Quantitative bioluminescence tomography using spectral derivative data.

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.

Photoacoustic imaging of human lymph nodes with endogenous lipid and hemoglobin contrast.

Lymph nodes play a central role in metastatic cancer spread and are a key clinical assessment target. Abnormal node vascularization, morphology, and size may be indicative of disease but can be difficult to visualize with sufficient accuracy using existing clinical imaging modalities. To explore the potential utility of photoacoustic imaging for the assessment of lymph nodes, images of ex vivo samples were obtained at multiple wavelengths using a high-resolution three-dimensional photoacoustic scanner. These images showed that hemoglobin based contrast reveals nodal vasculature and lipid-based contrast reveals the exterior node size, shape, and boundary integrity. These two sources of complementary contrast may allow indirect observation of cancer, suggesting a future role for photoacoustic imaging as a tool for the clinical assessment of lymph nodes.