Cardiovascular Magnetic Resonance Radiomics to Identify Components of the Extracellular Matrix in Dilated Cardiomyopathy.

BACKGROUND: Current cardiovascular magnetic resonance sequences cannot discriminate between different myocardial extracellular space (ECSs), including collagen, noncollagen, and inflammation. We sought to investigate whether cardiovascular magnetic resonance radiomics analysis can distinguish between noncollagen and inflammation from collagen in dilated cardiomyopathy. METHODS: We identified data from 132 patients with dilated cardiomyopathy scheduled for an invasive septal biopsy who underwent cardiovascular magnetic resonance at 3 T. Cardiovascular magnetic resonance imaging protocol included native and postcontrast T1 mapping and late gadolinium enhancement (LGE). Radiomic features were computed from the midseptal myocardium, near the biopsy region, on native T1, extracellular volume (ECV) map, and LGE images. Principal component analysis was used to reduce the number of radiomic features to 5 principal radiomics. Moreover, a correlation analysis was conducted to identify radiomic features exhibiting a strong correlation (r>0.9) with the 5 principal radiomics. Biopsy samples were used to quantify ECS, myocardial fibrosis, and inflammation. RESULTS: Four histopathological phenotypes were identified: low collagen (n=20), noncollagenous ECS expansion (n=49), mild to moderate collagenous ECS expansion (n=42), and severe collagenous ECS expansion (n=21). Noncollagenous expansion was associated with the highest risk of myocardial inflammation (65%). Although native T1 and ECV provided high diagnostic performance in differentiating severe fibrosis (C statistic, 0.90 and 0.90, respectively), their performance in differentiating between noncollagen and mild to moderate collagenous expansion decreased (C statistic: 0.59 and 0.55, respectively). Integration of ECV principal radiomics provided better discrimination and reclassification between noncollagen and mild to moderate collagen (C statistic, 0.79; net reclassification index, 0.83 [95% CI, 0.45-1.22]; P<0.001). There was a similar trend in the addition of native T1 principal radiomics (C statistic, 0.75; net reclassification index, 0.93 [95% CI, 0.56-1.29]; P<0.001) and LGE principal radiomics (C statistic, 0.74; net reclassification index, 0.59 [95% CI, 0.19-0.98]; P=0.004). Five radiomic features per sequence were identified with correlation analysis. They showed a similar improvement in performance for differentiating between noncollagen and mild to moderate collagen (native T1, ECV, LGE C statistic, 0.75, 0.77, and 0.71, respectively). These improvements remained significant when confined to a single radiomic feature (native T1, ECV, LGE C statistic, 0.71, 0.70, and 0.64, respectively). CONCLUSIONS: Radiomic features extracted from native T1, ECV, and LGE provide incremental information that improves our capability to discriminate noncollagenous expansion from mild to moderate collagen and could be useful for detecting subtle chronic inflammation in patients with dilated cardiomyopathy.

Present and Future Innovations in AI and Cardiac MRI.

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

MRI Assessment of Myocardial Deformation for Risk Stratification of Major Arrhythmic Events in Patients With Non-Ischemic Cardiomyopathy Eligible for Primary Prevention Implantable Cardioverter Defibrillators.

BACKGROUND: Implantable cardioverter-defibrillator (ICD) intervention is an established prophylactic measure. Identifying high-benefit patients poses challenges. PURPOSE: To assess the prognostic value of cardiac magnetic resonance imaging (MRI) parameters including myocardial deformation for risk stratification of ICD intervention in non-ischemic cardiomyopathy (NICM) while accounting for competing mortality risk. STUDY TYPE: Retrospective and prospective. POPULATION: One hundred and fifty-nine NICM patients eligible for primary ICD (117 male, 54 ± 13 years) and 49 control subjects (38 male, 53 ± 5 years). FIELD STRENGTH/SEQUENCE: Balanced steady state free precession (bSSFP) and three-dimensional phase-sensitive inversion-recovery late gadolinium enhancement (LGE) sequences at 1.5 T or 3 T. ASSESSMENT: Patients underwent MRI before ICD implantation and were followed up. Functional parameters, left ventricular global radial, circumferential and longitudinal strain, right ventricular free wall longitudinal strain (RV FWLS) and left atrial strain were measured (Circle, cvi42). LGE presence was assessed visually. The primary endpoint was appropriate ICD intervention. Models were developed to determine outcome, with and without accounting for competing risk (non-sudden cardiac death), and compared to a baseline model including LGE and clinical features. STATISTICAL TESTS: Wilcoxon non-parametric test, Cox's proportional hazards regression, Fine-Gray competing risk model, and cumulative incidence functions. Harrell's c statistic was used for model selection. A P value <0.05 was considered statistically significant. RESULTS: Follow-up duration was 1176 ± 960 days (median: 896). Twenty-six patients (16%) met the primary endpoint. RV FWLS demonstrated a significant difference between patients with and without events (-12.5% ± 5 vs. -16.4% ± 5.5). Univariable analyses showed LGE and RV FWLS were significantly associated with outcome (LGE: hazard ratio [HR] = 3.69, 95% CI = 1.28-10.62; RV FWLS: HR = 2.04, 95% CI = 1.30-3.22). RV FWLS significantly improved the prognostic value of baseline model and remained significant in multivariable analysis, accounting for competing risk (HR = 1.73, 95% CI = 1.12-2.66). DATA CONCLUSIONS: In NICM, RV FWLS may provide additional predictive value for predicting appropriate ICD intervention. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 5.

