Application of low-dose “one-stop” myocardial computed tomography perfusion imaging in coronary artery disease

Introduction

Coronary artery disease (CAD) remains the most common form of cardiovascular disease and is the leading cause of mortality worldwide (1,2). Invasive coronary angiography (CAG) is considered the reference standard for diagnosing CAD, which directly visualizes coronary stenosis and obstruction. However, its invasive nature limits its widespread use, especially in low- and intermediate-risk populations (3). Coronary computed tomography angiography (CCTA) has emerged as a robust, non-invasive alternative for anatomical evaluation of CAD. CCTA offers high negative predictive value (NPV) and diagnostic accuracy, particularly for assessing lesions in patients with a low-to-intermediate risk of CAD. Nevertheless, CCTA only provides anatomical details and cannot assess hemodynamic changes, which is significantly influenced by calcification and patient factors such as heart rate and respiration (4-7).

Computed tomography perfusion (CTP) imaging accurately reflects myocardial blood supply and has unique advantages in detecting microvascular disease and hypoperfused regions (8,9). Recently, some studies have suggested that CTP combined with CCTA could provide both hemodynamic and anatomical information, making it an ideal approach for CAD diagnosis while reducing invasive procedures. Several studies have demonstrated that CTP + CCTA improved diagnostic performance over CTP or CCTA alone (10-13). However, the conventional CTP + CCTA approach is associated with increased effective dose (ED), contrast media (CM), and prolonged scan time. Thus, to reduce the ED, CM, and scan time, the “one-stop” CTP protocol was established, which can acquire anatomical and hemodynamic information from a single CTP scan (14,15). Nevertheless, “one-stop” CTP still requires repeated acquisition for multi-phase CT images, resulting in elevated radiation exposure and CM usage. Maintaining diagnostic image quality at low radiation doses remains a challenge, and the functional assessment in prior protocols has frequently lacked comprehensiveness. Therefore, further technical optimization of the “one-stop” CTP protocol is essential.

This study aimed to investigate a low-dose “one-stop” CTP protocol as an innovative CT technique for multi-parameter functional assessment of suspected CAD by obtaining data on myocardial perfusion, systolic function, and quantitative myocardial strain in a single scan, providing a CCTA image with adequate quality for diagnosis using a deep learning image reconstruction (DLIR) algorithm. We hypothesized that this protocol could further reduce radiation exposure and CM volume while simultaneously providing more comprehensive information to support clinical diagnosis and therapeutic decision-making. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1560/rc).

Methods

This study prospectively enrolled 105 patients who underwent low-dose “one-stop” CTP and 62 age- and sex-matched patients who underwent conventional CCTA. The clinical data were collected from the Hospital Information System (HIS) and the Picture Archiving and Communication Systems (PACS). The CTP images were reconstructed with a deep learning algorithm to improve image quality for clinical diagnosis. The ED, CM, and image quality of the two groups were recorded and assessed. Patients were then categorized into three subgroups based on the degree of coronary artery stenosis. The cardiac function, myocardial strain, and perfusion data were statistically analyzed to compare the differences of subgroups.

Patient population

This prospective study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Biomedical Ethics Committee of West China Hospital, Sichuan University (No. 2024-527) and registered with the Chinese Clinical Trial Registry (ChiCTR2500110211). Written informed consent was provided by all participants. Suspected CAD patients were prospectively enrolled from May 2024 to January 2025 for “one-stop” CTP imaging.

The inclusion criteria were as follows: (I) patients with symptoms suggestive of CAD (e.g., chest pain) or healthy individuals serving as controls; (II) age >18 years. The exclusion criteria were as follows (16): (I) clinically unstable conditions [e.g., severe arrhythmias, left ventricular ejection fraction (LVEF) <35%, heart failure]; (II) other cardiomyopathies; (III) severe liver, kidney, brain diseases, or bleeding disorders; (IV) known bronchial asthma or active hyperthyroidism; (V) allergy to iodine contrast agents or renal insufficiency [estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m²]; (VI) pregnancy or lactation; (VII) implantable devices (e.g., pacemakers, cardiac resynchronization devices) or coronary stents; (VIII) body mass index (BMI) <27 kg/m² (14,17).

Image acquisition

Patients refrained from consuming caffeine for at least 24 hours before scanning. Images were acquired using a 256-row multidetector computed tomography (CT) scanner (Revolution Apex CT, GE Healthcare, Milwaukee, WI, USA) with a Z-axis coverage of 16 cm and a gantry rotation time of 0.28 seconds. Intravenous contrast medium was administered via a dual-head power injector (Stellant Dual Flow, Nihon Medrad KK, Osaka, Japan). A prospective electrocardiogram (ECG)-gated acquisition protocol was employed.

