Optical tomography using the SCIRun problem solving environment: Preliminary results for three-dimensional geometries and parallel processing

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.

Application of the photon average trajectories method to real-time reconstruction of tissue inhomogeneities in diffuse optical tomography of strongly scattering media

The possibility of application of the photon average trajectories (PAT) method to real-time reconstruction of tissue inhomogeneities in diffuse optical tomography of strongly scattering media has been substantiated. By this method, the inverse problem is reduced to solution of the integral equation with integration along a conditional PAT. Such an approach allows the standard fast algebraic algorithms commonly used in projection computed tomography to be applied to diffuse optical image reconstruction. To demonstrate the capabilities of the PAT method, a numerical experiment on cross-sectional reconstruction of cylindrical strongly scattering objects with absorbing inhomogeneities has been done. Relative shadows caused by inhomogeneities are simulated via numerical solution of the non-stationary diffusion equation. To solve the inverse problem, the QR-factorization least-squares algorithm and the multiplicative algebraic reconstruction technique are used. The results are compared with those obtained by a well-known software package for temporal optical absorption and scattering tomography based on multiple solution of the diffusion equation. It is shown that the PAT method allows reconstruction of the optical structure of objects with comparable accuracy while saving reconstruction time considerably.