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Li S, Wang B, Yu J, Kang D, He X, Guo H, He X. 3D-deep optical learning: a multimodal and multitask reconstruction framework for optical molecular tomography. OPTICS EXPRESS 2023; 31:23768-23789. [PMID: 37475220 DOI: 10.1364/oe.490139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 06/16/2023] [Indexed: 07/22/2023]
Abstract
Optical molecular tomography (OMT) is an emerging imaging technique. To date, the poor universality of reconstruction algorithms based on deep learning for various imaged objects and optical probes limits the development and application of OMT. In this study, based on a new mapping representation, a multimodal and multitask reconstruction framework-3D deep optical learning (3DOL), was presented to overcome the limitations of OMT in universality by decomposing it into two tasks, optical field recovery and luminous source reconstruction. Specifically, slices of the original anatomy (provided by computed tomography) and boundary optical measurement of imaged objects serve as inputs of a recurrent convolutional neural network encoded parallel to extract multimodal features, and 2D information from a few axial planes within the samples is explicitly incorporated, which enables 3DOL to recognize different imaged objects. Subsequently, the optical field is recovered under the constraint of the object geometry, and then the luminous source is segmented by a learnable Laplace operator from the recovered optical field, which obtains stable and high-quality reconstruction results with extremely few parameters. This strategy enable 3DOL to better understand the relationship between the boundary optical measurement, optical field, and luminous source to improve 3DOL's ability to work in a wide range of spectra. The results of numerical simulations, physical phantoms, and in vivo experiments demonstrate that 3DOL is a compatible deep-learning approach to tomographic imaging diverse objects. Moreover, the fully trained 3DOL under specific wavelengths can be generalized to other spectra in the 620-900 nm NIR-I window.
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Liu Y, Chu M, Guo H, Hu X, Yu J, He X, Yi H, He X. Multispectral Differential Reconstruction Strategy for Bioluminescence Tomography. Front Oncol 2022; 12:768137. [PMID: 35251958 PMCID: PMC8895370 DOI: 10.3389/fonc.2022.768137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Bioluminescence tomography (BLT) is a promising in vivo molecular imaging tool that allows non-invasive monitoring of physiological and pathological processes at the cellular and molecular levels. However, the accuracy of the BLT reconstruction is significantly affected by the forward modeling errors in the simplified photon propagation model, the measurement noise in data acquisition, and the inherent ill-posedness of the inverse problem. In this paper, we present a new multispectral differential strategy (MDS) on the basis of analyzing the errors generated from the simplification from radiative transfer equation (RTE) to diffusion approximation and data acquisition of the imaging system. Through rigorous theoretical analysis, we learn that spectral differential not only can eliminate the errors caused by the approximation of RTE and imaging system measurement noise but also can further increase the constraint condition and decrease the condition number of system matrix for reconstruction compared with traditional multispectral (TM) reconstruction strategy. In forward simulations, energy differences and cosine similarity of the measured surface light energy calculated by Monte Carlo (MC) and diffusion equation (DE) showed that MDS can reduce the systematic errors in the process of light transmission. In addition, in inverse simulations and in vivo experiments, the results demonstrated that MDS was able to alleviate the ill-posedness of the inverse problem of BLT. Thus, the MDS method had superior location accuracy, morphology recovery capability, and image contrast capability in the source reconstruction as compared with the TM method and spectral derivative (SD) method. In vivo experiments verified the practicability and effectiveness of the proposed method.
