MULTIMODAL LEARNING TO IMPROVE CARDIAC LATE MECHANICAL ACTIVATION DETECTION FROM CINE MR IMAGES.

This paper presents a multimodal deep learning framework that utilizes advanced image techniques to improve the performance of clinical analysis heavily dependent on routinely acquired standard images. More specifically, we develop a joint learning network that for the first time leverages the accuracy and reproducibility of myocardial strains obtained from Displacement Encoding with Stimulated Echo (DENSE) to guide the analysis of cine cardiac magnetic resonance (CMR) imaging in late mechanical activation (LMA) detection. An image registration network is utilized to acquire the knowledge of cardiac motions, an important feature estimator of strain values, from standard cine CMRs.

Fatty acid composition MRI of epicardial adipose tissue: Methods and detection of proinflammatory biomarkers in ST-segment elevation myocardial infarction patients.

Purpose: To develop a method for quantifying the fatty acid composition (FAC) of human epicardial adipose tissue (EAT) using accelerated MRI and identify its potential for detecting proinflammatory biomarkers in patients with ST-segment elevation myocardial infarction (STEMI).

Methods: A multi-echo radial gradient-echo sequence was developed for accelerated imaging during a breath hold using a locally low-rank denoising technique to reconstruct undersampled images. FAC mapping was achieved by fitting the multi-echo images to a multi-resonance complex signal model based on triglyceride characterization.

In Vivo Quantification of Ascending Thoracic Aortic Aneurysm Wall Stretch Using MRI: Relationship to Repair Threshold Diameter and Ex Vivo Wall Failure Behavior.

Ascending thoracic aortic aneurysms (aTAAs) can lead to life-threatening dissection and rupture. Recent studies have highlighted aTAA mechanical properties as relevant factors associated with progression. The aim of this study was to quantify in vivo aortic wall stretch in healthy participants and aTAA patients using displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging.

Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation.

Background: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.

Cardiac resynchronization therapy (CRT) can lead to marked symptom reduction and improved survival in selected patients with heart failure with reduced ejection fraction (HFrEF); however, many candidates for CRT based on clinical guidelines do not have a favorable response. A better way to identify patients expected to benefit from CRT that applies machine learning to accessible and cost-effective diagnostic tools such as the 12-lead electrocardiogram (ECG) could have a major impact on clinical care in HFrEF by helping providers personalize treatment strategies and avoid delays in initiation of other potentially beneficial treatments. This study addresses this need by demonstrating that a novel approach to ECG waveform analysis using functional principal component decomposition (FPCD) performs better than measures that require manual ECG analysis with the human eye and also at least as well as a previously validated but more expensive approach based on cardiac magnetic resonance (CMR).