Journal of Cardiovascular Imaging最新文献

{"title":"Preoperative global longitudinal strain predicts myocardial hypertrophy burden but not the magnitude of reverse remodeling after surgical aortic valve replacement in severe aortic stenosis.","authors":"Joy Wulansari Purba, Ario Soeryo Kuncoro, Rina Ariani, Estu Rudiktyo, Amiliana Mardiani Soesanto","doi":"10.1186/s44348-026-00089-2","DOIUrl":"10.1186/s44348-026-00089-2","url":null,"abstract":"<p><strong>Background: </strong>Global longitudinal strain (GLS) is a marker of subclinical myocardial dysfunction and predicts adverse outcomes in severe aortic stenosis. However, its ability to predict the magnitude of left ventricular (LV) reverse remodeling following surgical aortic valve replacement (SAVR) remains uncertain.</p><p><strong>Methods: </strong>In 94 patients with severe aortic stenosis and preserved ejection fraction undergoing SAVR, patients were stratified by baseline GLS (normal, < -16%; abnormal, ≥ -16%). Given incomplete follow-up, the principal analysis was a complete-case comparison of interventricular septal dimension (IVSd), LV mass index (LVMI), relative wall thickness, and ejection fraction across baseline, post-SAVR, and follow-up in 54 patients with complete data, supplemented by correlation analyses treating baseline GLS as a continuous variable. A mixed-effects model in all 94 patients was a secondary analysis.</p><p><strong>Results: </strong>In the complete-case cohort, the abnormal GLS group had greater IVSd and LVMI and lower ejection fraction at baseline, with these differences persisting at each time point. A significant GLS-by-time interaction was observed for LVMI (P = 0.005). Continuous GLS correlated with absolute LVMI and IVSd at all time points but not with the magnitude of change in any index. After adjustment for baseline disease severity, continuous GLS was not independently associated with any remodeling outcome (all P ≥ 0.121). Baseline GLS identified a greater myocardial hypertrophy burden but does not predict the magnitude of reverse remodeling after SAVR.</p><p><strong>Conclusions: </strong>Preoperative GLS reflects the severity of preexisting LV hypertrophy but, with the possible exception of LV mass regression, does not independently predict the magnitude of early reverse remodeling after SAVR once baseline disease severity is accounted for.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13543429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intraprocedural echocardiographic guidance in transcatheter aortic valve implantation.","authors":"Tai Meng Chen, Mohammed Rizwan Amanullah, See Hooi Ewe","doi":"10.1186/s44348-026-00094-5","DOIUrl":"10.1186/s44348-026-00094-5","url":null,"abstract":"<p><p>Transcatheter aortic valve implantation (TAVI) is an established treatment for severe aortic stenosis across all surgical risk groups. Traditionally performed under general anesthesia with transesophageal echocardiography (TEE) guidance, TAVI has evolved toward a minimalist approach using local anesthesia and conscious sedation (LACS), supported by transthoracic echocardiography (TTE) or fluoroscopy alone. Despite this shift, intraprocedural imaging remains valuable as it offers real-time imaging guidance throughout the procedures, allowing for rapid and accurate assessment of complication and procedural results. Echocardiographic modalities used during TAVI include TTE, TEE (adult-, mini-, or micro-TEE) and intracardiac echocardiography (ICE), each with its own advantages and limitations. While TTE is widely used in uncomplicated cases, its image quality may be suboptimal, leading to missing complications and underestimation of paravalvular leak (PVL). In contrast, TEE provides superior spatial resolution, enabling comprehensive real-time assessment and improved PVL detection and other complications. Moreover, 3D TEE offers accurate assessment of the aortic annulus and aids in sizing of the prosthesis when computed tomography is suboptimal or unavailable. ICE is an attractive alternative as it provides real-time high-resolution images and can be performed under LACS by the same operator, although it is costly with single use and requires additional venous access. ICE may be particularly helpful in patients with challenging TTE windows, or in those with contraindications for TEE. We propose an upfront intraprocedural TEE strategy in patients with high-risk anatomies undergoing TAVI, and those with poor TTE window or renal impairment in whom minimal contrast usage is preferred. Mini-TEE (with a smaller probe) is the preferred modality for intraprocedural guidance, which has 3D imaging with good resolution, and is well tolerated under LACS. In conclusion, imaging strategies in TAVI should be individualized. While minimalist approaches are feasible, TEE remains indispensable in complex cases to optimize outcomes and minimize complications.