Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action.

The environmental impact of magnetic resonance imaging (MRI) has recently come into focus. This includes its enormous demand for electricity compared to other imaging modalities and contamination of water bodies with anthropogenic gadolinium related to contrast administration. Given the pressing threat of climate change, addressing these challenges to improve the environmental sustainability of MRI is imperative. The purpose of this review is to discuss the challenges, opportunities, and the need for action to reduce the environmental impact of MRI and prepare for the effects of climate change. The approaches outlined are categorized as strategies to reduce greenhouse gas (GHG) emissions from MRI during production and use phases, approaches to reduce the environmental impact of MRI including the preservation of finite resources, and development of adaption plans to prepare for the impact of climate change. Co-benefits of these strategies are emphasized including lower GHG emission and reduced cost along with improved heath and patient satisfaction. Although MRI is energy-intensive, there are many steps that can be taken now to improve the environmental sustainability of MRI and prepare for the effects of climate change. On-going research, technical development, and collaboration with industry partners are needed to achieve further reductions in MRI-related GHG emissions and to decrease the reliance on finite resources. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography.

OBJECTIVE: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error. METHODS: Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20-01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2-4, and AI for 12 cases were manipulated to be incorrect (8 false positives (FP), 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions. RESULTS: Relative to the No AI condition (FN = 2.7%, FP = 51.4%), FN and FPs were higher in the Keep AI (No Box) (FN = 33.0%, FP = 86.0%), Delete AI (No Box) (FN = 26.7%, FP = 80.5%), and Keep AI (Box) (FN = to 20.7%, FP = 80.5%) conditions (all ps < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03). CONCLUSION: Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient's file or a box is provided around the region of interest. CLINICAL RELEVANCE STATEMENT: When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI. KEY POINTS: • When AI provided incorrect results, false negative and false positive rates among the radiologists increased. • False positives decreased when AI results were deleted, versus kept, in the patient's record. • False negatives and false positives decreased when AI visually outlined the region of suspicion.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Prognostic significance of T1 mapping parameters in heart failure with preserved ejection fraction: a systematic review.

Heart failure with preserved ejection fraction (HFpEF) accounts for almost one-half of all heart failure (HF) patients and continues to increase in prevalence. While mortality with heart failure with reduced ejection fraction (HFrEF) has decreased over the past few decades with use of evidence-based HFrEF therapy, mortality related to heart failure with HFpEF has not changed significantly over the same time period. The combination of poor prognosis and lack of effective treatment options creates a pressing need for novel strategies for better patient characterization. We conducted a systematic review to evaluate the prognostic value of cardiac magnetic resonance (CMR)-derived T1 relaxation time and extracellular volume fraction (ECV) in HFpEF patients. PubMed, Embase, and Cochrane Central were searched for relevant studies. The primary outcomes of interest were hospitalization for HF and all-cause mortality. Five studies with 2741 patients were included. Four studies reported correlation of outcomes with ECV, 2 studies reported correlation of outcomes with native T1 time, and 1 study reported correlation of outcomes with post-contrast T1 time. All five studies showed significant correlation of CMR-derived parameters with adverse outcomes including event-free survival to cardiac event, all cause, and cardiac mortality. CMR-determined ECV is strongly correlated with adverse outcomes in HFpEF cohorts.

Cardiovascular magnetic resonance findings in young adult patients with acute myocarditis following mRNA COVID-19 vaccination: a case series.

BACKGROUND: Messenger RNA (mRNA) coronavirus disease of 2019 (COVID-19) vaccine are known to cause minor side effects at the injection site and mild global systemic symptoms in first 24-48 h. Recently published case series have reported a possible association between acute myocarditis and COVID-19 vaccination, predominantly in young males. METHODS: We report a case series of 5 young male patients with cardiovascular magnetic resonance (CMR)-confirmed acute myocarditis within 72 h after receiving a dose of an mRNA-based COVID-19 vaccine. RESULTS: Our case series suggests that myocarditis in this setting is characterized by myocardial edema and late gadolinium enhancement in the lateral wall of the left ventricular (LV) myocardium, reduced global LV longitudinal strain, and preserved LV ejection fraction. All patients in our series remained clinically stable during a relatively short inpatient hospital stay. CONCLUSIONS: In conjunction with other recently published case series and national vaccine safety surveillance data, this case series suggests a possible association between acute myocarditis and COVID-19 vaccination in young males and highlights a potential pattern in accompanying CMR abnormalities.

