AIMC Topic: Myocardium

Clear Filters Showing 41 to 50 of 115 articles

Artificial Muscles and Soft Robotic Devices for Treatment of End-Stage Heart Failure.

Advanced materials (Deerfield Beach, Fla.)
Medical soft robotics constitutes a rapidly developing field in the treatment of cardiovascular diseases, with a promising future for millions of patients suffering from heart failure worldwide. Herein, the present state and future direction of artif...

Deep Learning-Based Image Registration in Dynamic Myocardial Perfusion CT Imaging.

IEEE transactions on medical imaging
Registration of dynamic CT image sequences is a crucial preprocessing step for clinical evaluation of multiple physiological determinants in the heart such as global and regional myocardial perfusion. In this work, we present a deformable deep learni...

Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.

Magnetic resonance in medicine
PURPOSE: To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoes (DENSE) and to use the model to develop a deep convolutional neural network to aid in first-order ...

Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.

Mathematical biosciences and engineering : MBE
Myocardial contrast echocardiography (MCE) has been proposed as a method to assess myocardial perfusion for the detection of coronary artery diseases in a non-invasive way. As a critical step of automatic MCE perfusion quantification, myocardium segm...

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

Circulation
BACKGROUND: Myocardial scars are assessed noninvasively using cardiovascular magnetic resonance late gadolinium enhancement (LGE) as an imaging gold standard. A contrast-free approach would provide many advantages, including a faster and cheaper scan...

Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data.

Sensors (Basel, Switzerland)
Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world's population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and ...

Impact of deep learning architectures on accelerated cardiac T mapping using MyoMapNet.

NMR in biomedicine
The objective of the current study was to investigate the performance of various deep learning (DL) architectures for MyoMapNet, a DL model for T estimation using accelerated cardiac T mapping from four T -weighted images collected after a single inv...

Provisional Decision-Making for Perioperative Blood Pressure Management: A Narrative Review.

Oxidative medicine and cellular longevity
Blood pressure (BP) is a basic determinant for organ blood flow supply. Insufficient blood supply will cause tissue hypoxia, provoke cellular oxidative stress, and to some extent lead to organ injury. Perioperative BP is labile and dynamic, and intra...

Deep Learning-based Post Hoc CT Denoising for Myocardial Delayed Enhancement.

Radiology
Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)-based post hoc denoising method supervised with averaged MDE CT data was developed. Purpose To assess the image quality of denoised MDE CT images and evaluate their d...

Motion correction for native myocardial T mapping using self-supervised deep learning registration with contrast separation.

NMR in biomedicine
In myocardial T mapping, undesirable motion poses significant challenges because uncorrected motion can affect T estimation accuracy and cause incorrect diagnosis. In this study, we propose and evaluate a motion correction method for myocardial T map...