The Society for Cardiovascular Magnetic Resonance Registry at 150,000.

OBJECTIVES: To summarize the status of the SCMR Registry at 150,000 exams. BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The SCMR Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine (DICOM) images. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and present a summary of its contents. RESULTS: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (~100 terabytes of storage). The human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% female, 72% Caucasian, and had a mortality rate of 8%. The most common indication was cardiomyopathy (27%), and most frequently used current procedural terminology (CPT) code was 75561 (35%). Macrocyclic gadolinium-based contrast agents represented 89% of contrast utilization after 2015. Short-axis cines were performed in 99% of scans, short-axis LGE in 66%, and stress perfusion sequences in 30%. Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction (LVEF) < 35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct late gadolinium enhancement (LGE), compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years. CONCLUSIONS: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility. CONDENSED ABSTRACT: The SCMR Registry is a central regulatory-compliant cloud-based repository for real-world clinical data and DICOM images for multicenter cardiovascular research, including outcomes-based data. The Registry contains 299,622,066 DICOM images and 456,678 patient-years follow-up. Data compiled from 154,458 CMR scans across 20 US sites demonstrated cardiomyopathy as the most common indication and 89% macrocyclic gadolinium contrast utilization after 2015. There was an overall mortality rate of 8%, with higher rates in those with LVEF<35%, abnormal wall motion, ischemia presence, or infarct LGE. The Registry aims to promote evidence-based CMR utilization through a collaborative effort to positively impact cardiovascular outcomes.

On in-silico estimation of left ventricular end-diastolic pressure from cardiac strains.

Left ventricular diastolic dysfunction (LVDD) is a group of diseases that adversely affect the passive phase of the cardiac cycle and can lead to heart failure. While left ventricular end-diastolic pressure (LVEDP) is a valuable prognostic measure in LVDD patients, traditional invasive methods of measuring LVEDP present risks and limitations, highlighting the need for alternative approaches. This paper investigates the possibility of measuring LVEDP non-invasively using inverse in-silico modeling. We propose the adoption of patient-specific cardiac modeling and simulation to estimate LVEDP and myocardial stiffness from cardiac strains. We have developed a high-fidelity patient-specific computational model of the left ventricle. Through an inverse modeling approach, myocardial stiffness and LVEDP were accurately estimated from cardiac strains that can be acquired from in vivo imaging, indicating the feasibility of computational modeling to augment current approaches in the measurement of ventricular pressure. Integration of such computational platforms into clinical practice holds promise for early detection and comprehensive assessment of LVDD with reduced risk for patients.

Contrast-Enhanced Magnetic Resonance Imaging Based T1 Mapping and Extracellular Volume Fractions Are Associated with Peripheral Artery Disease.

BACKGROUND: Extracellular volume fraction (ECV), measured with contrast-enhanced magnetic resonance imaging (CE-MRI), has been utilized to study myocardial fibrosis, but its role in peripheral artery disease (PAD) remains unknown. We hypothesized that T1 mapping and ECV differ between PAD patients and matched controls. METHODS AND RESULTS: A total of 37 individuals (18 PAD patients and 19 matched controls) underwent 3.0T CE-MRI. Skeletal calf muscle T1 mapping was performed before and after gadolinium contrast with a motion-corrected modified look-locker inversion recovery (MOLLI) pulse sequence. T1 values were calculated with a three-parameter Levenberg-Marquardt curve fitting algorithm. ECV and T1 maps were quantified in five calf muscle compartments (anterior [AM], lateral [LM], and deep posterior [DM] muscle groups; soleus [SM] and gastrocnemius [GM] muscles). Averaged peak blood pool T1 values were obtained from the posterior and anterior tibialis and peroneal arteries. T1 values and ECV are heterogeneous across calf muscle compartments. Native peak T1 values of the AM, LM, and DM were significantly higher in PAD patients compared to controls (all p < 0.028). ECVs of the AM and SM were significantly higher in PAD patients compared to controls (AM: 26.4% (21.2, 31.6) vs. 17.3% (10.2, 25.1), p = 0.046; SM: 22.7% (19.5, 27.8) vs. 13.8% (10.2, 19.1), p = 0.020). CONCLUSIONS: Native peak T1 values across all five calf muscle compartments, and ECV fractions of the anterior muscle group and the soleus muscle were significantly elevated in PAD patients compared with matched controls. Non-invasive T1 mapping and ECV quantification may be of interest for the study of PAD.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Complete spatiotemporal quantification of cardiac motion in mice through enhanced acquisition and super-resolution reconstruction.

