Quantitative volumetric effect of sublingual nitroglycerin on coronary lumen volume in CT angiography after normalization for body size

Quantitative volumetric effect of sublingual nitroglycerin on coronary lumen volume in CT angiography after normalization for body size | 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 Quantitative volumetric effect of sublingual nitroglycerin on coronary lumen volume in CT angiography after normalization for body size Ismail BALABAN, Banu ALICIOGLU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9156292/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Sublingual nitroglycerin is widely used before coronary CT angiography (CCTA) to induce coronary vasodilation and enhance visualization of distal coronary segments and small side branches. Ther aim of the study is to assess the effect of sublingual nitroglycerin on whole-vessel coronary lumen volume in CCTA and to determine whether this response varies after normalization for body size parameters. Materials and Methods This single-center retrospective case–control study included patients who underwent CCTA using a 128-detector CT scanner. Patients were classified according to pre-scan nitroglycerin administration. In the nitroglycerin group, 400–800 µg of sublingual nitroglycerin was administered 3–5 minutes before image acquisition. Age-matched patients who did not receive nitroglycerin served as controls. Coronary lumen volumes were calculated semi-automatically using dedicated software and normalized to body mass index (BMI) and body surface area (BSA). The number of coronary side branches and intraluminal contrast enhancement in proximal and distal segments were also assessed. Results A total of 194 patients were included. Significantly higher lumen volumes were observed in the left anterior descending (LAD), circumflex (LCx), and right coronary artery (RCA) in the nitroglycerin group (p < 0.001). The LAD/BMI, RCA/BMI, and LCx/BMI as well as LAD/BSA, RCA/BSA, and LCx/BSA ratios were significantly higher in the nitroglycerin group. The number of septal, diagonal, and obtuse marginal branches was also significantly higher in the nitroglycerin group (p < 0.001). The proximal-to-distal contrast enhancement ratio of the LAD and LCx arteries was significantly lower in the nitroglycerin group (p < 0.001). Conclusion Sublingual nitroglycerin significantly increases whole-vessel coronary lumen volume on CCTA. This quantitative volumetric effect persists after body-size normalization and may facilitate improved coronary visualization in patients with larger body habitus. Artificial intelligence coronary vessel computed tomography angiography nitroglycerin Figures Figure 1 Figure 2 Introduction Coronary CT angiography (CCTA) has increasingly been recognized as a first-line diagnostic modality in patients with stable chest pain and suspected obstructive coronary artery disease. Despite its high spatial and temporal resolution, visualization of small-diameter coronary arteries and their side branches may still be challenging. CCTA was initially introduced using 16-slice CT systems, which had substantially lower spatial and temporal resolution compared with contemporary scanners [ 1 ]. Owing to its vasodilatory effect, pre-scan administration of sublingual nitroglycerin has been incorporated into CCTA imaging protocols to enhance coronary artery visualization. Nitroglycerin-induced coronary vasodilation reduces partial volume effects, may decrease motion-related artifacts, and facilitates the assessment of stenosis severity, as well as the visualization of non-calcified and mixed atherosclerotic plaques [ 2 , 3 ]. Previous studies using 16-, 40-, and 64-slice CT scanners have demonstrated that sublingual nitroglycerin significantly increases coronary lumen diameter, improves visualization of distal segments and side branches, enhances the detection of obstructive lesions, and has a favorable safety profile [ 4 – 6 ]. However, diameter-based measurements may underestimate the true three-dimensional volumetric response of coronary arteries, and coronary volume scales non-linearly with body size. Consequently, the physiologic response to nitroglycerin may be misinterpreted if not normalized for body size. To our knowledge, the body size–normalized volumetric response of coronary arteries to sublingual nitroglycerin has not been systematically investigated in CCTA. On the other hand, the spatial and temporal resolutions of MDCT in the post–64-slice era have steadily improved over the past few years. Technological advancements in modern CT scanners, together with the incorporation of artificial intelligence–based reconstruction techniques, motion reduction algorithms, the quality of coronary artery CT imaging has further advanced. Recent evidence indicates that next-generation CT systems allow for reduced contrast agent usage and lower radiation doses, with fewer motion-related artifacts even in patients with elevated heart rates [ 7 – 9 ]. However, whether routine nitroglycerin administration continues to provide incremental benefits in the era of modern high-resolution CT scanners remains uncertain. With the advent of next-generation CT systems and advanced post-processing techniques, including artificial intelligence–based algorithms, quantitative assessment of coronary lumen volumes has become feasible. Therefore, this study aimed to quantify the effect of pre-scan nitroglycerin on coronary lumen volume, distal segment opacification, and side-branch visibility in CCTA performed with a 128-slice CT scanner in non-stenotic coronaries. We further examined whether this volumetric response should be interpreted after normalization for body size. Materials and Methods This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and its later amendments. Ethical approval was obtained from the Zonguldak Bülent Ecevit University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (approval date: March 3, 2025; decision no: 2025/05). The study was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the requirement for informed consent was waived. and the requirement for informed consent was waived due to the retrospective design. The study was conducted in accordance with the Declaration of Helsinki. Patient selection: This single center case-control retrospective study included patients who underwent CCTA between June 2020 and January 2025. All CCTA examinations were performed under routine clinical conditions, with the primary indications being atypical chest pain, non-specific ST elevation or dyspnea. CCTA reports were retrospectively retrieved and reviewed using the hospital information system. Among the CCTA reports of a total of 2,058 patients, those with < 50% coronary artery stenosis who received sublingual nitroglycerin prior to scanning were identified. Ninety-seven patients who received nitroglycerin constituted the study group, while 97 age-matched patients who did not receive nitrates were assigned to the control group. During scan preparation, patients’ current medications were reviewed, and height, weight, blood pressure, heart rate, and all premedications administered prior to scanning were recorded. Demographic data, etiologies, and clinical and laboratory findings were also retrieved from the hospital information system. Exclusion criteria included major coronary artery or cardiac anomalies, aplasia of distal segment of coronary, stenosis > 50%, coronary or aortic aneurysms, prior coronary stenting or bypass surgery, non-standard imaging studies, and patients < 18 years. Since motion-related artifacts in the coronary arteries may affect lumen volume measurements, such images were excluded from the study. Scanning protocol CCTA was performed using a 128-row single source CT scanner (Revolution CT, GE Healthcare, Tokyo, Japan). Imaging was performed in the supine position after a fasting period of at least 6–8 hours, in accordance with the standard protocol. Venous access was established in the antecubital region using a 20-gauge intravenous catheter. The target heart rate for optimal scanning was 60–65 beats per minute (bpm). In cases with heart rate (HR) > 70 bpm, beta-blocker (40 mg per oral propranolol 3–4 times and/or intravenous 5mg metoprolol 3–4 times with blood pressure monitoring) was administrated. Patients were monitored after placement of ECG electrodes prior