Purpose To determine whether cardiac MRI strain parameters independently predict PET/CT-defined myocardial inflammation in patients with cardiac sarcoidosis (CS). Materials and Methods This retrospective study included patients with definite or probable CS who underwent cardiac MRI and fluorine 18 fluorodeoxyglucose (FDG) PET/CT between January 2010 and October 2022 within 90 days per the Japanese Circulation Society criteria. Strain parameters, including the peak circumferential strain (PCS), peak longitudinal strain (PLS), and diastolic strain rates, were calculated using feature tracking on cine steady-state free precession images. Inflammation was defined by focal FDG uptake using the 17-segment American Heart Association model. T2-weighted and late gadolinium enhancement (LGE) images were qualitatively assessed. Segment-level comparisons were performed between inflamed and noninflamed segments. Multivariable logistic regression clustered at the patient level was adjusted for LGE, T2 signal abnormality, age, sex, and left ventricular end-diastolic volume index. Receiver operating characteristic (ROC) curve analysis was used to evaluate the added value of strain metrics over the left ventricular ejection fraction (LVEF) for inflammation. Results Among 125 included patients (mean age, 59.5 years ± 12.7 [SD]), 60% (n = 75) had at least one segment with inflammation on FDG PET/CT images. Of these 75 patients, 61% (n = 46) were male. Segment-level analysis revealed inflammation in 18.1% (385 of 2125) of the segments. The PCS and PLS were lower in inflamed segments than in noninflamed segments (-14.1% vs -18.4% and -10.3% vs -13.0%, respectively; P < .001 for both) and independently predicted inflammation in multivariable analysis (odds ratio, 1.79 and 1.62, respectively; P < .01 for both). Adding PCS to LVEF improved inflammation detection (area under the ROC curve, 0.72 vs 0.63; P = .04). Conclusion Circumferential and longitudinal strain from cardiac MRI independently predicted PET-defined myocardial inflammation. Keywords: MRI, PET/CT, Heart, Cardiomyopathy, Cardiac Strain, Cardiac Sarcoidosis, Myocardial Inflammation © RSNA, 2026.
Purpose To investigate the association between biventricular imbalance, as reflected by the left ventricular (LV) and right ventricular (RV) global longitudinal strain (GLS) ratio derived from cardiac MRI feature tracking, and all-cause death in isolated left ventricular myocardial infarction. Materials and Methods In this multicenter retrospective study, the primary end point was all-cause mortality. The GLS ratio was defined as the ratio of LVGLS and RVGLS. Long-axis (two, three, and four chambers) and short-axis cine sequences were used for strain analysis. Kaplan-Meier curves, Cox proportional hazards regression, and the C statistic were used for statistical analysis. Results In the internal testing set (919 patients; median age, 59 years [IQR, 51-66 years]; 769 [83.7%] male), 90 (9.8%) patients died over a median follow-up period of 60.9 months (IQR, 47.7-83.6 months). The GLS ratio was the strongest independent predictor for all-cause death (adjusted hazard ratio [HR], 1.69; 95% CI: 1.45, 1.97; P < .001). The model, along with clinical conventional imaging, RVGLS, and GLS ratio, demonstrated improved discrimination (C statistic, 0.82; 95% CI: 0.76, 0.87) and calibration (χ2, 71.88). A GLS ratio of more than 0.95 was responsible for a fourfold death risk after multivariable adjustment. In those with normal RVGLS (HR, 2.43; 95% CI: 1.15, 5.10; P = .02), reserved LVGLS (HR, 5.38; 95% CI: 2.46, 11.79; P < .001), and both (HR, 7.76; 95% CI: 2.16, 11.90; P = .002), a high GLS ratio (>0.95) still predicted all-cause death. Conclusion In isolated LV myocardial infarction, an elevated GLS ratio may provide additional prognostic information for all-cause death.