The quantification of cardiac motion using cardiac magnetic resonance imaging (CMR) has shown promise as an early-stage marker for cardiovascular diseases. Despite the growing popularity of CMR-based myocardial strain calculations, measures of complete spatiotemporal strains (i.e., three-dimensional strains over the cardiac cycle) remain elusive. Complete spatiotemporal strain calculations are primarily hampered by poor spatial resolution, with the rapid motion of the cardiac wall also challenging the reproducibility of such strains. We hypothesize that a super-resolution reconstruction (SRR) framework that leverages combined image acquisitions at multiple orientations will enhance the reproducibility of complete spatiotemporal strain estimation. Two sets of CMR acquisitions were obtained for five wild-type mice, combining short-axis scans with radial and orthogonal long-axis scans. Super-resolution reconstruction, integrated with tissue classification, was performed to generate full four-dimensional (4D) images. The resulting enhanced and full 4D images enabled complete quantification of the motion in terms of 4D myocardial strains. Additionally, the effects of SRR in improving accurate strain measurements were evaluated using an in-silico heart phantom. The SRR framework revealed near isotropic spatial resolution, high structural similarity, and minimal loss of contrast, which led to overall improvements in strain accuracy. In essence, a comprehensive methodology was generated to quantify complete and reproducible myocardial deformation, aiding in the much-needed standardization of complete spatiotemporal strain calculations.

Multi-Modality Deep Infarct: Non-invasive identification of infarcted myocardium using composite in-silico-human data learning.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, we develop a completely non-invasive methodology that identifies the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, we demonstrate the remarkable performance of a multi-fidelity ML model that combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. Our results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in-vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Assessing Regurgitation Severity, Adverse Remodeling, and Fibrosis with CMR in Primary Mitral Regurgitation.

PURPOSE OF REVIEW: This review offers an evidence-based analysis of established and emerging cardiovascular magnetic resonance (CMR) techniques used to assess the severity of primary mitral regurgitation (MR), identify adverse cardiac remodeling and its prognostic effect. The aim is to provide different insights regarding clinical decision-making and enhance the clinical outcomes of patients with MR. RECENT FINDINGS: Cardiac remodeling and myocardial replacement fibrosis are observed frequently in the presence of substantial LV volume overload, particularly in cases with severe primary MR. CMR serves as a useful diagnostic imaging modality in assessing mitral regurgitation severity, early detection of cardiac remodeling, myocardial dysfunction, and myocardial fibrosis, enabling timely intervention before irreversible damage ensues. Incorporating myocardial remodeling in terms of left ventricular (LV) dilatation and myocardial fibrosis with quantitative MR severity assessment by CMR may assist in defining optimal timing of intervention.

Association of Secondary Mitral Regurgitation and Right Ventricular Dysfunction among Patients with Non-Ischemic Cardiomyopathy.

AIMS: The association between secondary mitral regurgitation (MR) and right ventricular (RV) dysfunction in heart failure patients with non-ischemic cardiomyopathy (NICM) is unclear. Hence, our objective was to study the association between secondary MR and the occurrence of RV dysfunction among patients with NICM using cardiac magnetic resonance (CMR). METHODS AND RESULTS: Patients with NICM were enrolled in a prospective observational registry between 2008-2019. CMR was used to quantify MR severity along with RV function. RV dysfunction was defined as RV ejection fraction <45%. The outcome of the study was a composite event of all-cause death, heart transplantation, or left ventricular assist device implantation at follow-up. In the study cohort of 241 patients, RV dysfunction (RVEF < 45%) was present in 148 (61%). In comparison to patients without RV dysfunction, those with RV dysfunction had higher median MR volume (23 ml [IQR 16-31ml] vs 18 ml [IQR 12-25 ml], P=0.002) and MR fraction (33% [IQR 25-43%] vs 22% [IQR 15-29%], P<0.001). Furthermore, secondary MR was independently associated with RV dysfunction: MR volume ≥ 24ml (OR 3.21, 95% CI 1.26-8.15, P= 0.01) and MR fraction≥ 30% (OR 5.46, 95% 2.23-13.35, P=0.002). Increasing RVEF (every 1% increase) was independently associated with lower risk of adverse events (HR 0.98, 95% 0.95, 1.00, P=0.047). CONCLUSIONS: In patients with NICM, the severity of secondary MR is associated with an increased prevalence of RV dysfunction. RV dysfunction is not only associated with the severity of LV dysfunction, but also with the severity of secondary MR.

Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease.

Peripheral artery disease (PAD) is associated with impaired blood flow in the lower extremities and histopathologic changes of the skeletal calf muscles, resulting in abnormal microvascular perfusion. We studied the use of convolution neural networks (CNNs) to differentiate patients with PAD from matched controls using perfusion pattern features from contrast-enhanced magnetic resonance imaging (CE-MRI) of the skeletal calf muscles. We acquired CE-MRI based skeletal calf muscle perfusion in 56 patients (36 patients with PAD and 20 matched controls). Microvascular perfusion imaging was performed after reactive hyperemia at the midcalf level, with a temporal resolution of 409 ms. We analyzed perfusion scans up to 2 minutes indexed from the local precontrast arrival time frame. Skeletal calf muscles, including the anterior muscle, lateral muscle, deep posterior muscle group, and the soleus and gastrocnemius muscles, were segmented semiautomatically. Segmented muscles were represented as 3-dimensional Digital Imaging and Communications in Medicine stacks of CE-MRI perfusion scans for deep learning (DL) analysis. We tested several CNN models for the 3-dimensional CE-MRI perfusion stacks to classify patients with PAD from matched controls. A total of 2 of the best performing CNNs (resNet and divNet) were selected to develop the final classification model. A peak accuracy of 75% was obtained for resNet and divNet. Specificity was 80% and 94% for resNet and divNet, respectively. In conclusion, DL using CNNs and CE-MRI skeletal calf muscle perfusion can discriminate patients with PAD from matched controls. DL methods may be of interest for the study of PAD.

Experimental multiparametric magnetic resonance imaging characterization of iliocaval venous thrombosis pathological changes.

OBJECTIVE: Iliocaval thrombotic obstruction is a challenging condition, especially because thrombus age and corresponding pathological remodeling at presentation are unknown, which directly impacts management. Our aim was to assess the ability of magnetic resonance imaging (MRI) in determining age thresholds of experimentally created inferior vena cava (IVC) thrombosis in pigs. METHODS: We used a previously described swine model of IVC thrombosis. The animals underwent MRI at baseline, immediately after thrombosis creation, and after a follow-up period extending from 2 to 28 days. Thirteen pigs were divided into three groups according to disease chronicity: acute group (AG; n = 5), subacute group (SAG; n = 4), and chronic group (CG; n = 4), with a mean thrombosis age of 6.4 ± 2.5 days, 15.7 ± 2.8 days, and 28 ± 5.7 days, respectively. A T1-weighted volumetric interpolated breath-hold examination sequence was used to anatomically delineate IVC thrombus as a region of interest. Three other MRI sequences were used to assess the thrombus signal. RESULTS: The Kruskal-Wallis test showed a statistically significant difference in T1 relaxation times after contrast injection (P = .026) between the three groups of chronicity. The AG (360.2 ± 102.5 ms) was significantly different from the CG (336.7 ± 55.2 ms; P = .003), and the SAG (354.1 ± 89.7 ms) was significantly different from the AG (P = .027). There was a statistically significant difference in native T2 relaxation times (P = .038) between the three groups. The AG (160 ± 86.7 ms) was significantly different from the SAG (142.3 ± 55.4 ms; P = .027), and the SAG was significantly different from the CG (178.4 ± 11.7 ms; P = .004). CONCLUSIONS: This study highlighted MRI characteristics in a swine model that might have the potential to significantly differentiate subacute and chronic stages from an acute stage of deep vein thrombosis in humans. Further clinical studies in humans are warranted. CLINICAL RELEVANCE: In addition to providing a better understanding of venous thrombosis remodeling over time, magnetic resonance imaging has the potential to be a tool that could allow us to characterize the composition of venous thrombus over an interval, allowing for a refined analysis of the local evolution of venous thrombosis. We propose a noninvasive and innovative method to characterize different thresholds of chronicity with magnetic resonance imaging features of central deep vein thrombosis of the inferior vena cava experimentally obtained using a totally endovascular in vivo swine model, mimicking human pathophysiology. Being able to determine these features noninvasively is critical for vascular specialists when it comes to choosing between fibrinolytic therapy, percutaneous thrombectomy, or surgical management.

