Cardiovascular Magnetic Resonance for the Differentiation of Left Ventricular Hypertrophy

Left ventricular hypertrophy: do not forget Fabry disease. Diagnostic work-up and differential diagnosis

BACKGROUND

Left ventricular (LV) hypertrophy is a common clinical finding. Differential diagnosis includes Fabry disease, a rare and progressive, but treatable storage disease caused by deficiency of α-galactosidase A. However, diagnosis of Fabry is often hampered by its clinical heterogeneity, LV hypertrophy phenocopies and unawareness of the clinician.

METHODS

This review summarises clinical data, family history, electrocardiogram (ECG) and imaging (echocardiogram and cardiovascular magnetic resonance (CMR)) characteristics to differentiate aetiologies of LV hypertrophy including clues for the diagnosis of Fabry.

RESULTS

LV hypertrophy is a consequence of pressure overload mostly, but differential diagnosis includes hypertrophic cardiomyopathy and infiltrative diseases. Clinical data, ECG, type and degree of LV hypertrophy, functional and tissue characteristics differ among aetiologies. LV hypertrophy in Fabry is progressive and mostly concentric but may copy any hypertrophic cardiomyopathy. Dependent on residual alfa-galactosidase A enzyme activity, degree of LV hypertrophy in Fabry may vary. Initially, low myocardial CMR T1-map values are calculated. At a later stage, midwall late gadolinium enhancement of the inferolateral LV wall may occur. Global longitudinal strain may be depressed in the inferolateral wall. Voltage criteria for LV hypertrophy and short PQ interval are common. Right ventricular (RV) hypertrophy is frequent. In addition, multisystemic symptoms including neuropathic pain, hypohidrosis, proteinuria, renal insufficiency and familial young stroke are pointing to Fabry.

CONCLUSIONS

LV hypertrophy should raise suspicion of Fabry disease, especially if LV hypertrophy is unexplained and/or associated with RV hypertrophy. In Fabry, LV hypertrophy may be heterogeneous and mimic any hypertrophic cardiomyopathy. ECG, multisystemic symptoms and imaging may provide clues for Fabry.

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.

Cardiac magnetic resonance radiomics for disease classification

OBJECTIVES

This study investigated the discriminability of quantitative radiomics features extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and healthy (NOR) patients.

METHODS

The data of two hundred and eighty-three patients with HCM (n = 48) or DCM (n = 52) and NOR (n = 123) were extracted from two publicly available datasets. Ten feature selection methods were first performed on twenty-one different sets of radiomics features extracted from the left ventricle, right ventricle, and myocardium segmented from CMR images in the end-diastolic frame, end-systolic frame, and a combination of both; then, nine classical machine learning methods were trained with the selected radiomics features to distinguish HCM, DCM, and NOR. Ninety classification models were constructed based on combinations of the ten feature selection methods and nine classifiers. The classification models were evaluated, and the optimal model was selected. The diagnostic performance of the selected model was also compared to that of state-of-the-art methods.

RESULTS

The random forest minimum redundancy maximum relevance model with features based on LeastAxisLength, Maximum2DDiameterSlice, Median, MinorAxisLength, Sphericity, VoxelVolume, Kurtosis, Flatness, and Skewness was the highest performing model, achieving 91.2% classification accuracy. The cross-validated areas under the curve on the test dataset were 0.938, 0.966, and 0.936 for NOR, DCM, and HCM, respectively. Furthermore, compared with those of the state-of-the-art methods, the sensitivity and accuracy of this model were greatly improved.

CONCLUSIONS

A predictive model was proposed based on CMR radiomics features for classifying HCM, DCM, and NOR patients. The model had good discriminability.

KEY POINTS

• The first-order features and the features extracted from the LOG-filtered images have potential in distinguishing HCM patients from DCM patients. • The features extracted from the RV play little role in distinguishing DCM from HCM. • The VoxelVolume of the myocardium in the ED frame is important in the recognition of DCM.

Left Ventricular Hypertrophy and Ventricular Tachyarrhythmia: The Role of Biomarkers

Left ventricular hypertrophy (LVH) refers to a complex rebuilding of the left ventricle that can gradually lead to serious complications-heart failure and life-threatening ventricular arrhythmias. LVH is defined as an increase in the size of the left ventricle (i.e., anatomically), therefore the basic diagnosis detecting the increase in the LV size is the domain of imaging methods such as echocardiography and cardiac magnetic resonance. However, to evaluate the functional status indicating the gradual deterioration of the left ventricular myocardium, additional methods are available approaching the complex process of hypertrophic remodeling. The novel molecular and genetic biomarkers provide insights on the underlying processes, representing a potential basis for targeted therapy. This review summarizes the spectrum of the main biomarkers employed in the LVH valuation.

The impact of hypertension on the prognosis of patients with hypertrophic cardiomyopathy: a single-center retrospective study

Background

Hypertrophic cardiomyopathy (HCM) and hypertension coexist fairly frequently in clinical practice. However, the evidence about the impact of hypertension on the prognosis of HCM is limited. The present study aims to investigate the impact of hypertension on the prognosis of HCM patients.

Methods

A total of 468 HCM patients were enrolled, and patients were divided into hypertension group (31.8%) and non-hypertension group (68.2%). The primary study endpoint was HCM-related death, consisting of heart failure (HF)-related death, stroke-related death and sudden cardiac death (SCD). Associations between hypertension and HCM-related death were analyzed by Cox regression models with the use of propensity score matching (PSM) as primary analysis.

Results

There were 55 HCM-related death during a median follow-up time of 4.6 years, and the mortality rate was 2.53 per 100 person years. Kaplan-Meier analysis based on the crude cohort or PSM cohort revealed no significant difference regarding the HCM-related death between the two groups. In the crude cohort, both univariable and multivariable Cox regression analysis indicated that hypertension was not significantly associated with HCM-related death with hazard ratios (HR) at 0.74 (95% CI [0.40-1.36], p value: 0.329) and 0.77 (95% CI [0.35-1.71], p value: 0.521), respectively. Similarly, no strong evidence for an association was observed between hypertension and HCM-related death in the PSM cohort with unadjusted HR at 0.90 (95% CI [0.34-2.41]; p value: 0.838) and adjusted HR at 0.77 (95% CI [0.35-1.71]; p value: 0.521), respectively. Other propensity score methods, including overlap weighting and inverse probability treatment weighting demonstrated similar results. Sensitivity analysis also indicated that the concomitant hypertension did not significantly increase the risk of HF-related death, stroke-related death or SCD in HCM patients.

Conclusion

HCM-related death did not significantly differ between hypertension and non-hypertension groups, suggesting a negative impact of hypertension on the clinical prognosis of HCM patients.

Hypertrophic cardiomyopathy or athlete's heart? A systematic review of novel cardiovascular magnetic resonance imaging parameters

Hypertrophic cardiomyopathy (HCM) is a common cause of sudden cardiac death in athletes. Cardiac Magnetic Resonance (CMR) imaging is considered an excellent tool to differentiate between HCM and athlete's heart. The aim of this systematic review was to highlight the novel CMR-derived parameters with significant discriminative capacity between the two conditions. A systematic search in the MEDLINE, EMBASE and Cochrane Reviews databases was performed. Eligible studies were considered the ones comparing novel CMR-derived parameters on athletes and HCM patients. Therefore, studies that only examined Cine-derived volumetric parameters were excluded. Particular attention was given to binary classification results from multi-variate regression models and ROC curve analyses. Bias assessment was performed with the Quality Assessment on Diagnostic Accuracy Studies. Five (5) studies were included in the systematic review, with a total of 284 athletes and 373 HCM patients. Several novel indices displayed discriminatory potential, such as native T1 mapping and T2 values, LV global longitudinal strain, late gadolinium enhancement and whole-LV fractal dimension. Diffusion tensor imaging enabled quantification of the secondary eigenvalue angle and fractional anisotropy in one study, which also proved capable of reliably detecting HCM in a mixed athlete/patient sample. Several novel CMR-derived parameters, most of which are currently under development, show promising results in discerning between athlete's heart and HCM. Prospective studies examining the discriminatory capacity of all promising modalities side-by-side will yield definitive answers on their relative importance; diagnostic models can incorporate the best performing variables for optimal results.

