A Monte Carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy

Abstract

In order to quantify near-infrared spectroscopic (NIRS) data on an inhomogeneous medium, knowledge of the contribution of the various parts of the medium to the total NIRS signal is required. This is particularly true in the monitoring of cerebral oxygenation by NIRS, where the contribution of the overlying tissues must be known. The concept of the time point spread function (TPSF), which is used extensively in NIRS to determine the effective optical pathlength, is expanded to the more general inhomogeneous case. This is achieved through the introduction of the partial differential pathlength, which is the effective optical pathlength in the inhomogeneous medium, and an analytical proof of the applicability of the modified Beer-Lambert law in an inhomogeneous medium is shown. To demonstrate the use of partial differential pathlength, a Monte Carlo simulation of a two-concentric-sphere medium representing a simplified structure of the head is presented, and the possible contribution of the overlying medium to the total NIRS signal is discussed.

Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy

“…The forward solution of the diffusion equation can be obtained for arbitrary tissue geometries with spatially varying optical properties using finite-difference (Barnett et al, 2003;Hielscher et al, 1998), finite-element Okada et al, 1996a;Paulsen and Jiang, 1995), and Monte Carlo Graaff et al, 1993;Hayakawa et al, 2001;Hiraoka et al, 1993;Wang et al, 1995) methods. As diffuse optical imaging measurements on the adult human head typically have sourcedetector separations of 2-4 cm, the head can be considered a semiinfinite medium locally.…”

Optimization for Brain Activity Monitoring With Near Infrared Light in a Four-Layered Model of the Human Head

“…It is a very versatile method in solving numerical computing problems [13][14][15]. The principles of the Monte Carlo algorithm based on the variance reduction technique have already been described in [16]. The photon migration trajectory is determined by the scattering coefficient µ s , the scattering anisotropy factor g and the random number.…”

Near-Infrared Spectroscopy

“…(3) To solve the diffusion equation numerically by the finite-difference method, which has been successful under restricted conditions for an inhomogeneous medium. (4) To solve the diffusion equation by the finiteelement method, which can be applied to the complex geometries of an inhomogeneous medium and has the advantage of fast calculation time, but it does not calculate individual photon histories (Hiraoka et al, 1993).…”