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Bioluminescence tomography (BLT) is a powerful non-invasive molecular imaging tool for in vivo studies of glioma in mice. However, because of the light scattering and resulted ill-posed problems, it is challenging to develop a sufficient reconstruction method, which can accurately locate the tumor and define the tumor morphology in three-dimension. In this paper, we proposed a novel Gaussian weighted Laplace prior (GWLP) regularization method. It considered the variance of the bioluminescence energy between any two voxels inside an organ had a non-linear inverse relationship with their Gaussian distance to solve the over-smoothed tumor morphology in BLT reconstruction. We compared the GWLP with conventional Tikhonov and Laplace regularization methods through various numerical simulations and in vivo orthotopic glioma mouse model experiments. The in vivo magnetic resonance imaging and ex vivo green fluorescent protein images and hematoxylin-eosin stained images of whole head cryoslicing specimens were utilized as gold standards. The results demonstrated that GWLP achieved the highest accuracy in tumor localization and tumor morphology preservation. To the best of our knowledge, this is the first study that achieved such accurate BLT morphological reconstruction of orthotopic glioma without using any segmented tumor structure from any other structural imaging modalities as the prior for reconstruction guidance. This enabled BLT more suitable and practical for in vivo imaging of orthotopic glioma mouse models.

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Fluorescence molecular tomography (FMT) is a promising tomographic method in preclinical research, which enables noninvasive real-time three-dimensional (3-D) visualization for in vivo studies. The illposedness of the FMT reconstruction problem is one of the many challenges in the studies of FMT. In this paper, we propose a l 2,1 -norm optimization method using a priori information, mainly the structured sparsity of the fluorescent regions for FMT reconstruction. Compared to standard sparsity methods, the structured sparsity methods are often superior in reconstruction accuracy since the structured sparsity utilizes correlations or structures of the reconstructed image. To solve the problem effectively, the Nesterov's method was used to accelerate the computation. To evaluate the performance of the proposed l 2,1 -norm method, numerical phantom experiments and in vivo mouse experiments are conducted. The results show that the proposed method not only achieves accurate and desirable fluorescent source reconstruction, but also demonstrates enhanced robustness to noise. References and links1. W. R. Zipfel, R. M. Williams, and W. W. Webb, "Nonlinear magic: multiphoton microscopy in the biosciences," Nat. Biotechnol. 21(11), 1369-1377 (2003). 2. D. J. Stephens and V. J. Allan, "Light microscopy techniques for live cell imaging," Science 300(5616), 82-86 (2003). 3. V. Ntziachristos, J. Ripoll, L. V. Wang, and R. Weissleder, "Looking and listening to light: the evolution of whole-body photonic imaging," Nat. Biotechnol. 23(3), 313-320 (2005). 4. C. Qin, S. Zhu, and J. Tian, "New optical molecular imaging systems," Curr. Pharm. Biotechnol. 11(6), 620 (2010). 5. J. K. Willmann, N. van Bruggen, L. M. Dinkelborg, and S. S. Gambhir, "Molecular imaging in drug development," Nat. Rev. Drug Discov. 7(7), 591-607 (2008). 6. C. C. Leng and J. Tian, "Mathematical method in optical molecular imaging," Sci. China Life Sci. 58, 1-13 (2015). 7. V. Ntziachristos, "Going deeper than microscopy: the optical imaging frontier in biology," Nat. Methods 7(8), 603-614 (2010). XRay Sci. Technol. 16(4), 225-234 (2008). 43. G. Alexandrakis, F. R. Rannou, and A. F. Chatziioannou, "Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: a computer simulation feasibility study," Phys. Med. Biol. 50(17), 4225-4241 (2005). 44. X. Chen, X. Gao, D. Chen, X. Ma, X. Zhao, M. Shen, X. Li, X. Qu, J. Liang, J. Ripoll, and J. Tian, "3D reconstruction of light flux distribution on arbitrary surfaces from 2D multi-photographic images," Opt. Express 18(19), 19876-19893 (2010). 45. C. Chi, Y. Du, J. Ye, D. Kou, J. Qiu, J. Wang, J. Tian, and X. Chen, "Intraoperative imaging-guided cancer surgery: from current fluorescence molecular imaging methods to future multi-modality imaging technology," Theranostics 4(11), 1072-1084 (2014).