Abstract
The current approaches to imaging the tissue blood flow index (BFI) from diffuse correlation tomography (DCT) data are either an analytical solution or a finite element method, both of which are unable to simultaneously account for the tissue heterogeneity and fully utilize the DCT data. In this study, a new imaging concept for DCT, namely NL-DCT, was created by us in which the medical images are combined with light Monte Carlo simulation to provide geometrical and heterogeneous information in tissue. Moreover, the DCT data at multiple delay time are fully utilized via iterative linear regression. The unique merit of NL-DCT in utilizing the medical images as prior information, when combined with a split Bregman algorithm for total variation minimization (Bregman-TV), was validated on a realistic human head model. Computer simulation outcomes demonstrate the accuracy and robustness of NL-DCT in localizing and separating the flow anomalies as well as the capability to preserve edges of anomalies.
Highlights
Near-infrared diffuse optical spectroscopy (NIRS) is one of the major approaches to in vivo probe tissue blood oxygenation parameters, including oxy- and deoxy-hemoglobin concentrations (Δ[HbO2], Δ[Hb]), total hemoglobin concentration (THC) and oxygen saturation (StO2) [1,2,3,4,5]
Analytical solution of partial differential equation (PDE) has been used as diffuse correlation tomography (DCT) imaging algorithm, which requires a regular boundary condition that usually does not match the actual tissue geometry
The finite element method (FEM), which was borrowed from diffuse optical tomography (DOT), has applied to DCT in recent years [22, 23], because it takes the irregular tissue geometry into consideration
Summary
Near-infrared diffuse optical spectroscopy (NIRS) is one of the major approaches to in vivo probe tissue blood oxygenation parameters, including oxy- and deoxy-hemoglobin concentrations (Δ[HbO2], Δ[Hb]), total hemoglobin concentration (THC) and oxygen saturation (StO2) [1,2,3,4,5]. DOT measurement requires the placements of an array, containing a large number of source-detector (S-D) pairs, on the tissue surface, and an algorithm is used to reconstruct the 3D oxygenation imaging through modeling of diffusive light. Analytical solution to PDE was utilized for DOT image reconstruction, wherein a regular geometry (e.g., semi-infinite, cylinder, sphere, etc.) was assumed. The complicated geometry and tissue heterogeneity of biological tissue are difficult to be taken into consideration by analytical solution. Another solution for PDE, i.e., the finite element method (FEM), is becoming more popular in recent years [7,12]. Open-source FEM software, such as Nirfast (www.nirfast.org) and TOAST + + (http://web4.cs.ucl.ac.uk/research/vis/toast/), were established and public available, which triggered the widely clinical applications of DOT
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