Parametric level set reconstruction methods for hyperspectral diffuse optical tomography

A Parametric Level Set-Based Approach to Difference Imaging in Electrical Impedance Tomography

This paper presents a novel difference imaging approach based on the recently developed parametric level set (PLS) method for estimating the change in a target conductivity from electrical impedance tomography measurements. As in conventional difference imaging, the reconstruction of conductivity change is based on data sets measured from the surface of a body before and after the change. The key feature of the proposed approach is that the conductivity change to be reconstructed is assumed to be piecewise constant, while the geometry of the anomaly is represented by a shape-based PLS function employing Gaussian radial basis functions (GRBFs). The representation of the PLS function by using GRBF provides flexibility in describing a large class of shapes with fewer unknowns. This feature is advantageous, as it may significantly reduce the overall number of unknowns, improve the condition number of the inverse problem, and enhance the computational efficiency of the technique. To evaluate the proposed PLS-based difference imaging approach, results obtained via simulation, phantom study, and in vivo pig data are studied. We find that the proposed approach tolerates more modeling errors and leads to a significant improvement in image quality compared with the conventional linear approach.

Parameterized level-set based pharmacokinetic fluorescence optical tomography using the regularized Gauss-Newton filter

Pharmacokinetic tomography is emerging as an important methodology for detecting abnormalities in tissue based upon spatially varying estimation of the pharmacokinetic rates governing the leakage of an injected fluorophore between blood plasma and tissue. We present a shape-based reconstruction framework of a compartment-model based formulation of this dynamic fluorescent optical tomography problem to solve for the pharmacokinetic rates and concentrations of the fluorophore from time-varying log intensity measurements of the optical signal. The compartment-model based state variable model is set up in a radial basis function parameterized level set setting. The state (concentrations) and (pharmacokinetic) parameter estimation problem is solved with an iteratively regularized Gauss-Newton filter in a trust-region framework. Reconstructions obtained using this scheme for noisy data obtained from cancer mimicking numerical phantoms of near/sub-cm sizes show a good localization of the affected regions and reasonable estimates of the pharmacokinetic rates and concentration curves.

A Parametric Level Set Method for Electrical Impedance Tomography

This paper presents an image reconstruction method based on parametric level set (PLS) method using electrical impedance tomography. The conductivity to be reconstructed was assumed to be piecewise constant and the geometry of the anomaly was represented by a shape-based PLS function, which we represent using Gaussian radial basis functions (GRBF). The representation of the PLS function significantly reduces the number of unknowns, and circumvents many difficulties that are associated with traditional level set (TLS) methods, such as regularization, re-initialization and use of signed distance function. PLS reconstruction results shown in this article are some of the first ones using experimental EIT data. The performance of the PLS method was tested with water tank data for two-phase visualization and with simulations which demonstrate the most popular biomedical application of EIT: lung imaging. In addition, robustness studies of the PLS method w.r.t width of the Gaussian function and GRBF centers were performed on simulated lung imaging data. The experimental and simulation results show that PLS method has significant improvement in image quality compared with the TLS reconstruction.

Image reconstruction of oxidized cerebral cytochrome C oxidase changes from broadband near-infrared spectroscopy data

In diffuse optical tomography (DOT), overlapping and multidistance measurements are required to reconstruct depth-resolved images of oxy- ([Formula: see text]) and deoxy- (HHb) hemoglobin concentration changes occurring in the brain. These can be considered an indirect measure of brain activity, under the assumption of intact neurovascular coupling. Broadband systems also allow changes in the redox state of cytochrome c oxidase (oxCCO) to be measured, which can be an important biomarker when neurovascular coupling is impaired. We used DOT to reconstruct images of [Formula: see text], [Formula: see text], and [Formula: see text] from data acquired with a broadband system. Four healthy volunteers were measured while performing a visual stimulation task (4-Hz inverting checkerboard). The broadband system was configured to allow multidistance and overlapping measurements of the participants' visual cortex with 32 channels. A multispectral approach was employed to reconstruct changes in concentration of the three chromophores during the visual stimulation. A clear and focused activation was reconstructed in the left occipital cortex of all participants. The difference between the residuals of the three-chromophore model and of the two-chromophore model (recovering only [Formula: see text] and [Formula: see text]) exhibits a spectrum similar to that of oxCCO. These results form a basis for further studies aimed to further optimize image reconstruction of [Formula: see text].

A Novel Region Reconstruction Method for Fluorescence Molecular Tomography

Fluorescence molecular tomography (FMT) could exploit the distribution of fluorescent biomarkers that target tumors accurately and effectively, which enables noninvasive real-time 3-D visualization as well as quantitative analysis of small tumors in small animal studies in vivo. Due to the difficulties of reconstruction, continuous efforts are being made to find more practical and efficient approaches to accurately obtain the characteristics of fluorescent regions inside biological tissues. In this paper, we propose a region reconstruction method for FMT, which is defined as an L1-norm regularization piecewise constant level set approach. The proposed approach adopts a priori information including the sparsity of the fluorescent sources and the fluorescent contrast between the target and background. When the contrast of different fluorescent sources is low to a certain degree, our approach can simultaneously solve the detection and characterization problems for the reconstruction of FMT. To evaluate the performance of the region reconstruction method, numerical phantom experiments and in vivo bead-implanted mouse experiments were performed. The results suggested that the proposed region reconstruction method was able to reconstruct the features of the fluorescent regions accurately and effectively, and the proposed method was able to be feasibly adopted in in vivo application.

Shape-parameterized diffuse optical tomography holds promise for sensitivity enhancement of fluorescence molecular tomography

A fundamental approach to enhancing the sensitivity of the fluorescence molecular tomography (FMT) is to incorporate diffuse optical tomography (DOT) to modify the light propagation modeling. However, the traditional voxel-based DOT has been involving a severely ill-posed inverse problem and cannot retrieve the optical property distributions with the acceptable quantitative accuracy and spatial resolution. Although, with the aid of an anatomical imaging modality, the structural-prior-based DOT method with either the hard- or soft-prior scheme holds promise for in vivo acquiring the optical background of tissues, the low robustness of the hard-prior scheme to the segmentation error and inferior performance of the soft-prior one in the quantitative accuracy limit its further application. We propose in this paper a shape-parameterized DOT method for not only effectively determining the regional optical properties but potentially achieving reasonable structural amelioration, lending itself to FMT for comparably improved recovery of fluorescence distribution.

Parametric estimation of 3D tubular structures for diffuse optical tomography

We explore the use of diffuse optical tomography (DOT) for the recovery of 3D tubular shapes representing vascular structures in breast tissue. Using a parametric level set method (PaLS) our method incorporates the connectedness of vascular structures in breast tissue to reconstruct shape and absorption values from severely limited data sets. The approach is based on a decomposition of the unknown structure into a series of two dimensional slices. Using a simplified physical model that ignores 3D effects of the complete structure, we develop a novel inter-slice regularization strategy to obtain global regularity. We report on simulated and experimental reconstructions using realistic optical contrasts where our method provides a more accurate estimate compared to an unregularized approach and a pixel based reconstruction.