“One-stop” CTP protocol and reconstruction parameters

The “one-stop” CTP protocol simultaneously acquired anatomical, perfusion, and functional data (Figure 1). A test bolus scan was first conducted at the proximal ascending aorta to determine the time-to-peak enhancement using a 20-mL 1:1 mixture of iodine contrast (Iomeron, 100 mL:400 mg I, Bracco, Shanghai, China) and saline, injected at a rate of 4.5 mL/s. Subsequently, a contrast agent (0.7 times the patient’s body weight in mL) was injected at 4.5 mL/s, followed by 30 mL of saline to acquire perfusion data.

Figure 1 Diagram of steps in CT acquisition protocol. (A-D) The scan protocols. The blue circle in the picture B was the ROI for detecting the time density curve. (E) The time density curve acquired from the test bolus. (F) CTP maps of the CTP images. (G) The post treatment for cardiac function and myocardial strain. The green circle was the epicardial border, the red circle was the endocardial border. CCTA, coronary computed tomography angiography; CM, contrast media; CT, computed tomography; CTP, computed tomography perfusion; TTP, time-to-peak.

CT perfusion imaging was then performed using a tube voltage of 70 kV and tube current of 200 mA, with a reconstruction interval of 0.8 seconds across 25-time phases. Prospective ECG gating was applied, targeting 45–45% of the RR interval. The subsequent CCTA phase was acquired 2–3 seconds after the peak enhancement as determined from the test bolus scan. For this phase, tube current was adjusted between 500 and 600 mA using Smart mA. Prospective ECG gating was set at 40–50% and 70–80% of the RR interval. Additional multiphase reconstructions were performed from 5% to 95% of the RR interval at 50 mA to facilitate cardiac function assessment.

The CTP images were reconstructed with a 1.25-mm section thickness using the high-level DLIR algorithm from the raw data (DLIR-H; TrueFidelity, GE Healthcare). CCTA images were reconstructed using a 0.625-mm section thickness with the Snapshot Freeze algorithm (GE Healthcare). Two reconstruction methods were applied: high-level DLIR and standard adaptive statistical iterative reconstruction (ASIR-V, 50%). Cardiac functional images were reconstructed at 5% RR interval increments throughout the cardiac cycle using raw CCTA data, with a section thickness of 2.5 mm.

Conventional CCTA protocol and reconstruction parameters

In the conventional CCTA protocol, prospective ECG-triggered sequential acquisition was used. The scan was automatically triggered when the region of interest (ROI) placed at the ascending aorta reached the default attenuation value [200 Hounsfield units (HU)]. The contrast agent (0.9 mL/kg), the same as the CTP, was injected at a rate of 4.5 mL/s, followed by 30 mL saline at 4.5 mL/s. The CCTA imaging parameters were as follows: 100 kV, 600–900 mA tube current (Smart mA), 40–50%, and 70–80% RR intervals. Fifty percent ASIR-V algorithm and Snapshot Freeze technique were used.

Image evaluation

Subjective image quality was independently assessed by two radiologists with more than 6 years of experience in cardiac imaging using a 4-point Likert scale as described in previous studies (17,18). A score of 4 indicated excellent image quality with no artifacts. A score of 3 was considered good, with minor artifacts present, but adequate for evaluating the presence of luminal stenosis. A score of 2 was defined as fair, reflecting reduced image quality, though still sufficient to exclude obstructive CAD. A score of 1 represented poor image quality, with severe artifacts or calcification that impaired diagnostic interpretation.

The objective image quality was evaluated based on the CT attenuation values, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). An ROI with an approximately 5 mm² area was manually drawn at the orifices of the left anterior descending (LAD), left circumflex (LCX), and right coronary (RCA) arteries, avoiding calcifications and plaques as much as possible. The mean CT values were recorded. An ROI sized approximately 50 mm² was placed at the subcutaneous fat of the prothorax wall to obtain the mean CT attenuation values and standard deviations (SDs). The SDs of fat served as the noise of images. The SNR and CNR were calculated according to the following formula:

SNR=ROIvSDfat[1]

CNR=ROIv−ROIfatSDfat[2]

where ROI_fat and ROI_v represent the mean CT attenuation values of the vessels ROI and fat ROI, respectively, and SD_fat is SDs for fat ROI (19).

Data analysis

Data were analyzed using a dedicated post-processing workstation (GE AW VolumeShare 7, version 15.0; GE Healthcare). Coronary stenosis was graded per Society of Cardiovascular Computed Tomography (SCCT) guidelines. Stenosis ≥50% was defined as significant (20). Based on these criteria, patients who underwent the “one-stop” CTP protocol were categorized into three subgroups: the control group (no stenosis, 0%), Group I (non-significant stenosis, <50%), and Group II (significant stenosis, ≥50%). The patients without coronary stenosis served as a healthy control group. Myocardial segments were also grouped accordingly.

