Subclinical myocardial disease by cardiac magnetic resonance imaging and spectroscopy in healthy HIV/Hepatitis C virus-coinfected persons.

Objective The contribution of hepatitis C virus (HCV) infection to the risk of heart failure in human immunodeficiency virus (HIV)-coinfected persons is unknown. The objective was to characterize cardiac function and morphology in HIV-treated coinfected persons. Methods In a cross-sectional study, HIV-infected patients virologically suppressed on antiretroviral therapy without known cardiovascular disease or diabetes mellitus underwent cardiac magnetic resonance imaging and spectroscopy for measures of cardiac function, myocardial fibrosis, and steatosis. Results The study included 18 male patients with a median age of 44 years. Of these, 10 had untreated HCV coinfection and eight had HIV monoinfection. Global systolic and diastolic function in the cohort were normal, and median myocardial fat content was 0.48% (interquartile range 0.35-1.54). Left ventricular (LV) mass index and LV mass/volume ratio were significantly greater in the HIV/HCV-coinfected group compared with the HIV-monoinfected group. In the HIV-monoinfected group, there was more myocardial fibrosis as measured by extracellular volume fraction. Conclusions There were differences between HIV/HCV-coinfected and HIV-monoinfected patients in cardiac structure and morphology. Larger studies are needed to examine whether HIV and HCV independently contribute to mechanisms of heart failure

Myocardial strain in healthy adults across a broad age range as revealed by cardiac magnetic resonance imaging at 1.5 and 3.0T: associations of myocardial strain with myocardial region, age, and sex

Purpose: We assessed myocardial strain using cine displacement encoding with stimulated echoes (DENSE) using 1.5T and 3.0T MRI in healthy adults. Materials and Methods: Healthy adults without any history of cardiovascular disease underwent MRI at 1.5T and 3.0T within 2 days. The MRI protocol included b-SSFP, 2D cine-EPI-DENSE, and late gadolinium enhancement in subjects>45 years. Acquisitions were divided into 6 segments, global and segmental peak longitudinal and circumferential strain were derived and analyzed by field strength, age and gender. Results: 89 volunteers (mean age 44.8 ± 18.0 years, range: 18-87 years) underwent MRI at 1.5T, and 88 of these subjects underwent MRI at 3.0T (1.4±1.4 days between the scans). Compared with 3.0T, the magnitudes of global circumferential (-19.5±2.6% vs. -18.47±2.6%; p=0.001) and longitudinal (-12.47±3.2% vs -10.53±3.1%; p=0.004) strain were greater at 1.5T. At 1.5T, longitudinal strain was greater in females than in males: -10.17±3.4% vs. -13.67±2.4%; p=0.001. Similar observations occurred for circumferential strain at 1.5T (-18.72±2.2% vs. -20.10±2.7%; p=0.014) and at 3.0T (-17.92 ± 1.8% vs -19.1 ± 3.1%; p=0.047). At 1.5T, longitudinal and circumferential strain were not associated with age after accounting for sex (longitudinal strain p= 0.178, circumferential strain p= 0.733). At 3.0T, longitudinal and circumferential strain were associated with age. (p<0.05) Longitudinal strain values were greater in the apico-septal, basal-lateral and mid-lateral segments and circumferential strain in the inferior, infero-lateral and antero-lateral LV segments. Conclusion: Myocardial strain parameters as revealed by cine-DENSE at different MRI field strengths were associated with myocardial region, age and sex

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Presence of mechanical dyssynchrony in duchenne muscular dystrophy

<p>Abstract</p> <p>Background</p> <p>Cardiac dysfunction in boys with Duchenne muscular dystrophy (DMD) is a leading cause of death. Cardiac resynchronization therapy (CRT) has been shown to dramatically decrease mortality in eligible adult population with congestive heart failure. We hypothesized that mechanical dyssynchrony is present in DMD patients and that cardiovascular magnetic resonance (CMR) may predict CRT efficacy.</p> <p>Methods</p> <p>DMD patients (n = 236) were stratified into 4 groups based on age, diagnosis of DMD, left ventricular (LV) ejection fraction (EF), and presence of myocardial fibrosis defined as positive late gadolinum enhancement (LGE) compared to normal controls (n = 77). Dyssynchrony indices were calculated based on timing of CMR derived circumferential strain (e_cc). The calculated indices included cross-correlation delay (XCD), uniformity of strain (US), regional vector of variance (RVV), time to maximum strain (TTMS) and standard deviation (SD) of TTMS. Abnormal XCD value was defined as > normal + 2SD. US, RVV, TTMS and SD were calculated for patients with abnormal XCD.</p> <p>Results</p> <p>There was overall low prevalence of circumferential dyssynchrony in the entire DMD population; it increased to 17.1% for patients with abnormal EF and to 31.2% in the most advanced stage (abnormal EF with fibrosis). All but one DMD patient with mechanical dyssynchrony exhibited normal QRS duration suggesting absence of electrical dyssynchrony. The calculated US and RVV values (0.91 ± 0.09, 1.34 ± 0.48) indicate disperse rather than clustered dyssynchrony.</p> <p>Conclusion</p> <p>Mechanical dyssynchrony is frequent in boys with end stage DMD-associated cardiac dysfunction. It is associated with normal QRS complex as well as extensive lateral fibrosis. Based on these findings, it is unlikely that this patient population will benefit from CRT.</p

