MOCARTS: A lightweight radiation transport simulator for easy handling of complex sensing geometries

In functional neuroimaging (fNIRS), elaborated sensing geometries pairing multiple light sources and detectors arranged over the tissue surface are needed. A variety of software tools for probing forward models of radiation transport in tissue exist, but their handling of sensing geometries and specification of complex tissue architectures is, most times, cumbersome. In this work, we introduce a lightweight simulator, Monte Carlo Radiation Transport Simulator (MOCARTS) that attends these demands for simplifying specification of tissue architectures and complex sensing geometries. An object-oriented architecture facilitates such goal. The simulator core is evolved from the Monte Carlo Multi-Layer (mcml) tool but extended to support multi-channel simulations. Verification against mcml yields negligible error (RMSE~4-10e-9) over a photon trajectory. Full simulations show concurrent validity of the proposed tool. Finally, the ability of the new software to simulate multi-channel sensing geometries and to define biological tissue models in an intuitive nested-hierarchy way are exemplified.

[1]  B. Pogue,et al.  Optical image reconstruction using frequency-domain data: simulations and experiments , 1996 .

[2]  Gilberto Branco.  The development and evaluation of head probes for optical imaging of the infant head , 2007 .

[3]  S. Prahl.  The Adding-Doubling Method , 1995 .

[4]  S. Arridge,et al.  Optical tomography: forward and inverse problems , 2009, 0907.2586.

[5]  Hamid Dehghani,et al.  Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. , 2009, Communications in numerical methods in engineering.

[6]  M. Ferrari,et al.  Principles, techniques, and limitations of near infrared spectroscopy. , 2004, Canadian journal of applied physiology = Revue canadienne de physiologie appliquee.

[7]  P. Rolfe,et al.  In vivo near-infrared spectroscopy. , 2000, Annual review of biomedical engineering.

[8]  Tuan Vo-Dinh,et al.  Biomedical Photonics Handbook, Second Edition: Biomedical Diagnostics , 2014 .

[9]  Hamid Dehghani,et al.  Numerical modelling and image reconstruction in diffuse optical tomography , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  B. Pogue,et al.  Tutorial on diffuse light transport. , 2008, Journal of biomedical optics.

[11]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[12]  Henry R. Kang.  Kubelka-Munk Theory , 2006 .

[13]  S. A. Prahl,et al.  A Monte Carlo model of light propagation in tissue , 1989, Other Conferences.

[14]  E. Sevick-Muraca,et al.  Quantitative optical spectroscopy for tissue diagnosis. , 1996, Annual review of physical chemistry.

[15]  Martin Schweiger,et al.  The Toast++ software suite for forward and inverse modeling in optical tomography , 2014, Journal of biomedical optics.

[16]  L Wang,et al.  MCML--Monte Carlo modeling of light transport in multi-layered tissues. , 1995, Computer methods and programs in biomedicine.

[17]  David A Boas,et al.  Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. , 2009, Optics express.

[18]  J. Hammersley.  SIMULATION AND THE MONTE CARLO METHOD , 1982 .

[19]  Ashleyj . Welch,et al.  Optical-Thermal Response of Laser-Irradiated Tissue , 1995 .

[20]  Katarina Itrić,et al.  KUBELKA-MUNK THEORY IN DESCRIBING OPTICAL PROPERTIES OF PAPER (II) , 2012 .

[21]  D. Boas,et al.  Non-invasive neuroimaging using near-infrared light , 2002, Biological Psychiatry.

[22]  S. Arridge,et al.  Optical imaging in medicine: II. Modelling and reconstruction , 1997, Physics in medicine and biology.