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Computational Fluid Dynamics in Intracranial Atherosclerosis - Lessons from Cardiology: A Review of CFD in Intracranial Atherosclerosis
Intracranial atherosclerosis is a common cause of stroke with a high recurrence rate. Haemodynamically significant lesions are associated with a particularly high risk of recurrence. Computational fluid dynamics (CFD) is a tool that has been investigated to identify haemodynamically significant lesions. CFD in the intracranial vasculature benefits from the precedent set by cardiology, where CFD is an established clinical tool. This precedent is particularly important in CFD as models are very heterogenous. There are many decisions-points in the model-creation process, usually involving a trade-off between computational expense and accuracy.
Automatic Segmentation of Atherosclerotic Plaques in Transverse Carotid Ultrasound Images Using Deep Learning
Ultrasound imaging is commonly used for patients with atheroscelerotic plaques in the carotid artery. While B-mode ultrasound can be used for detection and measurement of these plaques, interpreting these images can be a subjective and time-consuming task. Deep learning algorithms have been proven to be an effective tool for interpreting medical images, especially for classification and segmentation tasks. Here, we propose a deep learning model to automatically detect and measure plaques in transverse B-mode images of the carotid artery.
Validation of Computational Fluid Dynamics Against PC-MRI Measurements of Blood Flow in Healthy Intracranial Arteries: On the Threshold to Clinical Utility for Stroke Prediction
Computational Fluid Dynamics (CFD) modeling of blood flow is a promising technique to obtain hemodynamic biomarkers and predict recurrent strokes. Few studies, however, have validated CFD against other methods for measuring hemodynamics, e.g. phase contrast MRI (PC-MRI). To resolve the uncertainties about the accuracy of CFD, this study validates the results of an automated software that combines deep learning and CFD for simulating blood flow given an MR angiogram against PC-MRI.
An Anatomy Corrector Conditional Generative Adversarial Network
The accurate segmentation of medical images has important consequences in clinical applications. Noisy and artefact-heavy images can result in erroneous image segmentation and often require expert understanding of the target anatomy by clinicians to interpret and compensate for missing and obfuscated data. This is especially true in ultrasound imaging where shadowing and speckle artefacts are common.
Automatic Detection and Measurement of Atherosclerotic Plaques in Carotid Ultrasound Using Deep Learning
Ultrasound imaging is commonly used for detection and measurement of carotid plaques, an important cause of ischemic stroke. Despite its importance, accurate interpretation of ultrasound can be difficult and subjective. Here, we evaluated the accuracy of our deep learning model for automatic detection of carotid plaques in b-mode ultrasound against expert interpretation of the images.
From Static Imaging to Hemodynamic Modelling: Validation of Intracranial Blood Flow Simulations Against Population-Based PC-MRI Studies
Computational models of blood flow in cerebral arteries present an opportunity to improve stroke prediction using hemodynamic biomarkers. Such models, however, are rarely validated against real clinical data. We evaluated the accuracy of a computational fluid dynamics (CFD) model which uses allometric scaling laws against population-based Phase Contrast MRI (PC-MRI) measurements of blood flow in the brain.
Automatic Segmentation of Cerebral Arteries in MRA TOF Using Deep Learning
Analysis of cerebral arteries in MRA TOF is challenging and time consuming. Established image processing methods suffer from artefacts, especially in the presence of pathology. We propose and assess the performance of a 3D convolutional neural network for automatic segmentation of cerebral arteries in MRA TOF which is robust to common MRI artefacts, thanks to implementation of a custom loss function.
Automated Detection and Measurement of Atherosclerotic Carotid Plaques in B-mode Ultrasound
Atherosclerotic carotid plaques are an important cause of ischemic stroke. B-Mode ultrasound imaging is commonly used for detection and measurement of these plaques, yet image interpretation is difficult and subjective. See-Mode’s software uses deep learning to automatically detect and measure plaque in B-mode ultrasound.
From Static Imaging to Hemodynamic Modelling: Automated NASCET Grading Using Computational Fluid Dynamics
Computational fluid dynamics (CFD) can be used to model blood flow based on static scans (like CT or MR angiograms), but such models are rarely validated in real clinical settings. We evaluated the accuracy of our automated vascular analysis software that simulates blood flow given a CT or MR angiogram against NASCET grading done by duplex ultrasound.
Detecting Intraplaque Hemorrhage in Carotid Ultrasound Using Machine Learning
Intraplaque hemorrhage increases the risk of stroke in patients with carotid artery atherosclerosis. Detecting intraplaque hemorrhage in the medical images is difficult even for expert clinicians. We evaluate the ability of our software that uses machine learning to improve intraplaque hemorrhage detection in b-mode ultrasound.