Cardiac Magnetic Resonance in Hypertensive Heart Disease: Time for a New Chapter

Hypertension is one of the most important cardiovascular risk factors, associated with significant morbidity and mortality. Chronic high blood pressure leads to various structural and functional changes in the myocardium. Different sophisticated imaging methods are developed to properly estimate the severity of the disease and to prevent possible complications. Cardiac magnetic resonance can provide a comprehensive assessment of patients with hypertensive heart disease, including accurate and reproducible measurement of left and right ventricle volumes and function, tissue characterization, and scar quantification. It is important in the proper evaluation of different left ventricle hypertrophy patterns to estimate the presence and severity of myocardial fibrosis, as well as to give more information about the benefits of different therapeutic modalities. Hypertensive heart disease often manifests as a subclinical condition, giving exceptional value to cardiac magnetic resonance as an imaging modality capable to detect subtle changes. In this article, we are giving a comprehensive review of all the possibilities of cardiac magnetic resonance in patients with hypertensive heart disease.

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.

Differential Diagnosis of Thick Myocardium according to Histologic Features Revealed by Multiparametric Cardiac Magnetic Resonance Imaging

Left ventricular (LV) wall thickening, or LV hypertrophy (LVH), is common and occurs in diverse conditions including hypertrophic cardiomyopathy (HCM), hypertensive heart disease, aortic valve stenosis, lysosomal storage disorders, cardiac amyloidosis, mitochondrial cardiomyopathy, sarcoidosis and athlete’s heart. Cardiac magnetic resonance (CMR) imaging provides various tissue contrasts and characteristics that reflect histological changes in the myocardium, such as cellular hypertrophy, cardiomyocyte disarray, interstitial fibrosis, extracellular accumulation of insoluble proteins, intracellular accumulation of fat, and intracellular vacuolar changes. Therefore, CMR imaging may be beneficial in establishing a differential diagnosis of LVH. Although various diseases share LV wall thickening as a common feature, the histologic changes that underscore each disease are distinct. This review focuses on CMR multiparametric myocardial analysis, which may provide clues for the differentiation of thickened myocardium based on the histologic features of HCM and its phenocopies.

Cardiac magnetic resonance imaging in preeclampsia complicated by pulmonary edema shows myocardial edema with normal left ventricular systolic function

BACKGROUND

Preeclampsia complicates approximately 5% of all pregnancies. When pulmonary edema occurs, it accounts for 50% of preeclampsia-related mortality. Currently, there is no consensus on the degree to which left ventricular systolic dysfunction contributes to the development of pulmonary edema.

OBJECTIVE

This study aimed to use cardiac magnetic resonance imaging to detect subtle changes in left ventricular systolic function and evidence of acute left ventricular dysfunction (through tissue characterization) in women with preeclampsia complicated by pulmonary edema compared with both preeclamptic and normotensive controls.

STUDY DESIGN

Cases were postpartum women aged ≥18 years presenting with preeclampsia complicated by pulmonary edema. Of note, 2 control groups were recruited: women with preeclampsia without pulmonary edema and women with normotensive pregnancies. All women underwent echocardiography and 1.5T cardiac magnetic resonance imaging with native T1 and T2 mapping. Gadolinium contrast was administered to cases only. Because of small sample sizes, a nonparametric test (Kruskal-Wallis) with pairwise posthoc analysis using Bonferroni correction was used to compare the differences between the groups. Cardiac magnetic resonance images were interpreted by 2 independent reporters. The intraclass correlation coefficient was calculated to assess interobserver reliability.

