IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 27, 2024
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme using physics-informed neural networks (PINNs) and a ...
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 27, 2024
The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data. This work reports on the development and validation of spatiotemporal deep learning models to asse...
BACKGROUND: Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize S...
Left ventricular (LV) global longitudinal strain (LVGLS) is versatile; however, it is difficult to obtain. We evaluated the potential of an artificial intelligence (AI)-generated electrocardiography score for LVGLS estimation (ECG-GLS score) to diagn...
PURPOSE: To develop an iterative deep learning (DL) reconstruction with spatio-coil regularization and multichannel k-space data consistency for accelerated cine imaging.
Medical & biological engineering & computing
Oct 17, 2024
Using echocardiography to assess the left ventricular function is one of the most crucial cardiac examinations in clinical diagnosis, and LV segmentation plays a particularly vital role in medical image processing as many important clinical diagnosti...
Cardiovascular engineering and technology
Oct 14, 2024
PURPOSE: Cardiovascular simulators are used in the preclinical testing phase of medical devices. Their reliability increases the more they resemble clinically relevant scenarios. In this study, a physiologically actuated soft robotic left ventricle (...
Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural networ...
OBJECTIVE: Achieving reliable automatic left ventricle (LV) segmentation from echocardiograms is challenging due to the inherent sparsity of annotations in the dataset, as clinicians typically only annotate two specific frames for diagnostic purposes...
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