TITLE:
Models and Algorithms for Diffuse Optical Tomographic System
AUTHORS:
Samir Kumar Biswas, Rajan Kanhirodan, Ram Mohan Vasu
KEYWORDS:
Diffuse Optical Tomography; Gauss Newton Methods; Broyden and Adjoint Broyden Approaches; Pseu-do-Dynamic Method
JOURNAL NAME:
International Journal of Communications, Network and System Sciences,
Vol.6 No.12,
December
16,
2013
ABSTRACT: Diffuse optical tomography (DOT) using near-infrared (NIR) light is
a promising tool for noninvasive imaging of deep tissue. The approach is
capable of reconstructing the quantitative optical parameters (absorption
coefficient and scattering coefficient) of a soft tissue. The motivation for
reconstructing the optical property variation is that it and, in particular,
the absorption coefficient variation, can be used to diagnose different
metabolic and disease states of tissue. In DOT, like any other medical imaging
modality, the aim is to produce a reconstruction with good spatial resolution
and in contrast with noisy measurements. The parameter recovery known as inverse problem
in highly scattering biological tissues is a nonlinear and ill-posed problem
and is generally solved through iterative methods. The algorithm uses a forward
model to arrive at a prediction flux density at the tissue boundary. The
forward model uses light transport models such as stochastic Monte Carlo
simulation or deterministic methods such as radioactive transfer equation (RTE)
or a simplified version of RTE namely the diffusion equation (DE). The finite
element method (FEM) is used for discretizing the diffusion equation. The
frequently used algorithm for solving the inverse problem is Newton-based Model
based Iterative Image Reconstruction (N-MoBIIR). Many Variants of Gauss-Newton
approaches are proposed for DOT reconstruction. The focuses of
such developments are 1) to reduce the computational complexity; 2) to
improve spatial recovery; and 3) to improve contrast recovery. These
algorithms are 1) Hessian based MoBIIR; 2) Broyden-based MoBIIR; 3)
adjoint Broyden-based MoBIIR; and 4) pseudo-dynamic approaches.