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Biomedical Imaging Technologies for Cardiovascular Disease

This reprint aims to provide an update on the biomedical imaging technologies that can be used for cardiovascular diseases, including Doppler echocardiography, magnetic resonance imaging and positron emission tomography. Cardiovascular disease imaging assessment includes anatomy, hemodynamics, and t...

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Guardat en:
Format: Online
Idioma:anglès
Publicat: MDPI - Multidisciplinary Digital Publishing Institute 2023
Matèries:
4D flow MRI
flow quantification
finite elements
left ventricle
dilated cardiomyopathy
hemodynamics
4D flow
self-gating
aorta
pulmonary hypertension
magnetic resonance imaging
4D-flow MRI
heart hemodynamics
aneurysm
hemodynamic
reproducibility
classification
left ventricular hypertrophy
CMR
machine learning
positron emission tomography
coronary heart disease
plaque imaging
perfusion imaging
viability imaging
18F-sodium fluoride
18F-fluorodeoxyglucose
Rubidium-82
13N-ammonia
15O-water
right ventricle
speckle-tracking
tricuspid tissue motion annular displacement
pulmonary arterial hypertension
children
diastolic disfunction
echocardiography
erectile dysfunction
nitric oxide signaling
ultrasmall superparamagnetic particles of iron oxide (USPIO)
cardiac magnetic resonance (CMR)
iron oxide nanoparticles
cardiac contractility modulation
right ventricular function
right ventricle–pulmonary artery coupling
n/a
thema EDItEUR::M Medicine and Nursing
thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJD Cardiovascular medicine
Accés en línia:ONIX_20230405_9783036570068_232
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Sumari:This reprint aims to provide an update on the biomedical imaging technologies that can be used for cardiovascular diseases, including Doppler echocardiography, magnetic resonance imaging and positron emission tomography. Cardiovascular disease imaging assessment includes anatomy, hemodynamics, and tissue biomarkers, which aid the stratification of patient risk and therapy. In addition, this Special Issue includes articles that discuss the recent integration of artificial intelligence and machine learning, in order to support novel approaches to personalized image-based diagnosis.

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