AIMC Topic: Perfusion

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Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regulari...
Neural Networks, Computer Tomography, X-Ray Computed Deep Learning Perfusion Signal-To-Noise Ratio
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Predicting Kidney Discard Using Machine Learning.

Transplantation Sep 1, 2021
BACKGROUND: Despite the kidney supply shortage, 18%-20% of deceased donor kidneys are discarded annually in the United States. In 2018, 3569 kidneys were discarded.
Humans Neural Networks, Computer Decision Support Techniques Kidney Transplantation Female Risk Factors Aged Young Adult Adult Male Middle Aged Risk Assessment Tissue Donors United States Kidney Function Tests Support Vector Machine Decision Trees Predictive Value of Tests Biopsy Perfusion Donor Selection
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Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry.

Journal of biomedical optics Jan 1, 2019
Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flow...
Humans Neural Networks, Computer Machine Learning Computer Simulation Reproducibility of Results Adult Male Image Processing, Computer-Assisted Models, Statistical Regional Blood Flow Perfusion Erythrocytes Lasers Calibration Monte Carlo Method Blood Flow Velocity Laser-Doppler Flowmetry Stochastic Processes Microcirculation
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