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13531935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148864703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Women's heart and echocardiography.","authors":"So Ree Kim, Seong-Mi Park","doi":"10.1186/s44348-026-00091-8","DOIUrl":"10.1186/s44348-026-00091-8","url":null,"abstract":"<p><p>Echocardiography is the primary imaging modality for evaluating cardiovascular disease, providing comprehensive assessment of cardiac structure, function, and hemodynamics. Its noninvasive nature, wide availability, and absence of radiation make it particularly suitable for repeated use, including in younger women and during pregnancy. Moreover, women-specific cardiovascular diseases are not uncommonly detected by echocardiography. However, smaller cardiac size, higher baseline ejection fraction, and technical limitations in women may affect interpretation. This review highlights the role of echocardiography across cardiovascular conditions relevant to women, including heart failure with preserved ejection fraction, coronary microvascular dysfunction, pulmonary hypertension, and valvular heart disease. It also addresses stress-induced cardiomyopathy, physiological changes during pregnancy, and peripartum cardiomyopathy.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13527923/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148864872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation.","authors":"Ran Heo, Seung-Ah Lee, Hyuk-Jae Chang","doi":"10.1186/s44348-026-00087-4","DOIUrl":"10.1186/s44348-026-00087-4","url":null,"abstract":"<p><p>Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including manual acquisition, sequential measurement, and expert interpretation face challenges from increased clinical demand, workforce shortage, and the physical burden of repetitive scanning. Artificial intelligence (AI) has begun to address these issues, transitioning from proof-of-concept to prospective clinical evaluations. Recent evidence suggests that AI integration reduces examination time and automates measurements, enabling more comprehensive data collection while mitigating sonographer fatigue and improving image quality. The sonographer's role is accordingly evolving from conventional measurement to active verification. AI applications in echocardiography now extend beyond ejection fraction to integrated assessments of myocardial texture and Doppler hemodynamics. New model architectures incorporate both structural and functional evaluations, reflecting clinical reasoning of the expert. These methods are being applied to valvular heart disease, cardiomyopathy, and pericardial disorders. Clinical implementation of AI in echocardiography requires more than high accuracy. Current evidence is limited by reliance on single-center studies, inconsistent performance across platforms, and the potential for automation bias in high-volume settings. This review evaluates current evidence, identifies existing gaps, and outlines the requirements for responsible clinical implementation of AI.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13508549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148828703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Left ventricular stroke volume index predicts outcomes in asymptomatic chronic severe primary mitral regurgitation.","authors":"Porntera Sethalao, Nithima Ratanasit, Decho Jakrapanichakul, Sasima Tongsai, Sirichai Jamnongprasatporn","doi":"10.1186/s44348-026-00088-3","DOIUrl":"10.1186/s44348-026-00088-3","url":null,"abstract":"<p><strong>Background: </strong>Severe primary mitral regurgitation (MR) linked to substantial morbidity and mortality. However, racial differences impact the left ventricular size, which subsequently influence specific echocardiographic parameters. In this context, the left ventricular stroke volume index (LVSVi) may serve as a valuable prognostic indicator. This study aims to examine the association between LVSVi and a composite endpoint of all-cause mortality or mitral valve (MV) surgery.</p><p><strong>Methods: </strong>We conducted a retrospective study on asymptomatic chronic severe primary MR patients over a 5-year follow-up period.</p><p><strong>Results: </strong>Among 77 patients with asymptomatic severe primary MR (mean age, 56.9 years; 55 male patients, 71.4%), an increase of 1 mL/m² in LVSVi, calculated from the left ventricular outflow tract area, was independently associated with an 8.2% reduction in the odds of reaching the composite endpoint of all-cause mortality or MV surgery (adjusted odds ratio, 0.918; 95% confidence interval, 0.856-0.986; P = 0.018). The receiver operating characteristic curve for LVSVi demonstrated an area under the curve of 0.726 (P = 0.002), with a threshold of ≤ 35 mL/m² yielding a sensitivity of 81% and specificity of 50% for predicting the composite outcome. The 5-year event-free survival was significantly lower in patients with LVSVi ≤ 35 mL/m² compared to those with LVSVi > 35 mL/m² (22.9% vs. 67.0%; Breslow test, P < 0.001).</p><p><strong>Conclusions: </strong>In patients with asymptomatic chronic severe primary MR, LVSVi was the significant echocardiographic parameter influencing the outcomes of MV surgery and mortality. This study supported the hypothesis generation that LVSVi may serve as a prognostic marker in this specific population.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148813076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction.","authors":"Nisanth Selvam, Dilip Parasu, Akshay Desale, Pranoti Toshniwal, Taniya Rachel Issac, Shine Kumar, Navaneetha Sasikumar, Shilpa Radhakrishnan, Raman Krishna Kumar","doi":"10.1186/s44348-026-00084-7","DOIUrl":"https://doi.org/10.1186/s44348-026-00084-7","url":null,"abstract":"<p><strong>Background: </strong>Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation.</p><p><strong>Methods: </strong>A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-systemic flow ratio (Qp:Qs) were evaluated by three experts and three trainees in pediatric cardiology and radiology using nine prespecified criteria. A separate set of 1,088 CXRs was used to train the DLM. The clinician-interpreted CXRs were subsequently assessed by the DLM to predict the Qp:Qs.</p><p><strong>Results: </strong>Intraclass correlation between Fick- and DLM-derived Qp:Qs was 0.782 (P < 0.001), with a root mean squared error of 0.395 (P < 0.001). The DLM showed a strong agreement with Fick-derived classification of pulmonary vascularity (κ = 0.770, P < 0.001; predicting Qp:Qs > 1.5: 90% specificity, 98% sensitivity, area under the receiver operating characteristic curve [AUROC] 98%; predicting Qp:Qs < 0.9: 92% specificity, 95% sensitivity, AUROC 98%). For interpreting pulmonary vascularity as increased or decreased, clinician concordance was 75% and 66%, respectively, whereas DLM concordance was 88% and 98%, respectively (P = 0.03). Among the structured criteria, the number of end-on vessels demonstrated strong interobserver agreement, whereas agreement for individual peripheral vessels, the size of end-on vessels, branch pulmonary arteries with their respective bronchi, and opacity of lung fields was modest.</p><p><strong>Conclusion: </strong>Deep learning-based analysis of CXRs enables accurate evaluation of pulmonary vascularity and outperforms structured assessment by clinicians.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13495393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148793635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.","authors":"Shreeyash Shashank Tulpule, Sammita Jagdish Jadhav","doi":"10.1186/s44348-026-00064-x","DOIUrl":"https://doi.org/10.1186/s44348-026-00064-x","url":null,"abstract":"<p><p>Artificial intelligence (AI) is emerging as a transformative tool in cardiovascular imaging, particularly in coronary angiography. With the growing interest in precision medicine, AI offers the potential to enhance clinical decision-making by improving diagnostic accuracy, predicting outcomes, and guiding interventions. This scoping review aims to examine the current landscape of AI applications in predicting clinical outcomes related to angiographic procedures, including diagnosis, risk stratification, and postprocedural monitoring. A total of 59 relevant studies published between 2015 and 2025 were included in this review. The selection was based on predefined inclusion criteria focused on AI-based models applied in coronary artery disease diagnosis, outcome prediction and restenosis detection. The studies were reviewed for the AI techniques used, clinical endpoints targeted, model performance, validation strategies, and limitations. Four primary themes were identified: (1) prediction of adverse cardiovascular events such as myocardial infarction and mortality; (2) detection of hemodynamically significant