AIMC Topic: Myocardial Perfusion Imaging

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"Virtual" attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is widely used for coronary artery disease (CAD) evaluation. Although attenuation correction is recommended to diminish image artifacts and improve d...

Fully automated deep learning powered calcium scoring in patients undergoing myocardial perfusion imaging.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: To assess the accuracy of fully automated deep learning (DL) based coronary artery calcium scoring (CACS) from non-contrast computed tomography (CT) as acquired for attenuation correction (AC) of cardiac single-photon-emission computed to...

Multi-task Deep Learning of Myocardial Blood Flow and Cardiovascular Risk Traits from PET Myocardial Perfusion Imaging.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize...

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.

European journal of nuclear medicine and molecular imaging
PURPOSE: To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive eval...

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging.

NMR in biomedicine
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast an...

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Computational and mathematical methods in medicine
Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression ...

Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: Attenuation correction can improve the quantitative accuracy of single-photon emission computed tomography (SPECT) images. Existing SPECT-only systems normally can only provide non-attenuation corrected (NC) images which are susceptible t...

Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.

Computational and mathematical methods in medicine
We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery d...

Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: We previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dos...

Impact of train/test sample regimen on performance estimate stability of machine learning in cardiovascular imaging.

Scientific reports
As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare different algorithms. While the machine learning community has...