Time-resolved tracking of the atrioventricular plane displacement in Cardiovascular Magnetic Resonance (CMR) images

Abstract

BackgroundAtrioventricular plane displacement (AVPD) is an indicator for systolic and diastolic function and accounts for 60% of the left ventricular, and 80% of the right ventricular stroke volume. AVPD is commonly measured clinically in echocardiography as mitral and tricuspid annular plane systolic excursion (MAPSE and TAPSE), but has not been applied widely in cardiovascular magnetic resonance (CMR). To date, there is no robust automatic algorithm available that allows the AVPD to be measured clinically in CMR with input in a single timeframe. This study aimed to develop, validate and provide a method that automatically tracks the left and right ventricular AVPD in CMR images, which can be used in the clinical setting or in applied cardiovascular research in multi-center studies.MethodsThe proposed algorithm is based on template tracking by normalized cross-correlation combined with a priori information by principal component analysis. The AVPD in each timeframe is calculated for the left and right ventricle separately using CMR long-axis cine images of the 2, 3, and 4-chamber views.The algorithm was developed using a training set (n = 40), and validated in a test set (n = 113) of healthy subjects, athletes, and patients after ST-elevation myocardial infarction from 10 centers. Validation was done using manual measurements in end diastole and end systole as reference standard. Additionally, AVPD, peak emptying velocity, peak filling velocity, and atrial contraction was validated in 20 subjects, where time-resolved manual measurements were used as reference standard. Inter-observer variability was analyzed in 20 subjects.ResultsIn end systole, the difference between the algorithm and the reference standard in the left ventricle was (mean ± SD) -0.6 ± 1.9 mm (R = 0.79), and −0.8 ± 2.1 mm (R = 0.88) in the right ventricle. Inter-observer variability in end systole was −0.6 ± 0.7 mm (R = 0.95), and −0.5 ± 1.4 mm (R = 0.95) for the left and right ventricle, respectively. Validation of peak emptying velocity, peak filling velocity, and atrial contraction yielded lower accuracy than the displacement measures.ConclusionsThe proposed algorithm show good agreement and low bias with the reference standard, and with an agreement in parity with inter-observer variability. Thus, it can be used as an automatic method of tracking and measuring AVPD in CMR.Electronic supplementary materialThe online version of this article (doi:10.1186/s12880-017-0189-5) contains supplementary material, which is available to authorized users.

Assessment of diastolic function and atrial remodeling by MRI - validation and correlation with echocardiography and filling pressure

“…Furthermore, our central hypothesis, that LV filling pressure is correlated with LA fibrosis, was demonstrated using these MRI-based measures. Evaluation of e 0 by feature-tracking has been proposed and validated (Leng et al 2015;Seemann et al 2017). For example, Leng et al (2015) obtained strong agreement when validating e 0 by MRI to TTE, as did this study which used a freely available image analysis tool.…”

“…and R.G. using Segment v2.2 R6324(http://segme nt.heiberg.se) (Heiberg et al 2010;Seemann et al 2017).…”

“…; Saba et al ; Seemann et al. ). For example, quantification of LA volumes is established (Wylie et al.…”

“…In cardiovascular magnetic resonance imaging (MRI), methods exist that can measure those diastolic parameters (Paelinck et al 2005;Bollache et al 2010;Duarte and Fernandez 2010;Leng et al 2015;Saba et al 2014;Seemann et al 2017). For example, quantification of LA volumes is established (Wylie et al 2008;Maceira et al 2010), but there is a lack of standardized methods for MRI assessment of diastolic parameters linked to valvular flow and motion (i.e., E, A, and especially e 0 ).…”

Valvular imaging in the era of feature‐tracking: A slice‐following cardiac MR sequence to measure mitral flow

“…In this current era of feature‐tracking, we propose a retrospectively gated PC sequence that obtains slice‐following by prospective updates of the imaging slice, in real‐time, based on offline valvular feature‐tracking . This method also enables correction for valvular through‐plane motion.…”