Purpose To evaluate the predictive value of cardiac MRI-derived left atrial strain (LAS) for new-onset atrial fibrillation (AF) in apical hypertrophic cardiomyopathy (AHCM). Materials and Methods This retrospective study included patients with AHCM with no prior history of AF who underwent 3.0-T cardiac MRI between May 2016 and December 2023. LAS parameters-reservoir strain (εs), conduit strain (εe), and booster pump strain (εa)-and minimum left atrial volume index (LAVImin) were measured. The clinical end point was new-onset AF as confirmed with electrocardiography, Holter monitoring, or implantable cardiac devices. Cox proportional hazards models and time-dependent receiver operating characteristic (ROC) analyses were performed to evaluate associations and discriminatory performance for new-onset AF. Results A total of 156 patients (mean age, 51.0 years ± 11.5 [SD]; 113 male) were included. During a median follow-up of 33 months, AF occurred in 21 patients. In multivariable Cox models adjusted for age, body mass index, and LAVImin, LAS parameters were independently associated with incident AF (εs: hazard ratio [HR], 0.79 [95% CI: 0.69, 0.90], P < .001; εa: HR, 0.65 [95% CI: 0.50, 0.83], P < .001; and εe: HR, 0.79 [95% CI: 0.65, 0.95], P = .01). In the nested Cox models, adding LAS to a baseline clinical-volumetric model improved model fit and discrimination, with the χ2 value improving from 36.22 to 54.90 and the concordance index value improving from 0.846 to 0.930 (P < .001 for both). The LAVImin provided complementary prognostic information, with its contribution varying across strain-specific models. Time-dependent ROC analyses demonstrated that LAS identified the development of AF well across prespecified time horizons. Conclusion Cardiac MRI-derived LAS was independently associated with new-onset AF in AHCM and provided incremental prognostic value beyond routine clinical and left atrial volumetric measures. Keywords: Apical Hypertrophic Cardiomyopathy, Atrial Fibrillation, Cardiac MRI, Left Atrial Strain, Heart, Cardiomyopathies, MR Imaging Supplemental material is available for this article. © RSNA, 2026.
Purpose To evaluate whether artificial intelligence (AI)-derived chamber volumetry from coronary artery calcium (CAC) CT improves heart failure (HF) risk prediction. Materials and Methods This retrospective study included asymptomatic patients without known cardiac disease undergoing CAC CT between 2010 and 2023. CAC CT images were analyzed using a validated AI model to calculate chamber volumes. HF events were identified based on International Classification of Diseases, Ninth and Tenth Revisions codes. Cox proportional hazard regression models incorporating chamber volumes, adjusted for Predicting Risk of cardiovascular disease EVENTs-Heart Failure (PREVENT-HF) score, were used to assess the association with HF. Time-dependent areas under the receiver operating characteristic curve (AUCs) at 3, 5, 8, and 10 years were calculated to compare the performance of volumetry, PREVENT-HF, CAC scoring, and their combination. Results A total of 5892 patients were included (mean age ± SD, 58.2 years ± 9.4; 3258 male). During a mean follow-up of 4 years ± 3, 377 patients (6.3%) developed HF. Larger left atrial, left ventricular, right atrial, and left ventricular myocardial volumes were associated with increased HF risk (P < .001). The composite, multivariable Cox regression model incorporating all chamber volumes, CAC score, and PREVENT-HF scores outperformed PREVENT-HF (AUC, 0.80 vs 0.76; ΔAUC, 0.04; P < .001) and CAC scores (AUC, 0.80 vs 0.70; ΔAUC, 0.10; P < .001) alone in predicting 10-year HF risk. A phase-volume interaction effect was identified, indicating that diastolic volumes were independently associated with higher HF risk than were systolic volumes (P < .001), adjusted for PREVENT-HF. Conclusion AI-derived cardiac chamber volumetry obtained from CAC CT improved HF risk prediction compared with PREVENT-HF or CAC scores alone. Keywords: Applications-CT, Deep Learning, Cardiac Supplemental material is available for this article. © RSNA, 2026.
An end-to-end, uncertainty-aware deep learning approach enabled direct quantification of functional lung MRI without multimodal segmentation, performing accurately across a range of pathologies, hospitals, and test-time augmentations.
In a scoping review of 55 studies, cardiac diffusion MRI demonstrated the potential for evaluating myocardial microstructure in hypertrophic cardiomyopathy and ischemic heart disease, although clinical adoption remains limited by heterogeneity and limited validation.
