Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method
Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not ac...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2020-10-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0027207 |
| _version_ | 1828886001073782784 |
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| author | Shenghan Ren Lin Wang Qi Zeng Duofang Chen Xueli Chen Jimin Liang |
| author_facet | Shenghan Ren Lin Wang Qi Zeng Duofang Chen Xueli Chen Jimin Liang |
| author_sort | Shenghan Ren |
| collection | DOAJ |
| description | Diffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction. |
| first_indexed | 2024-12-13T11:37:15Z |
| format | Article |
| id | doaj.art-99bd53e1807a49fa8b6c0f4787959d68 |
| institution | Directory Open Access Journal |
| issn | 2158-3226 |
| language | English |
| last_indexed | 2024-12-13T11:37:15Z |
| publishDate | 2020-10-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj.art-99bd53e1807a49fa8b6c0f4787959d682022-12-21T23:47:45ZengAIP Publishing LLCAIP Advances2158-32262020-10-011010105329105329-910.1063/5.0027207Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo methodShenghan Ren0Lin Wang1Qi Zeng2Duofang Chen3Xueli Chen4Jimin Liang5Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, Shaanxi 710126, ChinaSchool of Information Sciences and Technology, Northwest University, Xi’an, Shaanxi 710127, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, Shaanxi 710126, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, Shaanxi 710126, ChinaEngineering Research Center of Molecular and Neuro Imaging of Ministry of Education, Xi’an, Shaanxi 710126, ChinaSchool of Electronic and Engineering, Xidian University, Xi’an, Shaanxi 7101261, ChinaDiffusion equations (DEs) or simplified spherical harmonic equations are commonly used forward models in bioluminescence tomography (BLT), which are usually numerically calculated by the finite element method to construct the system matrix for reconstruction. However, the numerical solver is not accurate enough. The Monte Carlo (MC) method is regarded as the golden standard for modeling light propagation in biological tissue. In this paper, we proposed a GPU-accelerated inverse MC method for BLT reconstruction. The main feature is that the system matrix for BLT reconstruction is calculated by the MC method instead of the model-based numerical approximation. We evaluated the performance of the proposed method with both phantom-based simulation and animal-based in vivo experiment. The results show that, compared with the DE-based method, the proposed GPU-accelerated inverse MC method is more accurate and effective in BLT reconstruction.http://dx.doi.org/10.1063/5.0027207 |
| spellingShingle | Shenghan Ren Lin Wang Qi Zeng Duofang Chen Xueli Chen Jimin Liang Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method AIP Advances |
| title | Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method |
| title_full | Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method |
| title_fullStr | Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method |
| title_full_unstemmed | Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method |
| title_short | Effective reconstruction of bioluminescence tomography based on GPU-accelerated inverse Monte Carlo method |
| title_sort | effective reconstruction of bioluminescence tomography based on gpu accelerated inverse monte carlo method |
| url | http://dx.doi.org/10.1063/5.0027207 |
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