Accelerated myocardial fibrosis in young to middle-aged patients with hypertrophic cardiomyopathy.

BACKGROUND: The extent of late gadolinium enhancement (LGE) on cardiovascular magnetic resonance (CMR) in patients with hypertrophic cardiomyopathy (HCM) is associated with an increased risk of sudden cardiac death events. However, the clinical significance of age-specific longitudinal changes in LGE is not well characterized in HCM. We sought to assess whether the risk of LGE progression diverges between young to middle-aged (ages 20-59 years) and older (≥ 60) adults with HCM. METHODS: A total of 102 HCM patients (age <60 years; n=75, age ≥60 years; n=27) undergoing serial CMR studies from two tertiary medical centers were evaluated. The median time interval between initial and follow-up CMR scans was 3.7 years. LGE was semiautomatically quantified by measuring regions with signal intensity >6 SD above the nulled remote myocardium and manually adjusting a grayscale threshold. RESULTS: LGE was identified at baseline in 61 of the 102 HCM patients (60%), occupying 4.8 ± 3.9% of the left ventricular (LV) mass. At the end of the follow-up period, 53 of the 61 patients (87%) demonstrated an increase in the extent of LGE to 7.7 ± 5.4%, and 8 patients had no change. In 5 patients (5%), LGE increased to extensive with >15% of the LV mass. The rate of LGE progression was 0.7 ± 1.0%/year, including 21 patients (21%) with particularly accelerated progression of ≥1%/year. The risk of LGE progression ≥1%/year was significantly higher in patients <60 years than those ≥ 60 years (25% vs. 7%, p=0.03). The odds of LGE progression ≥1%/year was almost 4 times greater for patients <60 years compared with those ≥ 60 years (odds ratio, 4.2; 95%CI, 1.1-27.9). Age <60 years and LGE extent ≥ 10% were significant baseline predictors for future LGE progression ≥1%/year, even after adjustment for other potential risk factors. CONCLUSION: In HCM, progressive fibrosis occurs more frequently in young to middle-aged patients, underscoring the importance of repeating CMR to re-evaluate for potential LGE progression in this age group.

Cardiovascular magnetic resonance characterization of myocardial tissue injury in a miniature swine model of cancer therapy-related cardiovascular toxicity.

BACKGROUND: Left ventricular ejection fraction (LVEF) is the most commonly clinically used imaging parameter for assessing cancer therapy-related cardiac dysfunction (CTRCD). However, LVEF declines may occur late, after substantial injury. This study sought to investigate cardiovascular magnetic resonance (CMR) imaging markers of subclinical cardiac injury in a miniature swine model. METHODS: Female Yucatan miniature swine (n = 14) received doxorubicin (2 mg/kg) every 3 weeks for 4 cycles. CMR, including cine, tissue characterization via T1 and T2 mapping, and late gadolinium enhancement (LGE) were performed on the same day as doxorubicin administration and 3 weeks after the final chemotherapy cycle. In addition, magnetic resonance spectroscopy (MRS) was performed during the 3 weeks after the final chemotherapy in 7 pigs. A single CMR and MRS exam were also performed in 3 Yucatan miniature swine that were age- and weight-matched to the final imaging exam of the doxorubicin-treated swine to serve as controls. CTRCD was defined as histological early morphologic changes, including cytoplasmic vacuolization and myofibrillar loss of myocytes, based on post-mortem analysis of humanely euthanized pigs after the final CMR exam. RESULTS: Of 13 swine completing 5 serial CMR scans, 10 (77%) had histological evidence of CTRCD. Three animals had neither histological evidence nor changes in LVEF from baseline. No absolute LVEF <40% or LGE was observed. Native T1, extracellular volume (ECV), and T2 at 12 weeks were significantly higher in swine with CTRCD than those without CTRCD (1178 ms vs. 1134 ms, p = 0.002, 27.4% vs. 24.5%, p = 0.03, and 38.1 ms vs. 36.4 ms, p = 0.02, respectively). There were no significant changes in strain parameters. The temporal trajectories in native T1, ECV, and T2 in swine with CTRCD showed similar and statistically significant increases. At the same time, there were no differences in their temporal changes between those with and without CTRCD. MRS myocardial triglyceride content substantially differed among controls, swine with and without CTRCD (0.89%, 0.30%, 0.54%, respectively, analysis of variance, p = 0.01), and associated with the severity of histological findings and incidence of vacuolated cardiomyocytes. CONCLUSION: Serial CMR imaging alone has a limited ability to detect histologic CTRCD beyond LVEF. Integrating MRS myocardial triglyceride content may be useful for detection of early potential CTRCD.

