Wavelength band optimization in spectral near-infrared optical tomography improves accuracy while reducing data acquisition and computational burden.

Multispectral near-infrared (NIR) tomographic imaging has the potential to provide information about molecules absorbing light in tissue, as well as subcellular structures scattering light, based on transmission measurements. However, the choice of possible wavelengths used is crucial for the accurate separation of these parameters, as well as for diminishing crosstalk between the contributing chromophores. While multispectral systems are often restricted by the wavelengths of laser diodes available, continuous-wave broadband systems exist that have the advantage of providing broadband NIR spectroscopy data, albeit without the benefit of the temporal data. In this work, the use of large spectral NIR datasets is analyzed, and an objective function to find optimal spectral ranges (windows) is examined. The optimally identified wavelength bands derived from this method are tested using both simulations and experimental data. It is found that the proposed method achieves images as qualitatively accurate as using the full spectrum, but improves crosstalk between parameters. Additionally, the judicious use of these spectral windows reduces the amount of data needed for full spectral tomographic imaging by 50%, therefore increasing computation time dramatically.