A method for three-dimensional time-resolved optical tomography

Abstract

We present an overview of time‐resolved optical tomography together with the hardware and software methods that we have developed for a clinical instrument that implements this modality. The hardware is based on a multichannel photon‐counting technique that records the histograms of photons time‐of‐flight through highly scattering and attenuating media. The software is based on a finite element model that is iteratively updated in order to minimize the difference between measured and modeled data. We have presented a first experimental reconstruction of a three‐dimensional (3D) distribution of variable absorption and scattering coefficient, together with an ideal simulation of the same case. © 2000 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 2–11, 2000

In vivo quantitative imaging of normal and cancerous breast tissue using broadband diffuse optical tomography

“…Estimating the distribution of a ͑r͒ and D͑r͒ is a nonlinear inverse problem, which has been described in previous publications. 17,18 Briefly, a Newton-minimization approach is used to estimate a ͑r͒ and D͑r͒ based on repetitive solution of Eq. ͑1͒ using the finiteelement method ͑FEM͒.…”

Cortical Mapping of 3D Optical Topography in Infants

“…It has dimensions 2 M × 2 N , where M represents the number of measurements and N is the number of image pixels. The Jacobian was calculated for each wavelength using the software package TOAST (Temporal Optical Absorption and Scattering Tomography) [8]. It uses the finite element method (FEM) to model the propagation of light through highly scattering tissue using the diffusion equation, given an estimate of the medium optical properties and optode positions.…”

Linear and nonlinear reconstruction for optical tomography of phantoms with nonscattering regions

“…The same sets of difference data were also reconstructed with the nonlinear time-resolved optical absorption and scattering tomography (TOAST) reconstruction scheme, developed at UCL. 32,40 TOAST represents the image with a finite-element model and solves the nonlinear inverse problem by iteratively adjusting the internal properties using nonlinear optimization to minimize an error norm between the model and the data. Convergence occurs when the error norm either is reduced below a specified threshold or has ceased to reduce significantly between iterations.…”

“…Nonlinear reconstruction attempts to fit a model to the data and iteratively update the optical properties of the model until the simulated data match the measured data to within a specified tolerance. 1,32 Inevitably, errors occur when the model is unable to generate simulated data that match the measured data. This mismatch can arise owing to noise or error in the data, or because the model inadequately represents the object being imaged.…”