Evidence map›Paper›PMID 37710624›Full record

ArticleOptics express2023

Neu(t)ralMC: energy-efficient open source Monte Carlo algorithm for assessing photon transport in turbid media.

Abigail Clennell, Vinh Nguyen, Vladislav V Yakovlev, Alexander Doronin

Abstract read
In one paragraph

Article in Optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Anatomy-resolved digital twin framework for personalized light dosimetry in lung therapies.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Abigail Clennell
Vinh Nguyen
Vladislav V Yakovlev
Alexander Doronin

Funding

Brillouin Microscope for Biomedical ResearchR01GM127696 · NIGMS · TEXAS ENGINEERING EXPERIMENT STATION · PI YAKOVLEV, VLADISLAV V. · 2018 to 2021
$1.1M
Molecular and cellular imaging of bone biopsies using AI augmented deep UV Raman microscopyR21CA269099 · NCI · TEXAS ENGINEERING EXPERIMENT STATION · PI BEREZIN, MIKHAIL Y., YAKOVLEV, VLADISLAV V. · 2022 to 2024
$577k
Sensing local nano-environment with coherent Raman microspectroscopyR21GM142107 · NIGMS · TEXAS ENGINEERING EXPERIMENT STATION · PI YAKOVLEV, VLADISLAV V. · 2021 to 2022
$399k
NCI NIH HHS R21 CA269099NIGMS NIH HHS R01 GM127696NIGMS NIH HHS R21 GM142107
6 · The paper itself

Abstract

Light propagation in turbid mediums such as atmosphere, fluids, and biological tissues is a challenging problem which necessitates accurate simulation techniques to account for the effects of multiple scattering. The Monte Carlo method has long established itself as a gold standard and is widely adopted for simulating light transport, however, its computationally intensive nature often requires significant processing power and energy consumption. In this paper a novel, open source Monte Carlo algorithm is introduced which is specifically designed for use with energy-efficient processors, effectively addressing those challenges, while maintaining the accuracy/compatibility and outperforming existing solutions. The proposed implementation optimizes photon transport simulations by exploiting the unique capabilities of Apple's low-power, high-performance M-family of chips. The developed method has been implemented in an open-source software package, enabling seamless adaptation of developed algorithms for specific applications. The accuracy and performance are validated using comprehensive comparison with existing solvers commonly used for biomedical imaging. The results demonstrate that the new algorithm achieves comparable accuracy levels to those of existing techniques while significantly reducing computational time and energy consumption.

Identifiers

PMID37710624
PMCPMC10544956

What OpenQuestion holds

Textmetadata
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Registered trials

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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.