuthor":false,"prefix":"","firstName":"Jiliang","middleName":"","lastName":"Chen","suffix":""},{"id":516934234,"identity":"81bd535c-6af6-4225-a402-c46d1bdef551","order_by":6,"name":"Song Luo","email":"","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University,Nanjing","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Luo","suffix":""},{"id":516934237,"identity":"e4353c4d-bb8f-4a28-a5cf-57523a923315","order_by":7,"name":"Yane Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACNvnjhx//MKip7ydaC58ET5oxQ8ExxpkNxGqRk2AwkGb4wMy44QDRDpNuSDAuMGBjNj6evIHhR8U2IrTIHDzweIaBDJvZmWcFjD1nbhOhhSEhwYDHgI3H7EaOATNjG3FaDCR4DJgljGcQrUUiwUAaqMXAQIJoLTxn0gxnGBxLkAD65SBRfpFvbz/84MOfmgT+9uSND35UEKEFCSQYHCBJPVgLqTpGwSgYBaNghAAAML851CmhrR8AAAAASUVORK5CYII=","orcid":"","institution":"Geriatric Hospital of Nanjing Medical University,Nanjing","correspondingAuthor":true,"prefix":"","firstName":"Yane","middleName":"","lastName":"Zhao","suffix":""},{"id":516934243,"identity":"43c76586-77f6-4a0c-ab08-ea27d53b0625","order_by":8,"name":"Guang-ming Lu","email":"","orcid":"","institution":"Jinling Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guang-ming","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-06-07 16:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6843811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6843811/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12880-026-02162-0","type":"published","date":"2026-01-20T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91934632,"identity":"83316bbd-a055-4825-804d-cab3e3099463","added_by":"auto","created_at":"2025-09-23 02:40:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107989,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of different section thickness, VMI and QIR levels compared with CACS\u003csub\u003eTNC\u003c/sub\u003e.\u003csub\u003e \u003c/sub\u003eCACS\u003csub\u003ePC\u003c/sub\u003e are illustrated as the median. ns indicates not significant. ***P \u0026lt; 0.001; *P \u0026lt; 0.05; ns \u0026gt; = 0.05.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6843811/v1/3724f798923b70efe0b13aac.png"},{"id":91934654,"identity":"017b9a2c-d5f0-4c34-97c8-759ef8fbeeb7","added_by":"auto","created_at":"2025-09-23 02:40:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56568,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman analysis of CACS compared between TNC and PureCalicum algorithm at 1.5mm,65 keV, QIR 1\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6843811/v1/17d9e3f5f652e75a35abe89e.png"},{"id":91934631,"identity":"186e0e06-544a-4f27-acd8-8bb95808eecc","added_by":"auto","created_at":"2025-09-23 02:40:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":258323,"visible":true,"origin":"","legend":"\u003cp\u003eExample cases of axial CT reconstructions of CACS. Comparing CACS\u003csub\u003eTNC\u003c/sub\u003e (left column: 70 keV, quantum iterative reconstruction QIR 2), CACS\u003csub\u003ePC \u003c/sub\u003eat standard reconstruction (middle column: 3mm/1.5mm,55 keV, QIR 1 and 60 keV, QIR 4, as previously recommended) and CACS\u003csub\u003ePC\u003c/sub\u003e at the optimized reconstruction (right column: 1.5mm/1mm,55 keV, QIR 1). The upper row (patient 1) shows CT scans in a 75-year-old male and the lower row (patient 2) in a 61-year-old male. The optimized reconstruction improved the detection of tiny calcified plaques (circles) initially detected in TNC scans but not in standard PureCalcium reconstructions. LCX = left Circumflex Artery; RCA = right coronary artery\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6843811/v1/5e5f5ef14c19df22eadfaeed.png"},{"id":91934629,"identity":"e2815aa5-2ee2-43f5-91fe-367c9f0b306b","added_by":"auto","created_at":"2025-09-23 02:40:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114071,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagrams of change in Plaque Burden Classification at TNC and PureCalcium. A total of 8(6%) patients were misdiagnosed as P0 instead of P1 on Purecalcium images. 3 (2%)patients were misclassified as P1 instead of P2, and 1(0.8%) patient were misclassified as P3 instead of P2. P2 and P4 were misdiagnosed instead of P3 in 1(0.8%) and 4 (3%）participants, respectively. 1(0.8%) participant was misclassified as P3 instead of P4\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6843811/v1/6dc4fa2cd89fba751ab21c96.png"},{"id":101152600,"identity":"29e7a380-5b08-4a08-bb87-b47411f9d1a6","added_by":"auto","created_at":"2026-01-26 16:12:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6843811/v1/98fc9675-6dd8-4e7a-852f-7452283c4bbb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Slice Thickness on CACS Calculation with PureCalcium Algorithm in Photon-Counting CT","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery calcium score (CACS), which is obtained from non-contrast CT scan, has been demonstrated as an independent prognostic predictor for all-cause mortality in asymptomatic individuals\u003csup\u003e\u0026nbsp;[1]\u003c/sup\u003e. The progression of coronary calcification remained as an independent predictor for all-cause mortality after adjusting for baseline CACS, interval time of scan, and clinical risk factors\u003csup\u003e\u0026nbsp;[2]\u003c/sup\u003e. Nowadays, CACS is recommended at a class IIa level of the 2019 AHA guidelines for patients with borderline/intermediate risk and is helpful in guiding clinical management and treatment\u003csup\u003e\u0026nbsp;[3]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn routine clinical examinations, a separate non-contrast CT scan before coronary CT angiography (CCTA), was conducted to quantify the CACS, which may increase the examination time and lead to extra radiation dose. With the advent of dual-energy and spectral imaging techniques, several studies\u003csup\u003e\u0026nbsp;[4-6]\u003c/sup\u003e have explored the assessment of the CACS on virtual non-contrast (VNC) images. These studies using dual-energy techniques, and have demonstrated high correlations but underestimated CACS compared to the true non-contrast (TNC) images. With the advent of photon-counting detector CT (PCD-CT), it has been shown to have higher spatial resolution and reduced radiation dose. Additionally, PCD-CT data contain differentiated spectral information, which can be used for multi-material decomposition\u003csup\u003e\u0026nbsp;[7]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this context, a novel virtual noniodine (VNI) algorithm has been developed to create PureCalcium reconstructions on PCD-CT data. Previous studies have demonstrated that the PureCalcium algorithm achieves significantly higher accuracy in CACS quantification than VNC reconstructions\u003csup\u003e\u0026nbsp;[8]\u003c/sup\u003e. Of noted, VNI reconstructions still underestimate CACS compared with TNC images. The impact of automatic generation of virtual monoenergetic images (VMIs) at different levels of kilo-electron volt (keV), quantum iterative reconstruction (QIR) at various intensity levels, and other section thickness on the accuracy of CACS quantification has not been systematically investigated\u003csup\u003e\u0026nbsp;[9,10]\u003c/sup\u003e. In this study, we aimed to assess the impact of different VMIs, QIR levels, and section thickness on the accuracy of CACS quantification on the PureCalcium algorithm on PCD-CT.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 123 patients who underwent separate TNC and contrast-enhanced photon-counting CCTA due to suspected coronary artery disease between January 2024 and April 2024 were prospectively enrolled. Inclusion criteria included subjects older than 18 who underwent CCTA examinations by standard spatial resolution scanning. Exclusion criteria were as follows: (1) patients with CACS = 0; (2) patients who had a history of coronary revascularization (percutaneous coronary intervention or coronary artery bypass graft). This study was approved by our ethics committee (YJ-2024-044-1), and written informed consent was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients were scanned using a PCD-CT (Naeotom Alpha, Siemens Healthineers). TNC scans were performed using the prospective ECG-gated sequential technique, the tube voltage was set at 120 keV, and the tube current-time product was set to an IQ level of 19. CCTA scans were selected as prospective and retrospective based on the participants\u0026rsquo; heart rate and rhythm. The tube voltage was set at 120 keV, and the tube current-time product was set to an IQ level of 64. The contrast materials were applied using bolus tracking with an enhancement threshold of 100 HU in the descending aorta and a time delay of 6 seconds. A total of 45 to 60 mL of an iodinated contrast agent (350 mg iohexol/mL, Omnipaque; GE HealthCare) was injected at a flow rate of 4.5 mL/sec. Subsequently, 40 ml of saline was injected with the same rate. If not clinically contraindicated, 0.4 mg of nitroglycerin was sublingually administered approximately 5 minutes before the scan\u003csup\u003e\u0026nbsp;[11]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage Reconstruction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTNC images were reconstructed using a section thickness of 3.0 mm with an increment of 1.5 mm at 70 keV, QIR 2, and a recommended kernel Qr36 (given by the factory protocol). The standard-resolution CCTA images were post-processed using a novel VNI algorithm (PureCalcium, Siemens Healthineers) with Qr36\u0026nbsp;kernel. For all of the patient data, VMIs were reconstructed at 5-keV intervals from 55 to 75 keV with QIR level 1 or 4 for each keV level and section thickness of 3.0 mm with an increment of 1.5 mm or section thickness of 1.5 mm with an increment of 1.0 mm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCalcium Scoring Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TNC and PureCalcium datasets were evaluated on a semi-automated workflow (CT CaScoring, syngo.via VB60; Siemens Healthineers). Calcified lesion attenuation exceeding 130 HU and a minimum area of 0.5 mm\u003csup\u003e2\u003c/sup\u003e was automatically identified as calcium. Manual adjustments were made as necessary. The calcium scoring calculation was performed by two radiologists (H.X.Z. and Y.E.Z., with \u0026gt;10 years of experience in cardiovascular imaging) on anonymized data sets provided on a syngo.via server. They were blinded to clinical information and independently assessed all data. The CACS on PureCalcium algorithm (CACS\u003csub\u003ePC\u003c/sub\u003e) with standard reconstructions (at 55 keV, QIR 1 and 60 keV, QIR 4, section thickness 3.0 mm) and TNC (CACS\u003csub\u003eTNC\u003c/sub\u003e) (at 70 keV, QIR 2)\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e9\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e were compared to identify those patients of CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003e\u0026gt; 0 but not visible in the standard reconstruction. The smallest basis of CACS determined the combination of optimal reconstruction parameters for calcium detection compared to the true non-contrast scan.