Left ventricular modelling: a quantitative functional assessment tool based on cardiac magnetic resonance imaging

Abstract

We present the development and testing of a semi-automated tool to support the diagnosis of left ventricle (LV) dysfunctions from cardiac magnetic resonance (CMR). CMR short-axis images of the LVs were obtained in 15 patients and processed to detect endocardial and epicardial contours and compute volume, mass and regional wall motion (WM). Results were compared with those obtained from manual tracing by an expert cardiologist. Nearest neighbour tracking and finite-element theory were merged to calculate local myocardial strains and torsion. The method was tested on a virtual phantom, on a healthy LV and on two ischaemic LVs with different severity of the pathology. Automated analysis of CMR data was feasible in 13/15 patients: computed LV volumes and wall mass correlated well with manually extracted data. The detection of regional WM abnormalities showed good sensitivity (77.8%), specificity (85.1%) and accuracy (82%). On the virtual phantom, computed local strains differed by less than 14 per cent from the results of commercial finite-element solver. Strain calculation on the healthy LV showed uniform and synchronized circumferential strains, with peak shortening of about 20 per cent at end systole, progressively higher systolic wall thickening going from base to apex, and a 108 torsion. In the two pathological LVs, synchronicity and homogeneity were partially lost, anomalies being more evident for the more severely injured LV. Moreover, LV torsion was dramatically reduced. Preliminary testing confirmed the validity of our approach, which allowed for the fast analysis of LV function, even though future improvements are possible.

Sensitivity analysis of ventricular activation and electrocardiogram in tailored models of heart-failure patients

“…Late gadolinium-enhancement images for scar detection were obtained 10 minutes after intravenous infusion of gadolinium (Gadobutrol, 0.2 mmol/kg body weight) using a T-weighted gradient-echo pulse sequence with a phase-sensitive inversion recovery reconstruction. Semi-automatic segmentation of ventricular epicardium and endocardium was performed as described in a previous study [12]. Atrial cavities, aorta, inferior and superior vena cava, and pulmonary trunk were manually segmented using custom-made software for tracing contours.…”

Left ventricular strain and its pattern estimated from cine CMR and validation with DENSE

“…The estimated strains from the same animals were highly correlated to the values derived from implanted markers. Conti et al (2011) calculated strains with point tracking from cine CMR with a virtual heart phantom, and compared these data with strains from a finite element model. They found that local strains agreed well with the results from finite element modelling.…”

Images as drivers of progress in cardiac computational modelling

“…The possibility of obtaining accurate anatomical and functional information from a single imaging modality, the relatively “clean” appearance of the images (in comparison to echocardiography), and the relatively widespread availability of MRI have led to its use in many cardiac modelling studies, e.g. ( Plank et al., 2009; Vadakkumpadan et al., 2009; Conti et al., 2011; Bishop and Plank, 2012; Krishnamurthy et al., 2013 ), as will be illustrated in more detail in Sections 3 and 4 below.…”