Assessing Regurgitation Severity, Adverse Remodeling, and Fibrosis with CMR in Aortic Regurgitation.

PURPOSE OF REVIEW: Cardiac magnetic resonance (CMR) is emerging as a valuable imaging modality for the assessment of aortic regurgitation (AR). In this review, we discuss the assessment of AR severity, left ventricular (LV) remodeling, and tissue characterization by CMR while highlighting the latest studies and addressing future research needs. RECENT FINDINGS: Recent studies have further established CMR-based thresholds of AR severity and LV remodeling that are associated with adverse clinical outcomes, and lower than current guideline criteria. In addition, tissue profiling with late gadolinium enhancement (LGE) and extracellular volume (ECV) quantification can reliably assess adverse myocardial tissue remodeling which is also associated with adverse outcomes. The strengths and reproducibility of CMR in evaluating ventricular volumes, tissue characteristics, and regurgitation severity position it as an excellent modality in evaluating and following AR patients. Advanced CMR techniques for the detection of tissue remodeling have shown significant potential and merit further investigation.

Noninvasive Prediction of Pulmonary Capillary Wedge Pressure in Patients With Normal Left Ventricular Ejection Fraction: Comparison of Cardiac Magnetic Resonance With Comprehensive Echocardiography.

BACKGROUND: Cardiac magnetic resonance (CMR) was recently reported to predict mean pulmonary capillary wedge pressure (PCWP). However, there is a paucity of data on its accuracy for estimation of PCWP in patients with normal left ventricular (LV) ejection fraction (EF). We sought to examine its accuracy against the invasive gold standard and to compare it with the accuracy of comprehensive echocardiography. METHODS: Stable patients with EF of ≥50% who underwent right heart catheterization, CMR, and echocardiographic imaging within 1 week were included. Pulmonary capillary wedge pressure was estimated by CMR using a previously validated equation where PCWP is estimated based on the left atrial maximum volume and LV mass. Echocardiographic estimation of PCWP was based on 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging guidelines, taking into account the presence of myocardial disease. RESULTS: The mean age of the 79 patients was 55 ± 15 years, and 58.2% were female. There were 33 patients with PCWP >15 mm Hg by right heart catheterization. Cardiac magnetic resonance prediction of PCWP had an area under the curve (AUC) = 0.72. In comparison, echocardiographic prediction of PCWP showed a higher accuracy (AUC = 0.87 vs AUC = 0.72; P = .008). CONCLUSIONS: In patients with normal LV EF, CMR estimation of mean PCWP based on LV mass and left atrial volume has modest accuracy for detecting patients with mean PCWP >15 mm Hg. Comprehensive echocardiography predicts elevated PCWP with higher accuracy in comparison with CMR.

A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging.

Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.

Identifying Mitral Valve Prolapse at Risk for Arrhythmias and Fibrosis From Electrocardiograms Using Deep Learning.

