Radiology. Cardiothoracic imaging最新文献

Purpose To systematically compare MRI- and CT-based measurements of both the volume and quality of epicardial adipose tissue (EAT). Materials and Methods This prospective study included participants from a subset of the Swedish CArdioPulmonary bioImage Study (SCAPIS) who underwent MRI and CT between November 2017 and July 2018. Dixon fat-water separation MR images were manually segmented, and a threshold-based approach based on a fat signal fraction (FSF) map was used to obtain the EAT volume. Within this EAT volume, the mean FSF was quantified as a measure of fat quality. EAT segmentation from CT images was performed using deep learning techniques, and the EAT volume and its mean attenuation were quantified. Correlation between MRI- and CT-based measurements of EAT volume and quality was assessed using the Pearson correlation coefficient. Results Ninety-two participants (mean age, 59 years ± 5 [SD]; 60 male participants) were included. The intermodality correlation for EAT volume was very strong (r = 0.92, P < .001), with systematically larger values for CT versus MRI (P < .001). There was a strong negative correlation between MRI FSF and CT attenuation (r = -0.72, P < .001). Repeatability analysis for assessment of MRI EAT volume showed good interreader agreement (intraclass correlation coefficient, 0.86) and excellent intrareader agreement (intraclass correlation coefficient, 0.96). Conclusion Correlation between MRI and CT was very strong for EAT volume and strong for EAT quality. Keywords: Cardiac, Adipose Tissue (Obesity Studies), Epicardial Fat, Heart, Tissue Characterization, Comparative Studies, Magnetic Resonance Imaging, Computed Tomography, Fat Signal Fraction, Fat Attenuation Published under a CC BY 4.0 license.

Cardiac hemangioma is an uncommon benign heart tumor. It can either be an incidental finding at imaging because of its asymptomatic nature, or it can present with symptoms such as dyspnea, angina, or arrhythmias. Multimodality noninvasive imaging can help delineate the tumor as well as guide management. This is a case of a patient with an incidental finding of a cardiac hemangioma who was managed with serial imaging for close to 15 years with demonstrable increase in size and associated symptoms leading to eventual surgical resection. Keywords: Cardiac, CT, Echocardiography, Cardiac Hemangioma, MRI, CT, Conservative Management, Surgery Supplemental material is available for this article. © RSNA, 2025.

Four-dimensional (4D) flow MRI has emerged as a versatile technique for the three-dimensional evaluation of blood flow dynamics, offering the ability to visualize flow patterns qualitatively and allow for the retrospective quantification of standard and advanced hemodynamic parameters. Recent advancements in 4D flow MRI technology, including optimized acquisition protocols and improved hemodynamic analysis workflow efficiency, have facilitated its integration into standard clinical practice, enhancing the accessibility and applicability of this innovative imaging modality. A growing body of studies have demonstrated its clinical value for monitoring and informing the management of aortic pathologies, cementing its role in modern cardiovascular care. In this review, the authors provide a concise overview of data acquisition techniques and hemodynamics analysis methods for 4D flow MRI, with a specific focus on the thoracic aorta. The core of this article explores the clinical applications of aortic 4D flow MRI in patients with aortic valve disease, aortopathy, coarctation, dissection, connective tissue disorders, and age-related changes. Furthermore, the authors discuss the emerging role of artificial intelligence on improving 4D flow MRI acquisition and processing efficiencies. Keywords: Aorta, MR-Imaging, Vascular, Aortic Valve, 4D Flow MRI, Phase-Contrast, Hemodynamics, Clinical Applications © RSNA, 2025.

