The current fluorescence image-guided surgery technique does not provide accurate estimates of target depth and
transverse margins of fluorescing tumors. We adapted Spatial Frequency Domain Fluorescence Diffuse Optical Tomography to rapidly acquire the depth and transverse margins of fluorescent inclusions in a turbid media in two steps. First, we derive estimates of depth from normalized fluorescence responses to the spatially modulated light patterns. Second, using the estimated depth, we reconstruct the transverse margin in the target plane. We demonstrate the performance of our instrumentation and approach using a series of phantom experiments.
SignificanceRapid estimation of the depth and margins of fluorescence targets buried below the tissue surface could improve upon current image-guided surgery techniques for tumor resection.AimWe describe algorithms and instrumentation that permit rapid estimation of the depth and transverse margins of fluorescence target(s) in turbid media; the work aims to introduce, experimentally demonstrate, and characterize the methodology.ApproachSpatial frequency domain fluorescence diffuse optical tomography (SFD-FDOT) technique is adapted for rapid and computationally inexpensive estimation of fluorophore target depth and lateral margins. The algorithm utilizes the variation of diffuse fluorescence intensity with respect to spatial-modulation-frequency to compute target depth. The lateral margins are determined via analytical inversion of the data using depth information obtained from the first step. We characterize method performance using fluorescent contrast targets embedded in tissue-simulating phantoms.ResultsSingle and multiple targets with significant lateral size were imaged at varying depths as deep as 1 cm. Phantom data analysis showed good depth-sensitivity, and the reconstructed transverse margins were mostly within ∼30 % error from true margins.ConclusionsThe study suggests that the rapid SFD-FDOT approach could be useful in resection surgery and, more broadly, as a first step in more rigorous SFD-FDOT reconstructions. The experiments permit evaluation of current limitations.
We apply first order perturbation theory to the scalar radiative transport equation for the temporal field autocorrelation function to study DCT and SCOT sensitivity to changes in the Brownian motion of the constituent scattering particles.
In an effort to address Monte Carlo (MC) light prorogation shortcomings in terms of computational burden and light polarization-sensitivity, we report an efficient GPU-based MC for modeling polarized light in scattering medium.
Numerous methods consider the temporal field autocorrelation function in order to study the dynamical properties of a medium, e.g. diffuse correlation tomography (DCT) [1] and speckle contrast optical tomography (SCOT) [5]. In this paper, we calculate the field correlation function in the transport regime as the solution to the correlation transport equation (CTE) introduced in [1]. We show how perturbation theory can be applied to the CTE in order to calculate the sensitivity kernel relating the variation of the local Brownian motion of particles to the typical data. The Green’s function of the standard radiative transport equation (RTE) can be used to construct the sensitivity kernel in the first Born approximation where the correlation time is considered to be the small parameter. We stress that the sensitivity kernel is defined for every point within the scattering medium. The sensitivity kernel is then the Jacobian matrix required in DCT or SCOT in order to perform the image reconstruction [5]. Eventually, we demonstrate how the use of the CTE, instead of the correlation diffusion approximation, is increasing the resolution of reconstructed images of dynamical parts of a scattering medium.
We apply first order perturbation theory and reciprocity to the scalar radiative transport equation for the temporal field auto-correlation function to study its sensitivity to changes in the Brownian motion of the constituent scattering particles.
We describe a reciprocity relation for polarized radiative transport between arbitrarily positioned sources and detectors separated by a scattering medium. Applications to polarized Diffuse Optical Tomography are shown which allow for efficient computation of the sensitivity kernel.
We demonstrate numerically the increased resolution of the image of a pure
absorber as recorded by a scanning system composed of aligned source-detector when only
polarization-preserving photons are selected.
Diffuse optical tomography (DOT) has been employed to derive spatial maps of physiologically important chromophores in the human breast, but the fidelity of these images is often compromised by boundary effects such as those due to the chest wall. We explore the image quality in fast, data-intensive analytic and algebraic linear DOT reconstructions of phantoms with subcentimeter target features and large absorptive regions mimicking the chest wall. Experiments demonstrate that the chest wall phantom can introduce severe image artifacts. We then show how these artifacts can be mitigated by exclusion of data affected by the chest wall. We also introduce and demonstrate a linear algebraic reconstruction method well suited for very large data sets in the presence of a chest wall.
A spectral approach to the Lorenz-Mie problem was adopted to obtain a pole expansion of the Lorenz-Mie coefficients in the complex variable z = 4=(n2 - 1), where n2 is the dielectric permittivity of the scatterer. In the absence of magnetic properties (which is assumed), n is the refractive index of the scatterer. It is shown that the Lorenz-Mie coefficients are meromorphic functions of z with simple poles. The poles and the residues are functions of the size parameter x = ka = 2a/ and of the order of the Lorenz-Mie coefficient, l, but are independent of the material properties. This leads to a numerically efficient representation of the Lorenz-Mie coefficients.
We discuss image reconstruction algorithms for diffuse optical tomography that allow utilization of extremely large data sets. Image reconstruction is performed with experimental data obtained with the use of a CCD camera-based noncontact imager. We demonstrate that more than 107 measurements can be acquired and utilized. This is two orders of magintude or more larger than the data sets which are typically used in diffuse optical imaging.
We introduce a set of corrections to the integral equations of scattering theory within the diffusion approximation to the radiative transport equation. We use this result to obtain an image reconstruction algorithm for optical tomography with spatial resolution below the transport mean free path.
We consider the inverse scattering problem for the diffusion equation with general boundary conditions. Computer simulations are used to illustrate our approach in model systems.
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