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Affiliation(s)
- Yanqiu Liu
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Mengxiang Chu
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- Network and Data Center, Northwest University, Xi’an, China
| | - Hongbo Guo
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
- *Correspondence: Hongbo Guo, ; Xiaowei He,
| | - Xiangong Hu
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- Network and Data Center, Northwest University, Xi’an, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi’an, China
| | - Xuelei He
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Huangjian Yi
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
| | - Xiaowei He
- The Xi’an Key Laboratory of Radiomics and Intelligent Perception, Xi’an, China
- School of Information Sciences and Technology, Northwest University, Xi’an, China
- Network and Data Center, Northwest University, Xi’an, China
- *Correspondence: Hongbo Guo, ; Xiaowei He,
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Liu Y, Hu X, Chu M, Guo H, Yu J, He X. A Finite Element Mesh Regrouping Strategy-Based Hybrid Light Transport Model for Enhancing the Efficiency and Accuracy of XLCT. Front Oncol 2022; 11:751139. [PMID: 35111664 PMCID: PMC8801618 DOI: 10.3389/fonc.2021.751139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 12/10/2021] [Indexed: 11/19/2022] Open
Abstract
X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality in optical molecular imaging, which has attracted more attention and has been widely studied. In XLCT, the accuracy and operational efficiency of an optical transmission model play a decisive role in the rapid and accurate reconstruction of light sources. For simulation of optical transmission characteristics in XLCT, considering the limitations of the diffusion equation (DE) and the time and memory costs of simplified spherical harmonic approximation equation (SPN), a hybrid light transport model needs to be built. DE and SPN models are first-order and higher-order approximations of RTE, respectively. Due to the discontinuity of the regions using the DE and SPN models and the inconsistencies of the system matrix dimensions constructed by the two models in the solving process, the system matrix construction of a hybrid light transmission model is a problem to be solved. We provided a new finite element mesh regrouping strategy-based hybrid light transport model for XLCT. Firstly, based on the finite element mesh regrouping strategy, two separate meshes can be obtained. Thus, for DE and SPN models, the system matrixes and source weight matrixes can be calculated separately in two respective mesh systems. Meanwhile, some parallel computation strategy can be combined with finite element mesh regrouping strategy to further save the system matrix calculation time. Then, the two system matrixes with different dimensions were coupled though repeated nodes were processed according to the hybrid boundary conditions, the two meshes were combined into a regrouping mesh, and the hybrid optical transmission model was established. In addition, the proposed method can reduce the computational memory consumption than the previously proposed hybrid light transport model achieving good balance between computational accuracy and efficiency. The forward numerical simulation results showed that the proposed method had better transmission accuracy and achieved a balance between efficiency and accuracy. The reverse simulation results showed that the proposed method had superior location accuracy, morphological recovery capability, and image contrast capability in source reconstruction. In-vivo experiments verified the practicability and effectiveness of the proposed method.
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Affiliation(s)
- Yanqiu Liu
- Key Laboratory for Radiomics and Intelligent Sense of Xi'an, Northwest University, Xi'an, China.,School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Xiangong Hu
- Key Laboratory for Radiomics and Intelligent Sense of Xi'an, Northwest University, Xi'an, China.,Network and Data Center, Northwest University, Xi'an, China
| | - Mengxiang Chu
- Key Laboratory for Radiomics and Intelligent Sense of Xi'an, Northwest University, Xi'an, China.,Network and Data Center, Northwest University, Xi'an, China
| | - Hongbo Guo
- Key Laboratory for Radiomics and Intelligent Sense of Xi'an, Northwest University, Xi'an, China.,School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Jingjing Yu
- School of Physics and Information Technology, Shaanxi Normal University, Xi'an, China
| | - Xiaowei He
- Key Laboratory for Radiomics and Intelligent Sense of Xi'an, Northwest University, Xi'an, China.,School of Information Sciences and Technology, Northwest University, Xi'an, China.,Network and Data Center, Northwest University, Xi'an, China
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Wang L, Zhu W, Zhang Y, Chen S, Yang D. Harnessing the Power of Hybrid Light Propagation Model for Three-Dimensional Optical Imaging in Cancer Detection. Front Oncol 2021; 11:750764. [PMID: 34804938 PMCID: PMC8601256 DOI: 10.3389/fonc.2021.750764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 08/30/2021] [Indexed: 12/04/2022] Open
Abstract
Optical imaging is an emerging technology capable of qualitatively and quantitatively observing life processes at the cellular or molecular level and plays a significant role in cancer detection. In particular, to overcome the disadvantages of traditional optical imaging that only two-dimensionally and qualitatively detect biomedical information, the corresponding three-dimensional (3D) imaging technology is intensively explored to provide 3D quantitative information, such as localization and distribution and tumor cell volume. To retrieve these information, light propagation models that reflect the interaction between light and biological tissues are an important prerequisite and basis for 3D optical imaging. This review concentrates on the recent advances in hybrid light propagation models, with particular emphasis on their powerful use for 3D optical imaging in cancer detection. Finally, we prospect the wider application of the hybrid light propagation model and future potential of 3D optical imaging in cancer detection.
