Abstract
Purpose
To compare intra-individual percentage diameter stenosis (PDS) measurements of coronary artery stenoses between energy-integrating detector computed tomography (EID-CT) and a clinical photon-counting detector computed tomography (PCD-CT) systems using similar acquisition and reconstruction settings.
Methods
Patients (n = 23, mean age of 65 ± 12.1 years, out of these 16 (69.6%) male) were imaged on a conventional EID- and a clinical PCD-CT system with a median of 5.5 (3.0–12.5) days apart. Sequential CCTA scans were acquired and reconstructed using similar settings, including a vascular Bv36 kernel, a tube voltage of 110 kVp for EID-CT vs 120 kVp for PCD-CT, a slice thickness of 0.5 for EID-CT vs 0.6 for PCD-CT, and an iterative reconstruction strength of 3 on EID-CT vs a virtual monoenergetic reconstruction at 55 keV and quantum iterative reconstruction level of 3 on PCD-CT. Radiation dose, contrast volume, and injection parameters were matched as similarly as possible between the systems. PDS measurements were performed according to the coronary artery disease reporting and data system (CAD-RADS) by two trained readers and compared between the different modalities using the Wilcoxon rank sum test, Spearman correlation, and Bland-Altman analysis.
Results
PCD-CT measured significantly lower PDS values than EID-CT [PDSEID-CT: 45.1% (35.1%–64.0%) vs. PDSPCD-CT 44.2% (32.4%–61.0%), P < 0.0001]. This difference led to a mean bias of 1.8 (LoA −3.0/6.5) with an excellent ICC (0.99) value among EID- and PCD-CT. The mean intra-individual deviation between the examinations was 1.8% between the scanners. This led to CAD-RADS re-classification in 3/23 cases (13.0%, new-lower class) for the first reader, and in 4/23 cases (13.0%, new-lower and 4.4%, new-higher class) for the second reader. Inter-reader agreement between the two readers for each stenosis was very strong (ICC = 0.98).
Conclusions
Coronary artery stenosis measurements from PCD-CT correlate strongly to EID-CT-based measurements, despite the tendency of the measurement from PCD-CT being lower. This difference led to a change in CAD-RADS classification in 17.4% of patients. The effects on clinical decision-making, downstream testing, and prognosis have to be evaluated in future studies.
Introduction
Coronary artery disease (CAD) is the leading cause of death worldwide [1]. Over the last two decades, coronary CT angiography (CCTA) has emerged as one of the most commonly used modalities for the clinical evaluation of stable CAD [2, 3]. Accurate CT-based quantification of coronary artery stenosis is critical because stenosis severity in combination with calcium scoring guides clinical decision-making [4]. In this context, CCTA has already been shown to have excellent negative predictive value and moderate positive predictive value for the detection and evaluation of CAD, especially in patients with low or intermediate risk [5, 6]. However, the positive predictive value of CCTA suffers as the burden of calcium in the coronary arteries increases due to calcium blooming artifacts [7].
Recently, photon-counting detector computed tomography (PCD-CT) has become clinically available, enabling direct conversion of x-ray photon radiation into an electrical signal instead of using an intermediate conversion into visible light photons through a scintillator [8]. Compared with conventional energy integrating detector CT (EID-CT), PCD-CT offers several distinct advantages, including higher spatial resolution, lower image noise, and constantly available spectral image information [9–16]. These advantages have the potential to allow for more accurate stenosis quantification and overcome previous limitations in CT-based cardiac imaging. However, due to the novelty of this technique, analyses comparing intra-individual measurements from PCD- and EID-CT using similar settings are required to ensure transferability into clinical practice.
We hypothesized that percentage diameter stenosis (PDS) measurements with PCD-CT yield similar results compared to the conventional, clinical-standard EID-CT when acquired and reconstructed similarly. Thus, this study aims to intra-individually compare PDS measurements between a first-generation dual-source PCD-CT and a state-of-the-art conventional dual-source EID-CT.
Materials & methods
Patients
The protocol of this single-center, Health Insurance Portability and Accountability Act-compliant, study was approved by the local Institutional Review Board (IRB). Patients were prospectively enrolled based on the following inclusion criteria: (1) clinical indication for cardiac imaging; and (2) >18 years of age. The following exclusion criteria were applied: (1) contraindication to iodine-based contrast media; (2) reduced kidney function (glomerular filtration rate <45 mL min−1 m−2); (3) pregnancy or lactation, and (4) unable to be consented. All patients provided written informed consent and underwent an electrocardiogram (ECG)-gated CCTA examinations on both CT systems between August 2021 and March 2022.
Data acquisition and image reconstruction
Both the conventional EID-CT (SOMATOM Force, Siemens Healthineers, Erlangen, Germany) and a first-generation, clinical PCD-CT (NAEOTOM Alpha, Siemens) were used to perform CCTA examinations. The PCD-CT is equipped with two photon-counting cadmium telluride (CdTe) detectors, each with a collimation of 144 × 0.4 mm, which enables spectral CT data acquisition at a high temporal resolution. The tube voltage was set to 110 kVp for the EID-CT, which was the standard protocol used at our institution, while it was set to 120 kVp for the PCD-CT per vendor recommendations.
A sequential cardiac protocol was used for all scans, which included ECG triggering and a triphasic contrast injection protocol and a constant injection rate for all three phases (4 mL s−1). This involved injecting an initial bolus of nonionic iodinated contrast agent (50 mL, iopromide 370 mgI mL−1, Ultravist, Bayer Healthcare), followed by a 50% mixture of contrast agent and saline solution (20 mL), and a saline chaser (25 mL). If it was not clinically contraindicated, patients were given 0.4 mg nitroglycerin about 5 min before the scan, and those with heart rates above 70 beats per minute received 5 mg metoprolol intravenously.
CT images were directly reconstructed on the scanners. All reconstructions were performed in the phase with the least motion artifacts (best diastolic or systolic phase), using a body vascular kernel (Bv36) a slice thickness of 0.5 and 0.6 mm for the EID-CT and the PCD-CT, respectively. Iterative reconstruction algorithms were applied at strength level 3 for both scanners, using advanced modeled iterative reconstruction (ADMIRE) for the EID-CT and quantum iterative reconstructions (QIR) for the PCD-CT. Detailed acquisition and reconstruction parameters for both scanners can be found in Table 1.
CT acquisitions and reconstructions
| Modality | EID-CT | PCD-CT |
| Tube potential (kVp) | 110 | 120 |
| Monoenergetic level (keV) | n/a | 55 |
| Iterative reconstruction | Admire 3 | QIR 3 |
| Reconstruction kernel | Bv36 | Bv36 |
| Slice thickness (mm) | 0.5 | 0.6 |
| Field of View (mm) | 200 | 200 |
| Matrix size | 512 × 512 | 512 × 512 |
QIR, quantum iterative reconstruction; EID-CT, energy-integrating computed tomography; PCD-CT, photon-counting detector computed tomography.
Stenosis measurements
Measurements were performed by two readers with 2 and 4 years of experience in cardiovascular radiology and under the supervision of a board-certified cardiovascular radiologist with 12 years of experience. The supervisor selected relevant stenoses with at least 20% of vessel obstruction. Both readers independently assessed each stenosis in a blinded fashion.
Stenosis measurements were based on a semi-automated segmentation of the remaining coronary artery lumen using a commercially available software solution (CT Coronary, syngo.via Version VB60, Siemens). An example case illustrating the visualization for stenosis measurements is shown in Fig. 1. Quantitative analysis of the stenoses was conducted on cross-sectional images and according to established methods using the diameter proximal and distal to the stenosis as reference [17]. The reference diameters were measured in a plaque-free portion of the same vessel as close as possible to the selected stenosis.
Representative image in a patient scanned with the EID- and PCD-CT
Citation: Imaging 2023; 10.1556/1647.2023.00156
Representative image in a patient scanned with the EID- and PCD-CT
Citation: Imaging 2023; 10.1556/1647.2023.00156
Representative image in a patient scanned with the EID- and PCD-CT
Citation: Imaging 2023; 10.1556/1647.2023.00156
For reliable results, window-level settings were kept constant (C450 HU/W1500 HU) in the PCD- and EID-CT measurements. The readers also classified the plaques as being either calcified or mixed.
Furthermore, patients were assessed according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) depending on the measured PDS as follows: CAD-RADS 0 – 0%; CAD-RADS 1 – 1-24%; CAD-RADS 2 – 25-49%; CAD-RADS 3 – 50-74%; CAD-RADS 4 – 75-99%; CAD-RADS 4 – 100% [19].
Statistical analysis
Dedicated software (SPSS Statistics for Windows, Version 21.0, IBM Corp Armonk, NY; MedCalc for Windows, version 15.0, MedCalc Software, Ostend, Belgium; GraphPad Prism, Version 9 for macOS, San Diego, CA, USA) was used for statistical analysis. The data was tested for normality using the Kolmogorov-Smirnov test. For normally distributed data, mean ± standard deviation (SD) was used, while median with interquartile range was used for non-normally distributed data. Categorical variables were reported as frequencies and proportions. The means' distribution was compared with independent samples t-test for normally-distributed variables or Mann-Whitney U test for non-parametric variables. The difference between the two CT stenosis measurements was compared using the Wilcoxon rank sum test, and a p-value of 0.05 was deemed significant. The mean bias and the upper and lower limits of agreement (LoA) between the two approaches were evaluated using Bland-Altman analysis. The correlation between PDS values from EID- and PCD-CT was measured using the Spearman correlation coefficient (r). The agreements between the two CT systems and the two readers were measured using intraclass correlation coefficients (ICC), with the following interpretations: 0.0 to 0.3, lack of agreement; 0.31 to 0.5, weak agreement; 0.51 to 0.7, moderate agreement; 0.71 to 0.9, strong agreement; and 0.91 to 1.00, very strong agreement [20].
Results
The study included 23 patients (16 men) with a mean age of 65 ± 12.1 years and a total of 42 evaluated stenoses, comprised of 31 (73.8%) mixed and 11 (26.2%) calcified plaques. The PCD-CT scan was conducted a median of 5.5 (3.0–12.5) days after the EID-CT scan. The median radiation dose for scans obtained with PCD-CT was significantly lower compared to those performed with EID-CT [CTDIvol EID-CT: 38.3 (20.9–58.0) mGy vs PCD-CT: 28.0 (18.2–54.2) mGy, P < 0.05]. Clinical CT examinations included the following indications: 13 (56.5%) stable chest pain/CAD, 7 (30.4%) valvular disease, 3 (13.0%) structural heart disease. A detailed listing of the study population, scan conditions, and plaque characterization is given in Table 2.
Patient characteristics
| n | 23 | |
| Female (%) | 7 (30.4) | |
| Age (years) | 65 ± 12.1 | |
| BMI (kg m−2) | 30.1 ± 7.1 | |
| Time between scans (days) | 5.5 (3.0–12.5) | |
| EID-CT | PCD-CT | |
| Heart rate (bpm) | 64.1 ± 11.0, * | 65.3 ± 10.8, * |
| CTDIvol (mGy) | 38.3 (20.9–58.0), ** | 28.0 (18.2–54.2), ** |
| DLP (mGy*cm) | 647.8 (339.4–1055.1), *** | 557.5 ± 384.7, *** |
| Mixed Plaques | 31 (73.8%) | |
| Calcified Plaques | 11 (26.2%) | |
*P = 0.4713.
**P = 0.0116.
***P = 0.0046.
Values are mean ± standard deviation, median (interquartile range), n (frequencies).
BMI, body mass index; CTDI, computer tomography dose index; DLP, dose length product; EID-CT, energy-integrating computed tomography; PCD-CT, photon-counting detector computed tomography.
Stenosis evaluation
Overall, the PDSPCD-CT measurements were significantly lower than corresponding PDSEID-CT values (PDSEID-CT: 45.1% (35.1%–64.0%) vs. PDSPCD-CT: 44.2% (32.4%–61.0%), P < 0.0001, mean bias 1.8, LoA −3.0/6.5). Nevertheless, PDSPCD-CT showed very strong correlation and agreement with PDSEID-CT (r = 0.988, ICC = 0.99). The mean measurement deviation between individual stenoses amounted to a 1.8% difference between the scanners. Figure 2 demonstrates the correlation between PDS values from EID- and PCD-CT. Figure 3 displays the differences between the individual measurements from the EID- and PCD-CT.
