Evidence map›Paper›PMID 30696195›Full record

ArticleOptics express2019

Metropolis Monte Carlo simulation scheme for fast scattered X-ray photon calculation in CT.

Yuan Xu, Yusi Chen, Zhen Tian, Xun Jia, Linghong Zhou

Abstract read
In one paragraph

Article in Optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

5 authors.

Yuan Xu
Yusi Chen
Zhen Tian
Xun Jia
Linghong Zhou

Funding

Next generation small animal radiation research platformR37CA214639 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI JIA, XUN · 2018 to 2024
$3.8M
Precise image guidance for liver cancer stereotactic body radiotherapy using element-resolved motion-compensated cone beam CTR01CA227289 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI JIA, XUN · 2018 to 2022
$2.0M
Explore random sampling for dose reduction and scatter removal in cone beam CTR21EB021545 · NIBIB · UT SOUTHWESTERN MEDICAL CENTER · PI JIA, XUN, ZHU, LEI · 2016 to 2017
$463k
NCI NIH HHS R01 CA227289NCI NIH HHS R37 CA214639NIBIB NIH HHS R21 EB021545
6 · The paper itself

Abstract

Monte Carlo (MC) method is commonly considered as the most accurate approach for particle transport simulation because of its capability to precisely model physics interactions and simulation geometry. Conventionally, MC simulation is performed in a particle-by-particle fashion. In certain problems such as computing scattered X-ray photon signal at a detector of CT, the conventional simulation scheme suffers from low efficiency mainly due to the fact that abundant photons are simulated but do not reach the detector. The computational resources spent on those photons are therefore wasted. To solve this problem, this study develops a novel GPU-based Metropolis MC (gMMC) with a novel path-by-path simulation scheme and demonstrates its effectiveness in an example problem of scattered X-ray photon calculation in CT. In contrast to the conventional MC approach, gMMC samples an entire photon path extending from the X-ray source to the detector using Metropolis-Hasting algorithm. The path-by-path simulation scheme ensures contribution of every sampled event to the signal of interest, improving overall efficiency. We benchmark gMMC against an in-house developed GPU-based MC tool, gMCDRR, which performs simulations in the conventional particle-by-particle fashion. gMMC reaches speed up factors of 37~48 times in simple phantom cases and 20-34 times in real patient cases. The results calculated by gMCDRR and gMMC agree well with average differences < 3%.

Identifiers

PMID30696195
PMCPMC6410917

What OpenQuestion holds

Textmetadata
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.