Multi-spectral Near Infrared tomographic imaging has the potential to provide information about patho-physiological
function of soft tissue. However, the specific choice of wavelengths used is crucial for the accurate separation of such
parameters. It will be demonstrated that the conventionally believed choice of large set of wavelengths can be
detrimental in accurate recovery of tissue specific functions. The method of determining a set of optimized bands of
wavelengths will be presented and are tested using simulations and experimental data. It will be shown that the
optimization method achieves images as accurate as using the full spectrum, but improves crosstalk between
parameters. Additionally, a Jacobian normalization technique is presented which takes into account the varying
magnitude of different optical parameters within image reconstruction, creating a more uniform update within a
spectral image reconstruction model.
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.
Near-Infrared (NIR) Diffuse Optical Tomography (DOT) is a non-invasive imaging technique which is used to obtain
functional and physiological images of soft tissue, such as the female breast, specifically for the detection and
characterization of breast cancer. The vast majority of the work to date has been limited to two dimensional (2D)
models which have provided valuable insight into tissue function and physiology enabling a better understanding of
tumor development and treatment. Although the 2D image reconstruction approach is fast and computationally efficient,
it has limitations as it does not correctly represent the volume under investigation and therefore do not provide the most
accurate model for image reconstruction. Three dimensional (3D) modeling and image reconstruction is becoming more
accessible through the development of sophisticated numerical models and computationally fast algorithms. A robust
and general method is presented which reconstructs 3D functional images using a more accurate and realistic spectral
model of 3D light propagation in tissue. Results from a single patient example are presented to demonstrate the clinical
importance of 3D image reconstruction in optical tomography for the detection and characterization of breast cancer.
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