Abstract
A parametric level set method (PaLS) is implemented for image reconstruction for hyperspectral diffuse optical tomography (DOT). Chromophore concentrations and diffusion amplitude are recovered using a linearized Born approximation model and employing data from over 100 wavelengths. The images to be recovered are taken to be piecewise constant and a newly introduced, shape-based model is used as the foundation for reconstruction. The PaLS method significantly reduces the number of unknowns relative to more traditional level-set reconstruction methods and has been show to be particularly well suited for ill-posed inverse problems such as the one of interest here. We report on reconstructions for multiple chromophores from simulated and experimental data where the PaLS method provides a more accurate estimation of chromophore concentrations compared to a pixel-based method.
Highlights
Near-infrared light has proven to be useful for imaging the human body and providing functional information for applications including breast cancer detection and characterization [1,2,3,4,5]
In simulations the signal to noise ratio (SNR) is set to 30 dB, as it is defined by Eq (16) and Eq (17)
It should be noted that the runtime for each reconstruction for the parametric level set method (PaLS) method is significantly shorter compared to the pixel-based method
Summary
Near-infrared light has proven to be useful for imaging the human body and providing functional information for applications including breast cancer detection and characterization [1,2,3,4,5]. Inverting for multiple chromophores such as hemoglobin, lipids and water has been shown to provide an improved ability to localize tumours or other objects of interest in the breast cancer application [6,7,8]. While these and other advancements have been critical for moving DOT from the lab into the clinic, there remain significant obstacles to be addressed so that the method can be used to stably recover these quantities. Additional problems are encountered when recovering multiple chromophores such as crosstalk, e.g. when two spatially disjoint chromophores pollute the images of each other
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