Cardiac functions were analyzed using CVI42 software version 6.0.2 (CVI Circle Cardiovascular Imaging Inc., Calgary, AB, Canada). Endocardial and epicardial borders were delineated manually in serial short-axis and two-, three-, and four-chamber long-axis views on end-systolic and end-diastolic cardiac phases to calculate LVEF, stroke volume (SV), left ventricular end-diastolic volume (LVEDV), left ventricular end-systolic volume (LVESV) and LV global longitudinal strain (GLS), and global circumferential strain (GCS) and global radial strain (GRS).

CTP images were analyzed using GE AW VolumeShare 7, version 15.0. All CTP images were registered by the “CT cardiac dynamic registration” tool, and then loaded into the “Dynamic CT Myocardial Perfusion” model of the software. An ROI was automatically drawn at the ascending aorta to acquire the time-density curve. Myocardial boundaries were automatically identified after anatomic fixation points were marked, and any wrong area was corrected by manual editing of myocardial boundaries, then the 17-segment AHA myocardial model (bullseye plots) was generated and calculated the myocardial blood flow (MBF).

Radiation dose estimation

Radiation dose was calculated using dose-length product (DLP) values multiplied by a conversion factor (k=0.014 mSv/mGy/cm).

Statistical methods

All statistical analyses were performed using the software SPSS 25.0 (IBM Corp., Armonk, NY, USA). Continuous variables with normal distribution were expressed as mean ± SD, whereas nonparametric data were expressed as median with the interquartile range (IQR). Categorical variables were expressed as percentages or frequencies. The differences between two groups were analyzed using independent-samples t-test for normally distributed data and the Mann-Whitney U test for non-normally distributed data. For comparisons among three groups, one-way analysis of variance (ANOVA) was used for normally distributed data, and the Kruskal-Wallis H test for non-normally distributed data. When ANOVA results were significant, post-hoc pairwise comparisons were conducted using the Tukey honestly significant difference (HSD) test. In cases of a significant Kruskal-Wallis test, post-hoc pairwise comparisons were performed using Dunn’s test with a Bonferroni adjustment for multiple comparisons. Pearson correlation was used to analyze the relationships of parametric data, and Spearman correlation analysis was performed for nonparametric data. Inter-observer agreements in subjective scores of images were analyzed using the linear weighted interrater agreement (Kappa) test. A P value <0.05 was considered statistically significant.

Results

Patient population

A total of 105 participants with suspected CAD were initially enrolled. After the exclusion of 11 participants due to failed myocardial strain assessment (n=3) and severe arrhythmia (n=8), a final cohort of 94 patients proceeded with the low-dose “one-stop” CTP and subsequent analysis. In addition, 62 age- and sex-matched patients who underwent CCTA were included in the final analysis. No statistically significant differences were observed in baseline cardiac function parameters between the two groups.

Among the 94 patients in the CTP group, 14 (14.8%) patients with normal coronary arteries were identified as control group, and 31 (32.9%) and 49 (52.2%) patients were assigned to Group I and Group II, respectively. As for the three subgroups of CTP groups, a statistically significant difference in age was found in age between the control group and Group II, and no significant differences were found in height, body weight, BMI, and cardiac function parameters of three subgroups. Detailed baseline characteristics are presented in Table 1.

Imaging quality assessment

The objective imaging parameters, including CT attenuation values, SNR, CNR, and image noise, for patients in the “one-stop” CTP and conventional CCTA groups are listed in Table 2. Image noise in the CCTA phase derived from the CTP group was slightly higher than that in the CCTA images, but both groups had adequate image quality for diagnosis requiring that three coronary arteries and plaques could be clearly observed (Figures 2,3). No significant differences were found in SNRs and CNRs of LAD, LCX, and RCA between two groups (all P>0.05) (Table 2).

Figure 2 The CCTA phase acquired from “one-stop” CTP. A 69-year-old man (BMI: 23.83 kg/m²) with CAD. (A-C) Coronary trees visualization. (D-F) The CCTA revealed non-significant stenosis in the proximal LAD, and the non-significant stenosis in the proximal, mid and distal RCA. BMI, body mass index; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; CTP, computed tomography perfusion; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery.

Figure 3 The conventional CCTA. A 51-year-old woman (BMI: 20.7 kg/m²) with CAD. (A-C) Coronary trees visualization. (D-F) CCTA revealed non-significant stenosis in the proximal and distal LAD and proximal LCX, with significant mid-LAD. BMI, body mass index; CAD, coronary artery disease; CCTA, coronary computed tomography angiography; LAD, left anterior descending artery; LCX, left circumflex artery; RCA, right coronary artery.