\u003c/p\u003e\n\u003cp\u003eThe burden of calcium plaque in each patient was classified according to the\u0026nbsp;Coronary Artery Disease Reporting System\u0026nbsp;(P0 = score of 0 [none]; P1 = 1\u0026ndash;100 [mild]; P2 = 101\u0026ndash;300 [moderate]; P3 = 301\u0026ndash;999 [severe]; P4\u0026gt;=1000 [extensive])\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e11\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous data were described as mean \u0026plusmn; SD, while categorical data were expressed as frequencies and proportions. The Kolmogorov-Smirnov test was used to test the continuous data for normality. Spearman\u0026apos;s correlation coefficient (r) was used to assess the correlation between CACS\u003csub\u003eTNC\u003c/sub\u003e and CACS\u003csub\u003ePC\u003c/sub\u003e. The differences in calculated CACS with different reconstruction methods were compared with the referent standard (CACS\u003csub\u003eTNC\u003c/sub\u003e) using the Friedman test with Bonferroni correction. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used for agreement assessment. The agreement of plaque burden classification between CACS\u003csub\u003eTNC\u003c/sub\u003e and CACS\u003csub\u003ePC\u003c/sub\u003e was analyzed using Cohen\u0026apos;s kappa (\u0026kappa;) statistics. Statistical analysis was performed using SPSS 27 (IBM Corporation, Armonk, NY) and MedCalc 22.0 (MedCalc, Ostend, Belgium).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 123 patients, including 78 males, were enrolled in this study. The mean age was 69.9 \u0026plusmn; 9.5 years, with a range of 45\u0026ndash;89 years. Table 1 summarizes the demographic characteristics of the included patients in detailed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of CACS\u003csub\u003eTNC\u003c/sub\u003e and CACS\u003csub\u003ePC\u003c/sub\u003e using different\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ereconstruction\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe median value of CACS\u003csub\u003eTNC\u003c/sub\u003e was 109.7 (IQR: 36.9\u0026ndash;496.9). With the reconstruction parameters of 3 mm section thickness and an increment of 1.5 mm, the value of median CACS\u003csub\u003ePC\u0026nbsp;\u003c/sub\u003eranged from 83.2 (IQR: 5.5\u0026ndash;399.7; 75 keV, QIR 4) to 132.5 (IQR: 20.2\u0026ndash;570.1; 55keV, QIR 1) depending on different level of VMI and QIR. For the reconstruction parameters of 1.5 mm section thickness and an increment of 1 mm, the median value of CACS\u003csub\u003ePC\u003c/sub\u003e ranged from 76.9 (IQR: 10.8\u0026ndash;364.5; 75 keV, QIR 4) to 107.4 (IQR: 17.0\u0026ndash;474.6; 55kV, QIR 1). Regardless of whether the reconstruction section thickness is 3 mm or 1.5 mm, CACS\u003csub\u003ePC\u003c/sub\u003e strongly correlates with CACS\u003csub\u003eTNC\u003c/sub\u003e (r = 0.93 and r = 0.96, respectively, P \u0026lt; 0.001) and shows excellent agreement (ICC between 0.97 and 0.98 for all) for different keV and QIR levels. No statistical differences were observed in CACS\u003csub\u003ePC\u003c/sub\u003e at 3mm section thickness, 55 keV (QIR4), 60/65 keV (QIR1/4), and at 1.5 mm section thickness with 55 keV (QIR1/4), 60 keV (QIR1) compared with CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003e(Figure 1). Detailed results of correlation and agreement analysis are illustrated in Table 2. The smallest mean bias was obtained at 1.5mm section thickness, 55 keV with QIR 1 (CACS: 107.5 [IQR: 18.9\u0026ndash;480.4] compared to CACS\u003csub\u003eTNC\u003c/sub\u003e: 109.7 [IQR: 36.9\u0026ndash;496.9] mean bias, 2.3; LoA, (\u0026minus;182.7/187.4)) (Figure 2). Regardless of section thickness and QIR level, CACS\u003csub\u003ePC\u003c/sub\u003e values were gradually decreased with increased keV values (P \u0026lt; 0.001 for all) (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgreement between the 2 readers was excellent for both CACS\u003csub\u003ePC\u0026nbsp;\u003c/sub\u003e(ICC = 0.99 for all) and CACS\u003csub\u003eTNC\u003c/sub\u003e (ICC = 0.98 for all).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison of standard PureCalcium and optimized PureCalcium algorithms for tiny calcium detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 123 participants included, 21 (17.7%) participants had CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003e\u0026gt;0 but were not detected on standard PureCalcium\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eparameters reconstructions. The median CACS\u003csub\u003eTNC\u003c/sub\u003e was 12.4 (IQR: 6.4\u0026ndash;19.35). Of those, 13 (61.9%) participants were detected using optimized PureCalcium reconstructions with a section thickness of 1.5 mm and an increment of 1.0 mm, 55 keV, and QIR 1. Figure 3 shows representative cases of CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003eand section thickness 3.0 mm standard CACS\u003csub\u003ePC\u0026nbsp;\u003c/sub\u003ecompared with section thickness 1.5mm CACS\u003csub\u003ePC\u003c/sub\u003e using the optimized reconstruction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of Differences in CACS on Plaque Burden Classification\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the P classification of the Coronary Artery Disease Reporting System, 56, 26, 32, and 9 patients were classified as P1, P2, P3, and P4, respectively, using CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003eas the reference standard. No statistical difference was observed in P classification overall between CACS\u003csub\u003eTNC\u003c/sub\u003e and CACS\u003csub\u003ePC\u003c/sub\u003e images (P = 0.06). Of note, 18 patients had different P classification results between CACS\u003csub\u003ePC\u003c/sub\u003e and CACS\u003csub\u003eTNC\u003c/sub\u003e. On PureCalcium images, 8 patients were misdiagnosed as P0 instead of P1 on TNC images. In these patients, the median CACS\u003csub\u003ePC\u003c/sub\u003e was 3.95 (range: 0.3 to 10.2). Additionally, 3 patients were misclassified as P1 instead of P2, and 1 was misclassified as P3 instead of P2. P2 and P4 were misdiagnosed instead of P3 in 1 patient and 4 patients, respectively. Furthermore, 1 patient was misclassified as P3 instead of P4 (Table 3,4) (Fig.4). The categorical agreement of plaque classification between CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003eand CACS\u003csub\u003ePC\u003c/sub\u003e was excellent (\u0026kappa; = 0.87).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study aimed to evaluate\u0026nbsp;the feasibility of CACS quantification based on the PureCalcium algorithm from contrast-enhanced CCTA and the effects of different section thickness, VMI, and QIR levels during imaging reconstruction on the accuracy of CACS calculation. The findings of our study are as follows: First, the value of CACS\u003csub\u003ePC\u003c/sub\u003e showed excellent correlation and agreement compared to CASC\u003csub\u003eTNC\u003c/sub\u003e.\u0026nbsp;Second, the value of CACS\u003csub\u003ePC\u0026nbsp;\u003c/sub\u003eat a reconstruction parameter of a section thickness of 1.5 mm, 55 kV (QIR 1), is closest to CACS\u003csub\u003eTNC\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003eIn this study, only QIR1 and QIR4 were used for imaging reconstruction\u0026nbsp;\u003csup\u003e[\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e, consistent with previous studies that used different imaging techniques to evaluate the accuracy and consistency of calcium imaging. Compared with the energy integral detector CT (EID-CT), PCD-CT has higher spatial resolution and can improve dose efficiency. PCD-CT can utilize higher spectral separation technology for multi-substance separation and generate advanced spectral post-processed images, such as pure calcium images, which can significantly improve the accuracy of CACS calculation both in vitro and in vivo studies\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e9,12-13\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. However, standard PureCalcium reconstruction is unable to detect very low-density calcifications accurately. This may be attributed to the following reasons: the limited detectability of low-density calcifications and partial volume effects due to the section thickness of 3 mm\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e14-17\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Vliegenthart et al. showed that the calcium volumes were higher in 1.5 mm slices than in 3 mm in vitro and in vivo\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Georg et al. observed that a thinner section was associated with increased CACS values\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e19\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e. Our study demonstrated that the 1.5 mm sections can detect more tiny calcification, which can guide the use of this reconstruction parameter to detect small and low-density plaques\u003csup\u003e\u0026nbsp;[\u003c/sup\u003e\u003csup\u003e18\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAccording to CACS\u003csub\u003ePC\u003c/sub\u003e, the agreement on risk categorization was acceptable. In this study, 85% of patients\u0026apos; CACS\u003csub\u003ePC\u003c/sub\u003e burdens were correctly categorized using CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003eas the reference. It is essential for risk stratification and management recommendations. Although most CACS\u003csub\u003ePC\u003c/sub\u003e misdiagnoses occur at P1, which may result in false-negative CACS, our study partially addressed this issue by employing 1.5 mm sections. So, CACS calculation on the PureCalcium algorithm with a reconstruction section of 1.5mm may be a more accurate way for CACS quantification.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough this study has demonstrated the excellent correlation between CACS\u003csub\u003ePC\u003c/sub\u003e and CACS\u003csub\u003eTNC\u003c/sub\u003e and showed improved identification of tiny coronary calcifications by using 1.5 mm section thickness images, several limitations should be noted. Firstly, there were no detailed analytical results based on different QIR levels; only QIR1 and QIR4 were used in this study. The CACS calculated from other levels of QIRs should be further investigated. Secondly, the quantification of CACS can be affected by various factors; only VIMs, iterative reconstruction levels, and slice parameters were investigated; other parameters, such as different densities of calcification in terms of position, breath-hold depth, and heart rate, require further investigation. Thirdly, a relatively small number of participants were included for analysis; the consistency between CACS\u003csub\u003ePC\u003c/sub\u003e and CACS\u003csub\u003eTNC\u0026nbsp;\u003c/sub\u003ein