AIMC Topic: Heart

Clear Filters Showing 421 to 430 of 499 articles

Noninvasive reconstruction of cardiac transmembrane potentials using a kernelized extreme learning method.

Physics in medicine and biology
Non-invasively reconstructing the cardiac transmembrane potentials (TMPs) from body surface potentials can act as a regression problem. The support vector regression (SVR) method is often used to solve the regression problem, however the computationa...

Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
OBJECTIVE: We aimed to investigate if early revascularization in patients with suspected coronary artery disease can be effectively predicted by integrating clinical data and quantitative image features derived from perfusion SPECT (MPS) by machine l...

Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography.

IEEE transactions on bio-medical engineering
OBJECTIVE: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized computed tomography angiography gating (CTA).

Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI.

IEEE transactions on bio-medical engineering
OBJECTIVE: Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. Th...

Volumetric Medical Image Segmentation Through Dual Self-Distillation in U-Shaped Networks.

IEEE transactions on bio-medical engineering
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in U-shaped networks for volumetric medical image segmentation. DSD dist...

Adaptive Cardiorespiratory Separation With Harmonic Models and Filters: The Case of Electrical Impedance Tomography.

IEEE transactions on bio-medical engineering
Cardiorespiratory monitoring methods are vital in clinical and personal healthcare contexts, continuously delivering comprehensive insights into patient health. Among them, electrical impedance tomography, a non-invasive imaging modality, uniquely en...

Multifrequency Time-Dependent Deep Image Prior for Real-Time Free-Breathing Cardiac Imaging.

NMR in biomedicine
The aim of this study is to enable high temporal resolution functional cardiac imaging without breathholds or electrocardiogram (ECG) gating. Real-time MRI is essential for assessing heart function in patients with limited breathhold capacity or arrh...

Exploring advanced deep learning approaches in cardiac image analysis: A comprehensive review.

Computers in biology and medicine
BACKGROUND: Cardiac image analysis plays an important role in detecting and categorizing cardiovascular diseases (CVDs), such as coronary artery disease (CAD), heart failure, congenital heart defects, arrhythmias (irregular heartbeat), and valvular h...

Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta.

Radiological physics and technology
Magnetic resonance imaging (MRI) is time-consuming, posing challenges in capturing clear images of moving organs, such as cardiac structures, including complex structures such as the Valsalva sinus. This study evaluates a computed tomography (CT)-gui...