H. Shen and G. Wang, “A tetrahedon-based inhomogenous Monte Carlo
optical simulator,” Phys. Med. Biol. 55, 947–962 (2010).

N. Carbone, H. Di Rocco, D. I. Iriarte, and J. A. Pomarico, “Solution of the direct problem in turbid media
with inclusions using monte carlo simulations implemented in graphics
processing units: new criterion for processing transmittance
data,” J. Biomed. Opt. 15, 035002 (2010).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

N. Ren, J. Liang, X. Qu, J. Li, B. Lu, and J. Tian, “GPU-based Monte Carlo simulation for light
propagation in complex heterogeneous tissues,” Opt.
Express 18, 6811–6823 (2010).

Q. Fang and D. A. Boas, “Monte Carlo Simulation of Photon Migration in
3D Turbid Media Accelerated by Graphics Processing Units,” Opt. Express 17, 20178–20190 (2009).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

A. Badal and A. Badano, “Monte Carlo simulations in a graphics
processing unit,” Med. Phys. 36, 4878–4880 (2009).

W. C. Y. Lo, T. D. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for
photodynamic therapy treatment planning,” Proc. SPIE 7373, (2009).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing
units for high-speed Monte Carlo simulation of photon
migration,” J. Biomed. Opt. 13, 060504 (2008).

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon
migration,” J. Biomed. Opt. 13, 041304 (2008).

A. Johansson, J. Axelsson, S. Andersson-Engels, and J. Swartling, “Realtime light dosimetry software tools for
interstitial photodynamic therapy of the human prostate,” Med. Phys. 34, 4309 (2007).

G. Marsaglia, “Random number generators,” J. Mod. Appl. Stat. Meth. 2, 2–13 (2003).

C. K. Hayakawa, J. Spanier, F. Bevilacqua, A. K. Dunn, J. S. You, B. J. Tromberg, and V. Venugopalan, “Perturbation Monte Carlo methods to solve
inverse photon migration problems in heterogenous tissues,” Opt. Lett. 26, 1335–1337 (2001).

I. Meglinsky and S. Matcher, “Modelling the sampling volume for skin blood
oxygenation measurements,” Med. Biol. Eng. Comput. 39, 44–50 (2001).

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo
human skin and subcutaneous tissues measured using the Monte Carlo inversion
technique,” Phys. Med. Biol. 43, 2465–2478 (1998).

M. Matsumoto and T. Nishimura, “Mersnne Twister: a 623-dimensionally
equidistributed uniform pseudorandom number generator,” ACM T. Model. Comput. S. 8, 3–30 (1998).

L. Wang, S. Jacques, and L. Zheng, “CONV - convolution for responses to a finite
diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).

L. Wang, S. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport
in multi-layered tissues,” Comput. Meth. Prog. Biol. 47, 131–146 (1995).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

B. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux
distributions of light in tissue,” Med. Phys. 10, 824 (1983).

B. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux
distributions of light in tissue,” Med. Phys. 10, 824 (1983).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing
units for high-speed Monte Carlo simulation of photon
migration,” J. Biomed. Opt. 13, 060504 (2008).

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon
migration,” J. Biomed. Opt. 13, 041304 (2008).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “CUDAMCML - User manual and implementation
notes,” http://www.atomic.physics.lu.se/biophotonics/.

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon
migration,” J. Biomed. Opt. 13, 041304 (2008).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing
units for high-speed Monte Carlo simulation of photon
migration,” J. Biomed. Opt. 13, 060504 (2008).

A. Johansson, J. Axelsson, S. Andersson-Engels, and J. Swartling, “Realtime light dosimetry software tools for
interstitial photodynamic therapy of the human prostate,” Med. Phys. 34, 4309 (2007).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “CUDAMCML - User manual and implementation
notes,” http://www.atomic.physics.lu.se/biophotonics/.

A. Johansson, J. Axelsson, S. Andersson-Engels, and J. Swartling, “Realtime light dosimetry software tools for
interstitial photodynamic therapy of the human prostate,” Med. Phys. 34, 4309 (2007).

