CNN-based PET sinogram repair to mitigate defective block detectors.
Phys Med Biol
; 64(23): 235017, 2019 12 05.
Article
em En
| MEDLINE
| ID: mdl-31569075
ABSTRACT
Positron emission tomography (PET) scanners continue to increase sensitivity and axial coverage by adding an ever expanding array of block detectors. As they age, one or more block detectors may lose sensitivity due to a malfunction or component failure. The sinogram data missing as a result thereof can lead to artifacts and other image degradations. We propose to mitigate the effects of malfunctioning block detectors by carrying out sinogram repair using a deep convolutional neural network. Experiments using whole-body patient studies with varying amounts of raw data removed are used to show that the neural network significantly outperforms previously published methods with respect to normalized mean squared error for raw sinograms, a multi-scale structural similarity measure for reconstructed images and with regard to quantitative accuracy.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Tomografia Computadorizada por Raios X
/
Redes Neurais de Computação
/
Tomografia por Emissão de Pósitrons
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article