Regarding subjective image quality, despite higher noise in the CTP-derived CCTA phase, this did not significantly affect subjective image interpretation. No statistically significant differences were observed in mean subjective image quality scores of CCTA phase and conventional CCTA images for two readers (3.00±0.58 vs. 3.05±0.64; 2.94±0.62 vs. 3.03±0.57, all P>0.05). The detailed image quality score distributions are listed in the Table 3. Inter-observer agreement for subjective image quality was excellent, with κ values exceeding 0.80 for all assessments.

Radiation dose and contrast volume assessment

There was a significant decrease in the ED in “one-stop” CTP (4.13±0.33 vs. 7.56±1.43 mSv, P<0.05) and CCTA was reduced by 44.5% in the CTP group. A significant difference was also found in the DLP between the two groups (297.53±28.08 vs. 538.29±125.57 mGy·cm, P<0.05). In terms of CM usage, the “one-stop” CTP group also demonstrated a modest but statistically significant reduction in contrast volume compared with the CCTA group (51.96±5.54 vs. 55.74±9.05 mL; P<0.05), representing an 8% decrease.

Image data evaluation

Cardiac function

There were statistically significant differences in LVEF among the control group, Group I, and Group II (52.87%±2.75% vs. 48.26%±4.26% vs. 45.1%±3.79%, P<0.05 for all). SV and LVESV were also significantly different between the control group and Group II (72.40±12.56 vs. 62.21±11.13 mL; 64.17±8.77 vs. 76.02±15.15 mL, P<0.05 for all), but there were no differences in the LVEDV of the three subgroups. These results are summarized in Table 4.

The myocardial strain in patients with different degrees of coronary artery stenosis is also shown in Table 4. There were significant differences in LV GLS, GCS, and GRS of the control group, Group I, and Group II. As the degree of coronary artery stenosis increased, GRS, the absolute value of GLS (|GLS|), and GCS (|GCS|) decreased gradually. Correlation analysis revealed that LVEF, GLS, GCS, and GRS were significantly associated with the degree of coronary artery stenosis. The correlations were moderate to strong (LVEF: r=–0.56; GLS: r=0.61; GCS: r=0.54; GRS: r=–0.46; P<0.05 for all comparisons).

Myocardial perfusion

There were statistically significant differences in mean MBF of the control group, Group I, and Group II (139.96±5.3 vs. 133.95±3.7 vs. 125.53±4.55 mL/100 mL/min, P<0.05 for all) that statistical power >0.8. There was a strong negative correlation between the degrees of coronary artery stenosis and mean MBF (r=–0.65; P<0.05). As the degree of coronary artery stenosis increased, the mean MBF decreased gradually.

A total of 1,598 myocardial segments corresponding to 282 coronary arteries from 94 patients were analyzed. On CCTA phase, 95 coronary arteries (32.6%) of 94 patients showed a non-significant stenosis, among which LAD comprised about 52% (49/95), LCX 20% (19/95), and RCA 28% (27/95); significant stenosis was found in 71 coronary arteries (25.2%), including LAD 43% (31/71), LCX 20% (14/71), and RCA 37% (26/71). The MBF of the territories supplied by coronary arteries with significant and non-significant stenosis was 94.17±11.13 and 113.31±8.44 mL/100 mL/min, respectively, compared with 135.54±14.5 mL/100 mL/min in territories without relevant stenosis in the supplying vessel. There were significant differences in the MBF of the normal, non-significant, and significant myocardial segments (both P<0.05) with statistical power >0.8. For the patients without coronary stenosis (Figure 4), although there were some fluctuations in the MBF, it remained at a high level. In the patients with coronary stenosis (Figures 5,6), the MBF showed corresponding decreases in the myocardial segments supplied by relevant stenosis arteries.

Figure 4 Diagram of the cardiac strain in a patient with coronary stenosis. A 57-year-old man (BMI: 24.22 kg/m²) with non-significant coronary stenosis. (A) Coronary trees visualization. (B,C) CCTA revealed stenosis in the proximal LAD and RCA (red arrows). (D-F) The value of peak LS, CS, and RS decreased in the corresponding myocardial segments suggesting slightly reduction in the magnitude of myocardial shortening. BMI, body mass index; CCTA, coronary computed tomography angiography; CS, circumferential strain; LAD, left anterior descending artery; LS, longitudinal strain; RCA, right coronary artery; RS, radial strain.

Figure 5 An example case of a patient without coronary stenosis. A 27-year-old woman (BMI: 24.34 kg/m²) with normal coronary arteries. (A-C) CCTA phase showed no calcification or stenosis of three main coronary arteries. (D) Perfusion analysis showed no abnormalities in myocardial perfusion. BMI, body mass index; CCTA, coronary computed tomography angiography.