In bronchoscopic lung volume reduction (BLVR), one-way endobronchial valves induce atelectasis of a target lobe, resulting in lung volume reduction in patients with severe chronic obstructive pulmonary disease. Despite improvement in overall quality of life, these patients remain at high risk for lung cancer, including within the collapsed lobe, where nodule detection is particularly challenging. This article presents a case of lung cancer arising in a collapsed lobe after BLVR and highlights imaging features suggestive of a growing malignancy. Although a nodule may be visible at noncontrast CT, contrast-enhanced chest CT can improve nodule conspicuity in this setting. Keywords: Pulmonary, CT, Lung, Tracheobronchial Tree, Chronic Obstructive Pulmonary Disease, Neoplasms-Primary Supplemental material is available for this article. © RSNA, 2026
Purpose To assess the association of low-attenuation noncalcified plaque (LAP) morphologic features, including shape and degree of intraplaque embeddedness, using atherosclerotic imaging-enabled quantitative CT with acute coronary syndrome (ACS). Materials and Methods In this secondary analysis of the Incident Coronary Syndromes Identified by CT (ICONIC) study, a retrospective-nested, case-control, multicenter study of patients with future ACS after coronary CT angiography propensity matched with controls, atherosclerotic imaging-enabled quantitative CT analysis was performed between February and September 2022. LAP morphology was determined qualitatively according to geometric shape (crescent, lobular, spherical, or bean) and degree of intraplaque embeddedness (<90°, 90°-179°, 180°-269°, 270°-360°) within the vessel wall. Shapes were based on visual assessment of LAP contours, including features of curvature, symmetry, and lobulated and protruding components. Adverse LAP morphology (ALM) was defined by morphologic features associated with ACS using log-rank testing. Multivariable Cox regression was performed with ALM as the independent variable, adjusting for plaque burden and diameter stenosis. Results A total of 446 patients (mean age ± SD, 62.44 years ± 11.05; 277 male; 223 cases, 223 controls) with a mean follow-up of 2.45 years ± 2.49 were included in this study. Twenty-two patients were excluded due to missing images or poor image quality. Lobular, bean, and spherical LAP shape or LAP with a degree of intraplaque embeddedness greater than or equal to 180° was associated with increased ACS risk (log-rank P < .05 for each). These morphologic features thus defined ALM. Among 74 of 446 (16.6%) patients with ALM, 57 patients experienced ACS. Patients with ALM had a 3.24-fold increased risk for ACS (adjusted hazard ratio, 3.24 [95% CI: 1.44, 7.30]; P = .005). Conclusion ALM was independently associated with ACS. Keywords: Coronary Angiography, Coronary Arteries ClinicalTrials.gov identifier: NCT02959099 Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.
Purpose To generate high-resolution fibrosis maps using universal ventricular coordinates for spatial characterization of fibrotic patterns across disease severity in desmoplakin cardiomyopathy. Materials and Methods This retrospective study included patients with desmoplakin cardiomyopathy who underwent cardiac MRI between March 2012 and October 2024. Three-dimensional ventricular models were reconstructed from cine MRI acquisitions. Fibrosis identified at late gadolinium enhancement MRI was mapped to ventricular geometry using universal ventricular coordinates, enabling analysis within a common reference framework. Patients were classified as having mild, moderate, or severe disease based on fibrosis extent. Associations between fibrosis burden and left ventricular structural and functional metrics were evaluated using Tukey and Wald tests. Results Twenty-nine patients (mean age, 37.39 years [range, 10-77 years]; 20 female patients) were included. In mild disease, fibrosis was primarily located in the subepicardial midinferior region. With increasing fibrosis burden, moderate and severe disease demonstrated subepicardial circumferential involvement with a ringlike pattern in severe cases. Fibrosis burden correlated negatively with left ventricular ejection fraction (r = -0.80, P < .001) and positively with left ventricular end-diastolic volume index (r = 0.54, P = .002). Group comparisons showed significant differences between moderate and severe disease groups across all metrics (P < .05) but not between mild and moderate groups. Conclusion Use of universal ventricular coordinates enabled high-resolution mapping of fibrosis and demonstrated characteristic spatial patterns across disease severity in desmoplakin cardiomyopathy. Fibrosis was observed in nondilated ventricles, whereas higher fibrosis burden was associated with left ventricular dilatation and impaired systolic function. Keywords: Cardiomyopathies, Left Ventricle, Computer Applications-3D, MR Imaging Supplemental material is available for this article. © RSNA, 2026.