larger-scale populations with different severities of calcification should be further investigated. Fourthly, this study is a single center and only involved one CT vendor; the results should be interpreted carefully in other CT vendors with different virtual noniodine algorithms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, the CACS calculated from the PureCalcium algorithm showed excellent correlations with true non-contrast images. The section thickness, VMI, and QIR levels can affect the results of CACS. Compared to CACS\u003csub\u003eTNC\u003c/sub\u003e, the lower CACS\u003csub\u003ePC\u003c/sub\u003e can be mitigated by using thinner sections and lower keV reconstructions. This method facilitates accurate PureCalcium-based assessment of CACS in clinical practice and potentially eliminates the need for separate non-enhanced scans, thereby reducing radiation dose.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCACS, coronary artery calcium scoring\u003c/p\u003e\n\u003cp\u003ePCD-CT, photon-counting detector CT\u003c/p\u003e\n\u003cp\u003eVMIs, virtual monoenergetic images\u003c/p\u003e\n\u003cp\u003eQIR, quantum iterative reconstruction\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVNC, virtual non-contrast\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTNC, true non-contrast\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCCTA, coronary CT angiography\u003c/p\u003e\n\u003cp\u003eICC, Intraclass correlation coefficient\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis study was approved by the Ethics Committee of Geriatric Hospital of Nanjing Medical University(YJ-2024-044-1) in accordance with the Declaration of Helsinki\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;：\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e：The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThis research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQiuju hu: Writing original draft, Conceptualization, Project administration, Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuixin zhang: Data curation, Formal analysis,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBangju guo: Conceptualization, review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDongsheng Jin:\u0026nbsp;Resource,\u003c/p\u003e\n\u003cp\u003eMeirong Sun:Data curation Jiliang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJilang Chen: Methodology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSong Luo:Investigation, Resources, Software,Writing - review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eYane Zhao:Formal analysis,Methodology,Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eGuang-ming Lu:Investigation, Resource\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e：Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eParsa S, Saleh A, Raygor V, et al. Measurement and application of incidentally detected coronary calcium: JACC Review Topic of the Week. J Am Coll Cardiol, 2024, 83(16):1557-1567. https://doi.org/10.1016/j.jacc.2024.01.039\u003c/li\u003e\n \u003cli\u003eGolub IS, Termeie OG, Kristo S, et al. Major global coronary artery calcium guidelines. JACC Cardiovasc Imaging, 2023, 16(1):98-117. https://doi.org/10.1016/j.jcmg.2022.06.018\u003c/li\u003e\n \u003cli\u003eArnett DK, Blumenthal RS, Albert MA, et al. (2019) 2019 ACC/ AHA guideline on the primary prevention of cardiovascular disease: a report of the American College of Cardiology/American Heart Association Task Force on clinical practice guidelines. Circulation, 2019,140:e596-e646. https://doi.org/10.1161/CIR.0000000000000678.\u003c/li\u003e\n \u003cli\u003eGassert FG, Schacky CE, M\u0026uuml;ller-Leisse C, et al. Calcium scoring using virtual non-contrast images from a dual-layer spectral detector CT: comparison to true non-contrast data and evaluation of proportionality factor in a large patient collective. Eur Radiol. 2021;31(8):6193-6199. https://doi.org/10.1007/s00330-020-07677-w\u003c/li\u003e\n \u003cli\u003eYang P, Zhao R, Deng W, et al. Feasibility and accuracy of coronary artery calcium score on virtual non-contrast images derived from a dual-layer spectral detector CT: A retrospective multicenter study. Front Cardiovasc Med. 2023;10:1114058. Published 2023 Mar 2. https://doi.org/10.3389/fcvm.2023.1114058\u003c/li\u003e\n \u003cli\u003eLangenbach IL, Wienemann H, Klein K, et al. Coronary calcium scoring using virtual non-contrast reconstructions on a dual-layer spectral CT system: Feasibility in the clinical practice.Eur J Radiol. 2023;159:110681. https://doi.org/10.1016/j.ejrad.2022.110681\u003c/li\u003e\n \u003cli\u003eSandfort V, Persson M, Pourmorteza A, et al. Spectral photon-counting CT in cardiovascular imaging. J Cardiovasc Comput Tomogr. 2021;15(3):218-225. https://doi.org/10.1016/j.jcct.2020.12.005\u003c/li\u003e\n \u003cli\u003eEmrich T, Aquino G, Schoepf UJ, et al. Coronary Computed Tomography Angiography-Based Calcium Scoring: In Vitro and In Vivo Validation of a Novel Virtual Noniodine Reconstruction Algorithm on a Clinical, First-Generation Dual-Source Photon Counting-Detector System. Invest Radiol. 2022;57(8):536-543. https://doi.org/10.1097/RLI.0000000000000868\u003c/li\u003e\n \u003cli\u003eFink N, Zsarnoczay E, Schoepf UJ, et al. Photon Counting Detector CT-Based Virtual Noniodine Reconstruction Algorithm for In Vitro and In Vivo Coronary Artery Calcium Scoring: Impact of Virtual Monoenergetic and Quantum Iterative Reconstructions. Invest Radiol. 