AIMC Topic: Magnetic Resonance Imaging

Clear Filters Showing 5271 to 5280 of 6780 articles

Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics.

International journal of radiation oncology, biology, physics
PURPOSE: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional i...

A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods.

IEEE transactions on nanobioscience
This paper explored the hidden biomedical information from knee magnetic resonance (MR) images for osteoarthritis (OA) prediction. We have computed the cartilage damage index (CDI) information from 36 informative locations on tibiofemoral cartilage c...

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

Neural networks : the official journal of the International Neural Network Society
Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be...

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

NeuroImage
Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patterns from the input data. However, the successful implementation of deep neural networks using neuro...

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.

NeuroImage
Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a...

Improving resolution of MR images with an adversarial network incorporating images with different contrast.

Medical physics
PURPOSE: The routine MRI scan protocol consists of multiple pulse sequences that acquire images of varying contrast. Since high frequency contents such as edges are not significantly affected by image contrast, down-sampled images in one contrast may...

Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference From fMRI Data.

IEEE transactions on bio-medical engineering
In this work, we conduct comprehensive comparisons between four variants of independent component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) methods, both at the subject-level, by using synthesized fMRI data with gr...

Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Magnetic resonance in medicine
PURPOSE: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to asse...

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

IEEE transactions on medical imaging
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been th...

Machine Learning-based Texture Analysis of Contrast-enhanced MR Imaging to Differentiate between Glioblastoma and Primary Central Nervous System Lymphoma.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
PURPOSE: Although advanced MRI techniques are increasingly available, imaging differentiation between glioblastoma and primary central nervous system lymphoma (PCNSL) is sometimes confusing. We aimed to evaluate the performance of image classificatio...