A. Badal and A. Badano, “Monte Carlo simulations in a graphics
processing unit,” Med. Phys. 36, 4878–4880 (2009).

A. Badal and A. Badano, “Monte Carlo simulations in a graphics
processing unit,” Med. Phys. 36, 4878–4880 (2009).

C. K. Hayakawa, J. Spanier, F. Bevilacqua, A. K. Dunn, J. S. You, B. J. Tromberg, and V. Venugopalan, “Perturbation Monte Carlo methods to solve
inverse photon migration problems in heterogenous tissues,” Opt. Lett. 26, 1335–1337 (2001).

F. Bevilacqua, D. Piguet, P. Marquet, J. D. Gross, B. J. Tromberg, and C. Depeursinge, “In vivo local determination of tissue optical
properties: applications to human brain,” Appl. Opt. 38, 4939–4950 (1999).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

D. Boas and A. Dale, “Simulation study of magnetic resonance
imaging-guided cortically constrained diffuse optical tomography of human
brain function,” Appl. Opt. 44, 1957–1968 (2005).

D. Boas, J. Culver, J. Stott, and A. Dunn, “Three dimensional Monte Carlo code for photon
migration through complex heterogeneous media including the adult human
head.,” Opt. Express 10, 159–170 (2002).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

N. Carbone, H. Di Rocco, D. I. Iriarte, and J. A. Pomarico, “Solution of the direct problem in turbid media
with inclusions using monte carlo simulations implemented in graphics
processing units: new criterion for processing transmittance
data,” J. Biomed. Opt. 15, 035002 (2010).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo
human skin and subcutaneous tissues measured using the Monte Carlo inversion
technique,” Phys. Med. Biol. 43, 2465–2478 (1998).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

N. Carbone, H. Di Rocco, D. I. Iriarte, and J. A. Pomarico, “Solution of the direct problem in turbid media
with inclusions using monte carlo simulations implemented in graphics
processing units: new criterion for processing transmittance
data,” J. Biomed. Opt. 15, 035002 (2010).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo
human skin and subcutaneous tissues measured using the Monte Carlo inversion
technique,” Phys. Med. Biol. 43, 2465–2478 (1998).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

W. C. Y. Lo, T. D. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for
photodynamic therapy treatment planning,” Proc. SPIE 7373, (2009).

N. Carbone, H. Di Rocco, D. I. Iriarte, and J. A. Pomarico, “Solution of the direct problem in turbid media
with inclusions using monte carlo simulations implemented in graphics
processing units: new criterion for processing transmittance
data,” J. Biomed. Opt. 15, 035002 (2010).

L. Wang, S. Jacques, and L. Zheng, “CONV - convolution for responses to a finite
diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).

L. Wang, S. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport
in multi-layered tissues,” Comput. Meth. Prog. Biol. 47, 131–146 (1995).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

A. Johansson, J. Axelsson, S. Andersson-Engels, and J. Swartling, “Realtime light dosimetry software tools for
interstitial photodynamic therapy of the human prostate,” Med. Phys. 34, 4309 (2007).

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo
human skin and subcutaneous tissues measured using the Monte Carlo inversion
technique,” Phys. Med. Biol. 43, 2465–2478 (1998).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

W. C. Y. Lo, T. D. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for
photodynamic therapy treatment planning,” Proc. SPIE 7373, (2009).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

W. C. Y. Lo, T. D. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for
photodynamic therapy treatment planning,” Proc. SPIE 7373, (2009).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

G. Marsaglia, “Random number generators,” J. Mod. Appl. Stat. Meth. 2, 2–13 (2003).

I. Meglinsky and S. Matcher, “Modelling the sampling volume for skin blood
oxygenation measurements,” Med. Biol. Eng. Comput. 39, 44–50 (2001).

M. Matsumoto and T. Nishimura, “Mersnne Twister: a 623-dimensionally
equidistributed uniform pseudorandom number generator,” ACM T. Model. Comput. S. 8, 3–30 (1998).

M. Saito and M. Matsumoto, SIMD-oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator (Springer, 2008).

I. Meglinsky and S. Matcher, “Modelling the sampling volume for skin blood
oxygenation measurements,” Med. Biol. Eng. Comput. 39, 44–50 (2001).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

M. Matsumoto and T. Nishimura, “Mersnne Twister: a 623-dimensionally
equidistributed uniform pseudorandom number generator,” ACM T. Model. Comput. S. 8, 3–30 (1998).