2023;58(9):673-680. https://doi.org/10.1097/RLI.0000000000000959\u003c/li\u003e\n \u003cli\u003eKim SY, Suh YJ, Lee HJ, et al. Influence of computed tomography slice thickness on deep learning-based, automatic coronary artery calcium scoring software performance. Quant Imaging Med Surg. 2023;13(7):4257-4267. https://doi.org/10.21037/aims-22-835\u003c/li\u003e\n \u003cli\u003eLeipsic J, Abbara S, Achenbach S, et al. SCCT guidelines for the interpretation and reporting of coronary CT angiography: a report of the Society of Cardiovascular Computed Tomography Guidelines Committee. J Cardiovasc Comput Tomogr. 2014;8(5):342-358. https://doi.org/10.1016/j.jcct.2014.07.003\u003c/li\u003e\n \u003cli\u003eCury RC, Leipsic J, Abbara S, et al. CAD-RADS\u0026trade; 2.0 - 2022 Coronary Artery Disease - Reporting and Data System.An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North American Society of Cardiovascular Imaging (NASCI). J Am Coll Radiol. 2022;19(11):1185-1212. https://doi.org/10.1016/j.jacr.2022.09.012\u003c/li\u003e\n \u003cli\u003eSandstedt M, Marsh J Jr, Rajendran K, et al. Improved coronary calcification quantification using photon-counting-detector CT: an ex vivo study in cadaveric specimens. Eur Radiol. 2021;31(9):6621-6630. https://doi.org/10.1007/s00330-021-07780-6\u003c/li\u003e\n \u003cli\u003eVan der Werf NR, Booij R, Greuter MJW, et al. Reproducibility of coronary artery calcium quantification on dual-source CT and dual-source photon-counting CT: a dynamic phantom study. Int J Cardiovasc Imaging. 2022;38(7):1613-1619. https://doi.org/10.1007/s10554-022-02540-z\u003c/li\u003e\n \u003cli\u003eMergen V, Higashigaito K, Allmendinger T, et al. Tube voltage-independent coronary calcium scoring on a first-generation dual-source photon-counting CT proof-of-principle phantom study. Int J Cardiovasc Imaging. 2022;38(4):905-912. https://doi.org/10.1007/s10554-021-02466-y\u003c/li\u003e\n \u003cli\u003eSkoog S, Henriksson L, Gustafsson H, Sandstedt M, Elvelind S, Persson A. Comparison of the Agatston score acquired with photon-counting detector CT and energy-integrating detector CT: ex vivo study of cadaveric hearts. Int J Cardiovasc Imaging. 2022;38(5):1145-1155. https://doi.org/10.1007/s10554-021-02494-8\u003c/li\u003e\n \u003cli\u003evan Praagh GD, Wang J, van der Werf NR, et al. Coronary Artery Calcium Scoring: Toward a New Standard. Invest Radiol. 2022;57(1):13-22. https://doi.org/10.1097/RLI.0000000000000808\u003c/li\u003e\n \u003cli\u003eVliegenthart R, Song B, Hofman A, et al. Coronary calcification at electron-beam CT: effect of section thickness on calcium scoring in vitro and in vivo. Radiology. 2003;229(2):520-525. https://doi.org/10.1148/radiol.2292021305\u003c/li\u003e\n \u003cli\u003eM\u0026uuml;hlenbruch G, Thomas C, Wildberger JE, et al. Effect of varying slice thickness on coronary calcium scoring with multislice computed tomography in vitro and in vivo. Invest Radiol. 2005;40(11):695-699. https://doi.org/10.1097/01.rli.0000179523.07907.a6\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eCharacteristics of the enrolled patients.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eMale/Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e78/45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e69.9 \u0026plusmn; 9.5 (45-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eHeart rate during CCTA, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e68.0 \u0026plusmn; 17.3 (58-123)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e25.1 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eTotal CACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e109.7 (36.95/ 485.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 369px;\"\u003e\n \u003cp\u003eRisk factors, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 185px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41 (33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFamily history of coronary artery disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are mean \u0026plusmn; SD, median (interquartile range), or n (%). CCTA = coronary CT angiography; CACS = coronary artery calcium scoring\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eComparison of CACS\u003csub\u003eTNC\u003c/sub\u003e and CACS\u003csub\u003ePC\u003c/sub\u003e with different section thicknesses, VMI, and QIR\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"946\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\n \u003cp\u003eReconstruction plans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAgatston Score (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eFriedman P*\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eICC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eBias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLoA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eReconstruction methods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003esection thickness\u003c/p\u003e\n \u003cp\u003e/increment（mm/mm）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVMI (keV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eQIR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3/1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e109.7 (36.87,496.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"20\"\u003e\n \u003cp\u003ePureCalcium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"10\"\u003e\n \u003cp\u003e3/1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e132.5 (20.25,570.