Figure 6 An example case of a patient with significant coronary stenosis. A 70-year-old man (BMI: 24.39 kg/m²) with significant coronary stenosis. (A,B) Coronary trees visualization. (D,E) CCTA revealed stenosis in main artery, and the severe stenosis in the proximal and mid LAD and distal RCA (red arrows). (C) Perfusion analysis showed decreased MBF in the basal anterior, basal anteroseptal and apical inferior myocardial segments (red *), suggesting perfusion defects in the corresponding segments. BMI, body mass index; CCTA, coronary computed tomography angiography; LAD, left anterior descending artery; MBF, myocardial blood flow; RCA, right coronary artery.

Discussion

This prospective study evaluated the clinical utility of the low-dose “one-stop” CTP protocol in patients with CADs. The protocol reduced radiation dose and CM usage without the image quality decreasing compared to the conventional CCTA. Moreover, it enabled the acquisition of more comprehensive information about the cardiac function and LV global strain of patients in a single scan than the combined CCTA + CTP protocol.

Technical improvements of low-dose “one-stop” CTP

An improved “one-stop” CTP was developed in this study. This imaging protocol could acquire a CCTA phase from the CTP, which reduced ED and CM using low radiation dose with DLIR. The study employed the 70 kV tube voltage and 200 mA tube current setting, with increased tube voltage only during CCTA phase, thereby reducing the ED and contrast agent usage effectively. The polychromatic X-ray beam of 70 kV is close to the K-absorption edge of iodine, enhancing the photoelectric effect and contrast. Thus, the lower CM usage for this protocol could acquire a higher CT value of images. This finding is consistent with the known physical principle of increased iodine attenuation at lower kV levels (14), which is a key advantage of low-kV scanning. However, the higher noise caused by the low-kV scanning which affected the diagnosis could offset this advantage. To address this, a DLIR algorithm was used to suppress image noise and improve image quality for clinical diagnosis (21) that solved the key trade-off between dose reduction and image noise. However, in patients with high BMI, using a 70-kV tube voltage may not provide sufficient signal and could affect image quality. The higher kV setting may be needed depending on the patient’s body type.

In this study, the average ED for CTP alone was 3.59±0.32 mSv. In comparison, Yi et al. (15) also employed the 70 kV tube voltage setting, but their CTP scanning using CARE Dose 4D (tube voltage and current, 80 kV and 300 mAs) provided a higher tube current in the case of low dose, resulting in a higher ED of 3.8±1.4 mSv. The total average ED was 4.13±0.33 mSv. It was unsurprisingly higher than the ED of 70 kV CCTA (2.5±0.9 mSv), but lower than the 80 kV CCTA using a prospective “adaptive sequence” scan protocol (5.1±2.7 mSv) (22). Compared with a recent study combining 70kV CTP and CCTA, our approach achieved a further reduction of over 34.4% in ED (6.3±1.4 mSv) (23).

The clinical value of low-dose “one-stop” CTP

Several studies have provided a comprehensive evaluation of CAD by combining CTP with CCTA. These studies demonstrated that combined CCTA + CTP could improve diagnostic performance for patients with flow-limiting CAD compared to CCTA or CTP alone (10-13,24). The “one-stop” CTP, which obtained both anatomic and hemodynamic information from the same CTP acquisition, reduced the radiation dose, scan time, and CM usage, avoiding the occurrence of possible cross-contamination of contrast (15,25). In contrast to the combined CCTA + CTP and conventional “one-stop” CTP that provided only anatomy and hemodynamic information, the improved low-dose “one-stop” CTP protocol additionally enabled the assessment of cardiac function and LV global strain information, further minimizing the ED and CM usage.

In our study, the LVEF and the MBF gradually decreased as the coronary stenosis increased, consistent with the established pathophysiological principle (26). Wichmann (27) and Fogante et al. (23) also demonstrated a significant difference in the MBF between the significant and non-significant stenosis groups. MBF has been regarded as the most direct and accurate parameter to assess myocardial perfusion defects caused by functional impact of coronary lesions (28,29). It yielded the requirement of revascularization for clinical decision-making (30). The recent expert consensus recommended the combined CCTA + CTP for patients with ischemic heart disease, known CAD or significant calcifications (31). Previous studies have shown that CCTA + CTP that may be particularly useful for patients with an intermediate-to-high risk of CAD or multivessel disease (10,11). Beyond perfusion assessment, the gradual deterioration in the magnitudes of LV global strain with increasing coronary stenosis was observed in this study. Han et al. (32) similarly showed moderate negative correlations between cardiac strain and the severity of coronary stenosis. GLS is a more sensitive indicator of early systolic dysfunction than LVEF (33), making CT strain a valuable parameter for evaluating the functional exchange of coronary lesions and providing prognostic information (34). The LV global strain acquired from the low-dose “one-stop” CTP could detect LV systolic dysfunction sensitively in the subclinical stage while identifying the coronary artery plaques and degree of stenosis, which might help to comprehensively assess the adverse effects caused by myocardial ischemia in suspected CAD.

