Trabeculated Myocardium in Hypertrophic Cardiomyopathy: Clinical Consequences

Novel Magnetic Resonance Imaging Tools for Hypertrophic Cardiomyopathy Risk Stratification

Hypertrophic cardiomyopathy (HCM) is a common genetic disorder with a well described risk of sudden cardiac death; however, risk stratification has remained a challenge. Recently, novel parameters in cardiac magnetic resonance imaging (CMR) have shown promise in helping to improve upon current risk stratification paradigms. In this manuscript, we have reviewed novel CMR risk markers and their utility in HCM. The results of the review showed that T1, extracellular volume, CMR feature tracking, and other miscellaneous novel CMR variables have the potential to improve sudden death risk stratification and may have additional roles in diagnosis and prognosis. The strengths and weaknesses of these imaging techniques, and their potential utility and implementation in HCM risk stratification are discussed.

Improving a Deep Learning Model to Accurately Diagnose LVNC

Accurate diagnosis of Left Ventricular Noncompaction Cardiomyopathy (LVNC) is critical for proper patient treatment but remains challenging. This work improves LVNC detection by improving left ventricle segmentation in cardiac MR images. Trabeculated left ventricle indicates LVNC, but automatic segmentation is difficult. We present techniques to improve segmentation and evaluate their impact on LVNC diagnosis. Three main methods are introduced: (1) using full 800 × 800 MR images rather than 512 × 512; (2) a clustering algorithm to eliminate neural network hallucinations; (3) advanced network architectures including Attention U-Net, MSA-UNet, and U-Net++.Experiments utilize cardiac MR datasets from three different hospitals. U-Net++ achieves the best segmentation performance using 800 × 800 images, and it improves the mean segmentation Dice score by 0.02 over the baseline U-Net, the clustering algorithm improves the mean Dice score by 0.06 on the images it affected, and the U-Net++ provides an additional 0.02 mean Dice score over the baseline U-Net. For LVNC diagnosis, U-Net++ achieves 0.896 accuracy, 0.907 precision, and 0.912 F1-score outperforming the baseline U-Net. Proposed techniques enhance LVNC detection, but differences between hospitals reveal problems in improving generalization. This work provides validated methods for precise LVNC diagnosis.

Unusual Evolution of Hypertrophic Cardiomyopathy in Non-Compaction Myocardium in a Pompe Disease Patient

Classic infantile Pompe disease is characterized by a severe phenotype with cardiomyopathy and hypotonia. Cardiomyopathy is generally hypertrophic and rapidly regresses after enzyme replacement therapy. In this report, for the first time, we describe a patient with infantile Pompe disease and hypertrophic cardiomyopathy that evolved into non-compaction myocardium after treatment. The male newborn had suffered since birth with hypertrophic cardiomyopathy and heart failure. He was treated with standard enzyme replacement therapy (ERT) (alglucosidase alfa) and several immunomodulation cycles due to the development of anti-ERT antibodies, without resolution of the hypertrophic cardiomyopathy. At the age of 2.5 years, he was treated with a new combination of ERT therapy (cipaglucosidase alfa) and a chaperone (miglustat) for compassionate use. After 1 year, the cardiac hypertrophy was resolved, but it evolved into non-compaction myocardium. Non-compaction cardiomyopathy is often considered to be a congenital, primitive cardiomyopathy, due to an arrest of compaction of the myocardium wall during the embryonal development. Several genetic causes have been identified. We first describe cardiac remodeling from hypertrophic cardiomyopathy to a non-compaction form in a patient with infantile Pompe disease treated with a new ERT. This has important implications both for the monitoring of Pompe disease patients and for the understanding of the pathophysiological basis of non-compaction myocardium.

Left ventricular non-compaction cardiomyopathy automatic diagnosis using a deep learning approach

BACKGROUND AND OBJECTIVE

Left ventricular non-compaction (LVNC) is an uncommon cardiomyopathy characterised by a thick and spongy left ventricle wall caused by the high presence of trabeculae (hyper-trabeculation). Recently, the percentage of the trabecular volume to the total volume of the external wall of the left ventricle (VT%) has been proposed to diagnose this illness.

METHODS

This paper presents the use of a deep learning-based method to measure the (VT%) value and diagnose this rare cardiomyopathy. The population used in this research was composed of 277 patients suffering from hypertrophic cardiomyopathy. 134 patients only suffered hypertrophic cardiomyopathy, and 143 also suffered left ventricular non-compaction. Our deep learning solution is based on a 2D U-Net. This artificial neural network (ANN) was trained on short-axis magnetic resonance imaging to segment the left ventricle's internal cavity, external wall, and trabecular tissue. 5-fold cross-validation was performed to ensure the robustness of the results. The Dice coefficient of the three classes was computed as a measure of the precision of the segmentation. Based on this segmentation, the percentage of the trabecular volume (VT%) was computed. Two specialist cardiologists rated the segmentation produced by the neural network for 25 patients to evaluate the clinical validity of the outputs. The computed VT% was used to automatically diagnose the 277 patients depending on whether or not a given threshold was exceeded. A receiver operating characteristic analysis was also performed.

RESULTS

According to the cross-validation results, the average and standard deviation of the Dice coefficient for the internal cavity, external wall, and trabeculae were 0.96±0.00, 0.89±0.00, and 0.84±0.00, respectively. The cardiologists rated 99.5% of the evaluated segmentations as clinically valid for diagnosis, outperforming existing automatic traditional tools. The area under the ROC curve was 0.94 (95% confidence interval, 0.91-0.96). The accuracy, sensitivity, and specificity values of diagnosis using a threshold of 25% were 0.87, 0.93, and 0.80, respectively.

CONCLUSIONS

The U-Net neural network can achieve excellent results in the delineation of different cardiac structures of short-axis cardiac MRI. The high-quality segmentation allows for the correct measurement of left ventricular hyper-trabeculation and a definitive diagnosis of LVNC illness. Using this kind of solution could lead to more objective and faster analysis, reducing human error and time spent by cardiologists.

Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18-30, 31-40, 41-50, 51-60, and 61-80 years old. Subjects underwent 1.5 T, 2D CINE SSFP MRI. 35 pathologic cases from local clinical exams and the SCMR 2015 consensus contours dataset were also analyzed. A fully convolutional network (FCN) similar to U-Net trained on the U.K. Biobank was used to automatically segment LV endocardial and epicardial contours. FCN and manual segmentation were compared using Dice metrics and measurements of end-diastolic volume (EDV), end-systolic volume (ESV), mass (LVM), and ejection fraction (LVEF). Paired t-tests and linear regressions were used to analyze measurement differences with respect to sex and age. Dice metrics (median ± IQR) for n = 135 cases were 0.94 ± 0.04/0.87 ± 0.10 (ED endocardium/ES endocardium). Measurement biases (mean ± SD) among the healthy cohort were - 0.3 ± 10.1 mL for EDV, - 6.7 ± 9.6 mL for ESV, 4.6 ± 6.4% for LVEF, and - 2.2 ± 11.0 g for LVM; biases were independent of sex and age. Biases among the 35 pathologic cases were 0.1 ± 19 mL for EDV, - 4.8 ± 19 mL for ESV, 2.0 ± 7.6% for LVEF, and 1.0 ± 20 g for LVM. In conclusion, automatic segmentation by the Biobank-trained FCN was independent of age and sex. Improvements in end-systolic basal slice detection are needed to decrease bias and improve precision in ESV and LVEF.