The Mitral Coaptation to Ventricular Septal Space: Two- and Three-Dimensional Transesophageal Echocardiographic Assessment.

OBJECTIVES: To assess the dimensions and changes in the CSEPT (space between the ventricular septum and mitral coaptation point) before and after cardiopulmonary bypass (CPB) and to compare patients with and without aortic valve stenosis (AS) undergoing cardiac surgery. DESIGN: Retrospective review of intraoperative transesophageal echocardiographic examinations. SETTING: Single academic medical center. PARTICIPANTS: The study comprised 91 elective cardiac surgical patients-30 with AS scheduled for aortic valve replacement and 61 without AS (non-AS). INTERVENTIONS: Two- and 3-dimensional (2D and 3D) analysis of the CSEPT before and after CPB. MEASUREMENTS AND MAIN RESULTS: Assessment of the CSEPT distances and areas was performed using 2D and 3D imaging before and after CPB. Two-dimensional measures of CSEPT distances were performed using midesophageal 5-chamber and long-axis windows. Three-dimensional measures were performed offline using multiplanar reconstruction. The CSEPT space was smaller after CPB (p < 0.01). Before and after CPB, the midesophageal 5-chamber and long-axis windows were similar to each other, and both were larger than the pre-CPB 3D CSEPT distance. Patients with AS had smaller before and after CPB distances and areas compared with non-AS patients (p < 0.05). The change in CSEPT area in AS patients was 24%. CONCLUSIONS: The CSEPT space is smaller after CPB and more so for patients with AS undergoing aortic valve replacement. Two-dimensional CEPT distances vary compared with 3D CSEPT distances. Additional study using Doppler analysis will elucidate the added value of 3D assessment of the CSEPT space.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Detection of emergent large vessel occlusion stroke with CT angiography is high across all levels of radiology training and grayscale viewing methods.

OBJECTIVES: CT angiography (CTA) is essential in acute stroke to detect emergent large vessel occlusions (ELVO) and must be interpreted by radiologists with and without subspecialized training. Additionally, grayscale inversion has been suggested to improve diagnostic accuracy in other radiology applications. This study examines diagnostic performance in ELVO detection between neuroradiologists, non-neuroradiologists, and radiology residents using standard and grayscale inversion viewing methods. METHODS: A random, counterbalanced experimental design was used, where 18 radiologists with varying experiences interpreted the same patient images with and without grayscale inversion. Confirmed positive and negative ELVO cases were randomly ordered using a balanced design. Sensitivity, specificity, positive and negative predictive values as well as confidence, subjective assessment of image quality, time to ELVO detection, and overall interpretation time were examined between grayscale inversion (on/off) by experience level using generalized mixed modeling assuming a binary, negative binomial, and binomial distributions, respectively. RESULTS: All groups of radiologists had high sensitivity and specificity for ELVO detection (all > .94). Neuroradiologists were faster than non-neuroradiologists and residents in interpretation time, with a mean of 47 s to detect ELVO, as compared with 59 and 74 s, respectively. Residents were subjectively less confident than attending physicians. With respect to grayscale inversion, no differences were observed between groups with grayscale inversion vs. standard viewing for diagnostic performance (p = 0.30), detection time (p = .45), overall interpretation time (p = .97), and confidence (p = .20). CONCLUSIONS: Diagnostic performance in ELVO detection with CTA was high across all levels of radiologist training level. Grayscale inversion offered no significant detection advantage. KEY POINTS: • Stroke is an acute vascular syndrome that requires acute vascular imaging. • Proximal large vessel occlusions can be identified quickly and accurately by radiologists across all training levels. • Grayscale inversion demonstrated minimal detectable benefit in the detection of proximal large vessel occlusions.

Increased Left Atrial Appendage Density on Computerized Tomography is Associated with Cardioembolic Stroke.

