Two-step optimization for accelerating deep image prior-based PET image reconstruction.
Radiol Phys Technol
; 17(3): 776-781, 2024 Sep.
Article
em En
| MEDLINE
| ID: mdl-39096446
ABSTRACT
Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Método de Monte Carlo
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Tomografia por Emissão de Pósitrons
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Aprendizado Profundo
Limite:
Humans
Idioma:
En
Revista:
Radiol Phys Technol
Assunto da revista:
BIOFISICA
/
RADIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão