Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model
Photoacoustic tomography, a novel non-invasive imaging modality, combines the principles of optical and acoustic imaging for use in biomedical applications. In scenarios where photoacoustic signal acquisition is insufficient due to sparse-view sampling, conventional direct reconstruction methods sig...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Photoacoustics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213597924000879 |
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| author | Zilong Li Jiabin Lin Yiguang Wang Jiahong Li Yubin Cao Xuan Liu Wenbo Wan Qiegen Liu Xianlin Song |
| author_facet | Zilong Li Jiabin Lin Yiguang Wang Jiahong Li Yubin Cao Xuan Liu Wenbo Wan Qiegen Liu Xianlin Song |
| author_sort | Zilong Li |
| collection | DOAJ |
| description | Photoacoustic tomography, a novel non-invasive imaging modality, combines the principles of optical and acoustic imaging for use in biomedical applications. In scenarios where photoacoustic signal acquisition is insufficient due to sparse-view sampling, conventional direct reconstruction methods significantly degrade image resolution and generate numerous artifacts. To mitigate these constraints, a novel sinogram-domain priors guided extremely sparse-view reconstruction method for photoacoustic tomography boosted by enhanced diffusion model is proposed. The model learns prior information from the data distribution of sinograms under full-ring, 512-projections. In iterative reconstruction, the prior information serves as a constraint in least-squares optimization, facilitating convergence towards more plausible solutions. The performance of the method is evaluated using blood vessel simulation, phantoms, and in vivo experimental data. Subsequently, the transformation of the reconstructed sinograms into the image domain is achieved through the delay-and-sum method, enabling a thorough assessment of the proposed method. The results show that the proposed method demonstrates superior performance compared to the U-Net method, yielding images of markedly higher quality. Notably, for in vivo data under 32 projections, the sinogram structural similarity improved by ∼21 % over U-Net, and the image structural similarity increased by ∼51 % and ∼84 % compared to U-Net and delay-and-sum methods, respectively. The reconstruction in the sinogram domain for photoacoustic tomography enhances sparse-view imaging capabilities, potentially expanding the applications of photoacoustic tomography. |
| format | Article |
| id | doaj-art-4b1f1fbbb8af42b2993f3979deb2d132 |
| institution | DOAJ |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-4b1f1fbbb8af42b2993f3979deb2d1322025-08-20T03:01:00ZengElsevierPhotoacoustics2213-59792025-02-014110067010.1016/j.pacs.2024.100670Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion modelZilong Li0Jiabin Lin1Yiguang Wang2Jiahong Li3Yubin Cao4Xuan Liu5Wenbo Wan6Qiegen Liu7Xianlin Song8School of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaSchool of Information Engineering, Nanchang University, Nanchang 330031, ChinaCorresponding author.; School of Information Engineering, Nanchang University, Nanchang 330031, ChinaPhotoacoustic tomography, a novel non-invasive imaging modality, combines the principles of optical and acoustic imaging for use in biomedical applications. In scenarios where photoacoustic signal acquisition is insufficient due to sparse-view sampling, conventional direct reconstruction methods significantly degrade image resolution and generate numerous artifacts. To mitigate these constraints, a novel sinogram-domain priors guided extremely sparse-view reconstruction method for photoacoustic tomography boosted by enhanced diffusion model is proposed. The model learns prior information from the data distribution of sinograms under full-ring, 512-projections. In iterative reconstruction, the prior information serves as a constraint in least-squares optimization, facilitating convergence towards more plausible solutions. The performance of the method is evaluated using blood vessel simulation, phantoms, and in vivo experimental data. Subsequently, the transformation of the reconstructed sinograms into the image domain is achieved through the delay-and-sum method, enabling a thorough assessment of the proposed method. The results show that the proposed method demonstrates superior performance compared to the U-Net method, yielding images of markedly higher quality. Notably, for in vivo data under 32 projections, the sinogram structural similarity improved by ∼21 % over U-Net, and the image structural similarity increased by ∼51 % and ∼84 % compared to U-Net and delay-and-sum methods, respectively. The reconstruction in the sinogram domain for photoacoustic tomography enhances sparse-view imaging capabilities, potentially expanding the applications of photoacoustic tomography.http://www.sciencedirect.com/science/article/pii/S2213597924000879Photoacoustic tomographyNon-invasive imaging techniquesSparse-viewReconstructionsSinogram domainsDiffusion models |
| spellingShingle | Zilong Li Jiabin Lin Yiguang Wang Jiahong Li Yubin Cao Xuan Liu Wenbo Wan Qiegen Liu Xianlin Song Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model Photoacoustics Photoacoustic tomography Non-invasive imaging techniques Sparse-view Reconstructions Sinogram domains Diffusion models |
| title | Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model |
| title_full | Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model |
| title_fullStr | Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model |
| title_full_unstemmed | Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model |
| title_short | Ultra-sparse reconstruction for photoacoustic tomography: Sinogram domain prior-guided method exploiting enhanced score-based diffusion model |
| title_sort | ultra sparse reconstruction for photoacoustic tomography sinogram domain prior guided method exploiting enhanced score based diffusion model |
| topic | Photoacoustic tomography Non-invasive imaging techniques Sparse-view Reconstructions Sinogram domains Diffusion models |
| url | http://www.sciencedirect.com/science/article/pii/S2213597924000879 |
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