to scanning. Breath-hold training was provided to the patient. In the nitroglycerin receiving group, patients received sublingual nitroglycerin at a dose of 400–800 µg (1–2 sprays) approximately 3–5 minutes before image acquisition. The decision regarding pre-scan nitrate administration was made randomly. Scanning was performed in the craniocaudal direction, extending from the pulmonary apices to the inferior border of the heart. When the target HR was achieved, a retrospective ECG-gating technique was preferred. In cases with heart beat > 75 bpm or when the patient was younger than 40 years, a prospective gating technique was used to reduce radiation exposure. The scan range was defined to extend from just below the tracheal bifurcation to include the inferior border of the heart. The tube voltage was set to 100–120 kVp, the helical pitch: 0.22:1, tube rotation time: 0.35 seconds. Tube current was determined using automatic tube current modulation. Iohexol (300 mgI/mL) was administered intravenously at a dose of 1–1.5 mL/kg at an injection rate of 5 mL/s via automatic injector followed by half volume of saline. During image acquisition, axial images of the thorax were obtained, and a region of interest (ROI) was placed in the descending aorta. Scan acquisition was initiated using bolus tracking when luminal attenuation reached 140 Hounsfield units (HU). Diastolic-phase images were evaluated using thin-slice reconstructions. Subsequently, volumetric and curved reformatted images were generated to evaluate the coronary vascular structures. No allergic reactions or serious adverse events related to the contrast medium, beta-blockers, or nitrates were observed in any patient. Radiologic analysis: The CCTA images of 97 patients who received sublingual nitroglycerin spray before scanning and age matched 97 patients who did not receive nitroglycerin were analyzed. CCTA images were evaluated on a dedicated workstation (VolumeShare 7, GE Healthcare, Tokyo, Japan) using specialized cardiac software. Coronary artery volume measurement: Each coronary artery was manually segmented from the ostium to the distal end, after which curved reformat lumen images were automatically generated. The lumen volumes of the coronary arteries were calculated in cm 3 semi-automatically using artificial intelligence based plaque ID tool (Fig. 1 ). For statistical analysis, coronary artery volumes were normalized to each subject’s body surface area (BSA) and body mass index (BMI). The side branches of the coronaries were counted utilizing multiplanar reformation (MPR) and volume rendering 3-dimensional reconstructed images (Fig. 2 ). Intraluminal opacification of the coronaries was assessed on axial series. Circular ROI appropriately sized to the vessel lumen were manually placed in the proximal and distal segments, and the resulting contrast attenuation values were recorded in HU. Images were assessed by consensus of two readers, 5 years of radiology fellow and board certified, 15 years experience of cardiac CT senior radiologist. Statistical analysis: Statistical analysis was performed using SPSS (Version 29.0) software. The normality of continuous variables was assessed using the Shapiro-Wilk test. Continuous variables are presented as mean ± standard deviation or median (minimum–maximum), and categorical variables are expressed as number and percentage. For comparisons of continuous variables between two groups, either the independent samples t-test or the Mann-Whitney U test was applied, as appropriate. Associations between categorical variables were evaluated using the chi-square test. A p-value < 0.05 was considered statistically significant. Results The mean age of the 194 patients was 48 ± 11.1 years, and 50.5% were male. The mean BMI was 28.5 ± 4.6 kg/m², and the mean BSA was 1.9 ± 0.2 m². The demographic and clinical characteristics of the study groups are presented in Table 1 . The mean age did not differ significantly between the groups (47.8 ± 10.9 vs. 48.2 ± 11.2 years; p = 0.801). Sex distribution was also similar between groups (male: 56.7% vs. 44.3%, p = 0.085). Pre-scan β-blocker doses: In nitroglycerin group, the mean ± SD oral propranolol dose was 37.9 ± 28.5 (median: 40 mg, range: 0–80) mg, while the mean ± SD iv metoprolol dose was 0.05 ± 0.5 (median: 0 mg, range: 0–5) mg. In patients who did not receive nitroglycerin, the mean ± SD of oral propranolol dose was 31.5 ± 30 (median: 40 mg, range: 0–40) mg, and the mean ± SD iv metoprolol dose was 0.31 ± 1.2 (median: 0 mg, range: 0–5) mg. No statistically significant difference was observed between the nitroglycerin and control groups in terms of propranolol or metoprolol doses ( p = 0.11 and p = 0.06 respectively). The mean HR was 59.7 ± 7 BPM in the study group and 58.5 ± 5.9 BPM in the controls, with no statistically significant difference between the groups (p = 0.232). The mean BMI was 28.6 ± 4.5 kg/m² in the study group and 28.3 ± 4.6 kg/m² in the controls, with no significant difference between groups (p = 0.642). Similarly, the mean BSA was 1.9 ± 0.2 m² in both groups, with no statistically significant difference between them (p = 0.634). Interobserver reproducibility was assessed by a second independent observer who was blinded to the initial measurements. A random subset of 30 patients (15 from the nitroglycerin group and 15 from the control group) was reanalyzed. Agreement between observers was evaluated using the intraclass correlation coefficient (ICC). The ICC values for coronary lumen volume measurements were 0.82 for the LAD, 0.79 for the LCx, and 0.89 for the RCA, indicating good to excellent agreement. For proximal and distal ROI attenuation measurements of the LAD, LCx, and RCA, the corresponding ICC values were 0.75, 0.73, and 0.88, respectively.Significantly higher lumen volumes were observed in the left anterior descending (LAD), circumflex (LCx) and right coronary artery (RCA) in the study group (p < 0.001). When arterial luminal volumes were normalized using BMI and BSA, the LAD/BMI, RCA/BMI, and LCx/BMI ratios were significantly higher in the study group. Similarly, LAD/BSA, RCA/BSA, and LCx/BSA ratios were significantly higher in the nitroglycerin receiving patients. The number of septal, diagonal, and obtuse marginal branches was significantly higher in the study group (p < 0.001). The proximal/distal contrast enhancement ratio of the LAD and LCx arteries was significantly lower in the nitroglycerin receivings (p < 0.001) (Table 2 ). Table 2 Distribution of coronary arterial luminal volume parameters and lumen opacification on CCTA according to study groups. LAD: Left anterior descending artery, LCx: Circumflex artery, RCA: Right coronary artery. Values are presented as median (min–max). Variables Nitroglycerin group (n = 97) Controls (n = 97) p LAD/BMI 18.9 (3.7–41.0) 15.6 (4.4–38.8) 0.001 RCA/BMI 22.2 (4.0–56.5) 17.1 (7.5–57.4) 0.009 LCx/BMI 12.6 (4.3–39.7) 8.9 (2.8–24.1) 0.001 LAD/BSA 272.7 (54.1–591.4) 227.3 (65.1–557.4) 0.001 RCA/BSA 334.7 (49.1–719.3) 257.1 (114.3–701.0) 0.003 LCx/BSA 190.8 (59.3–521.0) 144.0 (39.6–337.4) 0.001 LAD volume 519 (103–1230) 429 (109–1344) 0.001 RCA volume 634 (100–1483) 468 (224–1490) 0.006 LCx volume 381 (113–938) 249 (75–658) 0.001 LAD proximal/distal ROI 1.8 (0.9–3.4) 2.1 (1.1–4.0) 0.001 RCA proximal/distal ROI 1.1 (0.8–3.0) 1.0 (0.6–2.4) 0.098 LCx proximal/distal ROI 1.4 (0.9–2.5) 1.5 (0.8–3.3) 0.038 Number of septal branches 5 (2–10) 4 (1–9) 0.001 Number of diagonal branches 5 (1–8) 3 (1–7) 0.001 Number of obtuse marginal branches 4 (1–9) 3 (1–7) 0.001 No adverse effects on image quality or coronary artery visualization were observed in any of the patients. Discussion Consistent with expectations, the lumen volumes of the LAD, RCA, and LCx arteries were significantly greater in the nitrate-premedicated group. No diagnostic limitations were observed in the visualization of the coronary ostia or the proximal, mid, and distal coronary segments in CCTA examinations performed without nitroglycerin. However a higher contrast enhancement in the distal LAD and LCx segments was observed in the nitrate group, indicating superior visualization of the distal coronary arteries. This finding suggests that nitrate administration promotes a more homogeneous distribution of blood flow toward the distal segments, thereby enhancing visualization of distal coronary