AIMC Topic: Cerebrovascular Circulation
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Improving Arterial Spin Labeling by Using Deep Learning.
Radiology
Dec 21, 2017
Purpose To develop a deep learning algorithm that generates arterial spin labeling (ASL) perfusion images with higher accuracy and robustness by using a smaller number of subtraction images. Materials and Methods For ASL image generation from pair-wi...
A brain-computer interface based on functional transcranial doppler ultrasound using wavelet transform and support vector machines.
Journal of neuroscience methods
Oct 7, 2017
BACKGROUND: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow.
Cerebrovascular Circulation
Space Perception
Imagination
Ultrasonography, Doppler, Transcranial
Brain-Computer Interfaces
Wavelet Analysis
Feasibility Studies
Blood Flow Velocity
Rotation
Neuropsychological Tests
Linear Models
Rest
Young Adult
Functional Neuroimaging
Male
Humans
Cognition
Brain
Support Vector Machine
Language
Female
Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis.
Computational intelligence and neuroscience
Oct 31, 2016
Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convo...
Regional muscle tissue saturation is an indicator of global inadequate circulation during cardiopulmonary bypass: a randomized porcine study using muscle, intestinal and brain tissue metabolomics.
Perfusion
Oct 17, 2016
BACKGROUND: Muscle tissue saturation (StO) measured with near-infrared spectroscopy has generally been considered a measurement of the tissue microcirculatory condition. However, we hypothesized that StO could be more regarded as a fast and reliable ...
Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.
Human brain mapping
Oct 12, 2014
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather tha...
A novel MRI-based deep learning imaging biomarker for comprehensive assessment of the lenticulostriate artery-neural complex.
BMC medical imaging
May 26, 2025
OBJECTIVES: To develop a deep learning network for extracting features from the blood-supplying regions of the lenticulostriate artery (LSA) and to establish these features as an imaging biomarker for the comprehensive assessment of the lenticulostri...
Artificial Intelligence in Transcranial Doppler Ultrasonography.
Current medical imaging
Jan 1, 2025
Transcranial Doppler is an instrumental ultrasound method capable of providing data on various brain pathologies, in particular, the study of cerebral hemodynamics in stroke, quickly, economically, and with repeatability of the data themselves. Howev...
Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.
Cerebral cortex (New York, N.Y. : 1991)
Jan 5, 2021
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...
[Accuracy of Classification of Cerebral Blood Flow Reduction Patterns Using Statistical Analysis Images Generated with Simulated SPECT Datasets via Deep Learning].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2021
PURPOSE: The aim of this study was to evaluate the classification accuracy of specific blood flow reduction patterns in clinical images by deep learning using simulation data.
Reducing False Alarm Rates in Neonatal Intensive Care: A New Machine Learning Approach.
Advances in experimental medicine and biology
Jan 1, 2020
UNLABELLED: In neonatal intensive care units (NICUs), 87.5% of alarms by the monitoring system are false alarms, often caused by the movements of the neonates. Such false alarms are not only stressful for the neonates as well as for their parents and...
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