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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| Vydané v: | Journal of biophotonics Ročník 17; číslo 1; s. e202300281 - n/a |
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| Hlavní autori: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Weinheim
WILEY‐VCH Verlag GmbH & Co. KGaA
01.01.2024
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| Abstract | 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. |
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| AbstractList | 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. 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.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. 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. |
| Author | Liu, Xuan Li, Zilong Xu, Xiaoling Liu, Qiegen Wang, Guijun Song, Xianlin Zhong, Wenhua Peng, Shuchong Zhang, Hongyu Dong, Jiaqing |
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| Keywords | multichannel autoencoder priors model-based iterative reconstruction sparse view photoacoustic tomography |
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| SubjectTerms | 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 |
| Title | Accelerated model‐based iterative reconstruction strategy for sparse‐view photoacoustic tomography aided by multi‐channel autoencoder priors |
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