Purpose To investigate the predictive efficacy of oxygen-enhanced MRI T1 mapping parameters for detecting chronic lung allograft dysfunction (CLAD)-related graft loss at 6-12 months and for an additional 2.5 years following pulmonary transplant over a 5.6-year observational interval. Materials and Methods In this single-center, longitudinal, and prospective study conducted between August 2013 and December 2018, parameters from oxygen-enhanced MRI T1 mapping (including oxygen transfer function [OTF], delta T1 oxygenated volume [OV]) were acquired from 141 clinically stable double-lung transplant recipients (6-12 months after transplant) and follow-up (2.5 years after baseline MRI). Applying Kaplan-Meier survival analysis and Cox proportional hazards model, all biomarkers were compared regarding the time to CLAD-related graft loss as the primary outcome measure. Results Participants (n = 141; mean age, 50 years ± 13 [SD], 76 men, 65 women) underwent baseline MRI, of which 132 were analyzed. Over the following 5.6-year observational period, 24 (18%) participants experienced graft loss related to CLAD. At baseline, oxygen-enhanced MRI parameters predicted graft loss within 5.6 years: delta T1 median (hazard ratio [HR], 3.50; 95% CI: 1.0, 9.4; P = .048), quartile coefficient of dispersion (HR, 3.43; 95% CI: 1.1, 8.7; P = .03), and delta T1 oxygenated volume (HR, 3.07; 95% CI 1.18, 7.22; P = .02), while OTF (P = .18) and spirometry (P = .32) did not. At follow-up (91 stable vs 11 graft loss), all parameter changes (follow-up/baseline value × 100 [%baseline]) predicted poorer survival: OTF (HR, 11.1; 95% CI: 2.5, 75.9; P = .001), delta T1 OV (HR, 6.47; 95% CI: 1.52, 27.53; P = .01), and forced expiratory volume in 1 second from spirometry (HR, 7.94; 95% CI: 2.16, 27.4; P = .003). Conclusion Oxygen-enhanced MRI parameters predict CLAD-related graft loss at 6-12 months following lung transplant and 2.5 years after baseline MRI. Delta T1 OV was the most consistent predictor of future graft loss. Keywords: MRI, Lung, Transplantation Supplemental material is available for this article. © RSNA, 2025.

Purpose To verify the accuracy of clinically used cardiac MRI T1 mapping sequences for measuring extracellular volume fraction (ECV) throughout the spectrum of pathologic values using an independent reference standard. Materials and Methods Acute myocardial ischemia was induced in six pigs using endovascular balloon occlusion in the left anterior descending artery between October 2018 and November 2018. After 7 days of reperfusion, ECV was measured in vivo using four sequences: three modified Look-Locker inversion recovery (MOLLI) and one saturation recovery single-shot acquisition (SASHA) sequences. ECV was also measured using ex vivo SPECT following injection of technetium 99m diethylenetriamine pentaacetic acid. ECV was calculated for corresponding regions of interest of necrosis on cardiac MR and SPECT images and in myocardium remote from injury. Mixed model analysis was used to test for correlation, while the paired t test was used to test for differences in T1 and ECV. Results ECV measurements with all T1 mapping sequences showed high correlation to the reference standard (r² range, 0.71-0.99). Measurements with MOLLI 5(3)3 and MOLLI 5s(3s)3s overestimated ECV in the infarct region compared with the reference standard (bias ± 2 SDs: 8.2% ± 8.5 and 4.9% ± 8.7; P = .005 and P = .04, respectively). All sequences showed overestimation of ECV in the remote region (range of bias, 3%-14% points, all MOLLI P values ≤ .001, SASHA P = .04). Conclusion This study verified the accuracy of clinically used T1 mapping sequences in measuring ECV in absolute values over a spectrum of pathologic values, using an independent radioisotope-based three-dimensionally acquired reference standard. Keywords: Animal Studies, MR Imaging, Cardiac, Edema, Imaging Sequences, Ischemia/Infarction Supplemental material is available for this article. © RSNA, 2025.

Purpose To evaluate the performance of a delayed-phase dynamic contrast-enhanced (dDCE) MRI model in quantitative assessment of myocardial tissue physiology and late gadolinium enhancement (LGE) within 5 minutes after contrast material injection in reperfused myocardial infarction (MI). Materials and Methods This animal study included 11 canines (seven female) with reperfused MI. A dDCE model using dynamic postcontrast T1 maps was adopted to depict an unbiased contrast material washout process. The dDCE parameters derived from the 30-minute contrast material washout process (dDCE_30min) and a 5-minute image subset (dDCE_5min) were compared. LGE images were synthesized from the dDCE_5min maps (LGE_dDCE) and compared LGE extent and transmurality with the standard LGE images acquired at 15 minutes after contrast material injection (LGE_standard). Statistical analyses used paired tests for dependent group comparisons. Results The dDCE_30min map demonstrated that extravascular extracellular volume and capillary permeability surface area product were higher in MI regions than in remote myocardium (61.12% ± 13.65 [SD] vs 13.43% ± 5.00; P = .02; 5.08 mL × g-1 × min-1 ± 4.10 vs 0.42 mL × g-1 × min-1 ± 0.55; P = .02, respectively). There was no evidence of differences between dDCE_5min parameters and dDCE_30min parameters (all P > .05). There was also no evidence of a difference between LGE_dDCE and LGE_standard in LGE area (30.65% ± 11.94 vs 30.66% ± 11.94; P = .99) or transmurality (54.87% ± 15.57 vs 53.27% ± 15.98; P = .06), but LGE_dDCE demonstrated a higher contrast-to-noise ratio (14.62 ± 13.03 vs 1.41 ± 0.97; P < .01). The area under the receiver operating characteristic curve of MI detection using LGE_dDCE was 0.97 (95% CI: 0.94, >0.99), with 94.4% sensitivity and 96.7% specificity. Conclusion The developed dDCE MRI model for myocardial tissue assessment shows potential to enhance lesion contrast, quantify clinically relevant physiologic parameters, and support comprehensive evaluation of myocardial injury in heart disease. Keywords: Dynamic Contrast-enhanced MRI, Myocardial Infarction, MRI, Pharmacokinetics, Ischemic Heart Disease Supplemental material is available for this article. © RSNA, 2025.