Purpose To evaluate the preliminary technical development, safety, and feasibility of dynamic contrast-enhanced MR lymphangiography (DCE-MRL) performed with nodal and/or dermal injection of gadopiclenol. Materials and Methods This institutional review board-approved retrospective study included consecutive patients referred to the complex lymphatic disorders clinic who underwent DCE-MRL at 1.5 T with gadopiclenol administered via inguinal nodal and/or interstitial transpedal injection. Technical performance, safety, and feasibility for imaging peripheral and central conducting lymphatic anatomy, flow, and drainage were assessed. Results A total of 23 patients (12 female) underwent DCE-MRL with gadopiclenol. Contrast material was administered via inguinal lymph nodes in all patients (23 of 23, 100%) and via interstitial transpedal injection in one patient (one of 23, 4.3%). No serious or nonserious adverse events were observed (0 of 23). Evaluation of central conducting lymphatic anatomy, flow, and drainage into the central venous system was feasible in all patients (23 of 23), and evaluation of lower extremity superficial lymphatic anatomy was feasible in one patient. Signal contrast increased with flip angle up to approximately 35° at 1.5 T, supporting protocol optimization. Preliminary results suggest a near-optimal flip angle of 35° at 1.5 T, a higher flip angle than when using other gadolinium-based contrast agents. Conclusion Dynamic gadopiclenol-enhanced nodal and pedal MR lymphangiography was safe and feasible for imaging peripheral and central conducting lymphatic anatomy. Keywords: Lymphangiography, MR-Angiography, Lymphatic, MR-Dynamic Contrast Enhanced, MR-Contrast Agent Supplemental material is available for this article. © RSNA, 2026.
Purpose To develop a deep learning (DL) algorithm for identification of cardiac chamber enlargement (CCE) on anteroposterior chest radiographs using same-day transthoracic echocardiography (TTE) as a reference standard. Materials and Methods Between January 2019 and December 2021, anteroposterior chest radiographs obtained within 24 hours of TTE were retrospectively collected and randomly assigned to training (n = 5158), validation (n = 655), and test (n = 654) sets. A pretrained EfficientNet-B6 model was adapted to predict the presence of any CCE and enlargement of individual cardiac chambers. Model performance was compared with manual cardiothoracic ratio (CTR) measurements on the test set and with assessments by three cardiothoracic radiologists on a subset of 200 test set chest radiographs. Results A total of 6467 anteroposterior chest radiographs from unique patients (mean age, 63.4 years ± 17.0 [SD]; 3820 [59.1%] male patients) were included, with CCE present in 4060 (62.8%) cases. For binary classification of CCE, the model achieved an area under the receiver operating characteristic curve (AUC) value of 0.80 in the validation set and 0.83 in the test set. Corresponding performance in the validation and test sets, respectively, was as follows: accuracy, 74% and 76%; sensitivity, 76% and 81%; specificity, 71% and 66%; and area under the precision-recall curve value, 0.87 and 0.89. On the test set, the model outperformed cardiothoracic ratio (CTR) measurements (AUC, 0.83 vs 0.74) and cardiothoracic radiologist assessment (accuracy, 77% vs 60.5%-67.5%; all P < .001). Conclusion In this proof-of-concept study, a DL model demonstrated robust performance for detection of CCE on anteroposterior chest radiographs and outperformed CTR measurements and cardiothoracic radiologist assessment in a controlled evaluation setting. Keywords: Deep Learning, Algorithm Development, Neural Networks, Echocardiography, Conventional Radiography, Cardiac, Data Science, Machine Learning, Mass Chest X-Ray, Transthoracic Echocardiography, Radiographic Image Interpretation-Computer-Assisted, Cardiomegaly Supplemental material is available for this article. © RSNA, 2026.