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-206.86/304.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e126.4 (18.87,552.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-197.32/284.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121.6 (17.12,513.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-185.98/225.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112.5 (11.37,502.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-180.13/210.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e108.4 (13.47,473.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-187.44/176.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.1 (7.47,470.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-13.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-203.21/175.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e100.2 (11.72,438.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-28.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-214.63/158.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.1 (5.50,438.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-34.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-228.95/159.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.7 (9.65, 408.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-47.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-256.27/161.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83.2 (5.52,399.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-54.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-271.89/163.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.5/1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107.5 (18.90,480.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-182.68/187.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e107.4 (17.02, 474.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-186.34/171.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.8 (17.12,452.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＞0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-18.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-190.94/154.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e99.4 (15.90,438.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-27.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-213.66/157.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e93.1 (15.05,429.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-36.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-226.26/152.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e90.1 (14.72,412.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-46.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-248.44/156.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e89.3 (63.16,441.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-52.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-264.78/160.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.7 (13.55,381.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-62.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-287.98/163.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.3 (11.92,389.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-64.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-297.70/168.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-75.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-326.30/175.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are median (interquartile range)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTNC indicates true noncontrast; VMI,virtual monoenergetic images; QIR, quantum iterative reconstruction; ICC, intraclass correlation; LoA, limits of agreement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;CACS Distribution in TNC and PureCalcium Reconstruction\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eScan Type and CACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eCACS burden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003ePureCalcium images\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003eTNC images\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e8 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e1-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e51 (41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e56 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e101-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e23 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e26 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e301-999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e29 (24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e32 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026gt;1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e12 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e9 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are numbers of patients, with percentages in parentheses\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;CAD-RADS P Classification Based on CACS\u003csub\u003eTNC\u003c/sub\u003e Images or CACS\u003csub\u003ePC\u003c/sub\u003e Images\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCAD-RADS P Classification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTNC P1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTNC P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTNC P3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTNC P4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePureCalcium P0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePureCalcium P1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePureCalcium P2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePureCalcium P3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePureCalcium P4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCAD-RADS = Coronary Artery Disease Reporting and Data System\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Coronary artery calcium scoring, spectral imaging, virtual non-iodine algorithm, coronary artery disease, photon counting CT","lastPublishedDoi":"10.21203/rs.3.rs-6843811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6843811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: This study aims to investigate the feasibility of coronary artery calcium scoring (CACS) calculating from PureCalcium virtual non-iodine algorithm on photon-counting detector CT (PCD-CT) and the potential impact of different section thickness, level of virtual monoenergetic images (VMIs), and quantum iterative reconstruction (QIR) on the accuracy of CACS quantification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e: A total of 123 patients who underwent coronary CT angiography on PCD-CT with a separate true non-contrast CACS (CACS\u003csub\u003eTNC\u003c/sub\u003e) scan were prospectively included. Agatston scores were calculated from the PureCalcium algorithm (CACS\u003csub\u003ePC\u003c/sub\u003e) using a section thickness of 3mm or 1.5mm, different VMI (55–75 kilo-electron volt (keV)) and QIR (strength 1,4) levels, respectively. CACS\u003csub\u003eTNC\u003c/sub\u003e at 70 keV and QIR 2 were used as reference standards. Differences in CACS of different reconstructions section thicknesses, various keV levels, and QIR strength were compared using the Wilcoxon rank sum test with Bonferroni correction. The intraclass correlation coefficients (ICCs) and Bland-Altman analysis were conducted to assessed the agreement. The agreement of plaque burden groups (based on CACS) at different reconstruction parameters was evaluated using weighted Cohen kappa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: At all investigated section thickness, VMI, and QIR levels, the CACS\u003csub\u003ePC\u003c/sub\u003e were strongly correlated with CACS\u003csub\u003eTNC\u003c/sub\u003e (ICC: 0.94–0.98, P \u0026lt; 0.001 for all). There were no statistical differences in CACS between CACS\u003csub\u003ePC\u003c/sub\u003e at 3mm section thickness, 60/65 keV (QIR1/4), and at 1.5 mm section thickness with 55 keV (QIR1/4), compared with CACS\u003csub\u003eTNC\u003c/sub\u003e. The smallest CACS bias was observed at a 1.5 mm section thickness, 55 keV, QIR 1, with mean bias of 2.4; LoA (IQR: −182.7, 187.4). CACS\u003csub\u003ePC\u003c/sub\u003e correctly identified 105 of 123 participants (85.4%) into the corresponding plaque burden group using CACS\u003csub\u003eTNC \u003c/sub\u003eas the referent standard (excellent agreement, κ = 0.904).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e CACS derived from the PureCalcium algorithm with optimized reconstruction parameters shows excellent correlation with true non-contrast scans derived values. Thus, it is may possible to use the PureCalcium virtual non-iodine algorithm to replace the true non-contrast scans for CACS quantification, without additional radiation dose exposure.\u003c/p\u003e","manuscriptTitle":"Impact of Slice Thickness on CACS Calculation with PureCalcium Algorithm in Photon-Counting CT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 02:40:26","doi":"10.21203/rs.3.rs-6843811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T04:04:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-14T15:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62115056122303436952555016873940354160","date":"2025-11-13T00:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94470231352910011309317071331462334358","date":"2025-11-12T15:39:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-18T20:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"80661945891712260343803490189984668229","date":"2025-10-14T06:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41885964085054304525136979799699479797","date":"2025-09-21T07:55:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292996960392585638619569104986595911330","date":"2025-09-14T16:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-12T16:33:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T04:44:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T07:19:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T07:19:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2025-06-07T15:53:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aff3f6c0-3f92-4696-93ee-2968fe9fcb14","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:09:04+00:00","versionOfRecord":{"articleIdentity":"rs-6843811","link":"https://doi.org/10.1186/s12880-026-02162-0","journal":{"identity":"bmc-medical-imaging","isVorOnly":false,"title":"BMC Medical Imaging"},"publishedOn":"2026-01-20 15:57:14","publishedOnDateReadable":"January 20th, 2026"},"versionCreatedAt":"2025-09-23 02:40:26","video":"","vorDoi":"10.1186/s12880-026-02162-0","vorDoiUrl":"https://doi.org/10.1186/s12880-026-02162-0","workflowStages":[]},"version":"v1","identity":"rs-6843811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6843811","identity":"rs-6843811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}