Highly accelerated free-breathing real-time myocardial tagging for exercise cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) myocardial tagging would enable quantification of myocardial deformation after exercise. However, current electrocardiogram (ECG)-segmented sequences are limited for Ex-CMR. METHODS: We developed a highly accelerated balanced steady-state free-precession real-time tagging technique for 3 T. A 12-fold acceleration was achieved using incoherent sixfold random Cartesian sampling, twofold truncated outer phase encoding, and a deep learning resolution enhancement model. The technique was tested in two prospective studies. In a rest study of 27 patients referred for clinical CMR and 19 healthy subjects, a set of ECG-segmented for comparison and two sets of real-time tagging images for repeatability assessment were collected in 2-chamber and short-axis views with spatiotemporal resolution 2.0 × 2.0 mm2 and 29 ms. In an Ex-CMR study of 26 patients with known or suspected cardiac disease and 23 healthy subjects, real-time images were collected before and after exercise. Deformation was quantified using measures of short-axis global circumferential strain (GCS). Two experienced CMR readers evaluated the image quality of all real-time data pooled from both studies using a 4-point Likert scale for tagline quality (1-excellent; 2-good; 3-moderate; 4-poor) and artifact level (1-none; 2-minimal; 3-moderate; 4-significant). Statistical evaluation included Pearson correlation coefficient (r), intraclass correlation coefficient (ICC), and coefficient of variation (CoV). RESULTS: In the rest study, deformation was successfully quantified in 90% of cases. There was a good correlation (r = 0.71) between ECG-segmented and real-time measures of GCS, and repeatability was good to excellent (ICC = 0.86 [0.71, 0.94]) with a CoV of 4.7%. In the Ex-CMR study, deformation was successfully quantified in 96% of subjects pre-exercise and 84% of subjects post-exercise. Short-axis and 2-chamber tagline quality were 1.6 ± 0.7 and 1.9 ± 0.8 at rest and 1.9 ± 0.7 and 2.5 ± 0.8 after exercise, respectively. Short-axis and 2-chamber artifact level was 1.2 ± 0.5 and 1.4 ± 0.7 at rest and 1.3 ± 0.6 and 1.5 ± 0.8 post-exercise, respectively. CONCLUSION: We developed a highly accelerated real-time tagging technique and demonstrated its potential for Ex-CMR quantification of myocardial deformation. Further studies are needed to assess the clinical utility of our technique.

The spatial ventricular gradient is associated with adverse outcomes in acute pulmonary embolism.

BACKGROUND: The spatial ventricular gradient (SVG) is a vectorcardiographic measurement that reflects cardiac loading conditions via electromechanical coupling. OBJECTIVES: We hypothesized that the SVG is correlated with right ventricular (RV) strain and is prognostic of adverse events in patients with acute pulmonary embolism (PE). METHODS: Retrospective, single-center study of patients with acute PE. Electrocardiogram (ECG), imaging, and outcome data were obtained. SVG components were regressed on tricuspid annular plane systolic excursion (TAPSE), qualitative RV dysfunction, and RV/left ventricular (LV) ratio. Odds of adverse outcomes (30-day mortality, vasopressor requirement, or advanced therapy) after PE were regressed on demographics, RV/LV ratios, traditional ECG signs of RV dysfunction, and SVG components using a logit model. RESULTS: ECGs from 317 patients (48% male, age 63.1 ± 16.6 years) with acute PE were analyzed; 36 patients (11.4%) experienced an adverse event. Worse RV hypokinesis, larger RV/LV ratio, and smaller TAPSE were associated with smaller SVG X and Y components, larger SVG Z components, and smaller SVG vector magnitude (p < .001 for all). In multivariable logistic regression, odds of adverse events after PE decreased with increasing SVG magnitude and TAPSE (OR 0.32 and 0.54 per standard deviation increase; p = .03 and p = .004, respectively). Receiver operating characteristic (ROC) analysis showed that, when combined with imaging, replacing traditional ECG criteria with the SVG significantly improved the area under the ROC from 0.70 to 0.77 (p = .01). CONCLUSION: The SVG is correlated with RV dysfunction and adverse outcomes in acute PE and has a better prognostic value than traditional ECG markers.

Relation of MRI Aortic Wall Area and Plaque to Incident Cardiovascular Events: The Framingham Heart Study.

