AIMC Topic: Positron-Emission Tomography
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Sub-2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single-ended readout using convolutional neural networks.
Medical physics
Feb 2, 2021
PURPOSE: Depth of interaction (DOI) readout in PET imaging has been researched in efforts to mitigate parallax error, which would enable the development of small diameter, high-resolution PET scanners. However, DOI PET has not yet been commercialized...
Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR.
Physics in medicine and biology
Feb 2, 2021
The purpose of this work was to develop and evaluate a deep learning approach for automatic rat brain image segmentation of magnetic resonance imaging (MRI) images in a clinical PET/MR, providing a useful tool for analyzing studies of the pathology a...
Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure.
European journal of nuclear medicine and molecular imaging
Feb 1, 2021
PURPOSE: To generate diagnostic F-FDG PET images of pediatric cancer patients from ultra-low-dose F-FDG PET input images, using a novel artificial intelligence (AI) algorithm.
Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy.
International journal of radiation oncology, biology, physics
Feb 1, 2021
PURPOSE: Novel actuarial deep learning neural network (ADNN) architectures are proposed for joint prediction of radiation therapy outcomes-radiation pneumonitis (RP) and local control (LC)-in stage III non-small cell lung cancer (NSCLC) patients. Unl...
Machine learning-based differentiation between multiple sclerosis and glioma WHO II°-IV° using O-(2-[18F] fluoroethyl)-L-tyrosine positron emission tomography.
Journal of neuro-oncology
Jan 27, 2021
INTRODUCTION: This study aimed to test the diagnostic significance of FET-PET imaging combined with machine learning for the differentiation between multiple sclerosis (MS) and glioma II°-IV°.
Denoising non-steady state dynamic PET data using a feed-forward neural network.
Physics in medicine and biology
Jan 26, 2021
The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies h...
Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging.
European journal of nuclear medicine and molecular imaging
Jan 25, 2021
PURPOSE: Tendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and increase patient comfort. This work aims to assess the performance of regular full-dose (FD) synthes...
Modeling autosomal dominant Alzheimer's disease with machine learning.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Jan 21, 2021
INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.
4D deep image prior: dynamic PET image denoising using an unsupervised four-dimensional branch convolutional neural network.
Physics in medicine and biology
Jan 14, 2021
Although convolutional neural networks (CNNs) demonstrate the superior performance in denoising positron emission tomography (PET) images, a supervised training of the CNN requires a pair of large, high-quality PET image datasets. As an unsupervised ...
A deep learning framework for F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy.
European journal of nuclear medicine and molecular imaging
Jan 9, 2021
PURPOSE: Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, F-fl...
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