BACKGROUND AND PURPOSE: While studies have stratified cardioembolic (CE) stroke risk by qualitative left atrial appendage (LAA) morphology and biomarkers of atrial dysfunction, the quantitative properties that underlie these observations are not well established. Accordingly, we hypothesized that LAA volume and contrast density (attenuation) on computerized tomography (CT) may capture the structural and hemodynamic processes that underlie CE stroke risk. METHODS: Data were collected from a single center prospective ischemic stroke database over 18 months and included all patients with ischemic stroke who previously underwent routine, nongated, contrast enhanced thin-slice (≤2.5 mm) chest CT. Stroke subtype was determined based on the inpatient diagnostic evaluation. LAA volume and attenuation were determined from CT studies performed for various clinically appropriate indications. Univariate and multivariable analyses were performed to determine factors associated with ischemic stroke subtype, including known risk factors and biomarkers, as well as LAA density and morphologic measures. RESULTS: We identified 311 patients with a qualifying chest CT (119 CE subtype, 109 Embolic Stroke of Undetermined Source (ESUS), and 83 non-CE). In unadjusted models, there was an association between CE (versus non-CE) stroke subtype and LAA volume (OR per mL increase 1.15, 95% CI 1.07-1.24, P < .001) and LAA density (4th quartile versus 1st quartile; OR 2.95, 95% CI 1.28-6.80, P = .011), but not with ESUS (versus non-CE) subtype. In adjusted models, only the association between LAA density and CE stroke subtype persisted (adjusted OR 3.71, 95% CI 1.37-10.08, P = .010). CONCLUSION: The LAA volume and density values on chest CT are associated with CE stroke subtype but not ESUS subtype. Patients with ESUS and increased LAA volume or attenuation may be a subgroup where the mechanism is CE and anticoagulation can be tested for secondary stroke prevention.

Serum Troponin Level in Acute Ischemic Stroke Identifies Patients with Visceral Infarcts.

BACKGROUND AND PURPOSE: Patients with ischemic stroke of cardioembolic origin are at risk of visceral (renal or splenic) infarction. We hypothesized that serum troponin level at time of ischemic stroke would be associated with presence of visceral infarction. METHODS: Data were abstracted from a single center prospective stroke database over 18 months and included all patients with ischemic stroke who underwent contrast-enhanced computerized tomography (CT) of the abdomen and pelvis for clinical purposes within 1 year of stroke. The primary predictor was troponin concentration ≥.1ng/mL. The primary outcome was visceral infarct (renal and/or splenic) on CT abdomen and pelvis. Univariate and multivariable logistic regression models were used to estimate the odds ratio and 95% confidence intervals (OR, 95% CI) for the association of troponin with visceral infarction. RESULTS: Of 1233 patients with ischemic stroke, 259 patients had a qualifying visceral CT. Serum troponin level on admission was measured in 237 of 259 patients (93.3%) and 41 of 237 (17.3%) had positive troponin. There were 25 patients with visceral infarcts: 16 renal, 7 splenic, and 2 both. In univariate models, patients with a positive troponin level (versus negative) were more likely to have visceral infarcts (39.1% [9/23] versus 15.0% [32/214], P = .008) and this association persisted in multivariable models (adjusted OR 3.83; 95% CI 1.42-10.31, P = .006). CONCLUSIONS: In ischemic stroke patients, elevated serum troponin levels may help identify patients with visceral infarcts. This suggests that troponin in the acute stroke setting is a biomarker of embolic risk. Larger studies with systematic visceral imaging are needed to confirm our findings.

Left Atrial Appendage Morphology and Embolic Stroke of Undetermined Source: A Cross-Sectional Multicenter Pilot Study.

