AIMC Topic: Tomography, Emission-Computed, Single-Photon
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Deep-Learning Generation of Synthetic Intermediate Projections Improves Lu SPECT Images Reconstructed with Sparsely Acquired Projections.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Aug 28, 2020
The aims of this study were to decrease the Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by adding deep-learning-generated synthesized projections. We constructed a deep convolutional U-net-shap...
Morphological Characterization of Functional Brain Imaging by Isosurface Analysis in Parkinson's Disease.
International journal of neural systems
Aug 12, 2020
Finding new biomarkers to model Parkinson's Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons main...
Deep learning, another important tool for improving acquisition efficiency in SPECT MPI Imaging.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
May 17, 2020
Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Apr 28, 2020
INTRODUCTION: The purpose of this work was to assess the feasibility of acquisition time reduction in MPI-SPECT imaging using deep leering techniques through two main approaches, namely reduction of the acquisition time per projection and reduction o...
Deep learning-based attenuation map generation for myocardial perfusion SPECT.
European journal of nuclear medicine and molecular imaging
Mar 26, 2020
PURPOSE: Attenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission sc...
A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation.
Medical physics
Jan 28, 2020
PURPOSE: The purpose of this study is to develop a deep learning (DL) method for producing four-dimensional computed tomography (4DCT) ventilation imaging and to evaluate the accuracy of the DL-based ventilation imaging against single-photon emission...
Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation.
Computational and mathematical methods in medicine
Jan 24, 2020
Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion re...
Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.
PloS one
Jan 24, 2020
OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT...
Application of data mining in a cohort of Italian subjects undergoing myocardial perfusion imaging at an academic medical center.
Computer methods and programs in biomedicine
Jan 16, 2020
INTRODUCTION: Coronary artery disease (CAD) is still one of the primary causes of death in the developed countries. Stress single-photon emission computed tomography is used to evaluate myocardial perfusion and ventricular function in patients with s...
Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports.
European journal of nuclear medicine and molecular imaging
Jan 7, 2020
PURPOSE: Basal/acetazolamide brain perfusion single-photon emission computed tomography (SPECT) has been used to evaluate functional hemodynamics in patients with carotid artery stenosis. We aimed to develop a deep learning model as a support system ...
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