Subendocardial contractile impairment in chronic ischemic myocardium: assessment by strain analysis of 3T tagged CMR

<p>Abstract</p> <p>Background</p> <p>The purpose of this study was to quantify myocardial strain on the subendocardial and epicardial layers of the left ventricle (LV) using tagged cardiovascular magnetic resonance (CMR) and to investigate the transmural degree of contractile impairment in the chronic ischemic myocardium.</p> <p>Methods</p> <p>3T tagged CMR was performed at rest in 12 patients with severe coronary artery disease who had been scheduled for coronary artery bypass grafting. Circumferential strain (C-strain) at end-systole on subendocardial and epicardial layers was measured using the short-axis tagged images of the LV and available software (Intag; Osirix). The myocardial segment was divided into stenotic and non-stenotic segments by invasive coronary angiography, and ischemic and non-ischemic segments by stress myocardial perfusion scintigraphy. The difference in C-strain between the two groups was analyzed using the Mann-Whitney U-test. The diagnostic capability of C-strain was analyzed using receiver operating characteristics analysis.</p> <p>Results</p> <p>The absolute subendocardial C-strain was significantly lower for stenotic (-7.5 ± 12.6%) than non-stenotic segment (-18.8 ± 10.2%, p < 0.0001). There was no difference in epicardial C-strain between the two groups. Use of cutoff thresholds for subendocardial C-strain differentiated stenotic segments from non-stenotic segments with a sensitivity of 77%, a specificity of 70%, and areas under the curve (AUC) of 0.76. The absolute subendocardial C-strain was significantly lower for ischemic (-6.7 ± 13.1%) than non-ischemic segments (-21.6 ± 7.0%, p < 0.0001). The absolute epicardial C-strain was also significantly lower for ischemic (-5.1 ± 7.8%) than non-ischemic segments (-9.6 ± 9.1%, p < 0.05). Use of cutoff thresholds for subendocardial C-strain differentiated ischemic segments from non-ischemic segments with sensitivities of 86%, specificities of 84%, and AUC of 0.86.</p> <p>Conclusions</p> <p>Analysis of tagged CMR can non-invasively demonstrate predominant impairment of subendocardial strain in the chronic ischemic myocardium at rest.</p

A novel hierarchical template matching model for cardiac motion estimation

Cardiovascular disease diagnosis and prognosis can be improved by measuring patient-specific in-vivo local myocardial strain using Magnetic Resonance Imaging. Local myocardial strain can be determined by tracking the movement of sample muscles points during cardiac cycle using cardiac motion estimation model. The tracking accuracy of the benchmark Free Form Deformation (FFD) model is greatly affected due to its dependency on tunable parameters and regularisation function. Therefore, Hierarchical Template Matching (HTM) model, which is independent of tunable parameters, regularisation function, and image-specific features, is proposed in this article. HTM has dense and uniform points correspondence that provides HTM with the ability to estimate local muscular deformation with a promising accuracy of less than half a millimetre of cardiac wall muscle. As a result, the muscles tracking accuracy has been significantly (p<0.001) improved (30%) compared to the benchmark model. Such merits of HTM provide reliably calculated clinical measures which can be incorporated into the decision-making process of cardiac disease diagnosis and prognosis

Iron overload in polytransfused patients without heart failure is associated with subclinical alterations of systolic left ventricular function using cardiovascular magnetic resonance tagging

BACKGROUND: It remains incompletely understood whether patients with transfusion related cardiac iron overload without signs of heart failure exhibit already subclinical alterations of systolic left ventricular (LV) dysfunction. Therefore we performed a comprehensive evaluation of systolic and diastolic cardiac function in such patients using tagged and phase-contrast CMR. METHODS: 19 patients requiring regular blood transfusions for chronic anemia and 8 healthy volunteers were investigated using cine, tagged, and phase-contrast and T2* CMR. LV ejection fraction, peak filling rate, end-systolic global midventricular systolic Eulerian radial thickening and shortening strains as well as left ventricular rotation and twist, mitral E and A wave velocity, and tissue e' wave and E/e' wave velocity ratio, as well as isovolumic relaxation time and E wave deceleration time were computed and compared to cardiac T2*. RESULTS: Patients without significant iron overload (T2* > 20 ms, n = 9) had similar parameters of systolic and diastolic function as normal controls, whereas patients with severe iron overload (T2* 20 ms) or normal controls. Patients with moderate iron overload (T2* 10-20 ms, n = 5), had preserved ejection fraction (59 ± 6%, p = NS vs. pts. with T2* > 20 ms and controls), but showed reduced maximal LV rotational twist (1.8 ± 0.4 degrees). The magnitude of reduction of LV twist (r = 0.64, p < 0.001), of LV ejection fraction (r = 0.44, p < 0.001), of peak radial thickening (r = 0.58, p < 0.001) and of systolic (r = 0.50, p < 0.05) and diastolic twist and untwist rate (r = -0.53, p < 0.001) in patients were directly correlated to the logarithm of cardiac T2*. CONCLUSION: Multiple transfused patients with normal ejection fraction and without heart failure have subclinical alterations of systolic and diastolic LV function in direct relation to the severity of cardiac iron overload. Among all parameters, left ventricular twist is affected earliest, and has the highest correlation to log (T2*), suggesting that this parameter might be used to follow systolic left ventricular function in patients with iron overload

