The use of cardiovascular magnetic resonance in acute myocardial infarction

Overview of complications of acute and chronic myocardial infarctions: revisiting pathogenesis and cross-sectional imaging

Myocardial infarction (MI) remains one of the leading contributors to overall mortality and morbidity in the modern world, even with recent advances in medicine. Various complications can arise following an MI, particularly with delayed or inadequate treatment. Even though many of these complications are uncommon, they can have a significant impact on patient outcomes. Some of these complications can be diagnosed based on clinical, laboratory and echocardiographic evaluation. Other times, however, cardiac MR and multidetector CT are necessary in their diagnosis and proper evaluation. Accurate detection of these complications is an important aspect of optimising prompt and effective patient care, leading to better clinical outcomes. It is the goal of this article to review the role of cross-sectional imaging in patients with post-MI as well as the characteristic imaging findings and differential diagnosis of common and uncommon complications of MI.

Comparison of diffusion-weighted with T2-weighted imaging for detection of edema in acute myocardial infarction

BACKGROUND

Recent studies, performed with the use of a commercially available diffusion weighted imaging (DWI) sequence, showed that they are sensitive to the increase of water content in the myocardium and may be used as an alternative to the standard T2-weighted sequences. The aim of this study was to compare two methods of myocardial edema imaging: DWI and T2-TIRM.

METHODS

The study included 91 acute and post STEMI patients. We applied a qualitative and quantitative image analysis. The qualitative analysis consisted of evaluation of the quality of blood suppression, presence of artifacts and occurrence of high signal (edema) areas. On the basis of edema detection in AMI and control (post STEMI) group, the sensitivity and specificity of TIRM and DWI were determined. Two contrast to noise ratios (CNR) were calculated: CNR1--the contrast between edema and healthy myocardium and CNR2--the contrast between edema and intraventricular blood pool. The area of edema was measured for both TIRM and DWI sequences and compared with the infarct size in LGE images.

RESULTS

Edema occurred more frequently in the DWI sequence. A major difference was observed in the inferior wall, where an edema-high signal was observed in 46% in T2-TIRM, whereas in the DWI sequence in 85%. An analysis of the image quality parameters showed that the use of DWI sequence allows complete blood signal suppression in the left ventricular cavity and reduces the occurrence of motion artifacts. However, it is connected with a higher incidence of magnetic susceptibility artifacts and image distortion. An analysis of the CNRs showed that CNR1 in T2-TIRM sequence depends on the infarct location and has the lowest value for the inferior wall. The area of edema measured on DWI images was significantly larger than in T2-TIRM.

CONCLUSIONS

DWI is a new technique for edema detection in patients with acute myocardial infarction which may be recommended for the diagnosis of acute injuries, especially in patients with slow-flow artifacts in TIRM images.

Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long- and short-axis information

Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in LGE CMR images. Given myocardial contours in cine images as a priori knowledge, the framework initially propagates the a priori segmentation from cine to LGE images via 2D translational registration. Two meshes representing respectively endocardial and epicardial surfaces are then constructed with the propagated contours. After construction, the two meshes are deformed towards the myocardial edge points detected in both short-axis and long-axis LGE images in a unified 3D coordinate system. Taking into account the intensity characteristics of the LV in LGE images, we propose a novel parametric model of the LV for consistent myocardial edge points detection regardless of pathological status of the myocardium (infarcted or healthy) and of the type of the LGE images (short-axis or long-axis). We have evaluated the proposed framework with 21 sets of real patient and four sets of simulated phantom data. Both distance- and region-based performance metrics confirm the observation that the framework can generate accurate and reliable results for myocardial segmentation of LGE images. We have also tested the robustness of the framework with respect to varied a priori segmentation in both practical and simulated settings. Experimental results show that the proposed framework can greatly compensate variations in the given a priori knowledge and consistently produce accurate segmentations.

A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly visualize nonviable myocardium with hyperenhanced intensities with respect to normal myocardium. For heart attack patients, it is crucial to facilitate the decision of appropriate therapy by analyzing and quantifying their LGE CMR images. To achieve accurate quantification, LGE CMR images need to be processed in two steps: segmentation of the myocardium followed by classification of infarcts within the segmented myocardium. However, automatic segmentation is difficult usually due to the intensity heterogeneity of the myocardium and intensity similarity between the infarcts and blood pool. Besides, the slices of an LGE CMR dataset often suffer from spatial and intensity distortions, causing further difficulties in segmentation and classification. In this paper, we present a comprehensive 3-D framework for automatic quantification of LGE CMR images. In this framework, myocardium is segmented with a novel method that deforms coupled endocardial and epicardial meshes and combines information in both short- and long-axis slices, while infarcts are classified with a graph-cut algorithm incorporating intensity and spatial information. Moreover, both spatial and intensity distortions are effectively corrected with specially designed countermeasures. Experiments with 20 sets of real patient data show visually good segmentation and classification results that are quantitatively in strong agreement with those manually obtained by experts.