Framework for denoising Monte Carlo photon transport simulations using deep learning.

SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resulting in high computational burdens. AIM: We aim to develop an effective image denoising technique using deep learning (DL) to dramatically improve the low-photon MC simulation result quality, equivalently bringing further acceleration to the MC method. APPROACH: We developed a cascade-network combining DnCNN with UNet, while extending a range of established image denoising neural-network architectures, including DnCNN, UNet, DRUNet, and deep residual-learning for denoising MC renderings (ResMCNet), in handling three-dimensional MC data and compared their performances against model-based denoising algorithms. We also developed a simple yet effective approach to creating synthetic datasets that can be used to train DL-based MC denoisers. RESULTS: Overall, DL-based image denoising algorithms exhibit significantly higher image quality improvements over traditional model-based denoising algorithms. Among the tested DL denoisers, our cascade network yields a 14 to 19 dB improvement in signal-to-noise ratio, which is equivalent to simulating 25 × to 78 × more photons. Other DL-based methods yielded similar results, with our method performing noticeably better with low-photon inputs and ResMCNet along with DRUNet performing better with high-photon inputs. Our cascade network achieved the highest quality when denoising complex domains, including brain and mouse atlases. CONCLUSIONS: Incorporating state-of-the-art DL denoising techniques can equivalently reduce the computation time of MC simulations by one to two orders of magnitude. Our open-source MC denoising codes and data can be freely accessed at http://mcx.space/.

Graphics processing units-accelerated adaptive nonlocal means filter for denoising three-dimensional Monte Carlo photon transport simulations.

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 3.5 × 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.

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms.

We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware. Furthermore, multiple thread-level and device-level load-balancing strategies are developed to obtain efficient simulations using multiple central processing units and GPUs.

Determination of Other Related Carotenoids Substances in Astaxanthin Crystals Extracted from Adonis amurensis.

Astaxanthin is a kind of important carotenoids with powerful antioxidation capacity and other health functions. Extracting from Adonis amurensis is a promising way to obtain natural astaxanthin. However, how to ensure the high purity and to investigate related substances in astaxanthin crystals are necessary issues. In this study, to identify possible impurities, astaxanthin crystal was first extracted from Adonis amurensis, then purified by saponification and separation. The concentration of total carotenoids in purified astaxanthin crystals was as high as 97% by weight when analyzed by UV-visible absorption spectra. After identified with TLC, HPLC and MS, besides free astaxanthin as main ingredient in the crystals, there existed four other unknown related substances, which were further investigated by HPLC/ESI/MS with the positive ion mode combining with other auxiliary reference data obtained in stress tests, at last it was confirmed that four related carotenoids substances were three structural isomers of semi-astacene and adonirubin.

Big Data Analysis on Puerto Rico Testsite for Exploring Contamination Threats.

In this paper, we present the use of Principal Component Analysis and customized software, to accelerate the spectral analysis of biological samples. The work is part of the mission of the National Institute of Environmental Health Sciences sponsored Puerto Rico Testsite for Exploring Contamination Threats Center, establishing linkages between environmental pollutants and preterm birth. This paper provides an overview of the data repository developed for the Center, and presents a use case analysis of biological sample data maintained in the database system.