Purpose To evaluate whether the artificial intelligence (AI)-quantified mean thoracic skeletal muscle (TSM) attenuation from coronary artery calcium (CAC) scans predicts incident cardiovascular disease (CVD), with a focus on atrial fibrillation (AF) and heart failure (HF). Materials and Methods Data from the Multi-Ethnic Study of Atherosclerosis, including participants without baseline CVD who underwent CAC scanning, were retrospectively analyzed. Myosteatosis was defined by sex-specific, AI-quantified mean TSM attenuation cutoffs. The Cox proportional hazards model was used to compare total CVD, AF, and HF risks between the bottom and top quartiles of TSM attenuation after adjusting for CVD risk factors, inflammatory markers, insulin resistance, Agatston score, TSM volume, and social determinants of health. Results Among 5739 participants (mean age, 62.1 years ± 10.3 [SD]; 3002 [52.3%] female), 1826 CVD events occurred over 19 years, including 1139 AF and 359 HF events. Myosteatosis was independently associated with increased risks of total CVD (hazard ratio [HR], 1.48 [95% CI: 1.25, 1.75]; P = .001), AF (HR, 1.68 [95% CI: 1.37, 2.07]; P < .001), and HF (HR, 1.61 [95% CI: 1.18, 2.19]; P < .002). Participants with both myosteatosis and high Agatston scores had markedly higher cumulative incidences compared with those with high Agatston scores alone (total CVD: 84.6% vs 68.6%; AF: 52.9% vs 42.4%; HF: 22.6% vs 16.1%). Adding myosteatosis to the Agatston score significantly improved prediction (time-dependent area under the receiver operating characteristic curve, total CVD: 0.74 vs 0.80, P < .001; AF: 0.68 vs 0.76, P < .001; HF: 0.73 vs 0.78, P = .007). Conclusion AI-quantified mean TSM attenuation on CAC scans independently predicted AF and HF and enhanced the Agatston score's predictive value. Keywords: Myosteatosis, Coronary Artery Calcium Scan, Atrial Fibrillation, Heart Failure, Artificial Intelligence, Applications-CT, Cardiac, Thorax, Muscular, Heart ClinicalTrials.gov NCT00005487 Supplemental material is available for this article. © RSNA, 2026.
Purpose To evaluate the reliability and clinical applicability of an artificial intelligence (AI)-based infarct size quantification method based on cardiac MR images in patients with ST-elevation myocardial infarction (STEMI). Materials and Methods This retrospective study included patients with acute STEMI who underwent cardiac MRI between January 2005 and October 2024. A convolutional neural network (CNN) was trained on 468 unique cardiac MRI examinations, with manual infarct segmentations serving as the reference standard. On a test set, correlations between manual and AI-determined infarct sizes and peak creatine kinase (CK) and cardiac troponin T (cTnT) levels were assessed using Pearson and Spearman correlation analyses. The predictive value of the manual and AI-based measurements for the occurrence of left ventricular adverse remodeling (LVAR) was compared using the DeLong test. Results The test set comprised 800 patients (median age, 58 years [IQR, 51-67 years]; 83% male). The CNN estimated a larger median infarct size than the manual measurements did (26.5 vs 20.1 mL; P < .001). The correlation with peak CK levels was greater (P < .001) for the automated measurements (r = 0.76, ρ = 0.80) than for manual segmentations (r = 0.68, ρ = 0.72). The same relationship was observed for peak cTnT levels (r = 0.66 vs r = 0.57; P = .004). Manual and AI-based measurements demonstrated comparable predictive value for LVAR (P = .24). Conclusion The AI-based infarct size quantification method based on cardiac MRI is comparable to manual measurement and is strongly correlated with cardiac biomarkers. Keywords: MR-Imaging, Cardiac, Heart, Ischemia/Infarction, Segmentation, Late Gadolinium Enhancement, ST Elevation Myocardial Infarction, Convolutional Neural Networks, Cardiac Biomarkers Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. ClinicalTrials.gov identifier: NCT04113356.
