Method of bioluminescence imaging for molecular imaging of physiological and pathological processes

Department of Nuclear Medicine, University Hospital Leuven, Herestraat 49, Leuven B-3000, Belgium.
Methods (Impact Factor: 3.65). 04/2009; 48(2):139-45. DOI: 10.1016/j.ymeth.2009.03.013
Source: PubMed


Molecular imaging has emerged as a powerful tool in basic, pre-clinical and clinical research for monitoring a variety of molecular and cellular processes in living organisms. Optical imaging techniques, mainly bioluminescence imaging, have extensively been used to study biological processes because of their exquisite sensitivity and high signal-to noise ratio. However, current applications have mainly been limited to small animals due to attenuation and scattering of light by tissues but efforts are ongoing to overcome these hurdles. Here, we focus on bioluminescence imaging by giving a brief overview of recent advances in instrumentation, current available reporter gene-reporter probe systems and applications such as cell trafficking, protein-protein interactions and imaging endogenous processes.

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    • "The general form of the FD-ERT, Eqs. (1),2), can be adapted to account for fluorescence and bioluminescence effects which have become increasingly important in recent years [30,34–37]. In the case of fluorescence, one equation (ERT 1) is used to model the excitation field inside the medium due to a modulated boundary source. "
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    ABSTRACT: We present the first algorithm for solving the equation of radiative transfer (ERT) in the frequency domain (FD) on three-dimensional block-structured Cartesian grids (BSG). This algorithm allows for accurate modeling of light propagation in media of arbitrary shape with air-tissue refractive index mismatch at the boundary at increased speed compared to currently available structured grid algorithms. To accurately model arbitrarily shaped geometries the algorithm generates BSGs that are finely discretized only near physical boundaries and therefore less dense than fine grids. We discretize the FD-ERT using a combination of the upwind-step method and the discrete ordinates (S(N)) approximation. The source iteration technique is used to obtain the solution. We implement a first order interpolation scheme when traversing between coarse and fine grid regions. Effects of geometry and optical parameters on algorithm performance are evaluated using numerical phantoms (circular, cylindrical, and arbitrary shape) and varying the absorption and scattering coefficients, modulation frequency, and refractive index. The solution on a 3-level BSG is obtained up to 4.2 times faster than the solution on a single fine grid, with minimal increase in numerical error (less than 5%).
    Biomedical Optics Express 10/2010; 1(3):861-878. DOI:10.1364/BOE.1.000861 · 3.65 Impact Factor
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    • "Current in vivo microendoscopy imaging techniques include bioluminescence [2] and fluorescence techniques [3], and one task in the applications for quantitative microendoscopy image computing is that the motion in the microendoscopy image sequences needs to be corrected for better visualization and stable quantitative measures. Many methods such as image registration [4] [5] [6] were proposed for motion correction. "
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    ABSTRACT: In multimodality image-guided intervention for cancer diagnosis, a needle with cannula is first punctured using CT or MRI -guided system to target the tumor, then microendoscopy can be performed using an optical fiber through the same cannula. With real-time optical imaging, the operator can directly determine the malignance of the tumor or perform fine needle aspiration biopsy for further diagnosis. During this operation, stable microendoscopy image series are needed to quantify the tissue properties, but they are often affected by respiratory and heart systole motion even when the interventional probe is held steadily. This paper proposes a microendoscopy motion correction (MMC) algorithm using normalized mutual information (NMI)-based registration and a nonlinear system to model the longitudinal global transformations. Cubature Kalman filter is thus used to solve the underlying longitudinal transformations, which yields more stable and robust motion estimation. After global motion correction, longitudinal deformations among the image sequences are calculated to further refine the local tissue motion. Experimental results showed that compared to global and deformable image registrations, MMC yields more accurate alignment results for both simulated and real data.
    Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Beijing, China, September 19-20, 2010. Proceedings; 01/2010

  • Methods 07/2009; 48(2):81-2. DOI:10.1016/j.ymeth.2009.05.012 · 3.65 Impact Factor
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