Effect of T1-mapping technique and diminished image resolution on quantification of infarct mass and its ability in predicting appropriate ICD therapy

Prognostic Value of Entropy Derived from Late Gadolinium Enhancement Images to Adverse Cardiac Events in Post-Myocardial Infarction Patients

RATIONALE AND OBJECTIVES

To explore the prognostic value of entropy derived from late gadolinium enhancement images on cardiac magnetic resonance (CMR) for major adverse cardiac events (MACE) in post-myocardial infarction (MI) patients.

MATERIALS AND METHODS

Participants with MI underwent 3.0T CMR were retrospectively enrolled. CMR parameters, including the entropy of infarct core (IC), peri-infarct border zone (BZ), and infarct core and peri-infarct border zone (IBZ) were analyzed. Patients were divided into the No-MACE group and the MACE group according to the absence or presence of MACE during the follow-up period.

RESULTS

Eighty-four patients were included, among whom 51 patients without MACE and 33 patients with MACE. The MACE group showed higher IC mass, IBZ mass, IC entropy, BZ entropy, IBZ entropy, and LV entropy and lower LVEF than those of the NO-MACE group. LVEF, BZ entropy, and IBZ entropy were independent predictors of MACE (p < 0.05). Receiver operating characteristic curve revealed that the predictive values of BZ entropy with AUC of 0.860, IBZ entropy with AUC of 0.930, the combined model of LVEF and BZ entropy with AUC of 0.923, and the combined model of LVEF and IBZ entropy with AUC of 0.954 were higher than that of LVEF with AUC of 0.797. Delong test illustrated there was no significant difference in AUC among the three models with AUC > 0.900 (p > 0.05).

CONCLUSION

BZ entropy and IBZ entropy were noninvasive parameters for better risk stratification of post-MI patients. MI Patients with MACE showed higher BZ entropy and IBZ entropy than patients without MACE.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism

OBJECTIVE

It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI.

METHODS

Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed.

RESULTS

By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively.

CONCLUSION

This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.

Assessment of left atrial fibrosis progression in canines following rapid ventricular pacing using 3D late gadolinium enhanced CMR images

Background

Atrial fibrillation (AF) is associated with extracellular matrix (ECM) remodelling and often coexists with myocardial fibrosis (MF); however, the causality of these conditions is not well established.

Objective

We aim to corroborate AF to MF causality by quantifying left atrial (LA) fibrosis in cardiac magnetic resonance (CMR) images after persistent rapid ventricular pacing and subsequent AF using a canine model and histopathological validation.

Methods

Twelve canines (9 experimental, 3 control) underwent baseline 3D LGE-CMR imaging at 3T followed by insertion of a pacing device and 5 weeks of rapid ventricular pacing to induce AF (experimental) or no pacing (control). Following the 5 weeks, pacing devices were removed to permit CMR imaging followed by excision of the hearts and histopathological imaging. LA myocardial segmentation was performed manually at baseline and post-pacing to permit volumetric %MF quantification using the image intensity ratio (IIR) technique, wherein fibrosis was defined as pixels > mean LA myocardium intensity + 2SD.

Results

Volumetric %MF increased by an average of 2.11 ± 0.88% post-pacing in 7 of 9 experimental dogs. While there was a significant difference between paired %MF measurements from baseline to post-pacing in experimental dogs (P = 0.019), there was no significant change in control dogs (P = 0.019 and P = 0.5, Wilcoxon signed rank tests). The median %MF for paced animals was significantly greater than that of non-paced dogs at the 5-week post-insertion time point (P = 0.009, Mann Whitney U test). Histopathological imaging yielded an average %MF of 19.42 ± 4.80% (mean ± SD) for paced dogs compared to 1.85% in one control dog.

Conclusion

Persistent rapid ventricular pacing and subsequent AF leads to an increase in LA fibrosis volumes measured by the IIR technique; however, quantification is limited by inherent image acquisition parameters and observer variability.

Multi-contrast volumetric imaging with isotropic resolution for assessing infarct heterogeneity: Initial clinical experience

BACKGROUND

To evaluate accelerated multi-contrast volumetric imaging with isotropic resolution reconstructed using low-rank and spatially varying edge-preserving constrained compressed sensing parallel imaging reconstruction (CP-LASER), for assessing infarct heterogeneity on post-infarction patients as a precursor to studies of utility for predicting ventricular arrhythmias.

METHODS

Eleven patients with prior myocardial infarction were included in the study. All subjects underwent cardiovascular magnetic resonance (CMR) scans including conventional two-dimensional late gadolinium enhancement (2D LGE) and three-dimensional multi-contrast late enhancement (3D MCLE) post-contrast. The extent of the infarct core and peri-infarct gray zone of a limited mid-ventricular slab were derived respectively by analyzing MCLE images with an isotropic resolution of 2.2 mm and an anisotropic resolution of 2.2×2.2×8.8 mm 3 , and LGE images with a resolution of 1.37×2.7×8 mm 3 ; the respective measures across all subjects were statistically compared.

RESULTS

Using 3D MCLE, the infarct core size measured with isotropic resolution was similar to that measured with anisotropic resolution, while the peri-infarct gray zone size measured with isotropic resolution was smaller than that measured with anisotropic resolution ( p<0.001 , Cohen's dz=1.33 ). Isotropic 3D MCLE yielded a significantly smaller measure of the peri-infarct gray zone size than conventional 2D LGE ( p=0.0016 , Cohen's dz=1.20 ). Overall, we have successfully shown the utility of isotropic 3D MCLE in a pilot patient study. Our results suggest that smaller voxels lead to more accurate differentiation between isotropic 3D MCLE-derived gray zone and core infarct because of diminished partial volume effect.

CONCLUSION

The CP-LASER accelerated 3D MCLE with isotropic resolution can be used in patients and yields excellent delineation of infarct and peri-infarct gray zone characteristics.

Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

PURPOSE OF REVIEW

Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images.

RECENT FINDINGS

With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.