A5089346758- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Dementia and Cognitive Impairment Research
- Radiation Detection and Scintillator Technologies
- Health, Environment, Cognitive Aging
- Industrial Vision Systems and Defect Detection
- Advanced Radiotherapy Techniques
- Color Science and Applications
- Frailty in Older Adults
- Advanced Computational Techniques and Applications
- Surface Roughness and Optical Measurements
Universitat Politècnica de València
2019-2024
Instituto de Instrumentación para Imagen Molecular
2019-2024
Universidad de Granada
2021
Consejo Superior de Investigaciones Científicas
2019
In positron emission tomography (PET) image reconstruction, loss of contrast and incorrect quantification activity are produced due to the effect Compton scattering. Thus, scatter correction becomes essential improve quality. this work, a machine learning approach based on supervised has been considered for in simulated multi-ring PET system using cylindrical phantom. Using positional energy information from both photons detected as input data, we able label each coincidence True or...
There has been a strong interest in using neural networks to solve several tasks PET medical imaging. One of the main problems faced when is quality, quantity, and availability data train algorithms. In order address this issue, we have developed pipeline that enables generation voxelized synthetic phantoms, simulates acquisition scan, reconstructs image from simulated data. achieve these results, pieces software are used different steps pipeline. This solves problem generating diverse...
In this paper we study up to what extent neural networks can be used accurately characterize LCD displays. Using a programmable colorimeter have taken extensive measures for DELL Ultrasharp UP2516D define training and testing data sets that are used, in turn, train validate two networks: one of them using tristimulus values, XYZ, as inputs the other color coordinates, xyY . Both same layer structure which has been experimentally determined. The errors from both models, terms ΔE00...
Abstract Purpose The purpose of this project is to develop and externally validate a Deep Learning (DL) FDG PET imaging algorithm able identify patients with Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) Dementia Lewy Bodies (DLB) among group Mild Cognitive Impairment (MCI). Methods A 3D Convolutional neural network, trained using images from the Neuroimaging Initiative (ADNI) database, was implemented. ADNI dataset used for training testing model consisted 822 subjects (472 AD...