PURPOSE: Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular diseases. Many segmentation frameworks based on convolutional neural networks (CNNs) or U-Net-like str...
Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of indiv...
This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 ...
AJNR. American journal of neuroradiology
Aug 12, 2021
BACKGROUND AND PURPOSE: The detection of cerebral aneurysms on MRA is a challenging task. Recent studies have used deep learning-based software for automated detection of aneurysms on MRA and have reported high performance. The purpose of this study ...
PURPOSE: To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute.
OBJECTIVE: Investigation of asymptomatic carotid stenosis treatment is hindered by the lack of a contemporary population-based disease cohort. We describe the use of natural language processing (NLP) to identify stenosis in patients undergoing caroti...
PURPOSE: To develop an end-to-end deep learning technique for nonrigid motion-corrected (MoCo) reconstruction of ninefold undersampled free-breathing whole-heart coronary MRA (CMRA).
Time-of-flight magnetic resonance angiography (TOF-MRA) is one of the most widely used non-contrast MR imaging methods to visualize blood vessels, but due to the 3-D volume acquisition highly accelerated acquisition is necessary. Accordingly, high qu...
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