Background Arterial arteriosclerosis and atherosclerosis reflect vascular disease, the subclinical detection of which allows opportunity for cardiovascular disease (CVD) prevention. Larger cohort studies simultaneously quantifying anatomic thoracic and abdominal aortic pathologic abnormalities are lacking in the literature. Purpose To investigate the association of aortic wall area (AWA) and atherosclerotic plaque presence and burden as measured on MRI scans with incident CVD in a community sample. Materials and Methods In this prospective cohort study, participants in the Framingham Heart Study Offspring Cohort without prevalent CVD underwent 1.5-T MRI (between 2002-2005) of the descending thoracic and abdominal aorta with electrocardiogram-gated axial T2-weighted black-blood acquisitions. The wall thickness of the thoracic aorta was measured at the pulmonary bifurcation level and used to calculate the AWA as the difference between cross-sectional vessel area and lumen area. For primary or secondary analyses, multivariable Cox proportional hazards regression models were used to examine the association of aortic MRI measures with risk of first-incident CVD events or stroke and coronary heart disease, respectively. Results In 1513 study participants (mean age, 64 years ± 9 [SD]; 842 women [56%]), 223 CVD events occurred during follow-up (median, 13.1 years), of which 97 were major events (myocardial infarction, ischemic stroke, or CVD death). In multivariable analysis, thoracic AWA and prevalent thoracic plaque were associated with incident CVD (hazard ratio [HR], 1.20 per SD unit [95% CI: 1.05, 1.37] [P = .006] and HR, 1.63 [95% CI: 1.12, 2.35] [P = .01], respectively). AWA and prevalent thoracic plaque were associated with increased hazards: 1.32 (95% CI: 1.07, 1.62; P = .01) and 2.20 (95% CI: 1.28, 3.79; P = .005), for stroke and coronary heart disease, respectively. Conclusion In middle-aged community-dwelling adults, thoracic aortic wall area (AWA), plaque prevalence, and plaque volumes measured with MRI were independently associated with incident cardiovascular disease, with AWA associated in particular with stroke, and plaque associated with coronary heart disease. Clinical trial registration no. NCT00041418 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Peshock in this issue.

2021-2022 state of our JCMR.

In 2021, there were 136 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR), including 122 original research papers, six reviews, four technical notes, one Society for Cardiovascular Magnetic Resonance (SCMR) guideline, one SCMR position paper, one study protocol, and one obituary (Nathaniel Reichek). The volume was up 53% from 2020 (n = 89) with a corresponding 21% decrease in manuscript submissions from 435 to 345. This led to an increase in the acceptance rate from 24 to 32%. The quality of the submissions continues to be high. The 2021 JCMR Impact Factor (which is released in June 2022) markedly increased from 5.41 to 6.90 placing us in the top quartile of Society and cardiac imaging journals. Our 5 year impact factor similarly increased from 6.52 to 7.25. Fifteen years ago, the JCMR was at the forefront of medical and medical society journal migration to the Open-Access format. The Open-Access system has dramatically increased the availability and JCMR citation. Full-text article requests in 2021 approached 1.5 M!. As I have mentioned, it takes a village to run a journal. JCMR is very fortunate to have a group of very dedicated Associate Editors, Guest Editors, Journal Club Editors, and Reviewers. I thank each of them for their efforts to ensure that the review process occurs in a timely and responsible manner. These efforts have allowed the JCMR to continue as the premier journal of our field. My role, and the entire editorial process would not be possible without the ongoing high dedication and efforts of our managing editor, Jennifer Rodriguez. Her premier organizational skills have allowed for streamlining of the review process and marked improvement in our time-to-decision (see later). As I conclude my 6th and final year as your editor-in-chief, I thank you for entrusting me with the JCMR editorship and appreciate the time I have had at the helm. I am very confident that our Journal will reach new heights under the stewardship of Dr. Tim Leiner, currently at the Mayo Clinic with a seamless transition occurring as I write this in late November. I hope that you will continue to send your very best, high quality CMR manuscripts to JCMR, and that our readers will continue to look to JCMR for the very best/state-of-the-art CMR publications.

2021 - State of our JCMR.

There were 89 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2020, including 71 original research papers, 5 technical notes, 6 reviews, 4 Society for Cardiovascular Magnetic Resonance (SCMR) position papers/guidelines/protocols and 3 corrections. The volume was up 12.7% from 2019 (n = 79) with a corresponding 17.9% increase in manuscript submissions from 369 to 435. This led to a slight increase in the acceptance rate from 22 to 23%. The quality of the submissions continues to be high. The 2020 JCMR Impact Factor (which is published in June 2020) slightly increased from 5.361 to 5.364 placing us in the top quartile of Society and cardiac imaging journals. Our 5 year impact factor increased from 5.18 to 6.52. Fourteen years ago, the JCMR was at the forefront of medical and medical society journal migration to the Open-Access format. The Open-Access system has dramatically increased the availability and citation of JCMR publications with accesses now exceeding 1.2 M! It takes a village to run a journal. JCMR is blessed to have a group of very dedicated Associate Editors, Guest Editors, Journal Club Editors, and Reviewers. I thank each of them for their efforts to ensure that the review process occurs in a timely and responsible manner. These efforts have allowed the JCMR to continue as the premier journal of our field. My role, and the entire process would not be possible without the dedication and efforts of our new managing editor, Jennifer Rodriguez, whose premier organizational efforts have allowed for streamlining of the review process and marked improvement in our time-to-decision (see later). As I begin my 6th and final year as your editor-in-chief, I thank you for entrusting me with the JCMR editorship. I hope that you will continue to send us your very best, high quality manuscripts for JCMR consideration and that our readers will continue to look to JCMR for the very best/state-of-the-art CMR publications. The editorial process continues to be a tremendously fulfilling experience and the opportunity to review manuscripts that reflect the best in our field remains a great joy and true highlight of my week!

