Ensemble of neural networks for 3D position estimation in monolithic PET detectors.
Phys Med Biol
; 64(19): 195010, 2019 10 04.
Article
en En
| MEDLINE
| ID: mdl-31416053
ABSTRACT
We propose an ensemble of multilayer feedforward neural networks to estimate the 3D position of photoelectric interactions in monolithic detectors. The ensemble is trained with data generated from optical Monte Carlo simulations only. The originality of our approach is to exploit simulations to obtain reference data, in combination with a variability reduction that the network ensembles offer, thus, removing the need of extensive per-detector calibration measurements. This procedure delivers an ensemble valid for any detector of the same design. We show the capability of the ensemble to solve the 3D positioning problem through testing four different detector designs with Monte Carlo data, measurements from physical detectors and reconstructed images from the MindView scanner. Network ensembles allow the detector to achieve a 2-2.4 mm FWHM, depending on its design, and the associated reconstructed images present improved SNR, CNR and SSIM when compared to those based on the MindView built-in positioning algorithm.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Encéfalo
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Redes Neurales de la Computación
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Tomografía de Emisión de Positrones
Tipo de estudio:
Health_economic_evaluation
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Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Phys Med Biol
Año:
2019
Tipo del documento:
Article