Abstract
The spectrally constrained diffuse optical tomography (DOT) method relies on incorporating spectral prior information directly into the image reconstruction algorithm, thereby correlating the underlying optical properties across multiple wavelengths. Although this method has been shown to provide a solution that is stable, the use of conventional Tikhonov-type regularization techniques can lead to additional crosstalk between parameters, particularly in linear, single-step dynamic imaging applications. This is due mainly to the suboptimal regularization of the spectral Jacobian matrix, which smoothes not only the image-data space, but also the spectral mapping space. In this work a novel regularization technique based on the singular value decomposition (SVD) is presented that preserves the spectral prior information while regularizing the Jacobian matrix, leading to dramatically reduced crosstalk between the recovered parameters. Using simulated data, images of changes in oxygenated and deoxygenated hemoglobin concentrations are reconstructed via the SVD-based approach and compared with images reconstructed by using non-spectral and conventional spectral methods. In a 2D, two wavelength example, it is shown that the proposed approach provides a 98% reduction in crosstalk between recovered parameters as compared with conventional spectral reconstruction algorithms, and 60% as compared with non-spectrally constrained algorithms. Using a subject specific multilayered model of the human head, a noiseless dynamic simulation of cortical activation is performed to further demonstrate such improvement in crosstalk. However, with the addition of realistic noise in the data, both non-spectral and proposed algorithms perform similarly, indicating that the use of spectrally constrained reconstruction algorithms in dynamic DOT may be limited by the contrast of the signal as well as the noise characteristics of the system.
Highlights
Through a series of proof-of-concept analyses using a 2D circular model, we have shown that the singular value decomposition (SVD)-based regularization technique followed by spectral mixing dramatically reduces the crosstalk between chromophores
While incorporating spectral prior information in spectral image reconstruction correlates the absorption coefficients across multiple wavelengths, providing a solution that is stable, the use of conventional regularization techniques can result in additional crosstalk between chromophores
A novel regularization technique that regularizes the Jacobian using the singular value decomposition (SVD) prior to spectral mixing has been presented in an attempt to reduce this crosstalk effect
Summary
Diffuse optical tomography (DOT) is finding widespread application in the diagnosis and characterization of breast cancer [1,2,3,4,5], monitoring brain function [6,7,8,9,10,11,12,13,14], and small animal imaging for the study of disease detection, progression and treatment [15,16,17,18]. The two primary chromophore species of interest in cerebral hemodynamics studies are oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) Given their difference in the nearinfrared absorption spectra (Fig. 1), measurements at two wavelengths are typically sufficient to recover the change in concentration for both chromophores [22]. Most existing optical neuroimaging systems are equipped with two wavelengths of light sources, including the CW5 (690 and 830 nm) [14,23], the DYNOT (760 and 830 nm) [12,14,24], and the highdensity-DOT imaging system at Washington University School of Medicine (750 and 850 nm) [9,10,13,25]. For this reason our analyses in this work will be focused on the dualwavelength setup using 750 and 850 nm
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