Purpose To investigate the prognostic value of left ventricular (LV) and scar entropy for major adverse cardiac events (MACEs) and ventricular arrhythmias (VAs) in patients who have experienced myocardial infarction (MI). Materials and Methods The medical records of patients who underwent late gadolinium enhancement (LGE) cardiac MRI following MI between October 2015 and September 2022 were retrospectively evaluated for MACEs and VAs. LV and scar entropy data were derived from the probability distributions of the pixel signal intensities of the LV myocardium and scar tissue, respectively. Cox proportional hazards regression was performed to investigate the prognostic value of LV and scar entropy for predicting MACEs and VAs. Results A total of 226 patients (mean age, 59 years ± 11 [SD], 170 [75.2%] male; LV ejection fraction, 40% ± 15) were followed for a median of 21 (IQR, 15-31) months and experienced 72 MACEs, which included VAs in 19 patients. Compared with patients in the low-LV entropy group, the risk of MACEs was higher in the high-LV entropy group (P < .001). The multivariable analysis showed that LV entropy was independently associated with MACEs (hazard ratio, 2.50 [95% CI: 1.55, 4.04]; P < .001). Multivariable analysis also showed that scar entropy was a significant predictor of VA (hazard ratio, 3.01 [95% CI: 1.02, 8.87]; P = .045). Conclusion LV entropy and scar entropy derived from LGE cardiac MRI were significant predictors of MACEs and VAs, respectively, in patients who have experienced MI. Keywords: Cardiac Imaging Techniques, MRI, Myocardial Infarction, Prognosis, Major Adverse Cardiac Events Supplemental material is available for this article. © RSNA, 2025.

Purpose To compare functional testing and management after cardiac CT-first versus invasive coronary angiography (ICA)-first strategies in participants with stable chest pain and low to intermediate probability of obstructive coronary artery disease (CAD) initially referred for ICA. Materials and Methods This study was a prespecified secondary analysis of the prospective, multicenter, randomized DISCHARGE (Diagnostic Imaging Strategies for Participants with Stable Chest Pain and Intermediate Risk of Coronary Artery Disease) trial (ClinicalTrials.gov no. NCT02400229) conducted between October 2015 and April 2019. The primary outcome was functional testing rates at each of the study sites after first test; secondary outcomes included revascularization, major postprocedure complications, and angina after a 3.5-year follow-up, all stratified by CAD severity. Comparisons were performed using adjusted multiple regression. Results Of 3561 participants (mean age, 60.1 years ± 10.1 [SD]; 2002 [56.2%] female), 3414 were included in the final analysis. CT-first resulted in more functional testing for obstructive CAD without high-risk anatomy as compared with ICA-first (114 of 214 [53.3%] vs 62 of 255 [24.3%]; adjusted odds ratio [OR], 3.55; 95% CI: 2.40, 5.28; P_interaction < .001). Revascularizations were lower for CT-first in obstructive CAD with high-risk anatomy (146 of 251 [58.2%] vs 161 of 196 [82.1%]; adjusted OR, 0.3; 95% CI: 0.19, 0.47) and without high-risk anatomy (81 of 214 [37.9%] vs 152 of 255 [59.6%]; adjusted OR, 0.41; 95% CI: 0.28, 0.60). ICA-first had more major complications with high-risk anatomy (11 of 196 [5.6%] vs five of 251 [2.0%]) and non-high-risk anatomy (11 of 255 [4.3%] vs two of 214 [0.9%]) than CT-first. Angina rates were similar (38 of 465 [8.2%] vs 32 of 451 [7.1%]; adjusted OR, 1.13; 95% CI: 0.69, 1.86). Conclusion A CT-first strategy increased functional testing, was influenced by CAD severity, and reduced revascularizations and major complications with similar angina rates after a 3.5-year follow-up compared with an ICA-first strategy in participants with stable chest pain. Keywords: CT Coronary Angiography, Coronary Arteries, Percutaneous, MR Perfusion, Cardiac, Heart, Comparative Studies Clinical trial registration no. NCT02400229 Supplemental material is available for this article. © RSNA, 2025.