An inline deep learning based free-breathing ECG-free cine for exercise cardiovascular magnetic resonance.

BACKGROUND: Exercise cardiovascular magnetic resonance (Ex-CMR) is a promising stress imaging test for coronary artery disease (CAD). However, Ex-CMR requires accelerated imaging techniques that result in significant aliasing artifacts. Our goal was to develop and evaluate a free-breathing and electrocardiogram (ECG)-free real-time cine with deep learning (DL)-based radial acceleration for Ex-CMR. METHODS: A 3D (2D + time) convolutional neural network was implemented to suppress artifacts from aliased radial cine images. The network was trained using synthetic real-time radial cine images simulated using breath-hold, ECG-gated segmented Cartesian k-space data acquired at 3 T from 503 patients at rest. A prototype real-time radial sequence with acceleration rate = 12 was used to collect images with inline DL reconstruction. Performance was evaluated in 8 healthy subjects in whom only rest images were collected. Subsequently, 14 subjects (6 healthy and 8 patients with suspected CAD) were prospectively recruited for an Ex-CMR to evaluate image quality. At rest (n = 22), standard breath-hold ECG-gated Cartesian segmented cine and free-breathing ECG-free real-time radial cine images were acquired. During post-exercise stress (n = 14), only real-time radial cine images were acquired. Three readers evaluated residual artifact level in all collected images on a 4-point Likert scale (1-non-diagnostic, 2-severe, 3-moderate, 4-minimal). RESULTS: The DL model substantially suppressed artifacts in real-time radial cine images acquired at rest and during post-exercise stress. In real-time images at rest, 89.4% of scores were moderate to minimal. The mean score was 3.3 ± 0.7, representing increased (P < 0.001) artifacts compared to standard cine (3.9 ± 0.3). In real-time images during post-exercise stress, 84.6% of scores were moderate to minimal, and the mean artifact level score was 3.1 ± 0.6. Comparison of left-ventricular (LV) measures derived from standard and real-time cine at rest showed differences in LV end-diastolic volume (3.0 mL [- 11.7, 17.8], P = 0.320) that were not significantly different from zero. Differences in measures of LV end-systolic volume (7.0 mL [- 1.3, 15.3], P < 0.001) and LV ejection fraction (- 5.0% [- 11.1, 1.0], P < 0.001) were significant. Total inline reconstruction time of real-time radial images was 16.6 ms per frame. CONCLUSIONS: Our proof-of-concept study demonstrated the feasibility of inline real-time cine with DL-based radial acceleration for Ex-CMR.

Impact of the COVID-19 pandemic on cardiology fellow echocardiography education at a large academic center.

BACKGROUND: In response to COVID-19 pandemic state restrictions, our institution deferred elective procedures from 3/15/2020 to 6/13/2020, and removed cardiology fellows from the echocardiography rotation to staff clinical services. We assessed the impact of the COVID-19 pandemic on fellow education and echocardiography volumes. METHODS: Our institutional database was used to examine volumes of transthoracic (TTE), stress (SE), and transesophageal echocardiograms (TEE) from 7/1/2018 to 10/10/2020. Study volumes were compared in three intervals: pre-pandemic (7/1/2018- 3/14/2020), pandemic (3/15/2020-6/13/2020), and pandemic recovery (6/14/2020-10/10/2020). We examined weekly number of TTEs performed or interpreted by cardiology fellows during the study period, and compared these to the two previous academic years. RESULTS: Weekly TTE volume declined by 54% during the pandemic, and increased by 99% during pandemic recovery, (p < 0.05). SE and TEE revealed similar trends. A strong correlation between weekly TTE volume and inpatient admissions was observed during the study period (rs=0.67, p < 0.05). Weekly fellow TTE scans declined by 78% during the pandemic, with a 380% increase during pandemic recovery (p < 0.05). Weekly fellow TTE interpretations declined by 56% during the pandemic, with a 76% increase during pandemic recovery (p < 0.05). CONCLUSION: COVID restrictions between 3/15/2020- 6/14/2020 coincided with a marked decline in TTE, SE, and TEE volumes, with an increase similar to near pre-pandemic volumes during the pandemic recovery period. A similar decline with the onset of COVID restrictions, and increase to pre-restriction volumes thereafter was observed with fellow scans and interpretations, but total academic year fellow training volumes remained depressed. With the ongoing COVID-19 pandemic and rise of multiple variants, training programs may need to adjust fellows' clinical responsibilities so as to support achievement of echocardiography training certification.

