Computer methods and programs in biomedicine
Oct 22, 2019
BACKGROUND AND OBJECTIVE: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to...
The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loa...
PURPOSE: To study the clinical potential of a deep learning neural network (convolutional neural networks [CNN]) as a supportive tool for detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA) by comparing...
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
May 8, 2019
Translation of many non-invasive hemodynamic MRI methods to cerebrovascular disease patients has been hampered by well-known artifacts associated with delayed blood arrival times and reduced microvascular compliance. Using machine learning and suppor...
IEEE transactions on bio-medical engineering
Feb 1, 2019
OBJECTIVE: To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD).
AJNR. American journal of neuroradiology
Dec 20, 2018
BACKGROUND AND PURPOSE: The rupture of an intracranial aneurysm is a serious incident, causing subarachnoid hemorrhage associated with high fatality and morbidity rates. Because the demand for radiologic examinations is steadily growing, physician fa...
Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images...
PURPOSE: The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences.
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