Novel l_2,1-norm optimization method for fluorescence molecular tomography reconstruction

Abstract

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).

Reconstruction method for fluorescence molecular tomography based on L1-norm primal accelerated proximal gradient

“…The conventional L1-norm regularization method cannot handle the high-noise condition. 14,29 The results usually are imprecise and have a lot of artifacts. In this work, we proposed a robust and efficient method based on PAPG and L1RP, 30,31 which can effectively find the optimal solution.…”

“…Besides, since only the photon distribution on the surface is measurable, the FMT reconstruction is always ill-conditioned. [12][13][14][15] Furthermore, since FMT reconstruction is sensitive to noise, it is difficult to obtain satisfactory results under the influence of the system noise, such as autofluorescence and the shot noise of the charge-coupled device (CCD) camera. 16 Therefore, how to precisely and efficiently solve the inverse problem is important for FMT study.…”

K-Nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography

“…The orthotopic glioma mouse models were established following the protocols of [15] , and 200 μL Tf-IRDye800 [15] were injected into each tumorbearing mouse through the tail vein. Six hours after the injection, the surface fluorescence images and CT data were first collected using a pentamodal imaging system [36,37] and the MRI data (M3TM, Aspect Imaging, Israel) was obtained subsequently.…”

Recent methodology advances in fluorescence molecular tomography

“…Besides the L 1 norm and L 2 norm regularization, other forms of Lp norm regularization were utilized in the reconstruction of FMT [100,114,117,119,120]. These regularization methods (0 ≤ p ≤ 1) not only make full use of the gradient information of the objective functions, like the Tikhonov method, but also retain the advantages of the sparsity regularizations in improving image quality.…”