Automated regional wall motion abnormality detection by combining rest and stress cardiac MRI: Correlation with contrast‐enhanced MRI

Abstract

Purpose: To correlate an automated regional wall motion abnormality (RWMA) detection method based on combined rest and dobutamine-stress cardiac MRI with the assessment of myocardial infarction from contrastenhanced MRI (CE-MRI), and to demonstrate that adding stress data improves the detection of scar segments compared with rest data alone. Materials and Methods:An automated RWMA detection method was built based on a statistical model of normokinetic myocardium from 41 healthy volunteers. The method was adapted to detect changes in RWMA from rest to stress. Twelve patients with myocardial infarction were included in the experiment. The correlation with CE-MRI was performed on two measurements: infarct transmurality and scar detection. Results:Compared with infarct transmurality, the probability of normokinetic motion decreased progressively as infarct transmurality increased. These probability values were 0.59 for non-scar segments, for <25% transmurality was 0.4 (SE ¼ 0.04), for 25-50% was 0.33 (SE ¼ 0.03), for 50-75% was 0.21 (SE ¼ 0.03) and for !75% was 0.10 (SE ¼ 0.03). For scar tissue detection, adding stress data significantly improved the performance (P < 0.001, confidence interval ¼ 99.9%). The sensitivity, specificity, and accuracy increased by 34%, 30%, and 32%, respectively. The area under the receiver operating characteristics curve was 0.63 when rest-only data was used, but it was improved to 0.87 when stress data was added. Conclusion:The presented automated RWMA assessment was capable of detecting wall motion improvements from rest to stress. The method correlated well with infarct transmurality from CE-MRI. Detection of scar regions was more accurate when rest and stress data were combined compared with rest data alone.

Automatic detection of anatomical landmarks on the knee joint using MRI data

“…Several methods of automated landmark detection have been proposed for medical images. The methods described in involve an optimization problem in detecting landmarks or morphological abnormality. The methods offer good landmark detection accuracy (within five pixels).…”

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.…”