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Optimization on artifacts in photoacoustic images based on spectrum analyses and signal extraction.
Nie, Shibo; Yin, Guanjun; Li, Pan; Guo, Jianzhong.
Afiliação
  • Nie S; Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China.
  • Yin G; Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China.
  • Li P; School of Physics and Electrical Engineering, Weinan Normal University, Wei'Nan 714099, China.
  • Guo J; Key Laboratory of Ultrasound of Shaanxi Province, School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710119, China.
J Acoust Soc Am ; 156(1): 503-510, 2024 Jul 01.
Article em En | MEDLINE | ID: mdl-39013038
ABSTRACT
Photoacoustic (PA) imaging is a promising technology for functional imaging of biological tissues, offering optical contrast and acoustic penetration depth. However, the presence of signal aliasing from multiple PA sources within the same imaging object can introduce artifacts and significantly impact the quality of the PA tomographic images. In this study, an optimized method is proposed to suppress these artifacts and enhance image quality effectively. By leveraging signal time-frequency spectrum, signals from each PA source can be extracted. Subsequently, the images are reconstructed using these extracted signals and fused together to obtain an optimized image. To verify this proposed method, PA imaging experiments were conducted on two phantoms and two in vitro samples and the distribution relative error and root mean square error of the images obtained through conventional and optimized methods were calculated. The results demonstrate that the proposed method successfully suppresses the artifacts and substantially improves the image quality.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article