RESULTS

Here, 20 women with preeclampsia complicated by pulmonary edema, 13 women with preeclampsia (5 with severe features and 8 without severe features), and 6 normotensive controls were recruited. There was no difference in the baseline characteristics between groups apart from the expected differences in blood pressure. Left atrial sizes were similar across all groups. Women with preeclampsia complicated by pulmonary edema had increased left ventricular mass (P=.01) but had normal systolic function compared with the normotensive controls. Furthermore, they had elevated native T1 values (P=.025) and a trend toward elevated T2 values (P=.07) in the absence of late gadolinium enhancement consistent with myocardial edema. Moreover, myocardial edema was present in all women with eclampsia or hemolysis, elevated liver enzymes, and low platelet count. Women with preeclampsia without severe features had similar findings to the normotensive controls. All cardiac magnetic resonance imaging measurements showed a very high level of interobserver correlation.

CONCLUSION

This study focused on cardiac magnetic resonance imaging in women with preeclampsia complicated by pulmonary edema, eclampsia, and hemolysis, elevated liver enzymes, and low platelet count. We have demonstrated normal systolic function with myocardial edema in women with preeclampsia with these severe features. These findings implicate an acute myocardial process as part of this clinical syndrome. The pathogenesis of myocardial edema and its relationship to pulmonary edema require further elucidation. With normal left atrial sizes, any hemodynamic component must be acute.

Magnetic Resonance for Differential Diagnosis of Left Ventricular Hypertrophy: Diagnostic and Prognostic Implications

BACKGROUND

Left ventricular hypertrophy (LVH) may be due to different causes, ranging from benign secondary forms to severe cardiomyopathies. Transthoracic Echocardiography (TTE) and ECG are the first-level examinations for LVH diagnosis. Cardiac magnetic resonance (CMR) accurately defines LVH type, extent and severity.

OBJECTIVES

to evaluate the diagnostic and prognostic role of CMR in patients with TTE and/or ECG evidence of LVH.

METHODS

We performed CMR in 300 consecutive patients with echocardiographic and/or ECG signs of LVH.

RESULTS

Overall, 275 patients had TTE evidence of LVH, with initial suspicion of hypertrophic cardiomyopathy (HCM) in 132 (44%), cardiac amyloidosis in 41 (14%), hypertensive LVH in 48 (16%), aortic stenosis in 4 (1%), and undetermined LVH in 50 (16%). The initial echocardiographic diagnostic suspicion of LVH was confirmed in 172 patients (57.3%) and changed in 128 patients (42.7%, p < 0.0001): the diagnosis of HCM increased from 44% to 71% of patients; hypertensive and undetermined LVH decreased significantly (respectively to 4% and 5%). CMR allowed for a diagnosis in 41 out of 50 (82%) patients with undetermined LVH at TTE. CMR also identified HCM in 17 out of 25 patients with apparently normal echocardiography but with ECG criteria for LVH. Finally, the reclassification of the diagnosis by CMR was associated with a change in survival risk of patients: after CMR reclassification, no events occurred in patients with undetermined or hypertensive LVH.

CONCLUSIONS

CMR changed echocardiographic suspicion in almost half of patients with LVH. In the subgroup of patients with abnormal ECG, CMR identified LVH (particularly HCM) in 80% of patients. This study highlights the indication of CMR to better characterize the type, extent and severity of LVH detected at echocardiography and suspected with ECG.

Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy

BACKGROUND

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent-free technology to replace LGE for faster and cheaper CMR scans.