arteries. However, no significant difference was observed between the groups in terms of contrast enhancement ratios in the distal RCA segment. This finding may be explained by the anatomical characteristics of the RCA, as it typically maintains a relatively uniform luminal diameter along its course, with tapering occurring only after bifurcation into the posterior descending and posterolateral branches. In contrast, the LAD and LCx arteries gradually taper as they course distally [ 10 ]. Visualization of the coronary artery side branches was also significantly superior in the nitrate-administered group. Furthermore, our findings demonstrate that nitrate administration enables visualization of longer coronary artery segments in patients with elevated BMI or BSA, even in the absence of weight-adjusted nitroglycerin dosing. Historically, CCTA was primarily focused on identifying major coronary anomalies and ostial significant stenoses; however, with advancements in contemporary CT technology, comprehensive assessment of longer segments, distal vessels, and small side branches has become feasible. Given that coronary artery diameters are subject to interindividual variability related to body size, anatomical variations, and cardiovascular performance, assessments based solely on diameter measurements may restrict the interpretability and generalizability of the findings. A more accurate assessment of the vasodilatory effect of nitroglycerin would require demonstration of differences in coronary diameter or volume before and after its administration. Nevertheless, performing such paired evaluations using CCTA is ethically and clinically questionable due to the associated radiation exposure and the need for nephrotoxic contrast agents. To the best of our knowledge, Okada et al [ 6 ] conducted the only study to date evaluating the vasodilatory effects of nitroglycerin within the same patient cohort, using a 64-slice CT scanner. They reported nitroglycerin-induced coronary vasodilation ranging from 7.54% to 22.26%, with dilation being more prominent in minor than in major coronary segments. Furthermore, although nitroglycerin administration was associated with a significant increase in coronary luminal attenuation (p < 0.01), this increase was not found to directly contribute to a marked improvement in image quality. Decramer et al. [ 5 ], using 64-slice multidetector CT, reported significant increases in coronary diameter and volume and a higher number of septal branches after nitrate administration in 42 subjects. Using a 16-slice CT scanner, Chun et al. [ 11 ] compared a nitroglycerin group (n = 46) with a control group (n = 30). Although sensitivity, specificity, positive predictive value, and negative predictive value were higher in the nitroglycerin group, the difference did not reach statistical significance (p = 0.25) to detect significant coronary stenoses (≥ 50%). However, higher diagnostic accuracy was reported in the proximal coronary segments among patients who received nitroglycerin. A study of 70 patients using 320-row detector CCTA demonstrated that nitroglycerin induced significant coronary artery dilation but did not improve the assessment of stenosis severity, thereby raising, for the first time, questions regarding the necessity of nitrate use in CCTA from a patient safety perspective [ 12 ]. The fact that nitroglycerin produces less dilation in diseased arteries reinforces this rationale [ 11 ]. As our study did not include patients with stenotic lesions, no conclusions can be drawn on this issue. Using 320-slice CT, Kim et al [ 13 ]. demonstrated a more homogeneous intraluminal contrast distribution following nitrate administration, reflected by less negative transluminal attenuation gradients, particularly in the LAD and LCx, findings consistent with our results. Nitroglycerin exerts its vasodilatory effects through relaxation of vascular smooth muscle, reducing myocardial oxygen demand and facilitating coronary dilation [ 14 – 16 ]. Although concerns exist regarding hypotension and reflex tachycardia, no clinically significant changes in heart rate or adverse events were observed in our cohort. In contrast, Okada et al. [ 6 ] reported an increase in heart rate following nitroglycerin administration; however, the optimal reconstruction phase of CCTA did not change significantly. Nitroglycerin may cause serious adverse effects when used in combination with other vasoactive agents. In particular, the concomitant use of nitroglycerin with phosphodiesterase type 5 (PDE5) inhibitors—commonly prescribed for the treatment of erectile dysfunction (e.g., sildenafil, vardenafil, and tadalafil)—is strictly contraindicated because of the risk of severe hypotension and cardiovascular collapse [ 17 ]. The use of PDE5 inhibitors has increased substantially in recent years [ 18 ] extending beyond erectile dysfunction to include indications such as idiopathic pulmonary hypertension, high-altitude illness, lower urinary tract symptoms, and angina [ 19 ]. A Danish nationwide study identified 249,541 male patients with ischemic heart disease between 2000 and 2018, of whom 42,073 were prescribed nitrates while continuing to use PDE5 inhibitors during the same period. However, no statistically significant association was found between the concomitant use of these medications and the occurrence of cardiovascular adverse events [ 20 ]. To the best of our knowledge, no cases of severe hypotension or cardiovascular collapse associated with the concomitant use of nitroglycerin and PDE inhibitors have been reported in the literature. Given the longer half-life of tadalafil (17.5 hours) relative to sildenafil (≈ 4 hours) and vardenafil (4–6 hours), [ 19 ] careful assessment of recent tadalafil use is warranted in patients requiring nitroglycerin administration. Furthermore, concomitant pre-scan beta-blocker administration may potentiate the vasodilatory effects of nitroglycerin, thereby increasing the risk of hypotension. This effect may be particularly relevant in elderly or volume-depleted patients [ 14 , 15 ]. Our study does not specifically address overall image quality aspects of CCTA. Image quality in CCTA is affected by multiple factors, including patient-related characteristics, HR and rhythm, scanner technology, acquisition and reconstruction protocols, pharmacologic premedication, and post-processing techniques [ 20 – 23 ]. The present study does not permit definitive conclusions regarding motion-related artifact formation; because all motion-related artifacts were excluded since they may affect lumen volume measurements. Motion artifacts mainly arise from irregular or high heart rates and inadequate breath-holding during image acquisition and are not directly attributable to nitroglycerin use. Advances in CT technology, particularly the introduction of ≥ 64-slice, wide-detector, and dual-source systems, have substantially improved spatial and temporal resolution in CCTA. Despite these improvements and the availability of advanced motion correction algorithms (‘snapshot freeze’ algorithm in our study), image quality at the segmental and distal coronary levels remains influenced by physiological factors such as vessel caliber and heart rate. In this context, adjunctive measures such as nitrate administration may continue to provide incremental benefit, particularly for distal vessel visualization [ 23 – 25 ]. Despite advances in CT technology, including scanners with more than 128 detector rows, beta-blocker use continues to play a crucial role in minimizing motion artifacts [ 26 ]. Modern CT-based CCTA enables visualization of coronary ostial and proximal segments without nitroglycerin administration. Pre-scan sublingual nitroglycerin increases coronary artery lumen volumes independently of individual body size and enhances attenuation in the distal segments of the coronary arteries and their side branches. This facilitates more comprehensive depiction of coronary tree anatomy and anastomotic connections. Beyond demonstrating patency of the main coronary vessels, this may provide clinical relevance by allowing indirect assessment of myocardial perfusion and the extent of physiological anastomoses and collateral circulation. Nevertheless, the inability to visualize distal coronary segments or collateral vessels is unlikely to result in any meaningful clinical consequence. A