Evidence map›Paper›PMID 31711051›Full record

ArticlePhysics in medicine and biology2019

gPET: a GPU-based, accurate and efficient Monte Carlo simulation tool for PET.

Youfang Lai, Yuncheng Zhong, Ananta Chalise, Yiping Shao, Mingwu Jin, Xun Jia, Yujie Chi

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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  8. HPhysics in medicine and biology · 2021
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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

7 authors.

Youfang LaiDepartment of Physics, University of Texas at Arlington, Arlington, TX 76019, United States of America.
Yuncheng ZhongInnovative Technology Of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Ananta ChaliseDepartment of Physics, University of Texas at Arlington, Arlington, TX 76019, United States of America.ORCID 0000-0003-4281-2771
Yiping ShaoDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Mingwu JinDepartment of Physics, University of Texas at Arlington, Arlington, TX 76019, United States of America.ORCID 0000-0003-3987-2896
Xun JiaInnovative Technology Of Radiotherapy Computation and Hardware (iTORCH) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Yujie ChiDepartment of Physics, University of Texas at Arlington, Arlington, TX 76019, United States of America.

Funding

Novel On-line PET Based Intra-Beam Range Verification and Delivery Optimization for Improved Particle Radiation TherapyR01CA218402 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI LU, WEIGUO, SHAO, YIPING · 2018 to 2022
$2.2M
Boosting photo-induced cancer therapies through real-time image guidanceR15CA199020 · NCI · UNIVERSITY OF TEXAS ARLINGTON · PI JIN, MINGWU · 2016 to 2016
$415k
NCI NIH HHS R01 CA218402NCI NIH HHS R15 CA199020
6 · The paper itself

Abstract

Monte Carlo (MC) simulation method plays an essential role in the refinement and development of positron emission tomography (PET) systems. However, most existing MC simulation packages suffer from long execution time for practical PET simulations. To fully address this issue, we developed and validated gPET, a graphics processing unit (GPU)-based MC simulation tool for PET. gPET was built on the NVidia CUDA platform. The simulation process was modularized into three functional parts and carried out by the GPU parallel threads: (1) source management, including positron decay, transport and annihilation; (2) gamma transport inside the phantom; and (3) signal detection and processing inside the detector. A hybrid of voxelized (for patient phantoms) and parametrized (for detectors) geometries were employed to sufficiently support particle navigations. Multiple inputs and outputs were available. Hence, a user can flexibly examine different aspects of a PET simulation. We evaluated the performance of gPET in three test cases with benchmark work from GATE8.0, in terms of the testing of the functional modules, the physics models used for gamma transport inside the detector, and the geometric configuration of an irregularly shaped PET detector. Both accuracy and efficiency were quantified. In all test cases, the differences between gPET and GATE for the coincidences with respect to the energy and crystal index distributions are below 3.18% and 2.54%, respectively. The speedup factor is 500 for gPET on a single Titan Xp GPU (1.58 GHz) over GATE8.0 on a single core of Intel i7-6850K CPU (3.6 GHz) for all test cases. In summary, gPET is an accurate and efficient MC simulation tool for PET.

Indexed as

Computer SimulationHumansMonte Carlo MethodPhantoms, ImagingReproducibility of ResultsSoftwareTomography, X-Ray Computed

Identifiers

PMID31711051
PMCPMC10593186

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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.