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Affiliation(s)
- Lin Wang
- School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Wentao Zhu
- Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, China
| | - Ying Zhang
- Zhejiang Lab, Research Center for Healthcare Data Science, Hangzhou, China
| | - Shangdong Chen
- School of Information Sciences and Technology, Northwest University, Xi'an, China
| | - Defu Yang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
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Yang D, Wang L, Chen D, Yan C, He X, Liang J, Chen X. Filtered maximum likelihood expectation maximization based global reconstruction for bioluminescence tomography. Med Biol Eng Comput 2018; 56:2067-2081. [PMID: 29770920 DOI: 10.1007/s11517-018-1842-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Accepted: 05/04/2018] [Indexed: 12/17/2022]
Abstract
The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment. Graphical abstract The filtered MLEM-based global reconstruction method for BLT.
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Affiliation(s)
- Defu Yang
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Lin Wang
- School of Information Sciences and Technology, Northwest University, Xi'an, 710126, China
| | - Dongmei Chen
- College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Chenggang Yan
- Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Xiaowei He
- School of Information Sciences and Technology, Northwest University, Xi'an, 710126, China
| | - Jimin Liang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710127, China
| | - Xueli Chen
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710127, China.
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Feng J, Jia K, Li Z, Pogue BW, Yang M, Wang Y. Bayesian sparse-based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational time. JOURNAL OF BIOPHOTONICS 2018; 11:e201700214. [PMID: 29119702 DOI: 10.1002/jbio.201700214] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 11/07/2017] [Indexed: 06/07/2023]
Abstract
Bioluminescence tomography (BLT) provides fundamental insight into biological processes in vivo. To fully realize its potential, it is important to develop image reconstruction algorithms that accurately visualize and quantify the bioluminescence signals taking advantage of limited boundary measurements. In this study, a new 2-step reconstruction method for BLT is developed by taking advantage of the sparse a priori information of the light emission using multispectral measurements. The first step infers a wavelength-dependent prior by using all multi-wavelength measurements. The second step reconstructs the source distribution based on this developed prior. Simulation, phantom and in vivo results were performed to assess and compare the accuracy and the computational efficiency of this algorithm with conventional sparsity-promoting BLT reconstruction algorithms, and results indicate that the position errors are reduced from a few millimeters down to submillimeter, and reconstruction time is reduced by 3 orders of magnitude in most cases, to just under a few seconds. The recovery of single objects and multiple (2 and 3) small objects is simulated, and the recovery of images of a mouse phantom and an experimental animal with an existing luminescent source in the abdomen is demonstrated. Matlab code is available at https://github.com/jinchaofeng/code/tree/master.
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Affiliation(s)
- Jinchao Feng
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124
| | - Kebin Jia
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
- Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124
| | - Zhe Li
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
| | - Brian W Pogue
- Thayer school of Engineering, Dartmouth College, Hanover, New Hampshire
| | - Mingjie Yang
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
| | - Yaqi Wang
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Beijing Laboratory of Advanced Information Networks, Beijing, China
- Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China
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Ancora D, Zacharopoulos A, Ripoll J, Zacharakis G. Fluorescence Diffusion in the Presence of Optically Clear Tissues in a Mouse Head Model. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1086-1093. [PMID: 28055860 DOI: 10.1109/tmi.2016.2646518] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Diffuse Optical Tomography commonly neglects or assumes as insignificant the presence of optically clear regions in biological tissues, estimating their contribution as a small perturbation to light transport. The inaccuracy introduced by this practice is examined in detail in the context of a complete, based on realistic geometry, virtual fluorescence Diffuse Optical Tomography experiment where a mouse head is imaged in the presence of cerebral spinal fluid. Despite the small thickness of such layer, we point out that an error is introduced when neglecting it from the model with possibly reduction in the accuracy of the reconstruction and localization of the fluorescence distribution within the brain. The results obtained in the extensive study presented here suggest that fluorescence diffuse neuroimaging studies can be improved in terms of quantitative and qualitative reconstruction by accurately taking into account optically transparent regions especially in the cases where the reconstruction is aided by the prior knowledge of the structural geometry of the specimen. Thus, this has only recently become an affordable choice, thanks to novel computation paradigms that allow to run Monte Carlo photon propagation on a simple graphic card, hence speeding up the process a thousand folds compared to CPU-based solutions.
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Xie T, Zaidi H. Development of computational small animal models and their applications in preclinical imaging and therapy research. Med Phys 2016; 43:111. [PMID: 26745904 DOI: 10.1118/1.4937598] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
The development of multimodality preclinical imaging techniques and the rapid growth of realistic computer simulation tools have promoted the construction and application of computational laboratory animal models in preclinical research. Since the early 1990s, over 120 realistic computational animal models have been reported in the literature and used as surrogates to characterize the anatomy of actual animals for the simulation of preclinical studies involving the use of bioluminescence tomography, fluorescence molecular tomography, positron emission tomography, single-photon emission computed tomography, microcomputed tomography, magnetic resonance imaging, and optical imaging. Other applications include electromagnetic field simulation, ionizing and nonionizing radiation dosimetry, and the development and evaluation of new methodologies for multimodality image coregistration, segmentation, and reconstruction of small animal images. This paper provides a comprehensive review of the history and fundamental technologies used for the development of computational small animal models with a particular focus on their application in preclinical imaging as well as nonionizing and ionizing radiation dosimetry calculations. An overview of the overall process involved in the design of these models, including the fundamental elements used for the construction of different types of computational models, the identification of original anatomical data, the simulation tools used for solving various computational problems, and the applications of computational animal models in preclinical research. The authors also analyze the characteristics of categories of computational models (stylized, voxel-based, and boundary representation) and discuss the technical challenges faced at the present time as well as research needs in the future.
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Affiliation(s)
- Tianwu Xie
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland; Geneva Neuroscience Center, Geneva University, Geneva CH-1205, Switzerland; and Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands
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Chen X, Yang D, Sun F, Cao X, Liang J. Adaptively Alternative Light-Transport-Model-Based Three-Dimensional Optical Imaging for Longitudinal and Quantitative Monitoring of Gastric Cancer in Live Animal. IEEE Trans Biomed Eng 2015; 63:2095-107. [PMID: 26700857 DOI: 10.1109/tbme.2015.2510369] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE The existence of void regions and the complexity of structural heterogeneity and optical specificity are two challenges encountered in three-dimensional (3-D) optical imaging of in situ gastric cancer. An adaptively alternative light-transport-model-based 3-D optical imaging method was developed for addressing these challenges. METHODS In this newly developed imaging method, both the anatomical structure and optical properties are considered as a priori information, which makes the full use of the specificity of the biological tissues and improves both the quality and efficiency of the reconstructed images. The soul of the adaptively alternative technique is technique is configured to first classify the tissues based on the anatomical structure and optical properties and then select various equations to specifically characterize the light transport in different categories of tissues. RESULTS A series of rigorous simulations were conducted to demonstrate the performance of the hybrid light transport model, and the quality of the reconstructed images was then evaluated using a digital-mouse-based gastric cancer mimicing simulation, which showed that the localization error was less than 1 mm and the quantification error was approximately 10%. Finally, the applicability of the proposed method for in vivo imaging of gastric cancer was illustrated using groups of gastric cancer-bearing mice, which demonstrated the ability of the proposed method for longitudinal and quantitative monitoring of gastric cancer. CONCLUSION The developed method offers improved probability and great potential in the applications of earlier detecting in situ gastric cancer and longitudinal and quantitative observation of its development.
