AIMC Topic: Perfusion

Clear Filters Showing 51 to 60 of 74 articles

Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence.

Stroke
BACKGROUND AND PURPOSE: The degree to which the coronavirus disease 2019 (COVID-19) pandemic has affected systems of care, in particular, those for time-sensitive conditions such as stroke, remains poorly quantified. We sought to evaluate the impact ...

Automatic attenuation map estimation from SPECT data only for brain perfusion scans using convolutional neural networks.

Physics in medicine and biology
In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq.[Formula: see text]). This correction generally...

Quantification of intravoxel incoherent motion with optimized b-values using deep neural network.

Magnetic resonance in medicine
PURPOSE: To develop a framework for quantifying intravoxel incoherent motion (IVIM) parameters, where a neural network for quantification and b-values for diffusion-weighted imaging are simultaneously optimized.

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been used to improve the diagnostic accuracy of coronary artery disease (CAD). This study was to design an...

Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.

European radiology
OBJECTIVES: Deep learning-based automatic segmentation (DLAS) helps the reproducibility of radiomics features, but its effect on radiomics modeling is unknown. We therefore evaluated whether DLAS can robustly extract anatomical and physiological MRI ...

Automatic Segmentation of Retinal Capillaries in Adaptive Optics Scanning Laser Ophthalmoscope Perfusion Images Using a Convolutional Neural Network.

Translational vision science & technology
PURPOSE: Adaptive optics scanning laser ophthalmoscope (AOSLO) capillary perfusion images can possess large variations in contrast, intensity, and background signal, thereby limiting the use of global or adaptive thresholding techniques for automatic...

Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.

Magnetic resonance in medicine
PURPOSE: Quantification of myocardial perfusion has the potential to improve the detection of regional and global flow reduction. Significant effort has been made to automate the workflow, where one essential step is the arterial input function (AIF)...

Deep learning-based attenuation map generation for myocardial perfusion SPECT.

European journal of nuclear medicine and molecular imaging
PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission sc...