Purpose To evaluate the diagnostic performance of artificial intelligence (AI) models in detecting and classifying aortic dissection (AD) from CT images through a systematic review and meta-analysis. Materials and Methods PubMed, Web of Science, Embase, and Medline were searched for articles published from January 2010 to October 2023. All primary studies were included. Quality of evidence was assessed using a composite tool based on the METhodological RadiomICs Score (ie, METRICS) and Checklist for Artificial Intelligence in Medical Imaging (ie, CLAIM) checklists, and risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (ie, QUADAS-2) tool. Univariate and bivariate meta-analyses were performed assessing individual and joint estimates of sensitivity and specificity. Results Thirteen studies were identified, with most using contrast-enhanced CT (CECT) imaging (n = 9) and the remainder using noncontrast CT (NCCT) imaging as their model input. Only three studies presented algorithms classifying AD by Stanford criteria. Univariate analysis of AI detection performance estimated sensitivity at 94% (95% CI: 88, 97; P = .049) and specificity at 88% (95% CI: 79, 94; P < .001). Bivariate analysis showed good overall model performances (area under the receiver operating characteristic curve [AUC], 0.97 [95% CI: 0.95, 0.99]; P = .49). Subgroup analyses revealed good performance for models using CECT images (sensitivity, 97% [95% CI: 81, 100; P = .007]; specificity, 93% [95% CI: 87, 97; P < .001]; AUC, 0.98 [95% CI: 0.93, 0.99; P = .09]) and NCCT images (sensitivity, 91% [95% CI: 83, 96; P = .33); specificity, 84% [95% CI: 69, 93; P < .001); AUC, 0.95 [95% CI: 0.90, 0.99; P = .14]). Most studies were of low quality and had high risk of bias. Conclusion AI can feasibly detect AD but does not demonstrate clinical applicability in its current form. Keywords: CT, Vascular, Cardiac, Aorta, Computer-aided Diagnosis (CAD), Meta-Analysis Supplemental material is available for this article. © RSNA, 2025.

Purpose Pericoronary adipose tissue attenuation (PCATa) measured at coronary CT angiography (CCTA) is an imaging biomarker of coronary inflammation associated with long-term adverse cardiac events. The authors hypothesized that PCATa may independently identify patients at risk for acute coronary syndromes (ACS). Materials and Methods The authors performed a retrospective substudy of the Incident Coronary Syndromes Identified by Computed Tomography (ICONIC) study, a propensity-matched case-control study of patients with CCTA followed by ACS. Two hundred analyzable case and control pairs were identified from the original 234 pairs. PCATa was measured using the adjusted attenuation of fat around proximal coronary vessels. The primary analysis applied conditional Cox models with cluster-robust standard errors to predict patient-level incident ACS, with adjustment for quantitative plaque volumes and clinical reporting-oriented findings of maximal stenosis and high-risk plaque features (HRPF). Results A total of 400 patients with 1174 matched measurable vessels were included. PCATa was not significantly different between patients with future ACS versus controls (-72.99 HU ± 9.42 vs -73.96 HU ± 9.47; P = .08). Conversely, PCATa was significantly associated with incident ACS events in Cox models (adjusted for noncalcified plaque hazard ratio [HR]: 1.015; 95% CI: 1.001, 1.028; P = .03; adjusted for total plaque HR: 1.015; 95% CI: 1.002, 1.029; P = .03; adjusted for stenosis and HRPF HR: 1.014; 95% CI: 1.000, 1.028; P = .049). Conclusion Limited quantitative difference in PCATa between patients and controls matched for risk factors and coronary artery disease suggests that PCATa may not be a useful single marker to identify future ACS. Nonetheless, significant differences seen in adjusted survival models identify a small biologic effect for increased risk of future ACS independent of traditional risk factors. Keywords: CT-Angiography, Inflammation, Coronary Arteries, Acute Coronary Syndrome, Pericoronary Adipose Tissue Attenuation, Noncalcified Plaque, ICONIC Study, Cardiovascular Risk Clinical trials registration no. NCT02959099 Supplemental material is available for this article. © RSNA, 2025.