Removing Artifacts in Transcranial Photoacoustic Imaging With Polarized Self-Attention Dense-UNet.
Ultrasound Med Biol
; 2024 Jul 15.
Article
em En
| MEDLINE
| ID: mdl-39013725
ABSTRACT
OBJECTIVE:
Photoacoustic imaging (PAI) is a promising transcranial imaging technique. However, the distortion of photoacoustic signals induced by the skull significantly influences its imaging quality. We aimed to use deep learning for removing artifacts in PAI.METHODS:
In this study, we propose a polarized self-attention dense U-Net, termed PSAD-UNet, to correct the distortion and accurately recover imaged objects beneath bone plates. To evaluate the performance of the proposed method, a series of experiments was performed using a custom-built PAI system.RESULTS:
The experimental results showed that the proposed PSAD-UNet method could effectively implement transcranial PAI through a one- or two-layer bone plate. Compared with the conventional delay-and-sum and classical U-Net methods, PSAD-UNet can diminish the influence of bone plates and provide high-quality PAI results in terms of structural similarity and peak signal-to-noise ratio. The 3-D experimental results further confirm the feasibility of PSAD-UNet in 3-D transcranial imaging.CONCLUSION:
PSAD-UNet paves the way for implementing transcranial PAI with high imaging accuracy, which reveals broad application prospects in preclinical and clinical fields.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Ultrasound Med Biol
Ano de publicação:
2024
Tipo de documento:
Article