Abstract
Structural image-guided near-infrared spectral tomography (NIRST) has been developed as a way to use diffuse NIR spectroscopy within the context of image-guided quantification of tissue spectral features. A direct regularization imaging (DRI) method for NIRST has the value of not requiring any image segmentation. Here, we present a comprehensive investigational study to analyze the impact of the weighting function implied when weighting the recovery of optical coefficients in DRI based NIRST. This was done using simulations, phantom and clinical patient exam data. Simulations where the true object is known indicate that changes to this weighting function can vary the contrast by 10%, the contrast to noise ratio by 20% and the full width half maximum (FWHM) by 30%. The results from phantoms and human images show that a linear inverse distance weighting function appears optimal, and that incorporation of this function can generally improve the recovered total hemoglobin contrast of the tumor to the normal surrounding tissue by more than 15% in human cases.
Highlights
Near-infrared spectral tomography (NIRST) uses near-infrared light (600-1000 nm) to image physiologically relevant optical properties of tissue, for breast cancer diagnosis [1,2,3] and functional brain mapping [4, 5]
To overcome the shortcoming of segmenting the MRI images, we have developed a NIRST reconstruction algorithm based on a direct regularization imaging (DRI) method [17, 18]
The direct regularization imaging method depends on a pre-specified weighting function
Summary
Near-infrared spectral tomography (NIRST) uses near-infrared light (600-1000 nm) to image physiologically relevant optical properties of tissue, for breast cancer diagnosis [1,2,3] and functional brain mapping [4, 5]. In hard-priors approach, anatomical images are segmented into several different regions with the different structure features, where each region is assumed to be optically uniform during NIRST reconstruction. With this approach, the number of unknown parameters in NIRST inverse problem is significantly reduced by lumping all nodes within these regions together into just a few homogenous regions [15]. The number of unknown parameters in NIRST inverse problem is significantly reduced by lumping all nodes within these regions together into just a few homogenous regions [15] This process has the peripheral benefit of significantly enhancing NIRST accuracy within the localized regions by reducing the illposedness of NIRST reconstruction. Its stability is critically dependent on the accuracy of the structural priors derived from the co-registered images, and performance is degraded when incomplete or distorted structural priors are employed
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