Purpose To quantify regional respiratory function using four-dimensional free-breathing dynamic MRI (dMRI) and evaluate vertical expandable prosthetic titanium rib surgery impact on diaphragm curvature in pediatric thoracic insufficiency syndrome (TIS) using pre- and postoperative and control comparisons. Materials and Methods Curvature was retrospectively analyzed in 149 pediatric patients with TIS from May 2010 to November 2022 (49 pre- and postoperative, 70 preoperative-only, 30 postoperative-only dMRI) and compared with 190 controls. Mean follow-up ± SD was 2.4 years ± 1.8. Diaphragm contours were delineated at end-expiration and end-inspiration, and curvature was quantified across 13 regions per hemidiaphragm. Analyses included paired t tests, one-way analysis of variance to compare with controls, and correlation analyses relating postoperative curvature to ventilatory status and thoracic Cobb angle. Results Patients with TIS (mean age, 3.5 years ± 3.5; 27 male) demonstrated region-, plane-, and phase-dependent preoperative curvature differences compared with controls (mean age, 11.9 years ± 3.6; 92 male). Significant pre- to postoperative curvature changes were limited to five region-plane-phase combinations. The right hemidiaphragm anterior-lateral region at end-expiration showed the only sagittal-plane change (6.3 m-1 ± 0.7 to 8.2 m-1 ± 0.6, P = .02), approaching control values (9.3 m-1 ± 2.6). Several regions were no longer different from controls, most prominently in the right hemidiaphragm coronal plane at end-inspiration, whereas others, particularly sagittal end-inspiration regions, remained different (P < .05). Postoperative curvature correlated with ventilatory status, strongest in central sagittal regions (ρ ≤ 0.392, P < .001), and with thoracic Cobb angle in posterior sagittal regions (ρ ≤ 0.385, P < .001). Conclusion Surgery resulted in plane- and phase-specific improvements in diaphragm curvature, with partial normalization toward control values predominantly in coronal-plane regions. Keywords: Pediatrics, MR-Functional Lung Imaging, MR-Imaging, Pulmonary, Diaphragm, Anatomy, Treatment Effects, Outcomes Analysis, Comparative Studies, Curvature, Dynamic MRI, Quantitative Radiology, Shape, Thoracic Insufficiency Syndrome (TIS) Supplemental material is available for this article. © RSNA, 2026.
Purpose To develop an automatic end-to-end deep learning pipeline for predicting the ventilation defect percentage (VDP) from coregistered functional hyperpolarized xenon 129 (129Xe) MRI and structural proton (1H) MRI scans without manual intervention. Materials and Methods In this retrospective study (2015-2024), 129Xe MRI and 1H MRI scans from healthy participants and patients with a range of pulmonary diseases were used to predict VDP and its associated prediction confidence via an uncertainty-aware convolutional neural network framework. Monte Carlo dropout was used to quantify model uncertainty. Model robustness was assessed using test-time augmentation to simulate test-retest repeatability. The proposed approach was evaluated on a stratified testing set via the median absolute error. Results The dataset comprised 574 paired 129Xe MRI and 1H MRI scans from 47 healthy participants (mean ± SD age, 28.3 years ± 17.3; 28 female participants) and 527 patients with a range of pulmonary pathologies (mean ± SD age, 44.9 years ± 21.9; 295 female patients). The proposed framework produced a median absolute error of 1.01% (IQR, 0.49-2.47) VDP compared with manually corrected, segmentation-derived VDPs; no evidence of difference was found (P = .70). Twenty Monte Carlo dropout iterations were completed, producing VDP prediction distributions that were subsequently clustered into confidence groupings. The proposed approach demonstrated clinical classification accuracy of 91% (95% CI: 68, 94; 32 of 35). Conclusion An uncertainty-aware, end-to-end deep learning approach enabled accurate prediction of VDP without manual segmentation, with performance comparable to segmentation-based methods and quantification of prediction uncertainty. Keywords: Functional Imaging, Lung, Multi-Modal, MRI, Uncertainty-Aware Supplemental material is available for this article. © RSNA, 2026.
