AIMC Topic: Magnetic Resonance Imaging

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Diagnosis and Treatment Effect of Convolutional Neural Network-Based Magnetic Resonance Image Features on Severe Stroke and Mental State.

Contrast media & molecular imaging Jul 26, 2021
The purpose of this paper is to explore the impact of magnetic resonance imaging (MRI) image features based on convolutional neural network (CNN) algorithm and conditional random field on the diagnosis and mental state of patients with severe stroke....
Aged Algorithms Magnetic Resonance Imaging Aged, 80 and over Prognosis Adult Humans Male Stroke Female Neural Networks, Computer Cognitive Dysfunction Middle Aged Cerebral Small Vessel Diseases Image Processing, Computer-Assisted Case-Control Studies Follow-Up Studies
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Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network.

Contrast media & molecular imaging Jul 26, 2021
The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) influence characteristics of cerebral infarction in critically ill patients based on the convolution...
Image Interpretation, Computer-Assisted Follow-Up Studies Magnetic Resonance Imaging Cerebral Infarction Aged Prognosis Male Critical Illness Image Processing, Computer-Assisted Female Humans Neural Networks, Computer Artificial Intelligence
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Bayesian Fully Convolutional Networks for Brain Image Registration.

Journal of healthcare engineering Jul 26, 2021
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two images are spatially consistent. Nonrigid medical image registration is a key step in medical image p...
Brain Image Processing, Computer-Assisted Magnetic Resonance Imaging Bayes Theorem Uncertainty Humans Neural Networks, Computer
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Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.

Zeitschrift fur medizinische Physik Jul 24, 2021
INTRODUCTION: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM) region only. Reducing the spatial coverage to the DGM vicinity can sub...
Brain Mapping Gray Matter Humans Algorithms Brain Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Representation learning of resting state fMRI with variational autoencoder.

NeuroImage Jul 23, 2021
Resting state functional magnetic resonance imaging (rsfMRI) data exhibits complex but structured patterns. However, the underlying origins are unclear and entangled in rsfMRI data. Here we establish a variational auto-encoder, as a generative model ...
Databases, Factual Brain Magnetic Resonance Imaging Humans Connectome Unsupervised Machine Learning Rest Individuality
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Evaluation of Deep Learning-Based Approaches to Segment Bowel Air Pockets and Generate Pelvic Attenuation Maps from CAIPIRINHA-Accelerated Dixon MR Images.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine Jul 22, 2021
Attenuation correction remains a challenge in pelvic PET/MRI. In addition to the segmentation/model-based approaches, deep learning methods have shown promise in synthesizing accurate pelvic attenuation maps (μ-maps). However, these methods often mis...
Humans Tomography, X-Ray Computed Pelvis Positron-Emission Tomography Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning.

NeuroImage. Clinical Jul 22, 2021
Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantificat...
Magnetic Resonance Imaging Spinal Cord Spinal Cord Neoplasms Brain Neoplasms Deep Learning Image Processing, Computer-Assisted Humans
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Deep learning to automate the labelling of head MRI datasets for computer vision applications.

European radiology Jul 20, 2021
OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development.
Area Under Curve Radiography Radiologists Humans Deep Learning Magnetic Resonance Imaging
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Deep learning for sex classification in resting-state and task functional brain networks from the UK Biobank.

NeuroImage Jul 20, 2021
Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. Whil...
United Kingdom Rest Databases, Factual Nerve Net Humans Male Brain Biological Specimen Banks Female Deep Learning Sex Characteristics Magnetic Resonance Imaging Psychomotor Performance
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Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images.

Contrast media & molecular imaging Jul 20, 2021
This study aimed to explore the application value of multimodal magnetic resonance imaging (MRI) images based on the deep convolutional neural network (Conv.Net) in the diagnosis of strokes. Specifically, four automatic segmentation algorithms were p...
Humans Neural Networks, Computer Algorithms Prognosis Imaging, Three-Dimensional Stroke Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging
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