Limitations

This study had some limitations. First, the research was conducted in a single-center. The sample size of this study was unbalanced. Multicenter studies are needed to further explore the feasibility of “one-stop” CTP protocol. Second, there were some sources of bias in the patient sample, as the enrolled patients had relatively low BMIs. Additionally, older patients tended to have more advanced coronary disease, potentially confounding some subgroup comparisons. Thirdly, there was no invasive reference standard used to validate the finding of the protocol. Further studies are needed to investigate the diagnostic performance of this study. Finally, stress dynamic CTP was not available in this study due to its limited clinical use in our hospital. Although resting CTP has shown value in perfusion assessment across age- and sex-matched groups (35), future studies should evaluate combined rest–stress protocols.

Conclusions

The low-dose “one-stop” CTP protocol significantly reduced radiation dose and contrast medium volume while providing comprehensive cardiac information, including coronary anatomy, left ventricular function, global myocardial strain, and perfusion—all in a single scan. This approach demonstrates potential for clinical application in the diagnosis and management of CAD across various risk levels.

Acknowledgments

We would like to thank the radiology departments of West China Hospital and GE Imaging Research Center for their support during patient enrollment and imaging acquisition.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1560/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1560/dss

Funding: This work was supported by National Natural Science Foundation of China (grant No. 82371927) to C.X.; Natural Science Foundation of Sichuan Province (grant No. 2024NSFSC1796) to C.X.; and 1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (grant No. ZYGD23024) to C.X.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1560/coif). W.R. is from GE Healthcare China. C.X. reports the funding from the Natural Science Foundation of China (grant No. 82371927); Natural Science Foundation of Sichuan Province (grant No. 2024NSFSC1796); and 1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (grant No. ZYGD23024). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by Biomedical Ethics Committee of West China Hospital, Sichuan University (No. 2024-527) and registered with the Chinese Clinical Trial Registry (ChiCTR2500110211). Written informed consent was taken from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Mensah GA, Fuster V, Murray CJL, Roth GA. Global Burden of Cardiovascular Diseases and Risks Collaborators. Global Burden of Cardiovascular Diseases and Risks, 1990-2022. J Am Coll Cardiol 2023;82:2350-473. [Crossref] [PubMed]
2. Murray CJL. The Global Burden of Disease Study at 30 years. Nat Med 2022;28:2019-26. [Crossref] [PubMed]
3. Unlu O, Fahed AC. Machine Learning in Invasive and Noninvasive Coronary Angiography. Curr Atheroscler Rep 2023;25:1025-33. [Crossref] [PubMed]
4. Meijboom WB, Meijs MF, Schuijf JD, Cramer MJ, Mollet NR, van Mieghem CA, Nieman K, van Werkhoven JM, Pundziute G, Weustink AC, de Vos AM, Pugliese F, Rensing B, Jukema JW, Bax JJ, Prokop M, Doevendans PA, Hunink MG, Krestin GP, de Feyter PJ. Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study. J Am Coll Cardiol 2008;52:2135-44. [Crossref] [PubMed]
5. Budoff MJ, Dowe D, Jollis JG, Gitter M, Sutherland J, Halamert E, Scherer M, Bellinger R, Martin A, Benton R, Delago A, Min JK. Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial. J Am Coll Cardiol 2008;52:1724-32. [Crossref] [PubMed]
6. Miller JM, Rochitte CE, Dewey M, Arbab-Zadeh A, Niinuma H, Gottlieb I, Paul N, Clouse ME, Shapiro EP, Hoe J, Lardo AC, Bush DE, de Roos A, Cox C, Brinker J, Lima JA. Diagnostic performance of coronary angiography by 64-row CT. N Engl J Med 2008;359:2324-36. [Crossref] [PubMed]
