Small animal imaging, which models almost all human diseases, has been widely used in preclinical research. In this context, optical imaging has attracted great attention over past several years, among which fluorescence imaging has the superior sensitivity due to exogenous contrast agent. In particular, fluorescence tomography (FLT) enables the three-dimensional (3D) quantitative recovery of fluorescent source in a non-invasive and non-radiative manner. However, it remains difficult to improve the FLT performance for more accurate and reliable quantification. One crucial fact is the missing of an accurate and fast model for in vivo light propagation. The popular diffusion approximation often fails in small animal imaging, while radiative transfer equation (RTE) is the most accurate of realistic models. It has shown by several groups that RTE-based reconstructions offer significantly better accuracy than DA-based ones. However, the major issue preventing RTE from being popular is its unpractical computational burden (e.g., it usually takes days to complete one reconstruction on 3D mouse model). To distinguish from most groups working on RTE-based reconstructions, we have recently dedicated in the development of numerical solver of RTE to improve its accuracy, efficiency and flexibility for practical use. In this project, we propose an ultra-fast solver of RTE so that RTE-based FLT is feasible (e.g., one reconstruction takes <2 hours), for which we will develop novel scattering-adaptive computation and implement the parallelization via graphics processing unit (GPU). The proposed solver of RTE will be first-of-its-kind to the best of our knowledge. On the other hand, FLT can be further improved by synergetic combination of linear complex-source formulation, simultaneous correction of optical background and various state-of-art reconstruction techniques, such as L1-promoted sparsity, framelet-regularized smoothness, Bregman method and multilevel approach. This proposal is featured by both an ultra-fast solver of RTE and innovative reconstruction techniques. The overall goal of this proposal is to develop fast, accurate and practical RTE-based FLT for small animal imaging. We are motivated by two main independent hypotheses that (1) GPU parallelization and scattering-adaptive computation will allow the ultra-fast solver of RTE, which makes RTE-based FLT feasible;(2) linear complex-source formulation and simultaneous correction of optical background will allow further significant quantitative improvement of FLT when combined with various start-of-art reconstruction techniques. Upon completion of this project, the proposed methods will have been validated in phantom experiments and applied to small animal studies. The software will be made publicly available on web.
In this project, we will use the most recent development in mathematical theory and computer architecture to improve fluorescence tomography with applications in small animal imaging for human cancer studies. An ultra-fast solver will be developed for the most accurate model - radiative transfer equation, and the state-of-art reconstruction techniques will be incorporated to significantly improve both accuracy and efficiency.
|Nouizi, Farouk; Luk, Alex; Thayer, Dave et al. (2016) Experimental validation of a high-resolution diffuse optical imaging modality: photomagnetic imaging. J Biomed Opt 21:16009|
|Nouizi, F; Erkol, H; Luk, A et al. (2016) An accelerated photo-magnetic imaging reconstruction algorithm based on an analytical forward solution and a fast Jacobian assembly method. Phys Med Biol 61:7448-7465|
|Erkol, H; Nouizi, F; Unlu, M B et al. (2015) An extended analytical approach for diffuse optical imaging. Phys Med Biol 60:5103-21|
|Lin, Yuting; Nouizi, Farouk; Kwong, Tiffany C et al. (2015) Simulation-based evaluation of the resolution and quantitative accuracy of temperature-modulated fluorescence tomography. Appl Opt 54:7612-21|
|Nouizi, Farouk; Kwong, Tiffany C; Cho, Jaedu et al. (2015) Implementation of a new scanning method for high-resolution fluorescence tomography using thermo-sensitive fluorescent agents. Opt Lett 40:4991-4|
|Erkol, Hakan; Nouizi, Farouk; Luk, Alex et al. (2015) Comprehensive analytical model for CW laser induced heat in turbid media. Opt Express 23:31069-84|
|Cai, Jian-Feng; Jia, Xun; Gao, Hao et al. (2014) Cine cone beam CT reconstruction using low-rank matrix factorization: algorithm and a proof-of-principle study. IEEE Trans Med Imaging 33:1581-91|
|Gao, Hao; Li, Longchuan; Zhang, Kai et al. (2014) PCLR: phase-constrained low-rank model for compressive diffusion-weighted MRI. Magn Reson Med 72:1330-41|
|Lin, Yuting; Gao, Hao; Thayer, David et al. (2013) Photo-magnetic imaging: resolving optical contrast at MRI resolution. Phys Med Biol 58:3551-62|
|Gao, Hao; Zhao, Hongkai (2013) ANALYSIS OF A NUMERICAL SOLVER FOR RADIATIVE TRANSPORT EQUATION. Math Comput 82:153-172|
Showing the most recent 10 out of 17 publications