BACKGROUND: Mitral valve prolapse (MVP) is a common valvulopathy, with a subset developing sudden cardiac death or cardiac arrest. Complex ventricular ectopy (ComVE) is a marker of arrhythmic risk associated with myocardial fibrosis and increased mortality in MVP. OBJECTIVES: The authors sought to evaluate whether electrocardiogram (ECG)-based machine learning can identify MVP at risk for ComVE, death and/or myocardial fibrosis on cardiac magnetic resonance (CMR) imaging. METHODS: A deep convolutional neural network (CNN) was trained to detect ComVE using 6,916 12-lead ECGs from 569 MVP patients from the University of California-San Francisco between 2012 and 2020. A separate CNN was trained to detect late gadolinium enhancement (LGE) using 1,369 ECGs from 87 MVP patients with contrast CMR. RESULTS: The prevalence of ComVE was 28% (160/569). The area under the receiver operating characteristic curve (AUC) of the CNN to detect ComVE was 0.80 (95% CI: 0.77-0.83) and remained high after excluding patients with moderate-severe mitral regurgitation [0.80 (95% CI: 0.77-0.83)] or bileaflet MVP [0.81 (95% CI: 0.76-0.85)]. AUC to detect all-cause mortality was 0.82 (95% CI: 0.77-0.87). ECG segments relevant to ComVE prediction were related to ventricular depolarization/repolarization (early-mid ST-segment and QRS from V1, V3, and III). LGE in the papillary muscles or basal inferolateral wall was present in 24% patients with available CMR; AUC for detection of LGE was 0.75 (95% CI: 0.68-0.82). CONCLUSIONS: CNN-analyzed 12-lead ECGs can detect MVP at risk for ventricular arrhythmias, death and/or fibrosis and can identify novel ECG correlates of arrhythmic risk. ECG-based CNNs may help select those MVP patients requiring closer follow-up and/or a CMR.

Stress Perfusion Cardiac Magnetic Resonance vs SPECT Imaging for Detection of Coronary Artery Disease.

BACKGROUND: GadaCAD2 was 1 of 2 international, multicenter, prospective, Phase 3 clinical trials that led to U.S. Food and Drug Administration approval of gadobutrol to assess myocardial perfusion and late gadolinium enhancement (LGE) in adults with known or suspected coronary artery disease (CAD). OBJECTIVES: A prespecified secondary objective was to determine if stress perfusion cardiovascular magnetic resonance (CMR) was noninferior to single-photon emission computed tomography (SPECT) for detecting significant CAD and for excluding significant CAD. METHODS: Participants with known or suspected CAD underwent a research rest and stress perfusion CMR that was compared with a gated SPECT performed using standard clinical protocols. For CMR, adenosine or regadenoson served as vasodilators. The total dose of gadobutrol was 0.1 mmol/kg body weight. The standard of reference was a 70% stenosis defined by quantitative coronary angiography (QCA). A negative coronary computed tomography angiography could exclude CAD. Analysis was per patient. CMR, SPECT, and QCA were evaluated by independent central core lab readers blinded to clinical information. RESULTS: Participants were predominantly male (61.4% male; mean age 58.9 ± 10.2 years) and were recruited from the United States (75.0%), Australia (14.7%), Singapore (5.7%), and Canada (4.6%). The prevalence of significant CAD was 24.5% (n = 72 of 294). Stress perfusion CMR was statistically superior to gated SPECT for specificity (P = 0.002), area under the receiver operating characteristic curve (P < 0.001), accuracy (P = 0.003), positive predictive value (P < 0.001), and negative predictive value (P = 0.041). The sensitivity of CMR for a 70% QCA stenosis was noninferior and nonsuperior to gated SPECT. CONCLUSIONS: Vasodilator stress perfusion CMR, as performed with gadobutrol 0.1 mmol/kg body weight, had superior diagnostic accuracy for diagnosis and exclusion of significant CAD vs gated SPECT.

Diastology with Cardiac MRI: A Practical Guide.