7. Marano R, De Cobelli F, Floriani I, Becker C, Herzog C, Centonze M, Morana G, Gualdi GF, Ligabue G, Pontone G, Catalano C, Chiappino D, Midiri M, Simonetti G, Marchisio F, Olivetti L, Fattori R, Bonomo L, Del Maschio ANIMISCAD Study Group. Italian multicenter, prospective study to evaluate the negative predictive value of 16- and 64-slice MDCT imaging in patients scheduled for coronary angiography (NIMISCAD-Non Invasive Multicenter Italian Study for Coronary Artery Disease). Eur Radiol 2009;19:1114-23. [Crossref] [PubMed]
8. Pontone G, Andreini D, Guaricci AI, Guglielmo M, Baggiano A, Muscogiuri G, et al. Quantitative vs. qualitative evaluation of static stress computed tomography perfusion to detect haemodynamically significant coronary artery disease. Eur Heart J Cardiovasc Imaging 2018;19:1244-52. [Crossref] [PubMed]
9. Zdanowicz A, Guzinski M, Pula M, Witkowska A, Reczuch K, Dynamic CT. Myocardial Perfusion: The Role of Functional Evaluation in the Diagnosis of Coronary Artery Disease. J Clin Med 2023;12:7062. [Crossref] [PubMed]
10. Pontone G, Andreini D, Guaricci AI, Baggiano A, Fazzari F, Guglielmo M, et al. Incremental Diagnostic Value of Stress Computed Tomography Myocardial Perfusion With Whole-Heart Coverage CT Scanner in Intermediate- to High-Risk Symptomatic Patients Suspected of Coronary Artery Disease. JACC Cardiovasc Imaging 2019;12:338-49. [Crossref] [PubMed]
11. Kitagawa K, Nakamura S, Ota H, Ogawa R, Shizuka T, Kubo T, et al. Diagnostic Performance of Dynamic Myocardial Perfusion Imaging Using Dual-Source Computed Tomography. J Am Coll Cardiol 2021;78:1937-49. [Crossref] [PubMed]
12. Nous FMA, Geisler T, Kruk MBP, Alkadhi H, Kitagawa K, Vliegenthart R, et al. Dynamic Myocardial Perfusion CT for the Detection of Hemodynamically Significant Coronary Artery Disease. JACC Cardiovasc Imaging 2022;15:75-87. [Crossref] [PubMed]
13. Kujala I, Nammas W, Maaniitty T, Stenström I, Klén R, Bax JJ, Knuuti J, Saraste A. Prognostic value of combined coronary CT angiography and myocardial perfusion imaging in women and men. Eur Heart J Cardiovasc Imaging 2023;24:1201-9. [Crossref] [PubMed]
14. Liu K, Diao K, Hu S, Xu X, Zhang J, Peng W, Xia C, Zhang K, Li Y, Guo Y, He S, He Y, Li Z. Achieving Low Radiation Dose in "One-Stop" Myocardial Computed Tomography Perfusion Imaging in Coronary Artery Disease Using 16-cm Wide Detector CT. Acad Radiol 2020;271531-9. [Crossref] [PubMed]
15. Yi Y, Xu C, Wu W, Shen ZJ, Lee W, Yun CH, Lu B, Zhang JY, Jin ZY, Wang YN. Low-dose CT perfusion with combined use of CTP and CTP-derived coronary CT angiography at 70 kVp: validation with invasive fractional flow reserve. Eur Radiol 2021;31:1119-29. [Crossref] [PubMed]
16. Narula J, Chandrashekhar Y, Ahmadi A, Abbara S, Berman DS, Blankstein R, Leipsic J, Newby D, Nicol ED, Nieman K, Shaw L, Villines TC, Williams M, Hecht HS. SCCT 2021 Expert Consensus Document on Coronary Computed Tomographic Angiography: A Report of the Society of Cardiovascular Computed Tomography. J Cardiovasc Comput Tomogr 2021;15:192-217. [Crossref] [PubMed]
17. Dewey M, Vavere AL, Arbab-Zadeh A, Miller JM, Sara L, Cox C, Gottlieb I, Yoshioka K, Paul N, Hoe J, de Roos A, Lardo AC, Lima JA, Clouse ME. Patient characteristics as predictors of image quality and diagnostic accuracy of MDCT compared with conventional coronary angiography for detecting coronary artery stenoses: CORE-64 Multicenter International Trial. AJR Am J Roentgenol 2010;194:93-102. [Crossref] [PubMed]
18. Chen MY, Shanbhag SM, Arai AE. Submillisievert median radiation dose for coronary angiography with a second-generation 320-detector row CT scanner in 107 consecutive patients. Radiology 2013;267:76-85. [Crossref] [PubMed]
19. Chen Y, Liu Z, Li M, Yu Y, Jia Y, Ma G, Hu Z, Wei D, Li D, He T. Reducing both radiation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V algorithm, in comparison with the conventional 100-kVp protocol. Eur Radiol 2019;29:3036-43. [Crossref] [PubMed]