Scatterplot (A) and Bland-Altman plot (B) show the correlation of percent diameter stenosis (PDS) between EID-CT and PCD-CT
Citation: Imaging 2023; 10.1556/1647.2023.00156
Scatterplot (A) and Bland-Altman plot (B) show the correlation of percent diameter stenosis (PDS) between EID-CT and PCD-CT
Citation: Imaging 2023; 10.1556/1647.2023.00156
Scatterplot (A) and Bland-Altman plot (B) show the correlation of percent diameter stenosis (PDS) between EID-CT and PCD-CT
Citation: Imaging 2023; 10.1556/1647.2023.00156
Box plot with line diagram shows the comparison of percent diameter stenosis (PDS) values between EID-CT and PCD-CT with a p-value from a paired Wilcoxon test
Citation: Imaging 2023; 10.1556/1647.2023.00156
Box plot with line diagram shows the comparison of percent diameter stenosis (PDS) values between EID-CT and PCD-CT with a p-value from a paired Wilcoxon test
Citation: Imaging 2023; 10.1556/1647.2023.00156
Box plot with line diagram shows the comparison of percent diameter stenosis (PDS) values between EID-CT and PCD-CT with a p-value from a paired Wilcoxon test
Citation: Imaging 2023; 10.1556/1647.2023.00156
Reclassification of CAD-RADS categories
Based on the degree of stenosis, patients were assigned a CAD-RADS score. Compared to the initial classification based on the EID-CT scan, measurements derived from PCD-CT scans led to a new-lower classification in three patients (3/23 cases, 13.0%) for both readers and to a new-higher classification in one patient (1/23 cases, 4.4%) for reader 2. This led to a per-patient CAD-RADS re-classification in 17.4% of cases. Details of the per-patient and per-vessel CAD-RADS category re-classifications are provided in Fig. 4, Table 3 and Supplementary Table S1. Agreement between the readers was very strong for stenosis measurements (ICC = 0.97) between the two CT systems.
Bar diagram show the original and re-classification of CAD-RADS categories for reader 1 and 2
Citation: Imaging 2023; 10.1556/1647.2023.00156
Bar diagram show the original and re-classification of CAD-RADS categories for reader 1 and 2
Citation: Imaging 2023; 10.1556/1647.2023.00156
Bar diagram show the original and re-classification of CAD-RADS categories for reader 1 and 2
Citation: Imaging 2023; 10.1556/1647.2023.00156
Re-classification of CAD-RADS categories
| Per-Patient Classification | ||||||
| CAD-RADS Score Category | Reader 1 | Reader 2 | ||||
| New-lower risk classification | Original classification | New-higher risk classification | New-lower risk classification | Original classification | New-higher risk classification | |
| 0 | 4 | 4 | ||||
| 1 | 3 | 4 | 1 | |||
| 2 | 1 | 6 | 5 | |||
| 3 | 5 | 1 | 8 | |||
| 4 | 2 | 5 | 2 | 2 | ||
| 5 | 0 | 0 | ||||
| Total | 3 | 23 | 3 | 23 | 1 | |
| Difference (%) | 13.0 | 13.0 | 4.4 | |||
Discussion
This prospective study investigated coronary artery stenosis measurements between EID- and PCD-CT in an intra-individual patient setting. Major findings were: 1) PCD-CT PDS values measured lower on average compared to the measurements on EID-CT. 2) This led to CAD-RADS re-classification in approximately 17% of patients. 3) Despite the instances of re-classification, there was still a very strong correlation and agreement of coronary stenosis measurements between the two CT systems.
CCTA is considered a first-line test in the evaluation of CAD due to its high sensitivity and negative predictive value in patients with low and intermediate risk of CAD [5, 6]. Additionally, CCTA enables the evaluation of coronary stenosis and plaque composition, providing valuable information for prognostic stratification [21, 22]. However, CCTA is restricted by a moderate positive predictive value due to artifacts that can lead to overestimation of stenoses, particularly from high-density calcifications or stents [23–25]. For high-density calcifications, errant stenosis measurements are induced by calcium blooming artifacts, which occur more frequently as calcium density increases [26]. The inappropriate stenosis measurements are caused by different tissue attenuations within the same imaged voxel [27]. Inaccurate PDS measurements with a stenosis overestimation can lead to unnecessary ICA [25, 26].
It has been demonstrated that PCD-CT reduces calcium blooming through increased spatial resolution and better decomposition of materials [28]. Despite similar reconstructions in our study between the PCD- and EID-CT systems, PCD-CT seemed to be less prone to calcium blooming and therefore resulted in lower PDS value measurements. However, the overestimation of PDS by standard resolution CCTA has been investigated by several phantom studies using EID- and PCD-CT [18, 29, 30]. Based on these prior publications, it can be implied that the PCD-CT in-vivo measurements in our study are more accurate than the EID-CT measurements, which is currently one of the state-of-the-art CT systems in clinical settings.