stenosis; (3) prediction of in-stent restenosis; and (4) identification of coronary arteries. Machine learning algorithms, especially random forests, support vector machines, and convolutional neural networks, have been commonly used. Many models have demonstrated superior performance compared to traditional statistical methods. However, limitations such as small sample sizes, lack of external validation, retrospective designs, and concerns about data privacy were frequently observed in these studies. AI demonstrates promising capabilities in outcome prediction within coronary angiography. Despite current limitations, its integration into clinical practice is feasible with prospective, multicentric studies and standardized validation frameworks. Ethical considerations and regulatory oversight will be crucial in ensuring safe and effective implementation.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13491615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148793700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Special article-EchoPeer: a standardized framework for assessing echocardiography reports in the era of artificial intelligence: a recommendation from the Korean Society of Echocardiography AI and Future Strategy Committee.","authors":"SungA Bae, Inki Moon, Jiesuck Park, Jaehyun Lim, Seung-Ah Lee, Yeonyee E Yoon, Goo-Yeong Cho","doi":"10.1186/s44348-026-00085-6","DOIUrl":"https://doi.org/10.1186/s44348-026-00085-6","url":null,"abstract":"<p><p>Artificial intelligence (AI) is rapidly advancing from automated measurement to full-report generation, yet existing frameworks do not provide a unified scoring approach for both human- and AI-authored reports. We developed EchoPeer, a three-step evaluation framework for echocardiography reports, scored against a reference standard. Step 0 (safety score, %) is a pass/fail safety gate for life-threatening findings (critical omission and hallucination). Step 1 (precision score, 0-80 scale) scores per-item diagnostic accuracy across 25 items in four anatomical domains. Step 2 (quality score, 0-20 scale) grades clinical utility across three checkpoints: relevance, synthesis, and clarity. The precision and quality scores are summed as a composite score (0-100 scale), with step 0 failures scored as 0. EchoPeer was applied to 30 adult transthoracic echocardiography cases interpreted by 11 human readers across three training levels. All three EchoPeer scores increased monotonically with training level. Median safety scores were 86.7%, 90.0%, and 96.7% for levels 1, 2, and 3, respectively (P = 0.005), and sensitivity for critical finding detection rose from 59.3% to 92.6% (P = 0.007). Median precision scores were 61.8, 64.7, and 70.9, respectively (P = 0.007), and median quality scores were 10.4, 12.9, and 15.1, respectively (P = 0.009). The composite score followed the same gradient (72.2, 77.7, and 86.2, respectively; P = 0.003), and the precision and quality scores correlated significantly at both the case level (Spearman ρ = 0.568) and the participant level (ρ = 0.888). EchoPeer discriminates clinical competence in human readers and produces an error profile that mirrors known challenges in echocardiographic reporting, providing a clinically grounded foundation for the future evaluation of AI-generated echocardiography reports.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13474789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148760448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing myocardial work to predict left ventricular remodeling after ST-segment elevation myocardial infarction: associations with left atrial and left ventricular strain.","authors":"Gomaa Abdelrazek, Mahmoud H Mahmoud, Gousay A Alkhazmari, Tarneem M Alghamdi, Khaled A Elkhashab, Hassan M Ebeid, Moustafa K Saad","doi":"10.1186/s44348-026-00081-w","DOIUrl":"10.1186/s44348-026-00081-w","url":null,"abstract":"<p><strong>Background: </strong>Left ventricular ejection fraction (LVEF) is the standard marker for systolic function, but it fails to detect subtle changes in myocardial contractility in patients after an ST-elevation myocardial infarction (STEMI). Thus, a new way to evaluate left ventricular (LV) function with high precision is needed to assess disease progression and guide therapeutic interventions. This study evaluates the prognostic significance of left atrial (LA) strain, LV strain, and myocardial work in patients who have undergone primary percutaneous coronary intervention (pPCI) post-STEMI.