AIMC Topic: Magnetic Resonance Imaging
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The Effect of Deep Learning-Based QSM Magnetic Resonance Imaging on the Subthalamic Nucleus.
Journal of healthcare engineering
Sep 15, 2021
In order to study the influence of quantitative magnetic susceptibility mapping (QSM) on them. A 2.5D Attention U-Net Network based on multiple input and multiple output, a method for segmenting RN, SN, and STN regions in high-resolution QSM images i...
Automated claustrum segmentation in human brain MRI using deep learning.
Human brain mapping
Sep 14, 2021
In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may ...
Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia.
Neurology
Sep 14, 2021
BACKGROUND AND OBJECTIVE: To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).
Mapping Epileptogenic Tissues in MRI-Negative Focal Epilepsy: Can Deep Learning Uncover Hidden Lesions?
Neurology
Sep 14, 2021
Modality-agnostic self-supervised deep feature learning and fast instance optimisation for multimodal fusion in ultrasound-guided interventions.
Computer methods and programs in biomedicine
Sep 13, 2021
BACKGROUND AND OBJECTIVE: Fast and robust alignment of pre-operative MRI planning scans to intra-operative ultrasound is an important aspect for automatically supporting image-guided interventions. Thus far, learning-based approaches have failed to t...
Performance improvement of weakly supervised fully convolutional networks by skip connections for brain structure segmentation.
Medical physics
Sep 13, 2021
PURPOSE: For the planning and navigation of neurosurgery, we have developed a fully convolutional network (FCN)-based method for brain structure segmentation on magnetic resonance (MR) images. The capability of an FCN depends on the quality of the tr...
Using synthetic data generation to train a cardiac motion tag tracking neural network.
Medical image analysis
Sep 10, 2021
A CNN based method for cardiac MRI tag tracking was developed and validated. A synthetic data simulator was created to generate large amounts of training data using natural images, a Bloch equation simulation, a broad range of tissue properties, and ...
Feasibility of late gadolinium enhancement (LGE) in ischemic cardiomyopathy using 2D-multisegment LGE combined with artificial intelligence reconstruction deep learning noise reduction algorithm.
International journal of cardiology
Sep 10, 2021
BACKGROUND: Despite the low spatial resolution of 2D-multisegment late gadolinium enhancement (2D-MSLGE) sequences, it may be useful in uncooperative patients instead of standard 2D single segmented inversion recovery gradient echo late gadolinium en...
[Artificial Intelligence and teleradiology in cardiovascular imaging by CT-Scan and MRI].
Annales de cardiologie et d'angeiologie
Sep 10, 2021
Cardiac CT-Scan and cardiac magnetic resonance imaging (MRI) are two booming cardiac imaging modalities especially in chest pain screening for CT-Scan and in surveillance of patients with known coronary artery disease for MRI. Artificial Intelligence...
Radiomics analysis combining unsupervised learning and handcrafted features: A multiple-disease study.
Medical physics
Sep 10, 2021
PURPOSE: To study and investigate the synergistic benefit of incorporating both conventional handcrafted and learning-based features in disease identification across a wide range of clinical setups.
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