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Tomography, Emission-Computed, Single-Photon

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A deep learning method for producing ventilation images from 4DCT: First comparison with technegas SPECT ventilation.

Medical physics
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
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
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
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
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 ...

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.

Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure.

Journal of medical imaging and radiation sciences
BACKGROUND: Our previous work with iodine meta-iodobenzylguanidine (I-mIBG) radionuclide imaging among patients with cardiomyopathy reported limitations associated with the prognostic power of global parameters derived from planar imaging [1]. Employ...

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

European journal of nuclear medicine and molecular imaging
PURPOSE: This study investigated the potential of deep convolutional neural networks (CNN) for automatic classification of FP-CIT SPECT in multi-site or multi-camera settings with variable image characteristics.

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

Small (Weinheim an der Bergstrasse, Germany)
Untethered small-scale robots have great potential for biomedical applications. However, critical barriers to effective translation of these miniaturized machines into clinical practice exist. High resolution tracking and imaging in vivo is one of th...

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

Journal of neurology
OBJECTIVES: To provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative I-FP-CIT SPECT striatal indices and support-vector-machine (SVM) analysis.