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Guo K, Zheng Z, Zhong W, Li Z, Wang G, Li J, Cao Y, Wang Y, Lin J, Liu Q, Song X. Score-based generative model-assisted information compensation for high-quality limited-view reconstruction in photoacoustic tomography. PHOTOACOUSTICS 2024; 38:100623. [PMID: 38832333 PMCID: PMC11144813 DOI: 10.1016/j.pacs.2024.100623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 05/11/2024] [Accepted: 05/17/2024] [Indexed: 06/05/2024]
Abstract
Photoacoustic tomography (PAT) regularly operates in limited-view cases owing to data acquisition limitations. The results using traditional methods in limited-view PAT exhibit distortions and numerous artifacts. Here, a novel limited-view PAT reconstruction strategy that combines model-based iteration with score-based generative model was proposed. By incrementally adding noise to the training samples, prior knowledge can be learned from the complex probability distribution. The acquired prior is then utilized as constraint in model-based iteration. The information of missing views can be gradually compensated by cyclic iteration to achieve high-quality reconstruction. The performance of the proposed method was evaluated with the circular phantom and in vivo experimental data. Experimental results demonstrate the outstanding effectiveness of the proposed method in limited-view cases. Notably, the proposed method exhibits excellent performance in limited-view case of 70° compared with traditional method. It achieves a remarkable improvement of 203% in PSNR and 48% in SSIM for the circular phantom experimental data, and an enhancement of 81% in PSNR and 65% in SSIM for in vivo experimental data, respectively. The proposed method has capability of reconstructing PAT images in extremely limited-view cases, which will further expand the application in clinical scenarios.
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Affiliation(s)
| | | | | | | | - Guijun Wang
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Jiahong Li
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Yubin Cao
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Yiguang Wang
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Jiabin Lin
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Qiegen Liu
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Xianlin Song
- School of Information Engineering, Nanchang University, Nanchang 330031, China
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Huang M, Liu W, Sun G, Shi C, Liu X, Han K, Liu S, Wang Z, Xie Z, Guo Q. Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data. BIOMEDICAL OPTICS EXPRESS 2024; 15:28-43. [PMID: 38223183 PMCID: PMC10783920 DOI: 10.1364/boe.506334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/31/2023] [Accepted: 11/21/2023] [Indexed: 01/16/2024]
Abstract
This study presents the Fourier Decay Perception Generative Adversarial Network (FDP-GAN), an innovative approach dedicated to alleviating limitations in photoacoustic imaging stemming from restricted sensor availability and biological tissue heterogeneity. By integrating diverse photoacoustic data, FDP-GAN notably enhances image fidelity and reduces artifacts, particularly in scenarios of low sampling. Its demonstrated effectiveness highlights its potential for substantial contributions to clinical applications, marking a significant stride in addressing pertinent challenges within the realm of photoacoustic acquisition techniques.
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Affiliation(s)
- Mengyuan Huang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Wu Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Guocheng Sun
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Chaojing Shi
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Xi Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Kaitai Han
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Shitou Liu
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zijun Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhennian Xie
- Xiyuan Hospital, Chinese Academy of Traditional Chinese Medicine, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
- School of Mechanical Engineering & Hydrogen Energy Research Centre, Beijing Institute of Petrochemical Technology, Beijing 102617, China
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Song X, Zhong W, Li Z, Peng S, Zhang H, Wang G, Dong J, Liu X, Xu X, Liu Q. Accelerated model-based iterative reconstruction strategy for sparse-view photoacoustic tomography aided by multi-channel autoencoder priors. JOURNAL OF BIOPHOTONICS 2024; 17:e202300281. [PMID: 38010827 DOI: 10.1002/jbio.202300281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023]
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.
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Affiliation(s)
- Xianlin Song
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Wenhua Zhong
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Zilong Li
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Shuchong Peng
- Ji luan Academy, Nanchang University, Nanchang, China
| | - Hongyu Zhang
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Guijun Wang
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Jiaqing Dong
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Xuan Liu
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Xiaoling Xu
- School of Information Engineering, Nanchang University, Nanchang, China
| | - Qiegen Liu
- School of Information Engineering, Nanchang University, Nanchang, China
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Song X, Wang G, Zhong W, Guo K, Li Z, Liu X, Dong J, Liu Q. Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration. PHOTOACOUSTICS 2023; 33:100558. [PMID: 38021282 PMCID: PMC10658608 DOI: 10.1016/j.pacs.2023.100558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/14/2023] [Accepted: 09/16/2023] [Indexed: 12/01/2023]
Abstract
As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in low-quality image in photoacoustic tomography. Here, a novel model-based sparse reconstruction method for photoacoustic tomography via diffusion model was proposed. A score-based diffusion model is designed for learning the prior information of the data distribution. The learned prior information is utilized as a constraint for the data consistency term of an optimization problem based on the least-square method in the model-based iterative reconstruction, aiming to achieve the optimal solution. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction compared with conventional reconstruction methods and U-Net. In particular, under the extreme sparse projection (e.g., 32 projections), the proposed method achieves an improvement of ∼ 260 % in structural similarity and ∼ 30 % in peak signal-to-noise ratio for in vivo data, compared with the conventional delay-and-sum method. This method has the potential to reduce the acquisition time and cost of photoacoustic tomography, which will further expand the application range.
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Affiliation(s)
| | | | - Wenhua Zhong
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Kangjun Guo
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Zilong Li
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Xuan Liu
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Jiaqing Dong
- School of Information Engineering, Nanchang University, Nanchang 330031, China
| | - Qiegen Liu
- School of Information Engineering, Nanchang University, Nanchang 330031, China
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Wang T, Chen C, Shen K, Liu W, Tian C. Streak artifact suppressed back projection for sparse-view photoacoustic computed tomography. APPLIED OPTICS 2023; 62:3917-3925. [PMID: 37706701 DOI: 10.1364/ao.487957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 04/21/2023] [Indexed: 09/15/2023]
Abstract
The development of fast and accurate image reconstruction algorithms under constrained data acquisition conditions is important for photoacoustic computed tomography (PACT). Sparse-view measurements have been used to accelerate data acquisition and reduce system complexity; however, reconstructed images suffer from sparsity-induced streak artifacts. In this paper, a modified back-projection (BP) method termed anti-streak BP is proposed to suppress streak artifacts in sparse-view PACT reconstruction. During the reconstruction process, the anti-streak BP finds the back-projection terms contaminated by high-intensity sources with an outlier detection method. Then, the weights of the contaminated back-projection terms are adaptively adjusted to eliminate the effects of high-intensity sources. The proposed anti-streak BP method is compared with the conventional BP method on both simulation and in vivo data. The anti-streak BP method shows substantially fewer artifacts in the reconstructed images, and the streak index is 54% and 20% lower than that of the conventional BP method on simulation and in vivo data, when the transducer number N=128. The anti-streak BP method is a powerful improvement of the BP method with the ability of artifact suppression.
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