3D Monte Carlo simulation of light distribution in mouse brain in quantitative photoacoustic computed tomography.

Journal Article (Journal Article)


Photoacoustic computed tomography (PACT) detects light-induced ultrasound (US) waves to reconstruct the optical absorption contrast of the biological tissues. Due to its relatively deep penetration (several centimeters in soft tissue), high spatial resolution, and inherent functional sensitivity, PACT has great potential for imaging mouse brains with endogenous and exogenous contrasts, which is of immense interest to the neuroscience community. However, conventional PACT either assumes homogenous optical fluence within the brain or uses a simplified attenuation model for optical fluence estimation. Both approaches underestimate the complexity of the fluence heterogeneity and can result in poor quantitative imaging accuracy.


To optimize the quantitative performance of PACT, we explore for the first time 3D Monte Carlo (MC) simulation to study the optical fluence distribution in a complete mouse brain model. We apply the MCX MC simulation package on a digital mouse (Digimouse) brain atlas that has complete anatomy information. To evaluate the impact of the brain vasculature on light delivery, we also incorporate the whole-brain vasculature in the Digimouse atlas. k-wave toolbox was used to investigate the effect of inhomogeneous illumination on the reconstructed images and chromophore concentration estimation.


The simulation results clearly show that the optical fluence in the mouse brain is heterogeneous at the global level and can decrease by a factor of five with increasing depth. Moreover, the strong absorption and scattering of the brain vasculature also induce the fluence disturbance at the local level.


Both global and local fluence heterogeneity contributes to the reduced quantitative accuracy of the reconstructed PACT images of mouse brain. Correcting the optical fluence distribution can improve the quantitative accuracy of PACT.

Full Text

Duke Authors

Cited Authors

  • Tang, Y; Yao, J

Published Date

  • March 2021

Published In

Volume / Issue

  • 11 / 3

Start / End Page

  • 1046 - 1059

PubMed ID

  • 33654676

Pubmed Central ID

  • PMC7829164

Electronic International Standard Serial Number (EISSN)

  • 2223-4306

International Standard Serial Number (ISSN)

  • 2223-4292

Digital Object Identifier (DOI)

  • 10.21037/qims-20-815


  • eng