Accelerated model‐based iterative reconstruction strategy for sparse‐view photoacoustic tomography aided by multi‐channel autoencoder priors

Photoacoustic tomography (PAT) commonly works in sparse view due to data acquisition limitations. However, reconstruction suffers from serious deterioration (e.g., severe artifacts) using traditional algorithms under sparse view. Here, a novel accelerated model‐based iterative reconstruction strateg...

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Bibliographic Details
Published in:Journal of biophotonics Vol. 17; no. 1; pp. e202300281 - n/a
Main Authors: Song, Xianlin, Zhong, Wenhua, Li, Zilong, Peng, Shuchong, Zhang, Hongyu, Wang, Guijun, Dong, Jiaqing, Liu, Xuan, Xu, Xiaoling, Liu, Qiegen
Format: Journal Article
Language:English
Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.01.2024
Wiley Subscription Services, Inc
Subjects:
Algorithms
Blood vessels
Computer Simulation
Constraint modelling
Data acquisition
Experimental data
Image Processing, Computer-Assisted - methods
Image reconstruction
Iterative methods
model‐based iterative reconstruction
multichannel autoencoder priors
Phantoms, Imaging
Photoacoustic effect
photoacoustic tomography
sparse view
Tomography
Tomography, X-Ray Computed - methods
multichannel autoencoder priors
model-based iterative reconstruction
sparse view
photoacoustic tomography
ISSN:1864-063X, 1864-0648, 1864-0648
Online Access:Get full text
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Summary:Photoacoustic tomography (PAT) commonly works in sparse view due to data acquisition limitations. However, reconstruction suffers from serious deterioration (e.g., severe artifacts) using traditional algorithms under sparse view. Here, a novel accelerated model‐based iterative reconstruction strategy for sparse‐view PAT aided by multi‐channel autoencoder priors was proposed. A multi‐channel denoising autoencoder network was designed to learn prior information, which provides constraints for model‐based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The performance of the proposed method was evaluated using blood vessel simulation data and experimental data. The results show that the proposed method can achieve superior sparse‐view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition (e.g., 32 projections) compared with the U‐Net method, with an improvement of 48% in PSNR and 12% in SSIM for in vivo experimental data. A novel accelerated model‐based iterative reconstruction strategy for sparse‐view PAT aided by multi‐channel autoencoder priors was proposed. A multi‐channel denoising autoencoder network was designed to learn prior information, which provides constraints for model‐based iterative reconstruction. This integration accelerates the iteration process, leading to optimal reconstruction outcomes. The results show that the proposed method can achieve superior sparse‐view reconstruction with a significant acceleration of iteration. Notably, the proposed method exhibits excellent performance under extremely sparse condition.
Bibliography:Xianlin Song, Wenhua Zhong, and Zilong Li contributed equally to this work.
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ISSN:1864-063X
1864-0648
1864-0648
DOI:10.1002/jbio.202300281