The lower PDS measurements would follow with a new-lower risk classification in patients and therefore has an impact on the CAD-RADS system. We demonstrated a new per-patient CAD-RADS classification in approximately 17% of our patients. Similarly, a previous study compared the intra-individual coronary artery calcium score using identical scan parameters between EID- and PCD-CT [12]. The study revealed a new-lower reclassification rate for the PCD-CT of approximately 5% compared to the EID-CT, which suggests a role in evaluating the plaque burden in patients. An accurate CT-based quantification of stenosis degrees is of great importance and represents the basis for the risk assessment of CAD, which guides clinical management [31]. However, most patients still received the same CAD-RADS category with the PCD-CT acquisition compared to the EID-CT, and there was a high degree of agreement between stenosis grading and CAD-RADS classification from both CT systems. On the other hand, through further improvements such as of the spatial resolution on the PCD-CT, it might have the potential to improve the positive predictive value of CCTA and thereby reduce unnecessary downstream testing in patients.
Despite the advantages demonstrated in this study using similar reconstructions between EID- and PCD-CT, PCD-CT systems offer various other advantages. For example, a new image reconstruction algorithm (PureLumen, Siemens) could improve stenosis assessment on the PCD-CT through removing calcified plaques and displaying the contrasted vessel [18]. Allmendinger et al. explored this concept in a phantom study that demonstrated better agreement of stenosis grading using PureLumen than a standard virtual monoenergetic reconstruction on PCD-CT. Furthermore, an ultra-high resolution (UHR) mode of the PCD-CT can be employed to reduce calcium blooming, leading to a higher image quality of calcified coronary arteries [32]. Koons et al. performed a study comparing stenosis measurements on a PCD- and EID-CT of a stationary phantom and showed more accurate measurements using UHR vs standard resolution (SR) [29]. They demonstrated better performance of the UHR in detecting the severity of differently arranged stenoses. These results were confirmed and expanded by Zsarnoczay et al. who demonstrated that the use of UHR led to more accurate PDS measurements in a motion phantom compared to SR acquisition over a range of different heart rates [30]. Generally, there was an overestimation of stenoses found in the phantom for both UHR and SR, but the UHR measurements were closer to the true values. In addition, these new approaches allow the reduction of calcium blooming artifacts to improve spatial resolution of the PCD-CT. However, in-vivo validation in prospective cohorts is currently lacking and is the subject of ongoing research protocols.
There are some limitations to our study: First, there were only 23 patients included, and a larger study group would be desirable. However, larger study cohorts with repeated CT scans performed within a short time range are rare. Second, our in-vivo study population consisted of an unequal distribution of calcifications. On the other hand, the distribution of the CAD-RADS classes was balanced over the patient population in this study. Third, differences in slice thickness/increment and the tube potential could have affected the results. Differences in radiation doses were due to ethical regulations requiring a lower or similar dose level for the PCD-CT. It would be expected that a lower radiation dose would worsen the image quality of PCD-CT images and potentially worsen stenosis measurements, but our results demonstrated improved PDS reading on the PCD-CT despite the lower radiation doses compared to the EID-CT images. Finally, it should be noted that this investigation lacks an invasive correlation, which is typically regarded as a ground truth, and thus warrants further investigation in clinical studies.
Conclusion
In conclusion, PDS measurements on PCD-CT are highly correlated but generally lower compared to the measurements from EID-CT using similar reconstruction settings. The discrepancy in stenosis measurements between the two CT systems resulted in per-patient CAD-RADS re-classification in 17.4% of cases. Further studies are expected to evaluate optimized PCD-CT imaging protocols for PDS evaluation and to investigate its effects on the clinical decision-making process.
Authors' contribution
EVW, CG, MCH and TE designed the study, interpreted the study data and drafted the manuscript. NF and EZ performed data analysis, supported statistical analysis, and substantially revised the manuscript. JOD advised data reconstruction, supervised data analysis and edited/revised the manuscript. AV-S and UJS supervised the study conception and data interpretation and substantially edited the manuscript. All authors read and approved the final manuscript.
