Abstract
Bioluminescence imaging (BLI) is a non-contact, optical imaging technique based on measurement of emitted light due to an internal source, which is then often directly related to cellular activity. It is widely used in pre-clinical small animal imaging studies to assess the progression of diseases such as cancer, aiding in the development of new treatments and therapies. For many applications, the quantitative assessment of accurate cellular activity and spatial distribution is desirable as it would enable direct monitoring for prognostic evaluation. This requires quantitative spatially-resolved measurements of bioluminescence source strength inside the animal to be obtained from BLI images. This is the goal of bioluminescence tomography (BLT) in which a model of light propagation through tissue is combined with an optimization algorithm to reconstruct a map of the underlying source distribution. As most models consider only the propagation of light from internal sources to the animal skin surface, an additional challenge is accounting for the light propagation from the skin to the optical detector (e.g. camera). Existing approaches typically use a model of the imaging system optics (e.g. ray-tracing, analytical optical models) or approximate corrections derived from calibration measurements. However, these approaches are typically computationally intensive or of limited accuracy. In this work, a new approach is presented in which, rather than directly using BLI images acquired at several wavelengths, the spectral derivative of that data (difference of BLI images at adjacent wavelengths) is used in BLT. As light at similar wavelengths encounters a near-identical system response (path through the optics etc.) this eliminates the need for additional corrections or system models. This approach is applied to BLT with simulated and experimental phantom data and shown that the error in reconstructed source intensity is reduced from 49% to 4%. Qualitatively, the accuracy of source localization is improved in both simulated and experimental data, as compared to reconstruction using the standard approach. The outlined algorithm can widely be adapted to all commercial systems without any further technological modifications.
Highlights
Bioluminescence Imaging (BLI) is a highly sensitive and non-invasive pre-clinical imaging technique based on the detection of visible and near-infrared light produced by, for example, luciferase-catalyzed reactions [1]
To achieve quantitatively accurate recovery of sources in a complex and heterogeneous model, there is a need to derive and construct a robust method that can accurately model light propagation in heterogeneous and complex tissue, using for example, the Finite Element Method (FEM) of light propagation in tissue [10]. 3D modeling and reconstruction algorithm as applied to multi-wavelength 3D bioluminescence tomography (BLT) image reconstruction have widely been developed and to improve the image recovery accuracy and computation time, we have previously reported the reciprocity approach [5], which is similar to that used in Diffuse Optical Tomography (DOT) and Fluorescence DOT [11]
It is apparent that assuming the offset n can be defined by the relationship shown in Fig. 2(b), the recovered maps of the spatial distribution of the bioluminescence source will be heavily corrupted through the inversion step of the image reconstruction
Summary
Bioluminescence Imaging (BLI) is a highly sensitive and non-invasive pre-clinical imaging technique based on the detection of visible and near-infrared light produced by, for example, luciferase-catalyzed reactions (bioluminescence) [1]. Accurate quantification of the spatial location and intensity of the light (which is often used to infer the cellular activity) cannot be established due to several factors, including the often limited number of wavelengths measured and inaccurate mapping of the measured signal on the 2D detector (often a CCD) onto the 3D surface of the animal (free-space light propagation mapping) as well as the unknown underlying and spectrally varying tissue optical properties [3]. Through both simulations and phantom data measurements, the benefits of using ‘logarithm of intensity’ for image reconstruction which allows for spectral derivate data to be utilized in BLT is highlighted which is shown to overcome this so called ‘free-space’ light propagation error
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