Evidence map›Paper›PMID 41867473›Full record

ArticleJournal of biomedical optics2026

Accelerating mesh-based Monte Carlo simulations using contemporary graphics ray-tracing hardware.

Shijie Yan, Douglas Dwyer, David R Kaeli, Qianqian Fang

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Shijie YanNortheastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States.ORCID https://orcid.org/0000-0002-1983-4625
Douglas DwyerNortheastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States.
David R KaeliNortheastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States.ORCID https://orcid.org/0000-0002-5692-0151
Qianqian FangNortheastern University, Department of Electrical and Computer Engineering, Boston, Massachusetts, United States.ORCID https://orcid.org/0000-0003-0805-935X

Funding

Next-generation Monte Carlo eXtreme Light Transport Simulation PlatformR01GM114365 · NIGMS · NORTHEASTERN UNIVERSITY · PI FANG, QIANQIAN · 2015 to 2023
$2.9M
Ultra-high-density, compressive and media- adaptive optical breast tomography platformR01CA204443 · NCI · NORTHEASTERN UNIVERSITY · PI FANG, QIANQIAN · 2016 to 2025
$2.5M
Next-generation optical brain functional imaging platformR01EB026998 · NIBIB · NORTHEASTERN UNIVERSITY · PI FANG, QIANQIAN · 2018 to 2019
$870k
NCI NIH HHS R01 CA204443NIBIB NIH HHS R01 EB026998NIGMS NIH HHS R01 GM114365
6 · The paper itself

Abstract

Significance: Monte Carlo (MC) methods are the gold standard for modeling light-tissue interactions due to their accuracy. Mesh-based MC (MMC) offers enhanced precision for complex tissue structures using tetrahedral mesh models. Despite significant speedups achieved on graphics processing units (GPUs), MMC performance remains hindered by the computational cost of frequent ray-boundary intersection tests. Aim: We propose a highly accelerated MMC algorithm, RT-MMC, which leverages the hardware-accelerated ray traversal and intersection capabilities of ray-tracing cores (RT-cores) on modern GPUs. Approach: Implemented using NVIDIA's OptiX platform, RT-MMC extends graphics ray-tracing pipelines toward volumetric ray-tracing in turbid media, eliminating the need for challenging tetrahedral mesh generation while delivering significant speed improvements through hardware acceleration. It also intrinsically supports wide-field sources without complex mesh retessellation. Results: RT-MMC demonstrates excellent agreement with traditional software-ray-tracing MMC algorithms while achieving 1.5× to 4.5× speedups across multiple GPU architectures. These performance gains significantly enhance the practicality of MMC for routine simulations. Conclusion: Migration from software- to hardware-based ray tracing not only greatly simplifies MMC simulation workflows but also results in significant speedups that are expected to increase further as ray-tracing hardware rapidly gains adoption. Adoption of graphics ray-tracing pipelines in quantitative MMC simulations enables leveraging of emerging hardware resources and benefits a wide range of biophotonics applications.

Indexed as

Computer GraphicsImage Processing, Computer-AssistedMonte Carlo MethodAlgorithmsComputer SimulationHumansParallel AlgorithmsSoftwarecomputer graphicsgraphics processing unitlight transportMesh-based Monte CarloMonte Carlo methodray-tracing

Identifiers

PMID41867473
PMCPMC12999973

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.