N. Carbone, H. Di Rocco, D. I. Iriarte, and J. A. Pomarico, “Solution of the direct problem in turbid media
with inclusions using monte carlo simulations implemented in graphics
processing units: new criterion for processing transmittance
data,” J. Biomed. Opt. 15, 035002 (2010).

M. Quinn, Parallel Computing: Theory and Practice (McGraw-Hill, 1994).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

W. C. Y. Lo, T. D. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for
photodynamic therapy treatment planning,” Proc. SPIE 7373, (2009).

M. Saito and M. Matsumoto, SIMD-oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator (Springer, 2008).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

H. Shen and G. Wang, “A tetrahedon-based inhomogenous Monte Carlo
optical simulator,” Phys. Med. Biol. 55, 947–962 (2010).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo
human skin and subcutaneous tissues measured using the Monte Carlo inversion
technique,” Phys. Med. Biol. 43, 2465–2478 (1998).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon
migration,” J. Biomed. Opt. 13, 041304 (2008).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing
units for high-speed Monte Carlo simulation of photon
migration,” J. Biomed. Opt. 13, 060504 (2008).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “CUDAMCML - User manual and implementation
notes,” http://www.atomic.physics.lu.se/biophotonics/.

A. Johansson, J. Axelsson, S. Andersson-Engels, and J. Swartling, “Realtime light dosimetry software tools for
interstitial photodynamic therapy of the human prostate,” Med. Phys. 34, 4309 (2007).

C. K. Hayakawa, J. Spanier, F. Bevilacqua, A. K. Dunn, J. S. You, B. J. Tromberg, and V. Venugopalan, “Perturbation Monte Carlo methods to solve
inverse photon migration problems in heterogenous tissues,” Opt. Lett. 26, 1335–1337 (2001).

F. Bevilacqua, D. Piguet, P. Marquet, J. D. Gross, B. J. Tromberg, and C. Depeursinge, “In vivo local determination of tissue optical
properties: applications to human brain,” Appl. Opt. 38, 4939–4950 (1999).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

H. Shen and G. Wang, “A tetrahedon-based inhomogenous Monte Carlo
optical simulator,” Phys. Med. Biol. 55, 947–962 (2010).

L. Wang, S. Jacques, and L. Zheng, “CONV - convolution for responses to a finite
diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).

L. Wang, S. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport
in multi-layered tissues,” Comput. Meth. Prog. Biol. 47, 131–146 (1995).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

B. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux
distributions of light in tissue,” Med. Phys. 10, 824 (1983).

L. Wang, S. Jacques, and L. Zheng, “CONV - convolution for responses to a finite
diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).

L. Wang, S. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport
in multi-layered tissues,” Comput. Meth. Prog. Biol. 47, 131–146 (1995).

M. Matsumoto and T. Nishimura, “Mersnne Twister: a 623-dimensionally
equidistributed uniform pseudorandom number generator,” ACM T. Model. Comput. S. 8, 3–30 (1998).

D. Boas and A. Dale, “Simulation study of magnetic resonance
imaging-guided cortically constrained diffuse optical tomography of human
brain function,” Appl. Opt. 44, 1957–1968 (2005).

G. Palmer and N. Ramanujam, “Monte Carlo-based inverse model for calculating
tissue optical properties. Part I: Theory and validation on synthetic
phantoms,” Appl. Opt. 45, 1062–1071 (2006).

F. Bevilacqua, D. Piguet, P. Marquet, J. D. Gross, B. J. Tromberg, and C. Depeursinge, “In vivo local determination of tissue optical
properties: applications to human brain,” Appl. Opt. 38, 4939–4950 (1999).

L. Wang, S. Jacques, and L. Zheng, “CONV - convolution for responses to a finite
diameter photon beam incident on multi-layered tissues,” Comput. Meth. Prog. Bio. 54, 141–150 (1997).

L. Wang, S. Jacques, and L. Zheng, “MCML - Monte Carlo modeling of light transport
in multi-layered tissues,” Comput. Meth. Prog. Biol. 47, 131–146 (1995).

