Multiplanar strain quantification for assessment of right ventricular dysfunction and non-ischemic fibrosis among patients with ischemic mitral regurgitation

Clinical Utility of Strain Imaging in Assessment of Myocardial Fibrosis

Myocardial fibrosis (MF) is a non-reversible process that occurs following acute or chronic myocardial damage. MF worsens myocardial deformation, remodels the heart and raises myocardial stiffness, and is a crucial pathological manifestation in patients with end-stage cardiovascular diseases and closely related to cardiac adverse events. Therefore, early quantitative analysis of MF plays an important role in risk stratification, clinical decision, and improvement in prognosis. With the advent and development of strain imaging modalities in recent years, MF may be detected early in cardiovascular diseases. This review summarizes the clinical usefulness of strain imaging techniques in the non-invasive assessment of MF.

Absence of cardiac damage induced by long-term intensive endurance exercise training: A cardiac magnetic resonance and exercise echocardiography analysis in masters athletes

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Endurance long-term high level of training induces significant cardiac remodelling involving all cardiac chambers, also known as ‘athletes-heart”.

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Both left and right ventricular longitudinal strain increases significantly at exercise.

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Cardiac extracellular volume is normal in master athletes and there is no evidence of cardiac fibrosis induced by long term endurance training in master athletes.

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There is no evidence of cardiac damage induced by intensive endurance training in healthy asymptomatic master athletes.

Objectives

It is under debate whether the long-term practice of intensive endurance exercise induces chronic cardiac damage such as myocardial fibrosis and ventricle contractile dysfunction. Multimodality analysis was performed to evaluate myocardial damage induced by long term intensive endurance training in master athletes.

Methods

Thirty-three asymptomatic endurance master athletes (47 ± 6 year-old, 9,6 ± 1,7 h training/week for 26 ± 6 years), were compared to 18 sedentary controls (49 ± 7 year-old). They underwent a CMR protocol including 4 chambers morphological and late gadolinium-enhancement (LGE) analysis, left (LV) and right ventricular (RV) T1 mapping and calculation of cardiac extracellular volume (ECV). A maximal exercise echocardiography with left and right ventricular longitudinal global strain (LGS) analysis was performed. Cardiac biomarkers of fibrosis (high sensitive cardiac Troponin T, N-Terminal pro brain natriuretic peptide, N-terminal propeptide of procollagen type I and N-terminal propeptide of procollagen type III) were analysed.

Results

Athletes had larger left and right atrial volume, LV and RV end diastolic volume and increased LV and RV mass compared to controls. LGE was not found in athletes. Native T1 values of LV and RV were not significantly different in athletes compared with controls. ECV was normal in both groups (21,5%± 1,6% [18.3 – 23%] in athletes, 22%± 2,2% [18.5 – 27%] in controls). LV and RV peak exercise LGS values were higher in athletes. Cardiac biomarkers levels were normal.

Conclusion

Despite significant physiological cardiac remodelling, consistent with previous descriptions of athlete's heart, there was no evidence of myocardial fibrosis or exercise left or right ventricular dysfunction or cardiac fibrosis in endurance athletes. Our results are not supporting the hypothesis of deleterious cardiac effects induced by long term and intensive endurance exercise training.

The Predictive Value of Right Ventricular Longitudinal Strain in Pulmonary Hypertension, Heart Failure, and Valvular Diseases