Comprehensive Echocardiographic and Cardiovascular Magnetic Resonance Evaluation Differentiates Between Patients with Heart Failure with Preserved Ejection Fraction, Hypertensive Patients and Healthy Controls

Objectives: The aim of this study was to investigate the utility of a comprehensive imaging protocol including echocardiography and cardiac magnetic resonance in the diagnosis and differentiation of hypertensive heart disease and heart failure with preserved ejection fraction (HFpEF). Background: Hypertension is present in up to 90% of patients with HFpEF and is a major etiological component. Despite current recommendations and diagnostic criteria for HFpEF, no noninvasive imaging technique has as yet shown the ability to identify any structural differences between patients with hypertensive heart disease and HFpEF. Methods: We conducted a prospective cross-sectional study of 112 well-characterized patients (62 with HFpEF, 22 with hypertension, and 28 healthy control subjects). All patients underwent cardiopulmonary exercise and biomarker testing and an imaging protocol including echocardiography with speckle-tracking analysis and cardiac magnetic resonance including T1 mapping pre- and post-contrast. Results: Echocardiographic global longitudinal strain (GLS) and extracellular volume (ECV) measured by cardiac magnetic resonance were the only variables able to independently stratify among the 3 groups of patients. ECV was the best technique for differentiation between hypertensive heart disease and HFpEF (ECV area under the curve: 0.88; GLS area under the curve: 0.78; p &#60; 0.001 for both). Using ECV, an optimal cutoff of 31.2% gave 100% sensitivity and 75% specificity. ECV was significantly higher and GLS was significantly reduced in subjects with reduced exercise capacity (lower peak oxygen consumption and higher minute ventilation–carbon dioxide production) (p &#60; 0.001 for both ECV and GLS). Conclusions: Both GLS and ECV are able to independently discriminate between hypertensive heart disease and HFpEF and identify patients with prognostically significant functional limitation. ECV is the best diagnostic discriminatory marker of HFpEF and could be used as a surrogate endpoint for therapeutic studies

Effects of steroids and angiotensin converting enzyme inhibition on circumferential strain in boys with Duchenne muscular dystrophy: a cross-sectional and longitudinal study utilizing cardiovascular magnetic resonance

<p>Abstract</p> <p>Background</p> <p>Steroid use has prolonged ambulation in Duchenne muscular dystrophy (DMD) and combined with advances in respiratory care overall management has improved such that cardiac manifestations have become the major cause of death. Unfortunately, there is no consensus for DMD-associated cardiac disease management. Our purpose was to assess effects of steroid use alone or in combination with angiotensin converting enzyme inhibitors (ACEI) or angiotension receptor blocker (ARB) on cardiovascular magnetic resonance (CMR) derived circumferential strain (ε_cc).</p> <p>Methods</p> <p>We used CMR to assess effects of corticosteroids alone (Group A) or in combination with ACEI or ARB (Group B) on heart rate (HR), left ventricular ejection fraction (LVEF), mass (LVM), end diastolic volume (LVEDV) and circumferential strain (ε_cc) in a cohort of 171 DMD patients >5 years of age. Treatment decisions were made independently by physicians at both our institution and referral centers and not based on CMR results.</p> <p>Results</p> <p>Patients in Group A (114 studies) were younger than those in Group B (92 studies)(10 ± 2.4 vs. 12.4 ± 3.2 years, p < 0.0001), but HR, LVEF, LVEDV and LVM were not different. Although ε_ccmagnitude was lower in Group B than Group A (-13.8 ± 1.9 vs. -12.8 ± 2.0, p = 0.0004), age correction using covariance analysis eliminated this effect. In a subset of patients who underwent serial CMR exams with an inter-study time of ~15 months, ε_ccworsened regardless of treatment group.</p> <p>Conclusions</p> <p>These results support the need for prospective clinical trials to identify more effective treatment regimens for DMD associated cardiac disease.</p

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis