AIMC Topic: Contrast Media

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Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.

Radiology Oct 23, 2017
Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This cl...
Carcinoma, Hepatocellular Contrast Media Diagnosis, Differential Bile Duct Neoplasms Cholangiocarcinoma Sensitivity and Specificity Middle Aged ROC Curve Liver Neoplasms Humans Neural Networks, Computer Machine Learning Retrospective Studies Tomography, X-Ray Computed Female Aged Aged, 80 and over
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A robotic C-arm cone beam CT system for image-guided proton therapy: design and performance.

The British journal of radiology Oct 9, 2017
OBJECTIVE: A ceiling-mounted robotic C-arm cone beam CT (CBCT) system was developed for use with a 190° proton gantry system and a 6-degree-of-freedom robotic patient positioner. We report on the mechanical design, system accuracy, image quality, ima...
Equipment Design Radiotherapy, Intensity-Modulated Phantoms, Imaging Proton Therapy Radiographic Image Enhancement Reproducibility of Results Cone-Beam Computed Tomography Contrast Media Robotics
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Head and Neck Cancer Tumor Segmentation Using Support Vector Machine in Dynamic Contrast-Enhanced MRI.

Contrast media & molecular imaging Sep 7, 2017
OBJECTIVE: We aimed to propose an automatic method based on Support Vector Machine (SVM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to segment the tumor lesions of head and neck cancer (HNC).
Magnetic Resonance Imaging Image Processing, Computer-Assisted Humans Head and Neck Neoplasms Databases, Factual Support Vector Machine Female Contrast Media Male
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Unsupervised Segmentation of 5D Hyperpolarized Carbon-13 MRI Data Using a Fuzzy Markov Random Field Model.

IEEE transactions on medical imaging Sep 4, 2017
Hyperpolarized MRI with C-labelled compounds is an emerging clinical technique allowing in vivo metabolic processes to be characterized non-invasively. Accurate quantification of C data, both for clinical and research purposes, typically relies on th...
Algorithms Computer Simulation Animals Rats Carbon Isotopes Unsupervised Machine Learning Contrast Media Markov Chains Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Fuzzy Logic Neoplasms, Experimental Models, Statistical Phantoms, Imaging
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Ensemble of expert deep neural networks for spatio-temporal denoising of contrast-enhanced MRI sequences.

Medical image analysis Aug 2, 2017
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain ba...
Neural Networks, Computer Algorithms Reproducibility of Results Sensitivity and Specificity Blood-Brain Barrier Humans Anisotropy Contrast Media Magnetic Resonance Imaging Brain Mapping Image Enhancement
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Gaussian process classification of superparamagnetic relaxometry data: Phantom study.

Artificial intelligence in medicine Jul 24, 2017
MOTIVATION: Superparamagnetic relaxometry (SPMR) is an emerging technology that holds potential for use in early cancer detection. Measurement of the magnetic field after the excitation of cancer-bound superparamagnetic iron oxide nanoparticles (SPIO...
Phantoms, Imaging Normal Distribution Numerical Analysis, Computer-Assisted Mice Contrast Media Magnetic Resonance Imaging Early Detection of Cancer Predictive Value of Tests Magnetite Nanoparticles Signal-To-Noise Ratio Magnetics Algorithms Neoplasms, Experimental Computer Simulation Reproducibility of Results Animals Image Processing, Computer-Assisted Machine Learning
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DCE-MRI prediction of survival time for patients with glioblastoma multiforme: using an adaptive neuro-fuzzy-based model and nested model selection technique.

NMR in biomedicine May 25, 2017
This pilot study investigates the construction of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the prediction of the survival time of patients with glioblastoma multiforme (GBM). ANFIS is trained by the pharmacokinetic (PK) parameters estimat...
Glioblastoma Survival Analysis Proportional Hazards Models Brain Neoplasms Male Female Middle Aged Aged Adolescent Time Factors Humans Aged, 80 and over Contrast Media Young Adult Magnetic Resonance Imaging Adult Fuzzy Logic Models, Neurological
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Impact of knowledge-based iterative model reconstruction on myocardial late iodine enhancement in computed tomography and comparison with cardiac magnetic resonance.

The international journal of cardiovascular imaging Apr 13, 2017
We evaluated the image quality and diagnostic performance of late iodine enhancement computed tomography (LIE-CT) with knowledge-based iterative model reconstruction (IMR) for the detection of myocardial infarction (MI) in comparison with late gadoli...
Coronary Artery Disease Myocardial Infarction Knowledge Bases Observer Variation Multidetector Computed Tomography Computed Tomography Angiography Coronary Circulation Myocardial Perfusion Imaging Gadolinium DTPA Contrast Media Predictive Value of Tests Myocardium Aged Magnetic Resonance Imaging, Cine Coronary Angiography Humans Male Cardiac-Gated Imaging Techniques Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Middle Aged Iopamidol Female Electrocardiography Reproducibility of Results
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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

Artificial intelligence in medicine Mar 27, 2017
This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from...
Artificial Intelligence Databases, Factual Retrospective Studies Automation Tomography, X-Ray Computed Humans Reproducibility of Results Liver Neoplasms Contrast Media Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted
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Autodelineation of cervical cancers using multiparametric magnetic resonance imaging and machine learning.

Acta oncologica (Stockholm, Sweden) Feb 8, 2017
BACKGROUND: Tumour delineation is a challenging, time-consuming and complex part of radiotherapy planning. In this study, an automatic method for delineating locally advanced cervical cancers was developed using a machine learning approach.
Organs at Risk Humans Radiotherapy Planning, Computer-Assisted Machine Learning Radiotherapy Dosage Algorithms Female Uterine Cervical Neoplasms Image Processing, Computer-Assisted Contrast Media Magnetic Resonance Imaging
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