Right ventricular (RV) systolic function has an important role in the prediction of adverse outcomes, including mortality, in a wide range of cardiovascular (CV) conditions. Because of complex RV geometry and load dependency of the RV functional parameters, conventional echocardiographic parameters such as RV fractional area change (FAC) and tricuspid annular plane systolic excursion (TAPSE), have limited prognostic power in a large number of patients. RV longitudinal strain overcame the majority of these limitations, as it is angle-independent, less load-dependent, highly reproducible, and measure regional myocardial deformation. It has a high predictive value in patients with pulmonary hypertension, heart failure, congenital heart disease, ischemic heart disease, pulmonary embolism, cardiomyopathies, and valvular disease. It enables detection of subclinical RV damage even when conventional parameters of RV systolic function are in the normal range. Even though cardiac magnetic resonance-derived RV longitudinal strain showed excellent predictive value, echocardiography-derived RV strain remains the method of choice for evaluation of RV mechanics primarily due to high availability. Despite a constantly growing body of evidence that support RV longitudinal strain evaluation in the majority of CV patients, its assessment has not become the part of the routine echocardiographic examination in the majority of echocardiographic laboratories. The aim of this clinical review was to summarize the current data about the predictive value of RV longitudinal strain in patients with pulmonary hypertension, heart failure and valvular heart diseases.

Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy

The diagnosis of cardiomyopathy states may benefit from machine-learning (ML) based approaches, particularly to distinguish those states with similar phenotypic characteristics. Three-dimensional myocardial deformation analysis (3D-MDA) has been validated to provide standardized descriptors of myocardial architecture and deformation, and may therefore offer appropriate features for the training of ML-based diagnostic tools. We aimed to assess the feasibility of automated disease diagnosis using a neural network trained using 3D-MDA to discriminate hypertrophic cardiomyopathy (HCM) from its mimic states: cardiac amyloidosis (CA), Anderson–Fabry disease (AFD), and hypertensive cardiomyopathy (HTNcm). 3D-MDA data from 163 patients (mean age 53.1 ± 14.8 years; 68 females) with left ventricular hypertrophy (LVH) of known etiology was provided. Source imaging data was from cardiac magnetic resonance (CMR). Clinical diagnoses were as follows: 85 HCM, 30 HTNcm, 30 AFD, and 18 CA. A fully-connected-layer feed-forward neural was trained to distinguish HCM vs. other mimic states. Diagnostic performance was compared to threshold-based assessments of volumetric and strain-based CMR markers, in addition to baseline clinical patient characteristics. Threshold-based measures provided modest performance, the greatest area under the curve (AUC) being 0.70. Global strain parameters exhibited reduced performance, with AUC under 0.64. A neural network trained exclusively from 3D-MDA data achieved an AUC of 0.94 (sensitivity 0.92, specificity 0.90) when performing the same task. This study demonstrates that ML-based diagnosis of cardiomyopathy states performed exclusively from 3D-MDA is feasible and can distinguish HCM from mimic disease states. These findings suggest strong potential for computer-assisted diagnosis in clinical practice.

Tissue-based markers of right ventricular dysfunction in ischemic mitral regurgitation assessed via stress cardiac magnetic resonance and three-dimensional echocardiography

Ischemic mitral regurgitation (iMR) augments risk for right ventricular dysfunction (RVDYS). Right and left ventricular (LV) function are linked via common coronary perfusion, but data is lacking regarding impact of LV ischemia and infarct transmurality-as well as altered preload and afterload-on RV performance. In this prospective multimodality imaging study, stress CMR and 3-dimensional echo (3D-echo) were performed concomitantly in patients with iMR. CMR provided a reference for RVDYS (RVEF < 50%), as well as LV function/remodeling, ischemia and infarction. Echo was used to test multiple RV performance indices, including linear (TAPSE, S'), strain (GLS), and volumetric (3D-echo) approaches. 90 iMR patients were studied; 32% had RVDYS. RVDYS patients had greater iMR, lower LVEF, larger global ischemic burden and inferior infarct size (all p < 0.05). Regarding injury pattern, RVDYS was associated with LV inferior ischemia and infarction (both p < 0.05); 80% of affected patients had substantial viable myocardium (< 50% infarct thickness) in ischemic inferior segments. Regarding RV function, CMR RVEF similarly correlated with 3D-echo and GLS (r = 0.81-0.87): GLS yielded high overall performance for CMR-evidenced RVDYS (AUC: 0.94), nearly equivalent to that of 3D-echo (AUC: 0.95). In multivariable regression, GLS was independently associated with RV volumetric dilation on CMR (OR - 0.90 [CI - 1.19 to - 0.61], p < 0.001) and 3D echo (OR - 0.43 [CI - 0.84 to - 0.02], p = 0.04). Among patients with iMR, RVDYS is associated with potentially reversible processes, including LV inferior ischemic but predominantly viable myocardium and strongly impacted by volumetric loading conditions.

