Abstract
.The Monte Carlo (MC) method is widely recognized as the gold standard for modeling light propagation inside turbid media. Due to the stochastic nature of this method, MC simulations suffer from inherent stochastic noise. Launching large numbers of photons can reduce noise but results in significantly greater computation times, even with graphics processing units (GPU)-based acceleration. We develop a GPU-accelerated adaptive nonlocal means (ANLM) filter to denoise MC simulation outputs. This filter can effectively suppress the spatially varying stochastic noise present in low-photon MC simulations and improve the image signal-to-noise ratio (SNR) by over 5 dB. This is equivalent to the SNR improvement of running nearly more photons. We validate this denoising approach using both homogeneous and heterogeneous domains at various photon counts. The ability to preserve rapid optical fluence changes is also demonstrated using domains with inclusions. We demonstrate that this GPU-ANLM filter can shorten simulation runtimes in most photon counts and domain settings even combined with our highly accelerated GPU MC simulations. We also compare this GPU-ANLM filter with the CPU version and report a threefold to fourfold speedup. The developed GPU-ANLM filter not only can enhance three-dimensional MC photon simulation results but also be a valuable tool for noise reduction in other volumetric images such as MRI and CT scans.
Highlights
The development of innovative biophotonics techniques relies on accurate and efficient photon propagation models, especially when imaging complex human anatomy
We describe a significantly improved GPUaccelerated adaptive nonlocal means (ANLM) filter and study its applications in denoising 3-D Monte Carlo (MC) photon transport simulation images
We show that the denoising step can generate an average 5-dB signal-to-noise ratio (SNR) improvement; this is equivalent to the result of running 3- to 3.5-fold more photons
Summary
The development of innovative biophotonics techniques relies on accurate and efficient photon propagation models, especially when imaging complex human anatomy. The importance of developing fast and accurate light propagation algorithms in general media is further highlighted by the increasing utility of model-based methods in optical image acquisition and image processing. The radiative transport equation (RTE) most realistically describes the light propagation in a general random media, such as human tissues. The diffusion equation (DE) provides a good approximation to the RTE in a scattering-dominant media[1,2] and can be computed efficiently using finite-element (FE)-based numerical solvers.[3,4] it has been shown that solving the DE in regions that contain low-scattering media, such as cerebral spinal fluid (CSF) in the brain and other void-like regions, can lead to erroneous solutions.[5,6]
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