Diastolic filling of the ventricle is a complex interplay of volume and pressure, contingent on active energy-dependent myocardial relaxation and myocardial stiffness. Abnormal diastolic function is the hallmark of the clinical entity of heart failure with preserved ejection fraction (HFpEF), which is now the dominant type of heart failure and is associated with significant morbidity and mortality. Although echocardiography is the current first-line imaging modality used in evaluation of diastolic function, cardiac MRI (CMR) is emerging as an important technique. The principal role of CMR is to categorize the cause of diastolic dysfunction (DD) and distinguish other entities that manifest similarly to HFpEF, particularly infiltrative and pericardial disorders. CMR also provides prognostic information and risk stratification based on late gadolinium enhancement and parametric mapping techniques. Advances in hardware, sequences, and postprocessing software now enable CMR to diagnose and grade DD accurately, a role traditionally assigned to echocardiography. Two-dimensional or four-dimensional velocity-encoded phase-contrast sequences can measure flow and velocities at the mitral inflow, mitral annulus, and pulmonary veins to provide diastolic functional metrics analogous to those at echocardiography. The commonly used cine steady-state free-precession sequence can provide clues to DD including left ventricular mass, left ventricular filling curves, and left atrial size and function. MR strain imaging provides information on myocardial mechanics that further aids in diagnosis and prognosis of diastolic function. Research sequences such as MR elastography and MR spectroscopy can help evaluate myocardial stiffness and metabolism, respectively, providing additional insights on diastolic function. The authors review the physiology of diastolic function, mechanics of diastolic heart failure, and CMR techniques in the evaluation of diastolic function. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Sex differences in myocardial remodeling and extracellular volume in aortic regurgitation.

Whether sex differences exist in the cardiac remodeling related to aortic regurgitation (AR) is unclear. Cardiac magnetic resonance (CMR) is the current non-invasive reference standard for cardiac remodeling assessment and can evaluate tissue characteristics. This prospective cohort included patients with AR undergoing CMR between 2011 and 2020. We excluded patients with confounding causes of remodeling. We quantified left ventricular (LV) volume, mass, AR severity, replacement fibrosis by late Gadolinium enhancement (LGE), and extracellular expansion by extracellular volume fraction (ECV). We studied 280 patients (109 women), median age 59.5 (47.2, 68.6) years (P for age = 0.25 between sexes). Women had smaller absolute LV volume and mass than men across the spectrum of regurgitation volume (RVol) (P ≤ 0.01). In patients with ≥ moderate AR and with adjustment for body surface area, indexed LV end-diastolic volume and mass were not significantly different between sexes (all P > 0.5) but men had larger indexed LV end systolic volume and lower LV ejection fraction (P ≥ 0.01). Women were more likely to have NYHA class II or greater symptoms than men but underwent surgery at a similar rate. Prevalence and extent of LGE was not significantly different between sexes or across RVol. Increasing RVol was independently associated with increasing ECV in women, but not in men (adjusted P for interaction = 0.03). In conclusion, women had lower LV volumes and mass than men across AR severity  but their ECV increased with higher regurgitant volume, while ECV did not change in men. Indexing to body surface area did not fully correct for the cardiac remodeling differences between men and women. Women were more likely to have symptoms but underwent surgery at a similar rate to men. Further research is needed to determine if differences in ECV would translate to differences in the course of AR and outcomes.

Imaging for implementation of heart failure guidelines.

The classification of heart failure with implications for pharmacological therapeutic interventions rests on defining ejection fraction (EF) which is an imaging parameter. Imaging can provide diagnostic clues as to aetiology of heart failure; it can also guide and help assess response to treatment. Echocardiography, CMR, cardiac computed tomography, positron emission tomography, and Tc 99 m pyrophosphate scanning provide information about the aetiology of heart failure. Further, echocardiography plays the primary role in the evaluation of LV diastolic function and the estimation of left ventricular (LV) filling pressures both at rest and with exercise during diastolic stress testing. Heart failure guidelines recognize four stages (A, B, C, and D) for heart failure. Cardiac imaging along with risk factors and clinical status is needed for identifying these stages. There are joint societal echocardiographic guidelines by American Society of Echocardiography (ASE) of Echocardiography and European Association of Cardiovascular Imaging that are applicable to the imaging of heart failure patients. There are also separate guidelines for the evaluation of patients being considered for LV assist device implantation and for multimodality imaging of patients with heart failure and preserved EF. Cardiac catheterization is needed in patients whose haemodynamic status is uncertain after clinical and echocardiographic evaluation and to evaluate for coronary artery disease. Myocardial biopsy can identify the presence of myocarditis or specific infiltrative diseases when the findings by non-invasive imaging are not conclusive.