Accurate modeling of photon propagation in small animals is critical to quantitatively obtain accurate
tomographic images. The diffusion approximation is used for biomedical optical diagnostic techniques in turbid
large media where absorption is low compared to scattering system. This approximation has considerable limitations
to accurately predict radiative transport in turbid small media and also in a media where absorption is high compared
to scattering systems. A radiative transport equation (RTE) is best suited for photon propagation in human tissues.
However, such models are quite expensive computationally. To alleviate the problems of the high computational
cost of RTE and inadequacies of the diffusion equation in a small volume, we use telegrapher equation (TE) in the
frequency domain for fluorescence-enhanced optical tomography problems. The telegrapher equation can
accurately and efficiently predict ballistic as well as diffusion-limited transport regimes which could simultaneously
exist in small animals. The telegrapher-based model is tested by comparing with the diffusion-based model using
stimulated data in a small volume. This work shows the telegrapher-based model is appropriate in small animal
optical tomography problems.
Fluorescent contrast agents have been proposed for near-infrared (NIR)
diagnostic breast imaging problems as the most efficient process for inducing
optical contrast. We have developed a penalty modified barrier function
method (PMBF) with constrained truncated Newton and trust region methods
(CONTN) for fluorescence-enhanced NIR diagnostic imaging for both
noncontact (area illumination/collection) and contact (point
illumination/collection) measurement techniques. A simple logarithmic
penalty function has been used in the PMBF/CONTN algorithm with linear
convergence. The motivation of this paper is to compare the efficiency and
performance of this method with many alternative penalty barrier function
methods, (a) hyperbolic penalty function, (b) quadratic-logarithm penalty
function, (c) logarithmic exponential penalty function for proper selection of a
penalty function that will be the most suitable for optical reconstruction
problems. In this paper a numerical comparison of different penalty function
methods has been made using experimental measured data in a clinically
relevance volume in three-dimensions. Our objective is to continue
development of sophisticated constrained optimization PMBF/CONTN method
to provide high resolution three dimensional tomography a reality.
We demonstrate fluorescence-enhanced optical imaging of single and multiple fluorescent targets within a large (~1081 cm3) phantom using frequency-domain photon migration measurements of fluorescence collected at individual points in response to illumination of excitation light at individual points on the boundary. The tissue phantom was filled with a 1% lipid solution with and without 0.01 µM Indocyanine Green (ICG) and targets consisted of vials filled with the 1% lipid containing 1–2.5 µM ICG. Measurements were acquired using a modulated intensified CCD imaging system under different experimental conditions. For 3-D image reconstruction, the gradient-based penalty modified barrier function (PMBF) method with simple bounds constrained truncated Newton with trust region method (CONTN) was used. Targets of 0.5, 0.6, and 1.0 cm3 at depths of 1.4–2.8 cm from the phantom surface were tomographically reconstructed. This work demonstrates the practicality of fluorescence-enhanced tomography in clinically relevant volumes.
Fluorescence-enhance optical tomography is performed using (i) point illumination and point collection and (ii) area illumination and area collection geometrics in 3D. In both measurement techniques, an image-intensified charge-coupled (ICCD) imaging system is used in the frequency-domain to image near-infrared contrast agents. The experimental measurements are compared to diffusion model predictions in least squares form in the inverse problem. For image recovery for both area and point illumination geometries, an efficient gradient-based optimization technique based on the Penalty/modified barrier function (PMBF) method and the constrained truncated Newton with trust region (CONTN) method is developed. Targets in 3D were reconstructed from experimental data under two conditions of (i) perfect uptake (1:0, target to background ratio) and (ii) imperfect uptake (100:1, target to background ratio). Parameters of absorption cross section due to fluorophore and lifetimes are reconstructed. The present work demonstrates that 3D fluorescence enhanced optical tomography reconstructions can be successfully performed from both point/area illumination and collection experimental measurements of the time-dependent light propagation on clinically relevant tissue phantoms using a gain-modulated ICCD camera.
Fluorescence enhanced optical tomography uses modulated NIR light and an exogenous fluorescent contrast agent in order to assess its spatial distribution deep within tissues. However, a spatial distribution of endogenous optical properties of absorption and scattering coefficients arises due to normal, structured anatomical background which can be expected to vary from patient to patient. This structured background can be a source of randomness (or “noise”) in the task of detecting a fluorescent target. In addition, there may be non-uniform distribution of exogenous fluorescent agent. Our objective is to develop the tools for OAIQ in order to assess the performance of image reconstruction algorithms in the presence of anatomical backgrounds owing to both endogenous and exogenous optical properties. We consider the lumpy-object model developed by Rolland and Barrett to simulate the normal background anatomy as a representation of the non-specific distribution of the fluorescent agent as well as the natural heterogeneity of the endogenous tissue optical properties. Reconstructed images show the insensitivity to endogenous optical property lumps in fluorescence enhanced optical imaging. The reconstructed images are more sensitive to uneven distribution of exogenous fluorophore in normal tissues, but can nonetheless be achieved.
Molecular targeting with exogenous near-infrared excitable fluorescent agents using time-dependent imaging techniques may enable diagnostic imaging of breast cancer and prognostic imaging of sentinel lymph nodes within the breast. However, prior to the administration of unproven contrast agents, phantom studies on clinically relevant volumes are essential to assess the benefits of fluorescence-enhanced optical imaging in humans. Diagnostic 3-D fluorescence-enhanced optical tomography is demonstrated using 0.5 to 1 cm3 single and multiple targets differentiated from their surroundings by indocyanine green (micromolar) in a breast-shaped phantom (10-cm diameter). Fluorescence measurements of referenced ac intensity and phase shift were acquired in response to point illumination measurement geometry using a homodyned intensified charge-coupled device system modulated at 100 MHz. Bayesian reconstructions show artifact-free 3-D images (3857 unknowns) from 3-D boundary surface measurements (126 to 439). In a reflectance geometry appropriate for prognostic imaging of lymph node involvement, fluorescence measurements were likewise acquired from the surface of a semi-infinite phantom (8×8×8 cm3) in response to area illumination (12 cm2) by excitation light. Tomographic 3-D reconstructions (24,123 unknowns) were recovered from 2-D boundary surface measurements (3194) using the modified truncated Newton's method. These studies represent the first 3-D tomographic images from physiologically relevant geometries for breast imaging.
In this contribution, the inverse optical imaging problem is formulated as both simply bound-constrained and unconstrained minimization problems in order to illustrate the reduction in computational time and storage. The Galerkin finite element method is used for the numerical solution of the excitation and emission diffusion equations. The inverse approach employs the truncated Newton method with trust region, and modified automatic reverse differentiation method is used to calculate the gradients thus sped up the computation of the optimization problem.
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