AIMC Journal:
Magnetic resonance in medicine

Showing 191 to 200 of 217 articles

Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting.

Magnetic resonance in medicine Jan 21, 2019
PURPOSE: MRSI has shown great promise in the detection and monitoring of neurologic pathologies such as tumor. A necessary component of data processing includes the quantitation of each metabolite, typically done through fitting a model of the spectr...
Brain Neoplasms Deep Learning User-Computer Interface Image Processing, Computer-Assisted Models, Theoretical Humans Neural Networks, Computer Software Algorithms Databases, Factual White Matter Computer Graphics Brain Mapping Magnetic Resonance Spectroscopy Glioblastoma Creatine Aspartic Acid Echo-Planar Imaging Choline Artifacts Signal-To-Noise Ratio
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Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.

Magnetic resonance in medicine Nov 13, 2018
PURPOSE: To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-spa...
Neural Networks, Computer Algorithms Brain Prognosis Female Humans Imaging, Three-Dimensional Treatment Outcome Stroke Brain Ischemia Neurites Anisotropy Reproducibility of Results Aged Male Middle Aged Deep Learning Diffusion Magnetic Resonance Imaging
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Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Magnetic resonance in medicine Nov 12, 2018
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response t...
Female Humans Docetaxel Young Adult Machine Learning Fourier Analysis Cognition Disorders Adult Algorithms Breast Neoplasms Epirubicin Brain Middle Aged Cancer Survivors Anisotropy Antineoplastic Agents Case-Control Studies Magnetic Resonance Imaging Regression Analysis Brain Mapping
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Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Magnetic resonance in medicine Sep 18, 2018
PURPOSE: To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data.
Heart Image Interpretation, Computer-Assisted Databases, Factual Image Enhancement Humans Brain Radionuclide Imaging Neural Networks, Computer Image Processing, Computer-Assisted Female Phantoms, Imaging Algorithms Male Young Adult Magnetic Resonance Imaging Middle Aged Adult Brain Mapping Deep Learning
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Machine learning improves classification of preclinical models of pancreatic cancer with chemical exchange saturation transfer MRI.

Magnetic resonance in medicine Sep 17, 2018
PURPOSE: We sought to assess whether machine learning-based classification approaches can improve the classification of pancreatic tumor models relative to more simplistic analysis methods, using T relaxation, CEST, and DCE MRI.
Machine Learning Algorithms Female Reproducibility of Results Sensitivity and Specificity Mice Magnetic Resonance Imaging Pancreatic Neoplasms Animals Cell Line, Tumor Image Processing, Computer-Assisted Humans Image Enhancement Normal Distribution Principal Component Analysis Area Under Curve Hypoxia Image Interpretation, Computer-Assisted Mice, SCID
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Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Magnetic resonance in medicine Sep 8, 2018
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability...
Fourier Analysis Breath Holding Magnetic Resonance Imaging, Cine Cardiac-Gated Imaging Techniques Male Middle Aged Deep Learning Adolescent Image Processing, Computer-Assisted Heart Image Interpretation, Computer-Assisted Artifacts Respiration Heart Defects, Congenital Humans Algorithms Retrospective Studies Young Adult Adult
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Assessment of the generalization of learned image reconstruction and the potential for transfer learning.

Magnetic resonance in medicine May 17, 2018
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...
Magnetic Resonance Imaging Models, Statistical Female Signal-To-Noise Ratio Aged Aged, 80 and over Protons Young Adult Adult Middle Aged Male Deep Learning Adolescent Humans Image Processing, Computer-Assisted Algorithms Contrast Media Databases, Factual
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Deep convolutional neural network for segmentation of knee joint anatomy.

Magnetic resonance in medicine May 17, 2018
PURPOSE: To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tiss...
Deep Learning Imaging, Three-Dimensional Image Processing, Computer-Assisted Knee Joint Cartilage Female Bone and Bones Humans Reproducibility of Results Knee Neural Networks, Computer Aged Tomography, X-Ray Computed Male Middle Aged
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Single-shot T mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network.

Magnetic resonance in medicine Apr 24, 2018
PURPOSE: An end-to-end deep convolutional neural network (CNN) based on deep residual network (ResNet) was proposed to efficiently reconstruct reliable T mapping from single-shot overlapping-echo detachment (OLED) planar imaging.
Deep Learning Humans Image Processing, Computer-Assisted Algorithms Phantoms, Imaging Echo-Planar Imaging Brain Computer Simulation Adult
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KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

Magnetic resonance in medicine Apr 6, 2018
PURPOSE: To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a ...
Neural Networks, Computer Humans Magnetic Resonance Imaging Algorithms Signal-To-Noise Ratio Databases, Factual Brain Image Processing, Computer-Assisted
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