AIMC Topic: Positron-Emission Tomography
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Range and dose verification in proton therapy using proton-induced positron emitters and recurrent neural networks (RNNs).
Physics in medicine and biology
Sep 4, 2019
Online proton range/dose verification based on measurements of proton-induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distr...
PET image denoising using unsupervised deep learning.
European journal of nuclear medicine and molecular imaging
Aug 29, 2019
PURPOSE: Image quality of positron emission tomography (PET) is limited by various physical degradation factors. Our study aims to perform PET image denoising by utilizing prior information from the same patient. The proposed method is based on unsup...
Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.
Medical image analysis
Aug 24, 2019
Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord. It is thus essential to measure the tissue myelin content to understand the physiopathology of MS, ...
An investigation of quantitative accuracy for deep learning based denoising in oncological PET.
Physics in medicine and biology
Aug 21, 2019
Reducing radiation dose is important for PET imaging. However, reducing injection doses causes increased image noise and low signal-to-noise ratio (SNR), subsequently affecting diagnostic and quantitative accuracies. Deep learning methods have shown ...
mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.
IEEE transactions on medical imaging
Aug 16, 2019
We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any ...
Prediction of Chemotherapy Response of Osteosarcoma Using Baseline F-FDG Textural Features Machine Learning Approaches with PCA.
Contrast media & molecular imaging
Jul 24, 2019
PURPOSE: Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning ...
Use of a Tracer-Specific Deep Artificial Neural Net to Denoise Dynamic PET Images.
IEEE transactions on medical imaging
Jul 5, 2019
Application of kinetic modeling (KM) on a voxel level in dynamic PET images frequently suffers from high levels of noise, drastically reducing the precision of parametric image analysis. In this paper, we investigate the use of machine learning and a...
Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD.
Magnetic resonance in medicine
Jul 4, 2019
PURPOSE: To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-New...
Brain
Computer Simulation
Female
Adult
Male
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Humans
Neural Networks, Computer
White Matter
Cerebrovascular Circulation
Normal Distribution
Least-Squares Analysis
Brain Mapping
Positron-Emission Tomography
Oxygen
Healthy Volunteers
Oxygen Consumption
Gray Matter
Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.
European journal of nuclear medicine and molecular imaging
Jul 1, 2019
OBJECTIVE: Quantitative PET/MR imaging is challenged by the accuracy of synthetic CT (sCT) generation from MR images. Deep learning-based algorithms have recently gained momentum for a number of medical image analysis applications. In this work, a no...
F-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma.
NeuroImage. Clinical
Jun 27, 2019
The differential diagnosis of primary central nervous system lymphoma from glioblastoma multiforme (GBM) is essential due to the difference in treatment strategies. This study retrospectively reviewed 77 patients (24 with lymphoma and 53 with GBM) to...
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