</p><p><strong>Methods: </strong>The study population was 100 patients diagnosed with STEMI, all of whom received pPCI for their first STEMI event. Patients were admitted to one of two centers, the cardiology department of Fayoum University in Egypt or the cardiac center at King Fahd University Hospital in Saudi Arabia, between November 2022 and March 2024. Conventional echocardiography and 2D speckle-tracking echocardiography (2D-STE) were performed on each participant within 24 h after pPCI and then repeated at least 6 months later. Myocardial work parameters, the global work index, global constructive work (GCW), global wasted work, and global work efficiency and LA strain parameters were derived from 2D-STE and analyzed for their association with subsequent LV remodeling.</p><p><strong>Results: </strong>Myocardial work parameters (e.g., global longitudinal strain) were associated with LV remodeling after STEMI treated with pPCI, with GCW showing the most significant association with LV remodeling among the myocardial work indices evaluated. Furthermore, LV remodeling was associated with significantly impaired cardiac structure and function. Patients who developed LV remodeling exhibited higher LV volumes, lower LVEFs, impaired myocardial work parameters, and longer door-to-balloon times than those without remodeling. Additionally, LA function (e.g., LA ejection fraction) was significantly compromised in the remodeling group.</p><p><strong>Conclusions: </strong>Myocardial work and strain parameters were associated with LV remodeling after STEMI treated with pPCI. GCW demonstrated the strongest association with LV remodeling in the present cohort.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13418148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148620244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low- versus high-concentration iodine contrast media for coronary computed tomography angiography and dynamic stress computed tomography perfusion: a study protocol for a prospective randomized trial.","authors":"Sung-Jin Cha, Pil-Hyun Jeon, Dong-Hee Kho, Hyosung Cho, Sung Min Ko","doi":"10.1186/s44348-026-00082-9","DOIUrl":"10.1186/s44348-026-00082-9","url":null,"abstract":"<p><strong>Background: </strong>This prospective randomized study protocol is designed to determine whether low-concentration iodine contrast media can maintain coronary enhancement on coronary computed tomography angiography (CCTA) compared with high-concentration iodine contrast media under standardized acquisition conditions. Dynamic stress computed tomography perfusion (CTP)-derived myocardial blood flow (MBF) will be assessed as a key secondary quantitative endpoint. Exploratory diagnostic performance analyses using invasive coronary angiography (ICA) and fractional flow reserve (FFR) will be performed only in clinically indicated invasive testing subgroups.</p><p><strong>Methods: </strong>The trial plans to enroll 258 adults (age ≥ 40 years) with known or suspected coronary artery disease referred for clinically indicated cardiac computed tomography, including CCTA with planned dynamic stress CTP. Participants will be randomized 1:1 to receive low-concentration iodine contrast media (270 mg I/mL) or high-concentration iodine contrast media (350 mg I/mL). The primary endpoint is quantitative coronary enhancement on CCTA. Dynamic stress CTP-derived MBF will be evaluated as a key secondary endpoint focused on quantitative comparability. ICA/FFR-based diagnostic performance analyses will be considered prespecified exploratory analyses in participants who undergo invasive testing as part of routine clinical care. The primary endpoint will be analyzed using linear mixed-effects models with a prespecified noninferiority margin, and MBF will be analyzed using mixed-effects models without claiming formal equivalence or noninferiority.</p><p><strong>Discussion: </strong>This study protocol is expected to clarify whether low-concentration iodine contrast media can maintain CCTA coronary enhancement and support quantitative dynamic stress CTP assessment while reducing iodine exposure. Because ICA and FFR are clinically driven rather than protocol mandated, diagnostic performance analyses will be interpreted cautiously and regarded as exploratory.</p><p><strong>Trial registration: </strong>CRIS identifier: KCT0011418. Registered on January 7, 2026.</p>","PeriodicalId":15229,"journal":{"name":"Journal of Cardiovascular Imaging","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13397706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148584526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}