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.

A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion.

Quantitative analysis of dynamic contrast-enhanced cardiovascular MRI (cMRI) datasets enables the assessment of myocardial blood flow (MBF) for objective evaluation of ischemic heart disease in patients with suspected coronary artery disease. State-of-the-art MBF quantification techniques use constrained deconvolution and are highly sensitive to noise and motion-induced errors, which can lead to unreliable outcomes in the setting of high-resolution MBF mapping. To overcome these limitations, recent iterative approaches incorporate spatial-smoothness constraints to tackle pixel-wise MBF mapping. However, such iterative methods require a computational time of up to 30 minutes per acquired myocardial slice, which is a major practical limitation. Furthermore, they cannot enforce robustness to residual nonrigid motion which can occur in clinical stress/rest studies of patients with arrhythmia. We present a non-iterative patch-wise deep learning approach for pixel-wise MBF quantification wherein local spatio-temporal features are learned from a large dataset of myocardial patches acquired in clinical stress/rest cMRI studies. Our approach is scanner-independent, computationally efficient, robust to noise, and has the unique feature of robustness to motion-induced errors. Numerical and experimental results obtained using real patient data demonstrate the effectiveness of our approach.Clinical Relevance- The proposed patch-wise deep learning approach significantly improves the reliability of high-resolution myocardial blood flow quantification in cMRI by improving its robustness to noise and nonrigid myocardial motion and is up to 300-fold faster than state-of-the-art iterative approaches.

Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation.

In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.

Contribution of mitral valve leaflet length and septal wall thickness to outflow tract obstruction in patients with hypertrophic cardiomyopathy.

We sought to examine whether elongation of the mitral valve leaflets in patients with hypertrophic cardiomyopathy (HCM) is synergistic to septal wall thickness (SWT) in the development of left ventricular outflow tract obstruction (LVOTO). HCM is a common genetic cardiac disease characterized by asymmetric septal hypertrophy and predisposition towards LVOTO. It has been reported that elongation of the mitral valve leaflets may be a primary phenotypic feature and contribute to LVOTO. However, the relative contribution of this finding versus SWT has not been studied. 152 patients (76 with HCM and 76 non-diseased age, race and BSA-matched controls) and 18 young, healthy volunteers were studied. SWT and the anterior mitral valve leaflet length (AMVLL) were measured using cine MRI. The combined contribution of these variables (SWT × AMVLL) was described as the Septal Anterior Leaflet Product (SALP). Peak LVOT pressure gradient was determined by Doppler interrogation and defined as "obstructive" if ≥ 30 mmHg. Patients with HCM were confirmed to have increased AMVLL compared with controls and volunteers (p < 0.01). Among HCM patients, both SWT and SALP were significantly higher in patients with LVOTO (N = 17) versus without. SALP showed modest improvement in predictive accuracy for LVOTO (AUC = 0.81) among the HCM population versus SWT alone (AUC = 0.77). However, in isolated patients this variable identified patients with LVOTO despite modest SWT. Elongation of the AMVLL is a primary phenotypic feature of HCM. While incremental contributions to LVOTO appear modest at a population level, specific patients may have dominant contribution to LVOTO. The combined marker of SALP allows for maintained identification of such patients despite modest increases in SWT.

Accelerated cardiac MR stress perfusion with radial sampling after physical exercise with an MR-compatible supine bicycle ergometer.

PURPOSE: To evaluate the feasibility of accelerated cardiac MR (CMR) perfusion with radial sampling using nonlinear image reconstruction after exercise on an MR-compatible supine bike ergometer. METHODS: Eight healthy subjects were scanned on two separate days using radial and Cartesian CMR perfusion sequences in rest and exercise stress perfusion. Four different methods (standard gridding, conjugate gradient SENSE [CG-SENSE], nonlinear inversion with joint estimation of coil-sensitivity profiles [NLINV] and compressed sensing with a total variation constraint [TV]) were compared for the reconstruction of radial data. Cartesian data were reconstructed using SENSE. All images were assessed by two blinded readers in terms of image quality and diagnostic value. RESULTS: CG-SENSE and NLINV were scored more favorably than TV (in both rest and stress perfusion cases, P < 0.05) and gridding (for rest perfusion cases, P < 0.05). TV images showed patchy artifacts, which negatively influenced image quality especially in the stress perfusion images acquired with a low number of radial spokes. Although CG-SENSE and NLINV received better scores than Cartesian sampling in both rest and exercise stress perfusion cases, these differences were not statistically significant (P > 0.05). CONCLUSION: We have demonstrated the feasibility of accelerated CMR perfusion using radial sampling after physical exercise using a supine bicycle ergometer in healthy subjects. For reconstruction of undersampled radial perfusion, CG-SENSE and NLINV resulted in better image quality than standard gridding or TV reconstruction. Further technical improvements and clinical assessment are needed before using this approach in patients with suspected coronary artery disease.

Low pregnenolone sulphate plasma concentrations in patients with generalized social phobia.

BACKGROUND: Animal studies have shown that neuroactive steroids modulate the activity of the gamma-aminobutyric acid type A/benzodiazepine receptor complex and that these steroids display anxiolytic or anxiogenic activity depending on their positive (e.g. allopregnanolone) or negative allosteric modulation (e.g. dehydroepiandrosterone sulphate) of this receptor. This study compared plasma levels of allopregnanolone, dehydroepiandrosterone sulphate and pregnenolone sulphate in healthy controls and in patients with generalized social phobia, as assessed with the Mini-International Neuropsychiatric Interview. METHODS: Plasma concentrations of allopregnanolone, pregnenolone sulphate, and dehydroepiandrosterone sulphate were measured in 12 unmedicated male patients with generalized social phobia and 12 matched healthy male volunteers. RESULTS: Concentrations of pregnenolone sulphate were significantly lower in patients with generalized social phobia than in healthy controls. No statistically significant differences were found for the concentrations of allopregnanolone and dehydroepiandrosterone sulphate in plasma. CONCLUSIONS: These results are particularly interesting since we also observed lower pregnenolone sulphate concentrations in male patients suffering from generalized anxiety disorder. Their relevance to the pathophysiology of social anxiety disorder remains to be determined.