20. Cury RC, Leipsic J, Abbara S, Achenbach S, Berman D, Bittencourt M, et al. CAD-RADSation and contrast doses in coronary CT angiography in lean patients on a 16-cm wide-detector CT using 70 kVp and ASiR-V ascular 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;19:1185-212. [Crossref] [PubMed]
21. Nam JG, Hong JH, Kim DS, Oh J, Goo JM. Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction. Eur Radiol 2021;31:5533-43. [Crossref] [PubMed]
22. Eijsvoogel NG, Hendriks BMF, Willigers JL, Martens B, Carati LF, Horehledova B, Kietselaer BLJH, Crijns HJGM, Wildberger JE, Das M. Personalization of injection protocols to the individual patient's blood volume and automated tube voltage selection (ATVS) in coronary CTA. PLoS One 2018;13:e0203682. [Crossref] [PubMed]
23. Fogante M, Paolini E, Cela F, Esposto Pirani P, Balardi L, Perna GP, Schicchi N. Feasibility, Added Value, and Radiation Dose of Combined Coronary CT Angiography and Stress Dynamic CT Myocardial Perfusion Imaging in Moderate Coronary Artery Disease: A Real-World Study. J Cardiovasc Dev Dis 2025;12:241. [Crossref] [PubMed]
24. Yang J, Dou G, He B, Jin Q, Chen Z, Jing J, Di Carli MF, Chen Y, Blankstein R. Stress Myocardial Blood Flow Ratio by Dynamic CT Perfusion Identifies Hemodynamically Significant CAD. JACC Cardiovasc Imaging 2020;13:966-76. [Crossref] [PubMed]
25. Sliwicka O, Sechopoulos I, Baggiano A, Pontone G, Nijveldt R, Habets J. Dynamic myocardial CT perfusion imaging-state of the art. Eur Radiol 2023;33:5509-25. [Crossref] [PubMed]
26. 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;41:407-77. [Crossref] [PubMed]
27. Wichmann JL, Meinel FG, Schoepf UJ, Lo GG, Choe YH, Wang Y, Vliegenthart R, Varga-Szemes A, Muscogiuri G, Cannaò PM, De Cecco CN. Absolute Versus Relative Myocardial Blood Flow by Dynamic CT Myocardial Perfusion Imaging in Patients With Anatomic Coronary Artery Disease. AJR Am J Roentgenol 2015;205:W67-72. [Crossref] [PubMed]
28. Nagao M, Yamasaki Y, Kamitani T, Kawanami S, Sagiyama K, Yamanouchi T, Shimomiya Y, Matoba T, Mukai Y, Odashiro K, Baba S, Maruoka Y, Kitamura Y, Nishie A, Honda H. Quantification of coronary flow using dynamic angiography with 320-detector row CT and motion coherence image processing: Detection of ischemia for intermediate coronary stenosis. Eur J Radiol 2016;85:996-1003. [Crossref] [PubMed]
29. Dantas RN Jr, Assuncao AN Jr, Marques IA, Fahel MG, Nomura CH, Avila LFR, Giorgi MCP, Soares J Jr, Meneghetti JC, Parga JR. Myocardial perfusion in patients with suspected coronary artery disease: comparison between 320-MDCT and rubidium-82 PET. Eur Radiol 2018;28:2665-74. [Crossref] [PubMed]
30. Katikireddy CK, Samim A. Myocardial viability assessment and utility in contemporary management of ischemic cardiomyopathy. Clin Cardiol 2022;45:152-61. [Crossref] [PubMed]
31. Patel AR, Bamberg F, Branch K, Carrascosa P, Chen M, Cury RC, Ghoshhajra B, Ko B, Nieman K, Pugliese F, Schoepf J, Blankstein R. Society of cardiovascular computed tomography expert consensus document on myocardial computed tomography perfusion imaging. J Cardiovasc Comput Tomogr 2020;14:87-100. [Crossref] [PubMed]
32. Han X, Cao Y, Ju Z, Liu J, Li N, Li Y, Liu T, Shi H, Gu J. Assessment of regional left ventricular myocardial strain in patients with left anterior descending coronary stenosis using computed tomography feature tracking. BMC Cardiovasc Disord 2020;20:362. [Crossref] [PubMed]
33. Smiseth OA, Torp H, Opdahl A, Haugaa KH, Urheim S. Myocardial strain imaging: how useful is it in clinical decision making? Eur Heart J 2016;37:1196-207. [Crossref] [PubMed]
34. Dodd JD, Leipsic JA. Evolving Developments in Cardiac CT. Radiology 2023;307:e222827. [Crossref] [PubMed]
35. Byrne C, Kühl JT, Zacho M, Nordestgaard BG, Fuchs A, Frestad D, Køber L, Kofoed KF. Sex- and age-related differences of myocardial perfusion at rest assessed with multidetector computed tomography. J Cardiovasc Comput Tomogr 2013;7:94-101. [Crossref] [PubMed]