Automated estimation of regional mean transition times and radial velocities from cine magnetic resonance images: Evaluation in normal subjects

Abstract

Purpose:To assess regional ventricular function via an accurate and automated definition of functional parameters. Materials and Methods:An automated method is proposed that estimates reliable regional normalized mean transition times (F mc ) and mean radial velocities (V m ) from cine images. This approach combines a quantitative parametric imaging method and an automated detection of the endocardial border, which is robust to the presence of papillary muscles and nonhomogeneities within the left ventricular cavity. Steadystate free-precession cine-magnetic resonance imaging (MRI) of 36 healthy subjects was analyzed.Results: The quality of the automated segmentation was assessed on a subgroup of 20 subjects by comparing the results with the manual contours traced by an expert. The comparison of functional parameters estimated consequently using the automated and the manual contours yielded (y ϭ 0.959x ϩ 0.016, R ϭ 0.964) for F mc and (y ϭ 0.883x ϩ 0.505, R ϭ 0.935) for V m . On the entire group, F mc was equal to 0.392 Ϯ 0.069 and V m to 5.4 Ϯ 2.3 cm/s. Increasing values of the temporal parameter from the apex to the base and larger values in the septal wall than in lateral wall were demonstrated and were in accordance with the physiology. Conclusion:The proposed method ensures an automated and robust assessment of regional wall motion parameters, which could be clinically useful.

Functional and anatomical medical image matching

“…In a future work, this method could be useful to accurately quantify the myocardial infarct extent on DE images to be then combined with segmental assessment of myocardial wall motion on cine images [12], so that could help to identify simultaneously motion abnormalities and myocardial viability in clinical studies.…”

Discriminative dictionary learning for local LV wall motion classification in cardiac MRI

“…The most relevant approaches that have been proposed for the automatic assessment of LV wall motion in cardiac cine-MRI can be classified in three main groups: i) Landmark-based shape analysis methods that provide a statistical shape modelling of cardiac contraction (Ordas & Frangi, 2005;Huang et al, 2006;Suinesiaputra et al, 2009Suinesiaputra et al, , 2011, ii) Methods based on image features extraction that consider the hypothesis that motion patterns of normal LV anatomical segments should be deviated away from patterns of abnormal LV segments (Lu et al, 2009;Punithakumar et al, 2013;Afshin et al, 2014), and iii) Methods based on parametric imaging-based quantification, that rely on the ability to integrate spatial and temporal information on LV wall motion in meaningful parametric images for motion abnormalities analysis (Caiani et al, 2004(Caiani et al, , 2006El-Berbari et al, 2009). Current techniques for LV wall motion assessment often consider a preprocessing step, followed by the segmentation of the myocardium, feature extraction and classification stages.…”

Regional myocardial radial velocities estimation from cine MR images Study of a patient-related coefficient effect

“…In a previous study [6], the parametric imaging-based quantification method was combined with an automated segmentation method [7] of the endocardial contour to automatically estimate mean time of contraction and radial velocity parameters on normal subjects. In this study, results of radial velocity for normal subjects were conforming to values in the literature [8,9].…”