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