METHODS

A CMR virtual native enhancement (VNE) imaging technology was developed using artificial intelligence. The deep learning model for generating VNE uses multiple streams of convolutional neural networks to exploit and enhance the existing signals in native T1 maps (pixel-wise maps of tissue T1 relaxation times) and cine imaging of cardiac structure and function, presenting them as LGE-equivalent images. The VNE generator was trained using generative adversarial networks. This technology was first developed on CMR datasets from the multicenter Hypertrophic Cardiomyopathy Registry, using hypertrophic cardiomyopathy as an exemplar. The datasets were randomized into 2 independent groups for deep learning training and testing. The test data of VNE and LGE were scored and contoured by experienced human operators to assess image quality, visuospatial agreement, and myocardial lesion burden quantification. Image quality was compared using a nonparametric Wilcoxon test. Intra- and interobserver agreement was analyzed using intraclass correlation coefficients (ICC). Lesion quantification by VNE and LGE were compared using linear regression and ICC.

RESULTS

A total of 1348 hypertrophic cardiomyopathy patients provided 4093 triplets of matched T1 maps, cines, and LGE datasets. After randomization and data quality control, 2695 datasets were used for VNE method development and 345 were used for independent testing. VNE had significantly better image quality than LGE, as assessed by 4 operators (n=345 datasets; P<0.001 [Wilcoxon test]). VNE revealed lesions characteristic of hypertrophic cardiomyopathy in high visuospatial agreement with LGE. In 121 patients (n=326 datasets), VNE correlated with LGE in detecting and quantifying both hyperintensity myocardial lesions (r=0.77-0.79; ICC=0.77-0.87; P<0.001) and intermediate-intensity lesions (r=0.70-0.76; ICC=0.82-0.85; P<0.001). The native CMR images (cine plus T1 map) required for VNE can be acquired within 15 minutes and producing a VNE image takes less than 1 second.

CONCLUSIONS

VNE is a new CMR technology that resembles conventional LGE but without the need for contrast administration. VNE achieved high agreement with LGE in the distribution and quantification of lesions, with significantly better image quality.

Diagnosis of wild-type transthyretin amyloid cardiomyopathy in Japan: red-flag symptom clusters and diagnostic algorithm

Wild‐type transthyretin amyloid cardiomyopathy (ATTRwt‐CM) is caused by the deposition of wild‐type transthyretin (TTR) amyloid fibrils in the heart. The age at diagnosis of ATTRwt‐CM is reported to be approximately 70–80 years, and patients commonly present with non‐disease‐specific cardiac abnormalities, such as heart failure with preserved ejection fraction and diastolic dysfunction. The disease can be fatal if left untreated, with an approximate survival of 3–5 years from diagnosis. An oral TTR stabilizer, tafamidis, has enabled early intervention for the treatment of ATTRwt‐CM. However, awareness of ATTRwt‐CM remains low, and misdiagnosis and a delay in diagnosis are common. This review discusses the epidemiology, characteristics, treatment strategy, and red‐flag symptoms and signs of ATTRwt‐CM based on the published literature, as well as recent advances in diagnostic modalities that enable early and accurate diagnosis of the disease. We also discuss an algorithm for early and accurate diagnosis of ATTRwt‐CM in daily clinical practice. In our diagnostic algorithm, a suspected diagnosis of ATTRwt‐CM should be triggered by unexplained left ventricular hypertrophy (LVH), which is LVH that cannot be explained by an increased afterload due to hypertension or valvular disease. In addition, heart failure symptoms, laboratory test results (N‐terminal pro‐B‐type natriuretic peptide, high‐sensitivity troponin T, or high‐sensitivity troponin I), electrocardiogram and imaging (echocardiogram or cardiac magnetic resonance) data, age (≥60 years), and medical history suggestive of ATTRwt‐CM (e.g. carpal tunnel syndrome) should be examined. Detailed examinations using bone scintigraphy and monoclonal protein detection tests followed by tissue biopsy, amyloid typing, and TTR genetic testing are warranted for a definite diagnosis of ATTRwt‐CM.