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Myocardium

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Machine learning based deconvolution of microarray atrial samples from atrial fibrillation patients reveals increased fractions of follicular CD4+ T lymphocytes and gamma-delta T cells.

Journal of physiology and pharmacology : an official journal of the Polish Physiological Society
A potential relationship between T cell immunity and development of atrial fibrillation (AF) has been proposed. Historically in AF patients it has been reported that peripheral blood had elevated CD4+ T cells. However few studies have explored whethe...

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 methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge.

Medical image analysis
A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min aft...

An Improved 3D Deep Learning-Based Segmentation of Left Ventricular Myocardial Diseases from Delayed-Enhancement MRI with Inclusion and Classification Prior Information U-Net (ICPIU-Net).

Sensors (Basel, Switzerland)
Accurate segmentation of the myocardial scar may supply relevant advancements in predicting and controlling deadly ventricular arrhythmias in subjects with cardiovascular disease. In this paper, we propose the architecture of inclusion and classifica...

Estimation of left ventricular parameters based on deep learning method.

Mathematical biosciences and engineering : MBE
Estimating material properties of personalized human left ventricular (LV) modelling is a central problem in biomechanical studies. In this work we use deep learning (DL) method to evaluating the passive myocardial mechanical properties inversely. In...

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...

Estimating Echocardiographic Myocardial Strain of Left Ventricle with Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The global longitudinal strain of the myocardial tissue has been shown to be a better indicator of cardiac pathologies in the subclinical stage than other indices, such as the ejection fraction. This article presents a new deep learning approach for ...

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...

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...