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Chen X, Sun F, Yang D, Ren S, Zhang Q, Liang J. Hybrid simplified spherical harmonics with diffusion equation for light propagation in tissues. Phys Med Biol 2015; 60:6305-22. [DOI: 10.1088/0031-9155/60/16/6305] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Yang D, Chen X, Cao X, Wang J, Liang J, Tian J. Performance investigation of SP3 and diffusion approximation for three-dimensional whole-body optical imaging of small animals. Med Biol Eng Comput 2015; 53:805-14. [PMID: 25850985 DOI: 10.1007/s11517-015-1293-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Accepted: 03/26/2015] [Indexed: 01/31/2023]
Abstract
The third-order simplified harmonic spherical approximation (SP3) and diffusion approximation (DA) equations have been widely used in the three-dimensional (3D) whole-body optical imaging of small animals. With different types of tissues, which were classified by the ratio of µ s'/µ ɑ, the two equations have their own application scopes. However, the classification criterion was blurring and unreasonable, and the scope has not been systematically investigated until now. In this study, a new criterion for classifying tissues was established based on the absolute value of absorption and reduced scattering coefficients. Using the newly defined classification criterion, the performance and applicability of the SP3 and DA equations were evaluated with a series of investigation experiments. Extensive investigation results showed that the SP3 equation exhibited a better performance and wider applicability than the DA one in most of the observed cases, especially in tissues of low-scattering-low-absorption and low-scattering-high-absorption range. For the case of tissues with the high-scattering-low-absorption properties, a similar performance was observed for both the SP3 and the DA equations, in which case the DA was the preferred option for 3D whole-body optical imaging. Results of this study would provide significant reference for the study of hybrid light transport models.
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Affiliation(s)
- Defu Yang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Xueli Chen
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
| | - Xu Cao
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Jing Wang
- Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Jimin Liang
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Jie Tian
- Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
- Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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Cerussi AE, Mishra N, You J, Bhandarkar N, Wong B. Monte Carlo modeling of light propagation in the human head for applications in sinus imaging. JOURNAL OF BIOMEDICAL OPTICS 2015; 20:035004. [PMID: 25781310 PMCID: PMC4362326 DOI: 10.1117/1.jbo.20.3.035004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2014] [Accepted: 02/05/2015] [Indexed: 06/04/2023]
Abstract
Sinus blockages are a common reason for physician visits, affecting one out of seven people in the United States, and often require medical treatment. Diagnosis in the primary care setting is challenging because symptom criteria (via detailed clinical history) plus objective imaging [computed tomography (CT) or endoscopy] are recommended. Unfortunately, neither option is routinely available in primary care. We previously demonstrated that low-cost near-infrared (NIR) transillumination correlates with the bulk findings of sinus opacity measured by CT. We have upgraded the technology, but questions of source optimization, anatomical influence, and detection limits remain. In order to begin addressing these questions, we have modeled NIR light propagation inside a three-dimensional adult human head constructed via CT images using a mesh-based Monte Carlo algorithm (MMCLAB). In this application, the sinus itself, which when healthy is a void region (e.g., nonscattering), is the region of interest. We characterize the changes in detected intensity due to clear (i.e., healthy) versus blocked sinuses and the effect of illumination patterns. We ran simulations for two clinical cases and compared simulations with measurements. The simulations presented herein serve as a proof of concept that this approach could be used to understand contrast mechanisms and limitations of NIR sinus imaging.
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Affiliation(s)
- Albert E. Cerussi
- University of California Irvine, Beckman Laser Institute, 1002 Health Sciences Road East, Irvine, California 92617, United States
| | - Nikhil Mishra
- University of California Irvine, Beckman Laser Institute, 1002 Health Sciences Road East, Irvine, California 92617, United States
| | - Joon You
- Praxis Biosciences, Irvine, California 92617, United States
| | - Naveen Bhandarkar
- University of California, Department of Head and Neck Surgery, 101 The City Drive South, Irvine, Orange, California 92868, United States
| | - Brian Wong
- University of California Irvine, Beckman Laser Institute, 1002 Health Sciences Road East, Irvine, California 92617, United States
- University of California, Department of Head and Neck Surgery, 101 The City Drive South, Irvine, Orange, California 92868, United States
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