Detection of tumor-like inclusions embedded within human liver tissue using a short-pulsed near-infrared laser beam: Parallel simulations with radiative transfer equation. (November 2015)

• Record Type: Journal Article
• Authors: Asllanaj, Fatmir; Addoum, Ahmad; Contassot-Vivier, Sylvain
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 165(2016:Mar.)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-11
• Subjects: Human liver tissue -- Short-pulsed near-infrared laser -- Tumor detection -- Time-dependent radiative transfer equation -- Finite-volume method -- Parallel computing; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2015.06.020
• Languages: English
• ISSNs: 0022-4073

Abstract

Abstract: An efficient solution to detect tumor-like inclusions embedded within a human liver tissue model is presented, using illumination by a short-pulsed laser beam. Light propagation was accurately solved using the time-dependent radiative transfer equation, with multithreaded parallel computing. A modified finite volume method based on unstructured grids and the fourth-order Runge–Kutta approach was employed to solve the equation in the (2-D/3-D) spatial and time domains. The normalization technique applied to the Henyey–Greenstein phase function was adopted to ensure numerical stability for values of the anisotropy factor that were close to unity. The presence of one or two circular/spherical inclusions was analyzed from the time and spatially resolved reflectance on the medium bounding surface. The results allowed a minimal size and a maximum distance for the detection of the inclusion to be highlighted. Abstract : Highlights: We detect tumor-like inclusions embedded within a (2D/3D) human liver tissue model. The technique is based on a short-pulsed near-infrared laser beam. We solve the (2D/3D) time-dependent RTE, with multithreaded parallel computing. The presence of one or two circular/spherical inclusions is analyzed. The results allows a minimal size and a maximum distance for the detection.