Conflict of interest
UJS received institutional research support and/or personal fees from Bayer, Bracco, Elucid Bioimaging, Guerbet, HeartFlow, Keya Medical, and Siemens. AV-S received institutional research support and/or personal fees from Elucid Bioimaging and Siemens. TE received a speaker fee, travel and institutional research support from Siemens Medical Solutions USA, Inc. JO’D is an employee of Siemens Medical Solutions USA, Inc.
Funding sources
This work was supported by a research grant from Siemens Healthineers. The MAInz-DOC Doctoral College and the Kaltenbach Doctoral Scholarship of the German Heart Foundation supported EVW.
Ethical statement
The studies involving human participants were reviewed and approved by Institutional Review Board, Medical University of South Carolina, SC, United States. The patients/participants provided their written informed consent to participate in this study.
Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1556/1647.2023.00156.
References
- [1]↑
Malakar AK, Choudhury D, Halder B, Paul P, Uddin A, Chakraborty S: A review on coronary artery disease, its risk factors, and therapeutics. J Cell Physiol 2019; 234(10): 16812–16823.
- [2]↑
Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C, et al.: 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J 2020; 41(3): 407–477.
- [3]↑
Moss AJ, Williams MC, Newby DE, Nicol ED: The updated NICE guidelines: cardiac CT as the first-line test for coronary artery disease. Curr Cardiovasc Imaging Rep 2017; 10(5): 15.
- [4]↑
Cury RC, Abbara S, Achenbach S, Agatston A, Berman DS, Budoff MJ, et al.: CAD-RADS(TM) coronary artery disease – reporting and data system. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology. J Cardiovasc Comput Tomogr 2016; 10(4): 269–281.
- [5]↑
Knuuti J, Ballo H, Juarez-Orozco LE, Saraste A, Kolh P, Rutjes AWS, et al.: The performance of non-invasive tests to rule-in and rule-out significant coronary artery stenosis in patients with stable angina: A meta-analysis focused on post-test disease probability. Eur Heart J 2018; 39(35): 3322–3330.
- [6]↑
Haase R, Schlattmann P, Gueret P, Andreini D, Pontone G, Alkadhi H, et al.: Diagnosis of obstructive coronary artery disease using computed tomography angiography in patients with stable chest pain depending on clinical probability and in clinically important subgroups: meta-analysis of individual patient data. BMJ 2019; 365: l1945.
- [7]↑
Chao SP, Law WY, Kuo CJ, Hung HF, Cheng JJ, Lo HM, et al.: The diagnostic accuracy of 256-row computed tomographic angiography compared with invasive coronary angiography in patients with suspected coronary artery disease. Eur Heart J 2010; 31(15): 1916–1923.
- [8]↑
Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: Technical principles and clinical prospects. Radiology 2018; 289(2): 293–312.
- [9]↑
Aquino GJ, O'Doherty J, Schoepf UJ, Ellison B, Byrne J, Fink N, et al.: Myocardial characterization with extracellular volume mapping with a first-generation photon-counting detector CT with MRI reference. Radiology 2023; 307(2):e222030.
- [10]
Emrich T, O'Doherty J, Schoepf UJ, Suranyi P, Aquino G, Kloeckner R, et al.: Reduced iodinated contrast media administration in coronary CT angiography on a clinical photon-counting detector CT system: a phantom study using a dynamic circulation model. Invest Radiol 2023; 58(2): 148–155.
- [11]
Graafen D, Emrich T, Halfmann MC, Mildenberger P, Duber C, Yang Y, et al.: Dose reduction and image quality in photon-counting detector high-resolution computed tomography of the chest: routine clinical data. J Thorac Imaging 2022; 37(5): 315–322.
- [12]↑
Wolf EV, Halfmann MC, Schoepf UJ, Zsarnoczay E, Fink N, Griffith JP, 3rd, et al.: Intra-individual comparison of coronary calcium scoring between photon counting detector- and energy integrating detector-CT: effects on risk reclassification. Front Cardiovasc Med 2022; 9: 1053398.
- [13]
Euler A, Higashigaito K, Mergen V, Sartoretti T, Zanini B, Schmidt B, et al.: High-pitch photon-counting detector computed tomography angiography of the aorta: intraindividual comparison to energy-integrating detector computed tomography at equal radiation dose. Invest Radiol 2022; 57(2): 115–121.
- [14]
Emrich T, Aquino G, Schoepf UJ, Braun FM, Risch F, Bette SJ, 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.
