AIMC Topic: Magnetic Resonance Angiography
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Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.
Magnetic resonance in medicine
May 22, 2019
PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.
Classifying intracranial stenosis disease severity from functional MRI data using machine learning.
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...
Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.
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).
Parallel imaging in time-of-flight magnetic resonance angiography using deep multistream convolutional neural networks.
Magnetic resonance in medicine
Jan 21, 2019
PURPOSE: To develop and evaluate a method of parallel imaging time-of-flight (TOF) MRA using deep multistream convolutional neural networks (CNNs).
Deep Learning-Based Detection of Intracranial Aneurysms in 3D TOF-MRA.
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...
Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.
Radiology
Oct 23, 2018
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...
Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.
Japanese journal of radiology
Sep 19, 2018
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.
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...
Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI.
IEEE transactions on bio-medical engineering
Dec 20, 2017
OBJECTIVE: Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurr...
Automated extraction and labelling of the arterial tree from whole-body MRA data.
Medical image analysis
May 21, 2015
In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts (i) 3D v...
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