The fluorescence protein has proven to be a powerful probe for investigating the functionalities of cells and organisms in vivo. The animal models of tumors that use a stable expression of fluorescent proteins have made it possible to observe directly cellular behaviors and biological interactions in living animals. Fluorescence protein imaging can be used to monitor physiological and pathological activities at molecular levels, specially visualize primary tumor growth, tumor cell metastasis, and the relationship between the tumor and its microenvironment. Current planar fluorescence protein imaging only provides good resolution near the skin surface, and cannot resolve depth and quantify features. Fluorescence protein tomography promises to offer a superior imaging performance to localize and quantify fluorescence proteins in vivo, and significantly enhance the utilities of fluorescent proteins in animal and human studies. However, the fluorescence proteins emit photons in the visible light spectrum, where the biological tissue absorbs photons much more strongly than in the near-infrared range. The popular diffusion approximation model is not suitable to describe the fluorescent photon propagation in biological tissues. The physical model mismatch would significantly compromise the quality of fluorescent tomographic reconstruction. Moreover, fluorescence protein tomography is a typical underdetermined problem. The uniqueness and stability of reconstruction remain major technical challenges. To solve the current major limitations of fluorescence tomographic imaging, the overall goal of this project is to develop a novel photon propagation model, associated reconstruction methods and an imaging system to localize and quantify the fluorescence cells in a living mouse.
The specific aims are to (1) design a novel three-mirror-based imaging system for simultaneous acquisition of multi-view photon signals. The system unifies the transillumination and epi-illumination imaging modes, and offers a notable capability of probing both deeply-seated probes and superficial targets in the mouse;(2) develop a novel phase approximation model to describe photon propagation accurately in the tissues over the visible light spectral range instead of using the popular diffusion approximation model;(3) establish an optimal numerical model to describe the mouse anatomy and tissue optical properties;(4) present a differential evolution (DE) approach for the fluorescent source reconstruction. This DE algorithm is able to find the true global optimization solution at a fast convergence rate, making the fluorescence tomographic imaging more accurate and stable, and (5) validate the proposed fluorescence protein tomography system and methods in numerical simulation, phantom experiments and mouse studies. Upon the completion of this project, the system will have been validated with <0.7mm accuracy in fluorescent source localization and <15% error in light energy estimation, which represent >30% improvement as compared to the performance of the current systems.
In this project, we propose a novel photon propagation model, associated reconstruction methods and an imaging system to solve the current major limitations of fluorescence tomographic imaging, which would significantly enhance the accuracy and stability of tomographic imaging of fluorescence proteins. This would be important to study a variety of physiological and pathological processes in living animals and patients, and monitor primary tumor growth, tumor cell metastasis, pharmacokinetics and therapeutic responses.
| Cong, W; Pfeiffer, F; Bech, M et al. (2012) X-ray dark-field imaging modeling. J Opt Soc Am A Opt Image Sci Vis 29:908-12 |
| Cong, Alexander X; Hofmann, Matthias C; Cong, Wenxiang et al. (2011) Monte Carlo fluorescence microtomography. J Biomed Opt 16:070501 |
| Cong, Wenxiang; Shen, Haiou; Wang, Ge (2011) Spectrally resolving and scattering-compensated x-ray luminescence/fluorescence computed tomography. J Biomed Opt 16:066014 |
| Cong, W; Wang, G (2011) X-ray scattering tomography for biological applications. J Xray Sci Technol 19:219-27 |
| Cong, Alexander; Cong, Wenxiang; Lu, Yujie et al. (2010) Differential evolution approach for regularized bioluminescence tomography. IEEE Trans Biomed Eng 57:2229-38 |
| Cong, W; Wang, G (2010) Bioluminescence tomography based on the phase approximation model. J Opt Soc Am A Opt Image Sci Vis 27:174-9 |
| Cong, Wenxiang; Wang, Ge (2010) Higher-order phase shift reconstruction approach. Med Phys 37:5238-42 |