- [15]
Fink N, Zsarnoczay E, Schoepf UJ, Griffith JP, 3rd, Wolf EV, O’Doherty J, 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. https://doi.org/10.1097/RLI.0000000000000959.
- [16]
Zsarnoczay E, Varga-Szemes A, Emrich T, Szilveszter B, van der Werf NR, Mastrodicasa D, et al.: Characterizing the heart and the myocardium with photon-counting CT. Invest Radiol 2023; 58(7): 505–514.
- [17]↑
Dewey M, Rutsch W, Schnapauff D, Teige F, Hamm B: Coronary artery stenosis quantification using multislice computed tomography. Invest Radiol 2007; 42(2): 78–84.
- [18]↑
Allmendinger T, Nowak T, Flohr T, Klotz E, Hagenauer J, Alkadhi H, et al.: Photon-counting detector CT-based vascular calcium removal algorithm: assessment using a cardiac motion phantom. Invest Radiol 2022; 57(6): 399–405.
- [19]↑
Cury RC, Leipsic J, Abbara S, Achenbach S, Berman D, Bittencourt M, et al.: CAD-RADS 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 America Society of Cardiovascular Imaging (NASCI). J Am Coll Radiol 2022; 19(11): 1185–1212.
- [20]↑
Koo TK, Li MY: A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15(2): 155–163.
- [21]↑
Mergen V, Eberhard M, Manka R, Euler A, Alkadhi H: First in-human quantitative plaque characterization with ultra-high resolution coronary photon-counting CT angiography. Front Cardiovasc Med 2022; 9: 981012.
- [22]↑
Gulati M, Levy PD, Mukherjee D, Amsterdam E, Bhatt DL, Birtcher KK, et al.: 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR guideline for the evaluation and diagnosis of chest pain: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2021; 144(22): e368–e454.
- [23]↑
Renker M, Nance JW, Jr., 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(2): 390–399.
- [24]
Zhang S, Levin DC, Halpern EJ, Fischman D, Savage M, Walinsky P: Accuracy of MDCT in assessing the degree of stenosis caused by calcified coronary artery plaques. AJR Am J Roentgenol 2008; 191(6): 1676–1683.
- [25]↑
Song YB, Arbab-Zadeh A, Matheson MB, Ostovaneh MR, Vavere AL, Dewey M, et al.: Contemporary discrepancies of stenosis assessment by computed tomography and invasive coronary angiography. Circ Cardiovasc Imaging 2019; 12(2): e007720.
- [26]↑
Vavere AL, Arbab-Zadeh A, Rochitte CE, Dewey M, Niinuma H, Gottlieb I, et al.: Coronary artery stenoses: Accuracy of 64-detector row CT angiography in segments with mild, moderate, or severe calcification–a subanalysis of the CORE-64 trial. Radiology 2011; 261(1): 100–108.
- [27]↑
Kalisz K, Buethe J, Saboo SS, Abbara S, Halliburton S, Rajiah P: Artifacts at cardiac CT: physics and solutions. Radiographics 2016; 36(7): 2064–2083.
- [28]↑
Symons R, De Bruecker Y, Roosen J, Van Camp L, Cork TE, Kappler S, et al.: Quarter-millimeter spectral coronary stent imaging with photon-counting CT: initial experience. J Cardiovasc Comput Tomogr 2018; 12(6): 509–515.
- [29]↑
Koons E, VanMeter P, Rajendran K, Yu L, McCollough C, Leng S: Improved quantification of coronary artery luminal stenosis in the presence of heavy calcifications using photon-counting detector CT. Proc SPIE Int Soc Opt Eng 2022; 12031.
- [30]↑
Zsarnoczay E, Fink N, Schoepf UJ, O'Doherty J, Allmendinger T, Hagenauer J, et al.: Ultra-high resolution photon-counting coronary CT angiography improves coronary stenosis quantification over a wide range of heart rates – a dynamic phantom study. Eur J Radiol 2023; 161: 110746.
- [31]↑
Saraste A, Knuuti J: Evaluation of coronary artery disease after computed tomography angiography. Eur Heart J Cardiovasc Imaging 2018; 19(4): 378–379.
- [32]↑
Mergen V, Sartoretti T, Baer-Beck M, Schmidt B, Petersilka M, Wildberger J, et al.: Ultra-high-resolution coronary CT angiography with photon-counting detector CT: feasibility and image characterization. Invest Radiol 2022; 57(12): 780–788.