limitation of our study is that the posterior descending artery and posterolateral artery were not included in the analysis, as these distal vessels are highly influenced by anatomic variations. Secondly, the assessment of stenosis severity, as well as the patency of coronary stents and bypass grafts, was not evaluated. Although our findings are consistent with previous literature and expected outcomes, the present study offers several strengths, including a relatively large sample size, the use of artificial intelligence–assisted coronary lumen volume quantification (a more reliable and reproducible method compared with the manual diameter measurements used in previous studies), and normalization of coronary artery volumes to individual body size parameters. Given the small diameter of the coronary arteries, spatial resolution constraints may compromise the reliability of manual measurements, including ROI sampling from distal coronary segments, as performed in our study. In conclusion, while the vasodilatory effect of nitroglycerin has been previously described, our study uniquely integrates AI-based volumetric quantification, body-size normalization, and distal coronary enhancement analysis in a contemporary CT setting. Modern CT technology generally allows sufficient visualization of the coronary ostia and proximal segments for routine diagnostic assessment; however, improved depiction of the distal coronary arteries theoretically provides additional diagnostic value by enabling better evaluation of diffuse disease burden, side-branch involvement, collateral circulation, and procedural planning. Nevertheless, the inability to visualize distal and side branches or anastomoses is not, in itself, a clinically decisive criterion. This observation invites further discussion regarding the extent to which nitroglycerin contributes to the diagnostic value of coronary CT angiography, particularly in light of the increasing use of vasoactive medications and the potential risk of serious cardiovascular complications associated with concomitant nitrate administration. Future studies should assess the impact of nitroglycerin on the evaluation of slow coronary flow, significant coronary stenosis, and post-interventional luminal patency using contemporary CT technology. Declarations Author contributions: IB: Data acquisition, data measurement, formal analysis, and writing – original draft. BA: Conceptualization, methodology, supervision, data measurement, formal analysis, and writing – review and editing. Conflict of Interest: The authors declare that they have no competing financial or non-financial interests related to this work. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data Availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. Human Ethics and Consent to Participate declarations: not applicable. References Antonopoulos AS, Simantiris S. 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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-9156292","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627229413,"identity":"279e7348-d857-465d-8051-11679fd97db7","order_by":0,"name":"Ismail BALABAN","email":"","orcid":"","institution":"Zonguldak Bülent Ecevit University","correspondingAuthor":false,"prefix":"","firstName":"Ismail","middleName":"","lastName":"BALABAN","suffix":""},{"id":627229414,"identity":"95e97947-5571-43cb-9955-ad1e386d55be","order_by":1,"name":"Banu ALICIOGLU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYBACAwYGxgMJDBIMDOwMjI8Rgvi1MEC0MDMwGyMEEwhoAbOYGdikidJizsD84MCDGgs5eWces+qCisN5DOzN2yQYf9zDqcWygc3gQMIxCWPDwzxmt2ecOVzMwHOsTIIhoRi3ww4AUWKDROLGZqAW3rbDQHaOGVALbpcZHGD/ANdSzPsPqEX+DSEtPBBb5jPzmDHzNoBs4cGvxbKZpwDsFwNmtmLpGcfSE9t40ootEtJwazFnb9/48EdNnZx8e/PGzwU11on97Ic33vhgg1sLMDrg4QABbCACjwYEkG8gRtUoGAWjYBSMSAAA7FZOIoPIH2sAAAAASUVORK5CYII=","orcid":"","institution":"Zonguldak Bülent Ecevit University","correspondingAuthor":true,"prefix":"","firstName":"Banu","middleName":"","lastName":"ALICIOGLU","suffix":""}],"badges":[],"createdAt":"2026-03-18 07:56:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9156292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9156292/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107616465,"identity":"86d6bda1-ba6b-4075-a2eb-3e5f0c256825","added_by":"auto","created_at":"2026-04-23 09:13:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276255,"visible":true,"origin":"","legend":"\u003cp\u003eThe lumen of the each main coronary artery was reconstructed using curved planar reformation (CPR). Automated segmentation and coronary lumen volume quantification were performed using PlaqID software, demonstrating three-dimensional volumetric assessment of the vessel lumen.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9156292/v1/f446874ca7180076f8c65ac0.png"},{"id":107707039,"identity":"9061278a-abed-46f7-95d4-9fbb04933827","added_by":"auto","created_at":"2026-04-24 09:19:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":253524,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUpper row:\u003c/strong\u003e CCTA images of a 37-year-old patient without nitroglycerin administration (A–D).\u003cbr\u003e\n \u003cstrong\u003eLower row:\u003c/strong\u003e CCTA images of a 65-year-old patient after nitroglycerin administration (E–H).Volume-rendered (VR) coronary tree images (A, E), curved reformatted images of the proximal (B, F) and distal (C, G) left anterior descending (LAD) artery segments, and maximum intensity projection (MIP) images of the apical interventricular septum (D, H). The patient who received nitroglycerin demonstrated more comprehensive visualization of the coronary tree and better delineation of side branches.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9156292/v1/be7826be8dee52c76a879e1b.png"},{"id":107709025,"identity":"f83fde9e-c350-436e-b4bc-03d4dc1f223e","added_by":"auto","created_at":"2026-04-24 09:34:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":836033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9156292/v1/f451a668-7db6-4268-a2aa-d1db413ecfa3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative volumetric effect of sublingual nitroglycerin on coronary lumen volume in CT angiography after normalization for body size","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary CT angiography (CCTA) has increasingly been recognized as a first-line diagnostic modality in patients with stable chest pain and suspected obstructive coronary artery disease. Despite its high spatial and temporal resolution, visualization of small-diameter coronary arteries and their side branches may still be challenging. CCTA was initially introduced using 16-slice CT systems, which had substantially lower spatial and temporal resolution compared with contemporary scanners [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOwing to its vasodilatory effect, pre-scan administration of sublingual nitroglycerin has been incorporated into CCTA imaging protocols to enhance coronary artery visualization. Nitroglycerin-induced coronary vasodilation reduces partial volume effects, may decrease motion-related artifacts, and facilitates the assessment of stenosis severity, as well as the visualization of non-calcified and mixed atherosclerotic plaques [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies using 16-, 40-, and 64-slice CT scanners have demonstrated that sublingual nitroglycerin significantly increases coronary lumen diameter, improves visualization of distal segments and side branches, enhances the detection of obstructive lesions, and has a favorable safety profile [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, diameter-based measurements may underestimate the true three-dimensional volumetric response of coronary arteries, and coronary volume scales non-linearly with body size. Consequently, the physiologic response to nitroglycerin may be misinterpreted if not normalized for body size. To our knowledge, the body size\u0026ndash;normalized volumetric response of coronary arteries to sublingual nitroglycerin has not been systematically investigated in CCTA.