Purpose To evaluate alterations in coronary arteries in individuals with esophageal squamous cell carcinoma (ESCC) who received chemotherapy or chemoradiotherapy using coronary CT angiography (CCTA) parameters and explore their association with major adverse cardiovascular events (MACEs). Materials and Methods Between July 2020 and August 2022, individuals with ESCC scheduled for chemotherapy or chemoradiotherapy who underwent CCTA were prospectively included. MACEs were defined as myocardial infarction, stroke, complete heart block, cardiovascular death, or rehospitalization due to heart failure or aggravated angina symptoms. The outcome time frame was measured from treatment initiation until the occurrence of MACEs or the end of follow-up, whichever occurred first. Associations between CCTA parameters and MACEs were analyzed by Cox regression analyses. Results Among the 140 enrolled participants (mean age ± SD, 65 years ± 8.39; 76 [54%] male), 60 received chemotherapy, and 80 received chemoradiotherapy. The CT fractional flow reserve (FFR) decreased and the fat attenuation index (FAI) increased after chemotherapy or chemoradiotherapy. Moreover, a lower CT FFR (left anterior descending artery [LAD], 0.75 vs 0.82; left circumflex artery [LCX], 0.74 vs 0.77; right coronary artery [RCA], 0.74 vs 0.77; P < .001) and higher FAI (LAD, -70.0 vs -75.0 HU; LCX, -75.0 vs -77.5 HU; RCA, -76.0 vs -78.5 HU; P < .001) were observed after chemoradiotherapy than after chemotherapy. The baseline FAIs of the LAD, LCX, and RCA were independently associated with the occurrence of MACE (hazard ratio = 3.99, 3.85, and 3.41, respectively; P < .001). Conclusion CT FFR decreased and the FAI increased after chemotherapy or chemoradiotherapy in individuals with ESCC. The baseline FAI was a predictor of MACEs. Keywords: CT-Coronary Angiography, Radiation Therapy/Oncology, Cardiac, Coronary Arteries, Esophagus, Esophageal Neoplasms, Chemoradiotherapy, Fractional Flow Reserve, Major Adverse Cardiac Events Supplemental material is available for this article. © RSNA, 2026.
Purpose To evaluate multiparametric cardiac MRI for assessing myocardial inflammation, fibrosis, and spatiotemporal distribution in autoimmune myocarditis, with histologic reference. Materials and Methods This study (December 2022 to January 2024) included 84 BALB/c mice (42 with experimental autoimmune myocarditis, 42 controls). Multiparametric cardiac MRI, including T1 mapping, T2 mapping, and extracellular volume fraction (ECV) mapping, was performed at 14 days, 28 days, and 3 months after immunization using a 9.4-T scanner. Cardiac tissues were collected at each time point for histopathologic assessment of inflammation (inflammatory index) and fibrosis (collagen volume fraction). Cardiac MRI parameters were analyzed at global, regional, and segmental levels to evaluate temporal and spatial changes. Group comparisons were performed using one-way analysis of variance with Tukey post hoc tests or Student t tests, and associations were assessed using Pearson or Spearman correlation coefficients, as appropriate. Results Among 81 mice analyzed, experimental autoimmune myocarditis (n = 39) demonstrated progression from inflammation to fibrosis. T2 values were elevated on days 14 and 28 (P < .001) and normalized at 3 months. Precontrast T1 and ECV increased progressively. T2 correlated with the inflammatory index (rho = 0.72, P < .001), and ECV correlated with collagen volume fraction (r ≥ 0.70, P < .01). T2 mapping showed the highest diagnostic performance on day 14 (area under the receiver operating characteristic curve [AUC], 0.89; 95% CI: 0.72, 0.99; P < .001) and day 28 (AUC, 0.81; 95% CI: 0.60, 0.97; P = .01), whereas ECV performed best at 3 months (AUC, 0.85; 95% CI: 0.69, 0.99; P < .001). Spatial distribution of abnormalities was heterogeneous across disease stages. Conclusion Multiparametric cardiac MRI characterizes myocardial inflammation and fibrosis and demonstrates stage-dependent spatial patterns in autoimmune myocarditis. Keywords: Animal Studies, MR-Imaging, Cardiac, Myocardium, Tissue Characterization Supplemental material is available for this article. © RSNA, 2026.
