Monte Carlo prediction of mean-time photon measurement density functions

Muneo Ono; Eiji Okada

doi:10.1117/12.334367

30 December 1998 Monte Carlo prediction of mean-time photon measurement density functions

Muneo Ono, Eiji Okada

Proceedings Volume 3566, Photon Propagation in Tissues IV; (1998) https://doi.org/10.1117/12.334367
Event: BiOS Europe '98, 1998, Stockholm, Sweden

Abstract

The scattering of tissue causes the ambiguity of volume of tissue interrogated by near infrared spectroscopy (NIRS) instruments. The sampling volume, which is the volume of tissue interrogated by NIRS instrument with CW light, can be predicted by calculating the distribution of light which arrives the detector. Recently, time-resolved and frequency modulated measurements has been applied to near-infrared spectroscopy instruments. Although the volume of tissue interrogated by the measurements of mean time, phase and so forth may be different from that by CW intensity measurement, there are very few studies on the volume of tissue interrogated by time-resolved and frequency domain measurements. In this study a Monte Carlo algorithm to predict the volume of tissue interrogated by mean time measurements is proposed and is applied to homogeneous and layered models. The volume of tissue interrogated by mean time measurement is significantly different from the sampling volume.

Citation Download Citation

Muneo Ono and Eiji Okada "Monte Carlo prediction of mean-time photon measurement density functions", Proc. SPIE 3566, Photon Propagation in Tissues IV, (30 December 1998); https://doi.org/10.1117/12.334367

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available