\u003c/p\u003e \u003cp\u003eOn the other hand, the spatial and temporal resolutions of MDCT in the post\u0026ndash;64-slice era have steadily improved over the past few years. Technological advancements in modern CT scanners, together with the incorporation of artificial intelligence\u0026ndash;based reconstruction techniques, motion reduction algorithms, the quality of coronary artery CT imaging has further advanced. Recent evidence indicates that next-generation CT systems allow for reduced contrast agent usage and lower radiation doses, with fewer motion-related artifacts even in patients with elevated heart rates [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, whether routine nitroglycerin administration continues to provide incremental benefits in the era of modern high-resolution CT scanners remains uncertain. With the advent of next-generation CT systems and advanced post-processing techniques, including artificial intelligence\u0026ndash;based algorithms, quantitative assessment of coronary lumen volumes has become feasible. Therefore, this study aimed to quantify the effect of pre-scan nitroglycerin on coronary lumen volume, distal segment opacification, and side-branch visibility in CCTA performed with a 128-slice CT scanner in non-stenotic coronaries. We further examined whether this volumetric response should be interpreted after normalization for body size.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and its later amendments. Ethical approval was obtained from the Zonguldak B\u0026uuml;lent Ecevit University Faculty of Medicine Non-Interventional Clinical Research Ethics Committee (approval date: March 3, 2025; decision no: 2025/05). The study was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the requirement for informed consent was waived.\u003c/p\u003e \u003cp\u003e and the requirement for informed consent was waived due to the retrospective design. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection:\u003c/h2\u003e \u003cp\u003eThis single center case-control retrospective study included patients who underwent CCTA between June 2020 and January 2025. All CCTA examinations were performed under routine clinical conditions, with the primary indications being atypical chest pain, non-specific ST elevation or dyspnea. CCTA reports were retrospectively retrieved and reviewed using the hospital information system. Among the CCTA reports of a total of 2,058 patients, those with \u0026lt;\u0026thinsp;50% coronary artery stenosis who received sublingual nitroglycerin prior to scanning were identified. Ninety-seven patients who received nitroglycerin constituted the study group, while 97 age-matched patients who did not receive nitrates were assigned to the control group.\u003c/p\u003e \u003cp\u003eDuring scan preparation, patients\u0026rsquo; current medications were reviewed, and height, weight, blood pressure, heart rate, and all premedications administered prior to scanning were recorded. Demographic data, etiologies, and clinical and laboratory findings were also retrieved from the hospital information system.\u003c/p\u003e \u003cp\u003eExclusion criteria included major coronary artery or cardiac anomalies, aplasia of distal segment of coronary, stenosis\u0026thinsp;\u0026gt;\u0026thinsp;50%, coronary or aortic aneurysms, prior coronary stenting or bypass surgery, non-standard imaging studies, and patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years. Since motion-related artifacts in the coronary arteries may affect lumen volume measurements, such images were excluded from the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eScanning protocol\u003c/strong\u003e \u003cp\u003eCCTA was performed using a 128-row single source CT scanner (Revolution CT, GE Healthcare, Tokyo, Japan). Imaging was performed in the supine position after a fasting period of at least 6\u0026ndash;8 hours, in accordance with the standard protocol. Venous access was established in the antecubital region using a 20-gauge intravenous catheter. The target heart rate for optimal scanning was 60\u0026ndash;65 beats per minute (bpm). In cases with heart rate (HR)\u0026thinsp;\u0026gt;\u0026thinsp;70 bpm, beta-blocker (40 mg per oral propranolol 3\u0026ndash;4 times and/or intravenous 5mg metoprolol 3\u0026ndash;4 times with blood pressure monitoring) was administrated. Patients were monitored after placement of ECG electrodes prior to scanning. Breath-hold training was provided to the patient. In the nitroglycerin receiving group, patients received sublingual nitroglycerin at a dose of 400\u0026ndash;800 \u0026micro;g (1\u0026ndash;2 sprays) approximately 3\u0026ndash;5 minutes before image acquisition. The decision regarding pre-scan nitrate administration was made randomly.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eScanning was performed in the craniocaudal direction, extending from the pulmonary apices to the inferior border of the heart. When the target HR was achieved, a retrospective ECG-gating technique was preferred. In cases with heart beat\u0026thinsp;\u0026gt;\u0026thinsp;75 bpm or when the patient was younger than 40 years, a prospective gating technique was used to reduce radiation exposure. The scan range was defined to extend from just below the tracheal bifurcation to include the inferior border of the heart. The tube voltage was set to 100\u0026ndash;120 kVp, the helical pitch: 0.22:1, tube rotation time: 0.35 seconds. Tube current was determined using automatic tube current modulation.\u003c/p\u003e \u003cp\u003eIohexol (300 mgI/mL) was administered intravenously at a dose of 1\u0026ndash;1.5 mL/kg at an injection rate of 5 mL/s via automatic injector followed by half volume of saline. During image acquisition, axial images of the thorax were obtained, and a region of interest (ROI) was placed in the descending aorta. Scan acquisition was initiated using bolus tracking when luminal attenuation reached 140 Hounsfield units (HU).\u003c/p\u003e \u003cp\u003eDiastolic-phase images were evaluated using thin-slice reconstructions. Subsequently, volumetric and curved reformatted images were generated to evaluate the coronary vascular structures. No allergic reactions or serious adverse events related to the contrast medium, beta-blockers, or nitrates were observed in any patient.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRadiologic analysis:\u003c/h3\u003e\n\u003cp\u003eThe CCTA images of 97 patients who received sublingual nitroglycerin spray before scanning and age matched 97 patients who did not receive nitroglycerin were analyzed. CCTA images were evaluated on a dedicated workstation (VolumeShare 7, GE Healthcare, Tokyo, Japan) using specialized cardiac software.\u003c/p\u003e \u003cp\u003eCoronary artery volume measurement: Each coronary artery was manually segmented from the ostium to the distal end, after which curved reformat lumen images were automatically generated. The lumen volumes of the coronary arteries were calculated in cm\u003csup\u003e3\u003c/sup\u003e semi-automatically using artificial intelligence based plaque ID tool (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For statistical analysis, coronary artery volumes were normalized to each subject\u0026rsquo;s body surface area (BSA) and body mass index (BMI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe side branches of the coronaries were counted utilizing multiplanar reformation (MPR) and volume rendering 3-dimensional reconstructed images (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIntraluminal opacification of the coronaries was assessed on axial series. Circular ROI appropriately sized to the vessel lumen were manually placed in the proximal and distal segments, and the resulting contrast attenuation values were recorded in HU.\u003c/p\u003e \u003cp\u003eImages were assessed by consensus of two readers, 5 years of radiology fellow and board certified, 15 years experience of cardiac CT senior radiologist.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using SPSS (Version 29.0) software. The normality of continuous variables was assessed using the Shapiro-Wilk test. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (minimum\u0026ndash;maximum), and categorical variables are expressed as number and percentage. For comparisons of continuous variables between two groups, either the independent samples t-test or the Mann-Whitney U test was applied, as appropriate. Associations between categorical variables were evaluated using the chi-square test. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe mean age of the 194 patients was 48\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 years, and 50.5% were male. The mean BMI was 28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 kg/m\u0026sup2;, and the mean BSA was 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 m\u0026sup2;.\u003c/p\u003e\n\u003cp\u003eThe demographic and clinical characteristics of the study groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age did not differ significantly between the groups (47.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 vs. 48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2 years; p\u0026thinsp;=\u0026thinsp;0.801). Sex distribution was also similar between groups (male: 56.7% vs. 44.3%, p\u0026thinsp;=\u0026thinsp;0.085).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003ePre-scan β-blocker doses: In nitroglycerin group, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD oral propranolol dose was 37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5 (median: 40 mg, range: 0\u0026ndash;80) mg, while the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD iv metoprolol dose was 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 (median: 0 mg, range: 0\u0026ndash;5) mg.\u003c/p\u003e \u003cp\u003eIn patients who did not receive nitroglycerin, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of oral propranolol dose was 31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;30 (median: 40 mg, range: 0\u0026ndash;40) mg, and the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD iv metoprolol dose was 0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 (median: 0 mg, range: 0\u0026ndash;5) mg. No statistically significant difference was observed between the nitroglycerin and control groups in terms of propranolol or metoprolol doses (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.11 and p\u0026thinsp;=\u0026thinsp;0.06\u003c/em\u003e respectively).\u003c/p\u003e \u003cp\u003eThe mean HR was 59.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7 BPM in the study group and 58.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 BPM in the controls, with no statistically significant difference between the groups (p\u0026thinsp;=\u0026thinsp;0.232).\u003c/p\u003e \u003cp\u003eThe mean BMI was 28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 kg/m\u0026sup2; in the study group and 28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 kg/m\u0026sup2; in the controls, with no significant difference between groups (p\u0026thinsp;=\u0026thinsp;0.642).\u003c/p\u003e \u003cp\u003eSimilarly, the mean BSA was 1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 m\u0026sup2; in both groups, with no statistically significant difference between them (p\u0026thinsp;=\u0026thinsp;0.634).\u003c/p\u003e \u003cp\u003eInterobserver reproducibility was assessed by a second independent observer who was blinded to the initial measurements. A random subset of 30 patients (15 from the nitroglycerin group and 15 from the control group) was reanalyzed. Agreement between observers was evaluated using the intraclass correlation coefficient (ICC). The ICC values for coronary lumen volume measurements were 0.82 for the LAD, 0.79 for the LCx, and 0.89 for the RCA, indicating good to excellent agreement. For proximal and distal ROI attenuation measurements of the LAD, LCx, and RCA, the corresponding ICC values were 0.75, 0.73, and 0.88, respectively.Significantly higher lumen volumes were observed in the left anterior descending (LAD), circumflex (LCx) and right coronary artery (RCA) in the study group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When arterial luminal volumes were normalized using BMI and BSA, the LAD/BMI, RCA/BMI, and LCx/BMI ratios were significantly higher in the study group. Similarly, LAD/BSA, RCA/BSA, and LCx/BSA ratios were significantly higher in the nitroglycerin receiving patients. The number of septal, diagonal, and obtuse marginal branches was significantly higher in the study group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proximal/distal contrast enhancement ratio of the LAD and LCx arteries was significantly lower in the nitroglycerin receivings (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of coronary arterial luminal volume parameters and lumen opacification on CCTA according to study groups. LAD: Left anterior descending artery, LCx: Circumflex artery, RCA: Right coronary artery. Values are presented as median (min\u0026ndash;max).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitroglycerin group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;97)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD/BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.9 (3.7\u0026ndash;41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.6 (4.4\u0026ndash;38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA/BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2 (4.0\u0026ndash;56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.1 (7.5\u0026ndash;57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCx/BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.6 (4.3\u0026ndash;39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9 (2.8\u0026ndash;24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD/BSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272.7 (54.1\u0026ndash;591.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227.3 (65.1\u0026ndash;557.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA/BSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334.7 (49.1\u0026ndash;719.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e257.1 (114.3\u0026ndash;701.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCx/BSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.8 (59.3\u0026ndash;521.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.0 (39.6\u0026ndash;337.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e519 (103\u0026ndash;1230)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e429 (109\u0026ndash;1344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e634 (100\u0026ndash;1483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e468 (224\u0026ndash;1490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCx volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e381 (113\u0026ndash;938)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e249 (75\u0026ndash;658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLAD proximal/distal ROI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (0.9\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (1.1\u0026ndash;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCA proximal/distal ROI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.8\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.6\u0026ndash;2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCx proximal/distal ROI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.9\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0.8\u0026ndash;3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of septal branches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (2\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of diagonal branches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of obtuse marginal branches\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo adverse effects on image quality or coronary artery visualization were observed in any of the patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsistent with expectations, the lumen volumes of the LAD, RCA, and LCx arteries were significantly greater in the nitrate-premedicated group. No diagnostic limitations were observed in the visualization of the coronary ostia or the proximal, mid, and distal coronary segments in CCTA examinations performed without nitroglycerin. However a higher contrast enhancement in the distal LAD and LCx segments was observed in the nitrate group, indicating superior visualization of the distal coronary arteries. This finding suggests that nitrate administration promotes a more homogeneous distribution of blood flow toward the distal segments, thereby enhancing visualization of distal coronary arteries. However, no significant difference was observed between the groups in terms of contrast enhancement ratios in the distal RCA segment. This finding may be explained by the anatomical characteristics of the RCA, as it typically maintains a relatively uniform luminal diameter along its course, with tapering occurring only after bifurcation into the posterior descending and posterolateral branches. In contrast, the LAD and LCx arteries gradually taper as they course distally [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Visualization of the coronary artery side branches was also significantly superior in the nitrate-administered group. Furthermore, our findings demonstrate that nitrate administration enables visualization of longer coronary artery segments in patients with elevated BMI or BSA, even in the absence of weight-adjusted nitroglycerin dosing.