BACKGROUND: The left atrial appendage (LAA) is the main source of thrombus in atrial fibrillation, and there is an association between non-chicken wing (NCW) LAA morphology and stroke. We hypothesized that the prevalence of NCW LAA morphology would be higher among patients with cardioembolic (CE) stroke and embolic stroke of undetermined source (ESUS) than among those with noncardioembolic stroke (NCS). METHODS: This multicenter retrospective pilot study included consecutive patients with ischemic stroke from 3 comprehensive stroke centers who previously underwent a qualifying chest computed tomography (CT) to assess LAA morphology. Patients underwent inpatient diagnostic evaluation for ischemic stroke, and stroke subtype was determined based on ESUS criteria. LAA morphology was determined using clinically performed contrast enhanced thin-slice chest CT by investigators blinded to stroke subtype. The primary predictor was NCW LAA morphology and the outcome was stroke subtype (CE, ESUS, NCS). RESULTS: We identified 172 patients with ischemic stroke who had a clinical chest CT performed. Mean age was 70.1 ± 14.3 years and 51.7% were male. Compared with patients with NCS, the prevalence of NCW LAA morphology was higher in patients with CE stroke (58.7% versus 46.3%, P = .1) and ESUS (58.8% versus 46.3%, P = .2), but this difference did not achieve statistical significance. CONCLUSION: The prevalence of NCW LAA morphology may be similar in patients with ESUS and CE, and may be higher than that in those with NCS. Larger studies are needed to confirm these associations.

Cardiac magnetic resonance imaging: a new tool to identify cardioaortic sources in ischaemic stroke.

Stroke of undetermined aetiology or 'cryptogenic' stroke accounts for 30-40% of ischaemic strokes despite extensive diagnostic evaluation. The role and yield of cardiac imaging is controversial. Cardiac MRI (CMR) has been used for cardiac disorders, but its use in cryptogenic stroke is not well established. We reviewed the literature (randomised trials, exploratory comparative studies and case series) on the use of CMR in the diagnostic evaluation of patients with ischaemic stroke. The literature on the use of CMR in the diagnostic evaluation of ischaemic stroke is sparse. However, studies have demonstrated a potential role for CMR in the diagnostic evaluation of patients with cryptogenic stroke to identify potential aetiologies such as cardiac thrombi, cardiac tumours, aortic arch disease and other rare cardiac anomalies. CMR can also provide data on certain functional and structural parameters of the left atrium and the left atrial appendage which have been shown to be associated with ischaemic stroke risk. CMR is a non-invasive modality that can help identify potential mechanisms in cryptogenic stroke and patients who may be targeted for enrolment into clinical trials comparing anticoagulation to antiplatelet therapy in secondary stroke prevention. Prospective studies are needed to compare the value of CMR as compared to transthoracic and transesophageal echocardiography in the diagnostic evaluation of cryptogenic stroke.

Quantitative Assessment of Right Ventricular Volumes and Ejection Fraction in Patients with Left Ventricular Systolic Dysfunction by Real Time Three-Dimensional Echocardiography versus Cardiac Magnetic Resonance Imaging.

AIMS: The aim of this study was to assess the accuracy and reproducibility of real time three-dimensional echocardiographic (RT3DE) for the determination of right ventricular (RV) volumes and function in patients with left ventricular (LV) systolic dysfunction. METHODS AND RESULTS: Dedicated RT3DE was prospectively performed to assess RV volumes and EF in patients with LV systolic function identified on routine clinical cardiac magnetic resonance (CMR) imaging. RV end-diastolic volume (RV EDV), RV end-systolic volume (RV ESV), and RV EF were obtained using an offline analysis software (TomTec) by two observers blinded to CMR results. In this population of 27 patients with LV systolic dysfunction with a mean LV EF of 36 ± 12%, RV RT3DE dataset could be assessed in 27 of 30 patients (90%). High correlation was noted between RT3DE and CMR for RV EDV, ESV, and EF (r = 0.90, 0.89, and 0.77, respectively). RV EDV was lower by RT3DE as compared to CMR (129 ± 52 vs. 142 ± 53 mL, P = 0.005) while there was no significant difference in RV ESV and RV EF (71 ± 37 vs. 77 ± 45 mL, P = 0.146; 45 ± 11 vs. 48 ± 13%, P = 0.134, respectively). The intraclass correlation coefficient ranged from 0.94 to 0.94 between measurements and from 0.84 to 0.96 between observers. CONCLUSION: Overall, RV volumes and EF assessed by RT3DE correlate well with CMR measurements in patients with LV dysfunction. RT3DE may be used as a more widely available and versatile alternative to CMR for the quantitative assessment of RV size and function in patients with LV dysfunction.