Improved Quantification of Myocardium Scar in Late Gadolinium Enhancement Images: Deep Learning Based Image Fusion Approach.

BACKGROUND: Quantification of myocardium scarring in late gadolinium enhanced (LGE) cardiac magnetic resonance imaging can be challenging due to low scar-to-background contrast and low image quality. To resolve ambiguous LGE regions, experienced readers often use conventional cine sequences to accurately identify the myocardium borders. PURPOSE: To develop a deep learning model for combining LGE and cine images to improve the robustness and accuracy of LGE scar quantification. STUDY TYPE: Retrospective. POPULATION: A total of 191 hypertrophic cardiomyopathy patients: 1) 162 patients from two sites randomly split into training (50%; 81 patients), validation (25%, 40 patients), and testing (25%; 41 patients); and 2) an external testing dataset (29 patients) from a third site. FIELD STRENGTH/SEQUENCE: 1.5T, inversion-recovery segmented gradient-echo LGE and balanced steady-state free-precession cine sequences ASSESSMENT: Two convolutional neural networks (CNN) were trained for myocardium and scar segmentation, one with and one without LGE-Cine fusion. For CNN with fusion, the input was two aligned LGE and cine images at matched cardiac phase and anatomical location. For CNN without fusion, only LGE images were used as input. Manual segmentation of the datasets was used as reference standard. STATISTICAL TESTS: Manual and CNN-based quantifications of LGE scar burden and of myocardial volume were assessed using Pearson linear correlation coefficients (r) and Bland-Altman analysis. RESULTS: Both CNN models showed strong agreement with manual quantification of LGE scar burden and myocardium volume. CNN with LGE-Cine fusion was more robust than CNN without LGE-Cine fusion, allowing for successful segmentation of significantly more slices (603 [95%] vs. 562 (89%) of 635 slices; P < 0.001). Also, CNN with LGE-Cine fusion showed better agreement with manual quantification of LGE scar burden than CNN without LGE-Cine fusion (%ScarLGE-cine = 0.82 × %Scarmanual , r = 0.84 vs. %ScarLGE = 0.47 × %Scarmanual , r = 0.81) and myocardium volume (VolumeLGE-cine = 1.03 × Volumemanual , r = 0.96 vs. VolumeLGE = 0.91 × Volumemanual , r = 0.91). DATA CONCLUSION: CNN based LGE-Cine fusion can improve the robustness and accuracy of automated scar quantification. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: 1.

Risk assessment of acute pulmonary embolism utilizing coronary artery calcifications in patients that have undergone CT pulmonary angiography and transthoracic echocardiography.

OBJECTIVE: To evaluate the relation of coronary artery calcifications (CAC) on non-ECG-gated CT pulmonary angiography (CTPA) with short-term mortality in patients with acute pulmonary embolism (PE). METHODS: We retrospectively included all in-patients between May 2007 and December 2014 with an ICD-9 code for acute PE and CTPA and transthoracic echocardiography available. CAC was qualitatively graded as absent, mild, moderate, or severe. Relations of CAC with overall and PE-related 30-day mortality were assessed using logistic regression analyses. The independence of those relations was assessed using a nested approach, first adjusting for age and gender, then for RV strain, peak troponin T, and cardiovascular risk factors for an overall model. RESULTS: Four hundred seventy-nine patients were included (63 ± 16 years, 52.8% women, 47.2% men). In total, 253 (52.8%) had CAC-mild: 143 (29.9%); moderate: 89 (18.6%); severe: 21 (4.4%). Overall mortality was 8.8% (n = 42) with higher mortality with any CAC (12.6% vs. 4.4% without; odds ratio [OR] 3.1 [95%CI 2.1-14.5]; p = 0.002). Mortality with severe (19.0%; OR 5.1 [95%CI 1.4-17.9]; p = 0.011), moderate (11.2%; OR 2.7 [95%CI 1.1-6.8]; p = 0.031), and mild CAC (12.6%; OR 3.1 [95%CI 1.4-6.9]; p = 0.006) was higher than without. OR adjusted for age and gender was 2.7 (95%CI 1.0-7.1; p = 0.050) and 2.6 (95%CI 0.9-7.1; p = 0.069) for the overall model. PE-related mortality was 4.0% (n = 19) with higher mortality with any CAC (5.9% vs. 1.8% without; OR 3.5 [95%CI 1.1-10.7]; p = 0.028). PE-related mortality with severe CAC was 9.5% (OR 5.8 [95%CI 1.0-34.0]; p = 0.049), with moderate CAC 6.7% (OR 4.0 [95%CI 1.1-14.6]; p = 0.033), and with mild 4.9% (OR 2.9 [95%CI 0.8-9.9]; p = 0.099). OR adjusted for age and gender was 4.2 (95%CI 0.9-20.7; p = 0.074) and 3.4 (95%CI 0.7-17.4; p = 0.141) for the overall model. Patients with sub-massive PE showed similar results. CONCLUSION: CAC is frequent in acute PE patients and associated with short-term mortality. Visual assessment of CAC may serve as an easy, readily available tool for early risk stratification in those patients. KEY POINTS: • Coronary artery calcification assessed on computed tomography pulmonary angiography is frequent in patients with acute pulmonary embolism. • Coronary artery calcification assessed on computed tomography pulmonary angiography is associated with 30-day overall and PE-related mortality in patients with acute pulmonary embolism. • Coronary artery calcification assessed on computed tomography pulmonary angiography may serve as an additional, easy readily available tool for early risk stratification in those patients.

