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Radiology Subcategories: Diagnostic Radiology Nuclear Medicine Therapeutic Radiology

Showing 988-1008 of 5,219 articles

Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

PURPOSE: This study investigated the potential of deep convolutional neural networks (CNN) for autom...

Aug 2019 31473800

PET image denoising using unsupervised deep learning.

PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradat...

Aug 2019 31468181

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the...

Aug 2019 31499318

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

Reducing radiation dose is important for PET imaging. However, reducing injection doses causes incre...

Aug 2019 31307019

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The...

Aug 2019 31425065

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer.

OBJECTIVE: Artificial neural network (ANN) technology has been developed for clinical use to analyze...

Jul 2019 31317398

High-Resolution SPECT Imaging of Stimuli-Responsive Soft Microrobots.

Untethered small-scale robots have great potential for biomedical applications. However, critical ba...

Jul 2019 31304653

Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.

OBJECTIVE: Using CT texture analysis and machine learning methods, this study aims to distinguish th...

Jul 2019 31219712

Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression for PET Attenuation Correction.

Given the complicated relationship between the magnetic resonance imaging (MRI) signals and the atte...

Jul 2019 31295129

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from ...

Jul 2019 31283475

Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.

OBJECTIVE: Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generatio...

Jul 2019 31264170

Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

INTRODUCTION: Recently there have been significant advances in the field of machine learning and art...

Jun 2019 31254036

A case of robotic assisted percutaneous coronary intervention of the left main coronary artery in a patient with very late baffle stenosis after surgical correction of anomalous left coronary artery from the pulmonary artery.

A 34-year-old woman with history of surgical correction (Takeuchi procedure) of anomalous left coron...

Jun 2019 31250510

F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (...

Jun 2019 31491820

Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

PURPOSE: Our goal was to use a generative adversarial network (GAN) with feature matching and task-s...

Jun 2019 31131901

Comparison of F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy.

AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of maligna...

Jun 2019 31202569

Radiology

Browse Categories

Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics.

PET image denoising using unsupervised deep learning.

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

An investigation of quantitative accuracy for deep learning based denoising in oncological PET.

mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.

Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer.

High-Resolution SPECT Imaging of Stimuli-Responsive Soft Microrobots.

Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.

Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression for PET Attenuation Correction.

Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.

Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.

Next generation research applications for hybrid PET/MR and PET/CT imaging using deep learning.

A case of robotic assisted percutaneous coronary intervention of the left main coronary artery in a patient with very late baffle stenosis after surgical correction of anomalous left coronary artery from the pulmonary artery.

F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.

Ultra-low-dose PET reconstruction using generative adversarial network with feature matching and task-specific perceptual loss.

Comparison of F-FDG avidity at PET of benign and malignant pure ground-glass opacities: a paradox? Part II: artificial neural network integration of the PET/CT characteristics of ground-glass opacities to predict their likelihood of malignancy.

Automated Bone Scan Index as an Imaging Biomarker to Predict Overall Survival in the Zometa European Study/SPCG11.

Higher SNR PET image prediction using a deep learning model and MRI image.

Machine-learned target volume delineation of F-FDG PET images after one cycle of induction chemotherapy.

Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal I-FP-CIT indices.

Deep learning only by normal brain PET identify unheralded brain anomalies.

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