\u003c/p\u003e \u003cp\u003eHistorically, CCTA was primarily focused on identifying major coronary anomalies and ostial significant stenoses; however, with advancements in contemporary CT technology, comprehensive assessment of longer segments, distal vessels, and small side branches has become feasible.\u003c/p\u003e \u003cp\u003eGiven that coronary artery diameters are subject to interindividual variability related to body size, anatomical variations, and cardiovascular performance, assessments based solely on diameter measurements may restrict the interpretability and generalizability of the findings. A more accurate assessment of the vasodilatory effect of nitroglycerin would require demonstration of differences in coronary diameter or volume before and after its administration. Nevertheless, performing such paired evaluations using CCTA is ethically and clinically questionable due to the associated radiation exposure and the need for nephrotoxic contrast agents. To the best of our knowledge, Okada et al [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] conducted the only study to date evaluating the vasodilatory effects of nitroglycerin within the same patient cohort, using a 64-slice CT scanner. They reported nitroglycerin-induced coronary vasodilation ranging from 7.54% to 22.26%, with dilation being more prominent in minor than in major coronary segments. Furthermore, although nitroglycerin administration was associated with a significant increase in coronary luminal attenuation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), this increase was not found to directly contribute to a marked improvement in image quality.\u003c/p\u003e \u003cp\u003eDecramer et al. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], using 64-slice multidetector CT, reported significant increases in coronary diameter and volume and a higher number of septal branches after nitrate administration in 42 subjects. Using a 16-slice CT scanner, Chun et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] compared a nitroglycerin group (n\u0026thinsp;=\u0026thinsp;46) with a control group (n\u0026thinsp;=\u0026thinsp;30). Although sensitivity, specificity, positive predictive value, and negative predictive value were higher in the nitroglycerin group, the difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.25) to detect significant coronary stenoses (\u0026ge;\u0026thinsp;50%). However, higher diagnostic accuracy was reported in the proximal coronary segments among patients who received nitroglycerin.\u003c/p\u003e \u003cp\u003eA study of 70 patients using 320-row detector CCTA demonstrated that nitroglycerin induced significant coronary artery dilation but did not improve the assessment of stenosis severity, thereby raising, for the first time, questions regarding the necessity of nitrate use in CCTA from a patient safety perspective [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The fact that nitroglycerin produces less dilation in diseased arteries reinforces this rationale [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As our study did not include patients with stenotic lesions, no conclusions can be drawn on this issue.\u003c/p\u003e \u003cp\u003eUsing 320-slice CT, Kim et al [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. demonstrated a more homogeneous intraluminal contrast distribution following nitrate administration, reflected by less negative transluminal attenuation gradients, particularly in the LAD and LCx, findings consistent with our results. Nitroglycerin exerts its vasodilatory effects through relaxation of vascular smooth muscle, reducing myocardial oxygen demand and facilitating coronary dilation [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although concerns exist regarding hypotension and reflex tachycardia, no clinically significant changes in heart rate or adverse events were observed in our cohort. In contrast, Okada et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] reported an increase in heart rate following nitroglycerin administration; however, the optimal reconstruction phase of CCTA did not change significantly.\u003c/p\u003e \u003cp\u003eNitroglycerin may cause serious adverse effects when used in combination with other vasoactive agents. In particular, the concomitant use of nitroglycerin with phosphodiesterase type 5 (PDE5) inhibitors\u0026mdash;commonly prescribed for the treatment of erectile dysfunction (e.g., sildenafil, vardenafil, and tadalafil)\u0026mdash;is strictly contraindicated because of the risk of severe hypotension and cardiovascular collapse [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The use of PDE5 inhibitors has increased substantially in recent years [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] extending beyond erectile dysfunction to include indications such as idiopathic pulmonary hypertension, high-altitude illness, lower urinary tract symptoms, and angina [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A Danish nationwide study identified 249,541 male patients with ischemic heart disease between 2000 and 2018, of whom 42,073 were prescribed nitrates while continuing to use PDE5 inhibitors during the same period. However, no statistically significant association was found between the concomitant use of these medications and the occurrence of cardiovascular adverse events [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To the best of our knowledge, no cases of severe hypotension or cardiovascular collapse associated with the concomitant use of nitroglycerin and PDE inhibitors have been reported in the literature. Given the longer half-life of tadalafil (17.5 hours) relative to sildenafil (\u0026asymp;\u0026thinsp;4 hours) and vardenafil (4\u0026ndash;6 hours), [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] careful assessment of recent tadalafil use is warranted in patients requiring nitroglycerin administration. Furthermore, concomitant pre-scan beta-blocker administration may potentiate the vasodilatory effects of nitroglycerin, thereby increasing the risk of hypotension. This effect may be particularly relevant in elderly or volume-depleted patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study does not specifically address overall image quality aspects of CCTA. Image quality in CCTA is affected by multiple factors, including patient-related characteristics, HR and rhythm, scanner technology, acquisition and reconstruction protocols, pharmacologic premedication, and post-processing techniques [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The present study does not permit definitive conclusions regarding motion-related artifact formation; because all motion-related artifacts were excluded since they may affect lumen volume measurements. Motion artifacts mainly arise from irregular or high heart rates and inadequate breath-holding during image acquisition and are not directly attributable to nitroglycerin use. Advances in CT technology, particularly the introduction of \u0026ge;\u0026thinsp;64-slice, wide-detector, and dual-source systems, have substantially improved spatial and temporal resolution in CCTA. Despite these improvements and the availability of advanced motion correction algorithms (\u0026lsquo;snapshot freeze\u0026rsquo; algorithm in our study), image quality at the segmental and distal coronary levels remains influenced by physiological factors such as vessel caliber and heart rate. In this context, adjunctive measures such as nitrate administration may continue to provide incremental benefit, particularly for distal vessel visualization [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Despite advances in CT technology, including scanners with more than 128 detector rows, beta-blocker use continues to play a crucial role in minimizing motion artifacts [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModern CT-based CCTA enables visualization of coronary ostial and proximal segments without nitroglycerin administration. Pre-scan sublingual nitroglycerin increases coronary artery lumen volumes independently of individual body size and enhances attenuation in the distal segments of the coronary arteries and their side branches. This facilitates more comprehensive depiction of coronary tree anatomy and anastomotic connections. Beyond demonstrating patency of the main coronary vessels, this may provide clinical relevance by allowing indirect assessment of myocardial perfusion and the extent of physiological anastomoses and collateral circulation. Nevertheless, the inability to visualize distal coronary segments or collateral vessels is unlikely to result in any meaningful clinical consequence.