2020 - State of our JCMR.

There were 79 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR) in 2019, including 65 original research papers, 2 reviews, 8 technical notes, 1 Society for Cardiovascular Magnetic Resonacne (SCMR) guideline, and 3 corrections. The volume was down slightly from 2018 (n = 89) with a corresponding 5.5% increase in manuscript submissions from 345 to 366. This led to a slight decrease in the acceptance rate from 25 to 22%. The quality of the submissions continues to be high. The 2019 JCMR Impact Factor (which is published in June 2020) increased from 5.07 to 5.36. The 2020 impact factor means that on average, each JCMR published in 2017 and 2018 was cited 5.36 times in 2019. Our 5 year impact factor was 5.2. We are now finishing the 13th year of JCMR as an open-access publication with BMC. As outlined in this report, the Open-Access system has dramatically increased the reading and citation of JCMR publications. I hope that our authors will continue to send their very best, high quality manuscripts for JCMR consideration and that our readers will continue to look to JCMR for the very best/state-of-the-art publications in our field. It takes a village to run a journal. JCMR is blessed to have very dedicated Associate Editors, Guest Editors, and Reviewers. I thank each of them for their efforts to ensure that the review process occurs in a timely and responsible manner. These efforts have allowed the JCMR to continue as the premier journal of our field. My role, and the entire process would not be possible without the dedication and efforts of our managing editor, Diana Gethers (who will leaving the journal in the coming months) and our assistant managing editor, Jennifer Rodriguez, who has agreed to increase her reponsibilities. Finally, I thank you for entrusting me with the editorship of the JCMR. As I begin my 5th year as your editor-in-chief, please know that I fully recognize we are not perfect in our review process. We try our best to objectively assess every submission in a timely manner, but sometimes don't get it "right." The editorial process is a tremendously fulfilling experience for me. The opportunity to review manuscripts that reflect the best in our field remains a great joy and a highlight of my week!

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study.

Background Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important marker for outcome prediction in patients with hypertrophic cardiomyopathy (HCM); however, its clinical application is hindered by a lack of measurement standardization. Purpose To develop and evaluate a three-dimensional (3D) convolutional neural network (CNN)-based method for automated LGE scar quantification in patients with HCM. Materials and Methods We retrospectively identified LGE MRI data in a multicenter (n = 7) and multivendor (n = 3) HCM study obtained between November 2001 and November 2011. A deep 3D CNN based on U-Net architecture was used for LGE scar quantification. Independent CNN training and testing data sets were maintained with a 4:1 ratio. Stacks of short-axis MRI slices were split into overlapping substacks that were segmented and then merged into one volume. The 3D CNN per-site and per-vendor performances were evaluated with respect to manual scar quantification performed in a core laboratory setting using Dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analyses. Furthermore, the performance of 3D CNN was compared with that of two-dimensional (2D) CNN. Results This study included 1073 patients with HCM (733 men; mean age, 49 years ± 17 [standard deviation]). The 3D CNN-based quantification was fast (0.15 second per image) and demonstrated excellent correlation with manual scar volume quantification (r = 0.88, P < .001) and ratio of scar volume to total left ventricle myocardial volume (%LGE) (r = 0.91, P < .001). The 3D CNN-based quantification strongly correlated with manual quantification of scar volume (r = 0.82-0.99, P < .001) and %LGE (r = 0.90-0.97, P < .001) for all sites and vendors. The 3D CNN identified patients with a large scar burden (>15%) with 98% accuracy (202 of 207) (95% confidence interval [CI]: 95%, 99%). When compared with 3D CNN, 2D CNN underestimated scar volume (r = 0.85, P < .001) and %LGE (r = 0.83, P < .001). The DSC of 3D CNN segmentation was comparable among different vendors (P = .07) and higher than that of 2D CNN (DSC, 0.54 ± 0.26 vs 0.48 ± 0.29; P = .02). Conclusion In the hypertrophic cardiomyopathy population, a three-dimensional convolutional neural network enables fast and accurate quantification of myocardial scar volume, outperforms a two-dimensional convolutional neural network, and demonstrates comparable performance across different vendors. © RSNA, 2019 Online supplemental material is available for this article.

Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar.

