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.
A frequency-domain photon migration (FDPM) imager employing an image-intensified CCD camera for fast data acquisition on a large tissue-mimicking phantom (1087 ml) is described. Fluorescence-enhanced imaging is performed employing frequency-domain techniques at 100 MHz in order to obtain the boundary measurements of phase and amplitude and to recover the interior optical maps using the first principles of light propagation. The effect of refractive-index parameter in the boundary condition of the light propagation model is not significant due to the large phantom volume and its curvilinear nature. Initial experiments were performed under perfect (1:0 contrast) and imperfect (100:1 contrast) uptake cases using indocyanine green as the contrast agent. Preliminary 3D image reconstructions using the approximate extended Kalman filter (AEKF) algorithm are presented.
We present results of ongoing research in 3-D fluorescence tomography on large clinically-relevant tissue-mimicking domains. Finite element predictions of excitation and emission phase shift and amplitude attenuation are compared to experimental data from both column-shaped and breast-shaped tissue mimicking phantoms containing embedded fluorophore; system noise and measurement noise are characterized and utilized in image reconstruction using the Bayesian APPRIZE algorithm.
The approximate extended Kalman filter (AEKF) has been suggested as an appropriate inverse method for reconstructing fluorescent properties in large tissue samples from frequency domain data containing measurement error. The AEKF is an “optimal” estimator, in that it seeks to minimize the predicted error variances of the estimated optical properties in relation to measurement and system errors. However, due to non-linearities in the recursive estimation process, the updates are actually suboptimal. Furthermore, the computational overhead is large for the full AEKF algorithm when applied to large datasets. In this contribution we developed three hybrid forms of the AEKF algorithm that may improve the performance in frequency domain fluorescence tomography. Numerical results of image reconstruction from actual frequency domain emission data show that one hybrid form of the AEKF outperforms the traditional full AEKF in both image quality and computational efficiency for the two cases tested.
Proc. SPIE. 4979, Micromachining and Microfabrication Process Technology VIII
KEYWORDS: Mathematical modeling, Chemical vapor deposition, 3D modeling, Process control, Microlens, Optical simulations, Chemical reactions, Deposition processes, Optimization (mathematics), Chemical lasers
A laser-induced chemical vapor deposition (LCVD) process is capable of producing high aspect ratio microstructures of arbitrary shape and is rapid, flexible, and relatively inexpensive to operate. To achieve high resolution and accurate fabrication, predictive models must be developed for process control and optimization. In this paper, we present an inverse model for predicting and optimizing the scanning pattern of the laser beam on the surface of deposit in order to produce accurate microstructures with the desired geometry. We demonstrate the applicability of the model by simulating and optimizing the process for fabricating a microlens with a pre-specified geometry.
Laser-induced Chemical Vapor Deposition (LCVD) is an emerging technique in freeform fabrication of high aspect ratio microstructures with many practical applications. The LCVD process is kinetically limited at low temperatures and pressure. The growth rate rises exponentially with temperature and becomes mass transport limited beyond a certain threshold. While the surface temperature drives the deposition rate of a heterogeneous pyrolytic reaction, the rate obtained depends on the reaction activation energy and the ability of the precursor reactants and by-products to transport to and from the surface. To achieve precise control of the thermal deposition near the focus of a laser beam, a mathematical model for 3-D LCVD is developed taking into account both kinetically limited and mass transport limited reactions. The model describes heat transport in the substrate and deposit as well as the gas-phase mass transport and temperature in the reaction zone in order to determine growth rate. A finite difference method is developed for solving the governing equations and an iterative algorithm is presented for simulating the process. The applicability of the model is demonstrated by growing a rod from silicon deposited on a graphite substrate.