AIMC Topic: Magnetic Resonance Imaging

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MRI-based automated detection of implanted low dose rate (LDR) brachytherapy seeds using quantitative susceptibility mapping (QSM) and unsupervised machine learning (ML).

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology Sep 19, 2018
BACKGROUND AND PURPOSE: Permanent seed brachytherapy is an established treatment option for localized prostate cancer. Currently, post-implant dosimetry is performed on CT images despite challenging target delineation due to limited soft tissue contr...
Humans Unsupervised Machine Learning Prostatic Neoplasms Iodine Radioisotopes Brachytherapy Tomography, X-Ray Computed Male Contrast Media Magnetic Resonance Imaging Radiotherapy Dosage Phantoms, Imaging
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Artificial intelligence using neural network architecture for radiology (AINNAR): classification of MR imaging sequences.

Japanese journal of radiology Sep 19, 2018
PURPOSE: The confusion of MRI sequence names could be solved if MR images were automatically identified after image data acquisition. We revealed the ability of deep learning to classify head MRI sequences.
Reproducibility of Results Diffusion Magnetic Resonance Imaging Aged Aged, 80 and over Male Artificial Intelligence Brain Humans Retrospective Studies Neural Networks, Computer Female Middle Aged Cognitive Dysfunction Magnetic Resonance Imaging Magnetic Resonance Angiography
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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.
Image Enhancement Radionuclide Imaging Phantoms, Imaging Magnetic Resonance Imaging Brain Mapping Heart Image Interpretation, Computer-Assisted Brain Female Young Adult Adult Male Middle Aged Deep Learning Image Processing, Computer-Assisted Humans Neural Networks, Computer Algorithms Databases, Factual
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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.
Image Interpretation, Computer-Assisted Mice, SCID Image Enhancement Normal Distribution Animals Principal Component Analysis Algorithms Cell Line, Tumor Magnetic Resonance Imaging Humans Area Under Curve Female Pancreatic Neoplasms Image Processing, Computer-Assisted Hypoxia Reproducibility of Results Machine Learning Mice Sensitivity and Specificity
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Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy.

Physics in medicine and biology Sep 17, 2018
Convolutional neural networks (CNNs) have become the state-of-the-art method for medical segmentation. However, repeated pooling and striding operations reduce the feature resolution, causing loss of detailed information. Additionally, tumors of diff...
Neural Networks, Computer Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted Magnetic Resonance Imaging Rectal Neoplasms
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Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

European journal of radiology Sep 14, 2018
PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to differentiate a glioblastoma multiforme (GBM) from a primary cerebral nervous system lymphoma (PCNSL).
Brain Neoplasms Male Middle Aged ROC Curve Logistic Models Lymphoma Magnetic Resonance Imaging Diagnosis, Differential Retrospective Studies Female Aged Adult Humans Glioblastoma Machine Learning Image Enhancement Brain
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Recognition of Schizophrenia with Regularized Support Vector Machine and Sequential Region of Interest Selection using Structural Magnetic Resonance Imaging.

Scientific reports Sep 14, 2018
Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the indiv...
Male Case-Control Studies Image Processing, Computer-Assisted Support Vector Machine Humans Magnetic Resonance Imaging Female Adult Schizophrenia Gray Matter
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Temporal Correlation Structure Learning for MCI Conversion Prediction.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 13, 2018
In Alzheimer's research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer's. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention an...
Cohort Studies Reproducibility of Results Humans Sensitivity and Specificity Magnetic Resonance Imaging Artificial Intelligence Disease Progression Image Interpretation, Computer-Assisted Algorithms Cognitive Dysfunction Brain
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A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 13, 2018
Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been considered as promising biomarkers for early Alzheimer's disease (AD) diagnosis, it is still challenging to...
Magnetic Resonance Imaging Humans Alzheimer Disease Reproducibility of Results Neural Networks, Computer Image Interpretation, Computer-Assisted Sensitivity and Specificity Machine Learning Deep Learning Algorithms Cognitive Dysfunction Brain
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Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Sep 13, 2018
Deep learning has become the new state-of-the-art for many problems in image analysis. However, large datasets are often required for such deep networks to learn effectively. This poses a difficult challenge for many medical image analysis problems i...
Humans Magnetic Resonance Imaging Sensitivity and Specificity Deep Learning Child Neural Networks, Computer Image Processing, Computer-Assisted Machine Learning Autism Spectrum Disorder Reproducibility of Results
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