\u003c/p\u003e \u003cp\u003eA limitation of our study is that the posterior descending artery and posterolateral artery were not included in the analysis, as these distal vessels are highly influenced by anatomic variations. Secondly, the assessment of stenosis severity, as well as the patency of coronary stents and bypass grafts, was not evaluated.\u003c/p\u003e \u003cp\u003eAlthough our findings are consistent with previous literature and expected outcomes, the present study offers several strengths, including a relatively large sample size, the use of artificial intelligence\u0026ndash;assisted coronary lumen volume quantification (a more reliable and reproducible method compared with the manual diameter measurements used in previous studies), and normalization of coronary artery volumes to individual body size parameters. Given the small diameter of the coronary arteries, spatial resolution constraints may compromise the reliability of manual measurements, including ROI sampling from distal coronary segments, as performed in our study.\u003c/p\u003e \u003cp\u003eIn conclusion, while the vasodilatory effect of nitroglycerin has been previously described, our study uniquely integrates AI-based volumetric quantification, body-size normalization, and distal coronary enhancement analysis in a contemporary CT setting. Modern CT technology generally allows sufficient visualization of the coronary ostia and proximal segments for routine diagnostic assessment; however, improved depiction of the distal coronary arteries theoretically provides additional diagnostic value by enabling better evaluation of diffuse disease burden, side-branch involvement, collateral circulation, and procedural planning. Nevertheless, the inability to visualize distal and side branches or anastomoses is not, in itself, a clinically decisive criterion. This observation invites further discussion regarding the extent to which nitroglycerin contributes to the diagnostic value of coronary CT angiography, particularly in light of the increasing use of vasoactive medications and the potential risk of serious cardiovascular complications associated with concomitant nitrate administration. Future studies should assess the impact of nitroglycerin on the evaluation of slow coronary flow, significant coronary stenosis, and post-interventional luminal patency using contemporary CT technology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIB: Data acquisition, data measurement, formal analysis, and writing \u0026ndash; original draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBA:\u003c/strong\u003e Conceptualization, methodology, supervision, data measurement, formal analysis, and writing \u0026ndash; review and editing.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that they have no competing financial or non-financial interests related to this work.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAntonopoulos AS, Simantiris S. 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World J Cardiol. 2013;5:453\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4330/wjc.v5.i12\u003c/span\u003e\u003cspan address=\"10.4330/wjc.v5.i12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Artificial intelligence, coronary vessel, computed tomography angiography, nitroglycerin","lastPublishedDoi":"10.21203/rs.3.rs-9156292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9156292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSublingual nitroglycerin is widely used before coronary CT angiography (CCTA) to induce coronary vasodilation and enhance visualization of distal coronary segments and small side branches. Ther aim of the study is to assess the effect of sublingual nitroglycerin on whole-vessel coronary lumen volume in CCTA and to determine whether this response varies after normalization for body size parameters.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThis single-center retrospective case\u0026ndash;control study included patients who underwent CCTA using a 128-detector CT scanner. Patients were classified according to pre-scan nitroglycerin administration. In the nitroglycerin group, 400\u0026ndash;800 \u0026micro;g of sublingual nitroglycerin was administered 3\u0026ndash;5 minutes before image acquisition. Age-matched patients who did not receive nitroglycerin served as controls. Coronary lumen volumes were calculated semi-automatically using dedicated software and normalized to body mass index (BMI) and body surface area (BSA). The number of coronary side branches and intraluminal contrast enhancement in proximal and distal segments were also assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 194 patients were included. Significantly higher lumen volumes were observed in the left anterior descending (LAD), circumflex (LCx), and right coronary artery (RCA) in the nitroglycerin group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The LAD/BMI, RCA/BMI, and LCx/BMI as well as LAD/BSA, RCA/BSA, and LCx/BSA ratios were significantly higher in the nitroglycerin group. The number of septal, diagonal, and obtuse marginal branches was also significantly higher in the nitroglycerin group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The proximal-to-distal contrast enhancement ratio of the LAD and LCx arteries was significantly lower in the nitroglycerin group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSublingual nitroglycerin significantly increases whole-vessel coronary lumen volume on CCTA. This quantitative volumetric effect persists after body-size normalization and may facilitate improved coronary visualization in patients with larger body habitus.\u003c/p\u003e","manuscriptTitle":"Quantitative volumetric effect of sublingual nitroglycerin on coronary lumen volume in CT angiography after normalization for body size","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:13:25","doi":"10.21203/rs.3.rs-9156292/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-03T10:46:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T07:17:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T01:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264182279891996494890676433513698089081","date":"2026-04-23T00:13:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157390355590976866982815046621880548075","date":"2026-04-22T11:34:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T17:22:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113281654101859871588423640399448284277","date":"2026-04-20T15:43:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116351394766562885831704317698563232580","date":"2026-04-20T15:09:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T17:39:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-25T12:53:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T11:01:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T11:01:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-03-18T07:44:32+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":"f6698625-37bd-44a8-b7b4-be161bfb02b1","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-03T10:46:24+00:00","index":63,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T07:17:30+00:00","index":62,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:13:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:13:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9156292","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9156292","identity":"rs-9156292","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}