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

BACKGROUND

Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.

METHODS

A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor.

RESULTS

Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25).

CONCLUSION

Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

Strain measurement on four-dimensional dynamic-ventilation CT: quantitative analysis of abnormal respiratory deformation of the lung in COPD

Purpose

Strain measurement is frequently used to assess myocardial motion in cardiac imaging. This study aimed to apply strain measurement to pulmonary motion observed by four-dimensional dynamic-ventilation computed tomography (CT) and to clarify motion abnormality in COPD.

Materials and methods

Thirty-two smokers, including ten with COPD, underwent dynamic-ventilation CT during spontaneous breathing. CT data were continuously reconstructed every 0.5 seconds. In the series of images obtained by dynamic-ventilation CT, five expiratory frames were identified starting from the peak inspiratory frame (first expiratory frame) and ending with the fifth expiratory frame. Strain measurement of the scanned lung was performed using research software that was originally developed for cardiac strain measurement and modified for assessing deformation of the lung. The measured strain values were divided by the change in mean lung density to adjust for the degree of expiration. Spearman’s rank correlation analysis was used to evaluate associations between the adjusted strain measurements and various spirometric values.

Results

The adjusted strain measurement was negatively correlated with FEV₁/FVC (ρ=−0.52, P<0.01), maximum mid-expiratory flow (ρ=−0.59, P<0.001), and peak expiratory flow (ρ=−0.48, P<0.01), suggesting that abnormal deformation of lung motion is related to various patterns of expiratory airflow limitation.

Conclusion

Abnormal deformation of lung motion exists in COPD patients and can be quantitatively assessed by strain measurement using dynamic-ventilation CT. This technique can be expanded to dynamic-ventilation CT in patients with various lung and airway diseases that cause abnormal pulmonary motion.

Value of tissue-tracking tricuspid annular plane by speckle-tracking echocardiography for the assessment of right ventricular systolic dysfunction

BACKGROUND

Assessment of right ventricular (RV) function remains challenging because of its complex geometry. Application of speckle-tracking echocardiography (STE) to the tricuspid annulus provides rapid and automated assessment of the midpoint of the tricuspid annular plane displacement (TAD). The aim of this study was to investigate the value of tissue-tracking TAD for the assessment of RV systolic dysfunction.

METHODS

We retrospectively studied 61 patients in whom RV ejection fraction (EF) measured by 3-dimensional echocardiography was performed. STE-derived displacement of the midpoint between the septal and lateral tricuspid annulus and its percentage of RV length at end-diastole (MTAD) were automatically assessed. We performed comparative analyses between the RVEF ≥45% group and the RVEF <45% group in each parameter for the assessment of RV systolic function.

RESULTS

MTAD was successfully assessed in 56 (91.2%). According to receiver operating characteristics analysis, RVEF <45% was best detected by MTAD <14.7% with area under curve (AUC) 0.97, sensitivity 93%, specificity 95%, followed by RV free wall longitudinal strain (AUC 0.86), RV fractional area change (AUC 0.84), tricuspid annular plane systolic excursion (AUC 0.79), and systolic peak velocity of tricuspid annulus (AUC 0.70), although there was no significant difference between MTAD and RV free wall strain (P = 0.14).

CONCLUSION

The present study showed that MTAD was simple index and useful for the assessment of RV systolic dysfunction.