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Myocardial Perfusion Imaging

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A method using deep learning to discover new predictors from left-ventricular mechanical dyssynchrony for CRT response.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Studies have shown that the conventional parameters characterizing left ventricular mechanical dyssynchrony (LVMD) measured on gated SPECT myocardial perfusion imaging (MPI) have their own statistical limitations in predicting cardiac res...

Deep Learning-Based Attenuation Correction Improves Diagnostic Accuracy of Cardiac SPECT.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
To improve diagnostic accuracy, myocardial perfusion imaging (MPI) SPECT studies can use CT-based attenuation correction (AC). However, CT-based AC is not available for most SPECT systems in clinical use, increases radiation exposure, and is impacted...

Deep Learning for Explainable Estimation of Mortality Risk From Myocardial Positron Emission Tomography Images.

Circulation. Cardiovascular imaging
BACKGROUND: We aim to develop an explainable deep learning (DL) network for the prediction of all-cause mortality directly from positron emission tomography myocardial perfusion imaging flow and perfusion polar map data and evaluate it using prospect...

Polar map-free 3D deep learning algorithm to predict obstructive coronary artery disease with myocardial perfusion CZT-SPECT.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep learning (DL) models have been shown to outperform total perfusion deficit (TPD) quantification in predicting obstructive coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, previously published methods have ...

Deep learning for myocardial ischemia auxiliary diagnosis using CZT SPECT myocardial perfusion imaging.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: The World Health Organization reported that cardiovascular disease is the most common cause of death worldwide. On average, one person dies of heart disease every 26 min worldwide. Deep learning approaches are characterized by the appropr...

Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning.

JACC. Cardiovascular imaging
BACKGROUND: Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed.

Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.

European journal of nuclear medicine and molecular imaging
PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the ...

AI-based classification algorithms in SPECT myocardial perfusion imaging for cardiovascular diagnosis: a review.

Nuclear medicine communications
In the last few years, deep learning has made a breakthrough and established its position in machine learning classification problems in medical image analysis. Deep learning has recently displayed remarkable applicability in a range of different med...

Multi-center, multi-vendor validation of deep learning-based attenuation correction in SPECT MPI: data from the international flurpiridaz-301 trial.

European journal of nuclear medicine and molecular imaging
PURPOSE: Although SPECT myocardial perfusion imaging (MPI) is susceptible to artifacts from soft tissue attenuation, most scans are performed without attenuation correction. Deep learning-based attenuation corrected (DLAC) polar maps improved diagnos...

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...