W. C. Y. Lo, K. Redmond, J. Luu, P. Chow, J. Rose, and L. Lilge, “Hardware acceleration of a Monte Carlo
simulation for photodynamic therapy treatment planning,” J. Biomed. Opt. 14, 014019 (2009).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing
units for high-speed Monte Carlo simulation of photon
migration,” J. Biomed. Opt. 13, 060504 (2008).

N. Carbone, H. Di Rocco, D. I. Iriarte, and J. A. Pomarico, “Solution of the direct problem in turbid media
with inclusions using monte carlo simulations implemented in graphics
processing units: new criterion for processing transmittance
data,” J. Biomed. Opt. 15, 035002 (2010).

E. Alerstam, S. Andersson-Engels, and T. Svensson, “White Monte Carlo for time-resolved photon
migration,” J. Biomed. Opt. 13, 041304 (2008).

G. Marsaglia, “Random number generators,” J. Mod. Appl. Stat. Meth. 2, 2–13 (2003).

S. Flock, S. Jacques, B. Wilson, W. Star, and M. van Gemert, “Optical properties of Intralipid: a phantom
medium for light propagation studies,” Laser. Surg.
Med. 12, 510–510 (1992).

I. Meglinsky and S. Matcher, “Modelling the sampling volume for skin blood
oxygenation measurements,” Med. Biol. Eng. Comput. 39, 44–50 (2001).

B. Wilson and G. Adam, “A Monte Carlo model for the absorption and flux
distributions of light in tissue,” Med. Phys. 10, 824 (1983).

A. Badal and A. Badano, “Monte Carlo simulations in a graphics
processing unit,” Med. Phys. 36, 4878–4880 (2009).

A. Johansson, J. Axelsson, S. Andersson-Engels, and J. Swartling, “Realtime light dosimetry software tools for
interstitial photodynamic therapy of the human prostate,” Med. Phys. 34, 4309 (2007).

A. Custo, D. Boas, D. Tsuzuki, I. Dan, R. Mesquita, B. Fischl, W. Grimson, and W. Wells III, “Anatomical atlas-guided diffuse optical
tomography of brain activation,” NeuroImage 49 (2010).

Q. Fang and D. A. Boas, “Monte Carlo Simulation of Photon Migration in
3D Turbid Media Accelerated by Graphics Processing Units,” Opt. Express 17, 20178–20190 (2009).

N. Ren, J. Liang, X. Qu, J. Li, B. Lu, and J. Tian, “GPU-based Monte Carlo simulation for light
propagation in complex heterogeneous tissues,” Opt.
Express 18, 6811–6823 (2010).

D. Boas, J. Culver, J. Stott, and A. Dunn, “Three dimensional Monte Carlo code for photon
migration through complex heterogeneous media including the adult human
head.,” Opt. Express 10, 159–170 (2002).

S. Davidson, R. Weersink, M. Haider, M. Gertner, A. Bogaards, D. Giewercer, A. Scherz, M. Sherar, M. Elhilali, and J. Chinet al., “Treatment planning and dose analysis
for interstitial photodynamic therapy of prostate cancer,” Phys. Med. Biol. 54, 2293–2313 (2009).

C. R. Simpson, M. Kohl, M. Essenpreis, and M. Cope, “Near-infrared optical properties of ex vivo
human skin and subcutaneous tissues measured using the Monte Carlo inversion
technique,” Phys. Med. Biol. 43, 2465–2478 (1998).

H. Shen and G. Wang, “A tetrahedon-based inhomogenous Monte Carlo
optical simulator,” Phys. Med. Biol. 55, 947–962 (2010).

W. C. Y. Lo, T. D. Han, J. Rose, and L. Lilge, “GPU-accelerated Monte Carlo simulation for
photodynamic therapy treatment planning,” Proc. SPIE 7373, (2009).

E. Alerstam, T. Svensson, and S. Andersson-Engels, “CUDAMCML - User manual and implementation
notes,” http://www.atomic.physics.lu.se/biophotonics/.

NVIDIA Corporation “CUDA Programming Guide 3.0,” (2010).

NVIDIA Corporation, “NVIDIA’s Next Generation CUDA Compute Architecture:
Fermi,” (2010).

M. Saito and M. Matsumoto, SIMD-oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator (Springer, 2008).

M. Quinn, Parallel Computing: Theory and Practice (McGraw-Hill, 1994).