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Myocardial Perfusion Imaging

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Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease.

Computational and mathematical methods in medicine
The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the ...

Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest.

Medical image analysis
Cardiac computed tomography angiography (CTA) is a non-invasive method for anatomic evaluation of coronary artery stenoses. However, CTA is prone to artifacts that reduce the diagnostic accuracy to identify stenoses. Further, CTA does not allow for d...

Diagnostic Performance of Artificial Neural Network for Detecting Ischemia in Myocardial Perfusion Imaging.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: The purpose of this study was to apply an artificial neural network (ANN) in patients with coronary artery disease (CAD) and to characterize its diagnostic ability compared with conventional visual and quantitative methods in myocardial p...

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...

Deep Learning-based Quantitative CT Myocardial Perfusion Imaging and Risk Stratification of Coronary Artery Disease.

Radiology
Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantificat...

Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study.

European journal of clinical investigation
BACKGROUND: Machine learning-based analysis can be used in myocardial perfusion imaging data to improve risk stratification and the prediction of major adverse cardiovascular events for patients with suspected or established coronary artery disease. ...

Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by 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 dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces ...

Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion.

Journal of thoracic imaging
During the latest years, artificial intelligence, and especially machine learning (ML), have experienced a growth in popularity due to their versatility and potential in solving complex problems. In fact, ML allows the efficient handling of big volum...

[Perfusion-Metabolic Myocardial Scintigraphy in Prognosis of Left Ventricular Remodeling After Complex Surgical Treatment of Ischemic Cardiomyopathy].

Kardiologiia
PURPOSE: To study capabilities of perfusion-metabolic myocardial scintigraphy for prediction of the left ventricular (LV) reverse remodeling after comprehensive surgical treatment of ischemic cardiomyopathy (ICMP).