BACKGROUND: In patients with suspected or known hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) provides diagnostic and prognostic value. However, contraindications and long-term retention of gadolinium have raised concern about repeated gadolinium administration in this population. Alternatively, native T1 -mapping enables identification of focal fibrosis, the substrate of LGE. However HCM-specific heterogeneous fibrosis distribution leads to subtle T1 -maps changes that are difficult to identify. PURPOSE: To apply radiomic texture analysis on native T1 -maps to identify patients with a low likelihood of LGE(+), thereby reducing the number of patients exposed to gadolinium administration. STUDY TYPE: Retrospective interpretation of prospectively acquired data. SUBJECTS: In all, 188 (54.7 ± 14.4 years, 71% men) with suspected or known HCM. FIELD STRENGTH/SEQUENCE: A 1.5T scanner; slice-interleaved native T1 -mapping (STONE) sequence and 3D LGE after administration of 0.1 mmol/kg of gadobenate dimeglumine. ASSESSMENT: Left ventricular LGE images were location-matched with native T1 -maps using anatomical landmarks. Using a split-sample validation approach, patients were randomly divided 3:1 (training/internal validation vs. test cohorts). To balance the data during training, 50% of LGE(-) slices were discarded. STATISTICAL TESTS: Four sets of texture descriptors were applied to the training dataset for capture of spatially dependent and independent pixel statistics. Five texture features were sequentially selected with the best discriminatory capacity between LGE(+) and LGE(-) T1 -maps and tested using a decision tree ensemble (DTE) classifier. RESULTS: The selected texture features discriminated between LGE(+) and LGE(-) T1 -maps with a c-statistic of 0.75 (95% confidence interval [CI]: 0.70-0.80) using 10-fold cross-validation during internal validation in the training dataset and 0.74 (95% CI: 0.65-0.83) in the independent test dataset. The DTE classifier provided adequate labeling of all (100%) LGE(+) patients and 37% of LGE(-) patients during testing. DATA CONCLUSION: Radiomic analysis of native T1 -images can identify ~1/3 of LGE(-) patients for whom gadolinium administration can be safely avoided. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020. J. Magn. Reson. Imaging 2020;52:906-919.

Society for Cardiovascular Magnetic Resonance (SCMR) guidance for the practice of cardiovascular magnetic resonance during the COVID-19 pandemic.

The aim of this document is to provide general guidance and specific recommendations on the practice of cardiovascular magnetic resonance (CMR) in the era of the COVID-19 pandemic. There are two major considerations. First, continued urgent and semi-urgent care for the patients who have no known active COVID-19 should be provided in a safe manner for both patients and staff. Second, when necessary, CMR on patients with confirmed or suspected active COVID-19 should focus on the specific clinical question with an emphasis on myocardial function and tissue characterization while optimizing patient and staff safety.

Society for Cardiovascular Magnetic Resonance (SCMR) guidance for re-activation of cardiovascular magnetic resonance practice after peak phase of the COVID-19 pandemic.

During the peak phase of the COVID-19 pandemic, alterations of standard operating procedures were necessary for health systems to protect patients and healthcare workers and ensure access to vital hospital resources. As the peak phase passes, re-activation plans are required to safely manage increasing clinical volumes. In the context of cardiovascular magnetic resonance (CMR), re-activation objectives include continued performance of urgent CMR studies and resumption of CMR in patients with semi-urgent and elective indications in an environment that is safe for both patients and health care workers.

Right ventricular strain in patients with pulmonary embolism and syncope.

Patients with acute pulmonary embolism (PE) can present with various clinical manifestations including syncope. The mechanism of syncope in PE is not fully elucidated and data of right ventricular (RV) function in patients has been limited. We retrospectively identified 477 consecutive patients hospitalized with acute PE diagnosed with a computed tomogram (CT) who also had a transthoracic echocardiogram (TTE) 24 h prior to or 48 h after diagnosis. Parameters of RV strain on CT, TTE, electrocardiogram (ECG), and clinical characteristics and adverse outcomes were collected. Patients with all three studies available for assessment were included (n = 369) and those with syncope (n = 34) were compared to patients without syncope (n = 335). Patients with syncope were more likely to demonstrate RV strain on all three modes of assessment compared to those without syncope [17 (50%) vs. 67 (20%); p = 0.001], and those patients were more likely to receive advanced therapies [9 (53%) vs. 15 (22%); p = 0.02]. PE-related mortality was highest among those presenting with high-risk PE and syncope (36%, OR 20.1, 95% CI 5.3-81.1; p < 0.001) and was low in patients with syncope without criteria for high-risk PE (3%, OR 1.2, 95% CI 0.2-10.0; p < 0.001). In conclusion, acute PE patients with syncope are more likely to demonstrate multimodality evidence of RV strain and to receive advanced therapies. Syncope was only associated with increased PE-related mortality in patients presenting with a high-risk PE. Syncope alone without evidence of RV strain is associated with low short-term adverse events and is similar to those without syncope.