Evidence map›Paper›PMID 33263311›Full record

ArticlePhysics in medicine and biology2020

GPU-accelerated Monte Carlo simulation of MV-CBCT.

Mengying Shi, Marios Myronakis, Matthew Jacobson, Dianne Ferguson, Christopher Williams, Mathias Lehmann, Paul Baturin, Pascal Huber, Rony Fueglistaller, Ingrid Valencia Lozano and 3 more

Open access · greenAbstract read
In one paragraph

Article in Physics in medicine and biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 37% of its field
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

1 citing paper in PubMed, 10 citations in OpenAlex.

  1. 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

13 authors at 5 institutions in 2 countries.

Mengying ShiMedical Physics Program, Department of Physics and Applied Physics, University of Massachusetts Lowell, Lowell, MA, United States of America.
Marios MyronakisBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Matthew JacobsonBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Dianne FergusonBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Christopher WilliamsBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Mathias LehmannVarian Medical Systems, Baden, Switzerland.
Paul BaturinVarian Medical Systems, Palo Alto, CA, United States of America.
Pascal HuberVarian Medical Systems, Baden, Switzerland.
Rony FueglistallerVarian Medical Systems, Baden, Switzerland.
Ingrid Valencia LozanoBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Thomas HarrisBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Daniel MorfVarian Medical Systems, Baden, Switzerland.
Ross I BerbecoBrigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America.
Dana-Farber Brigham Cancer Center · USVarian Medical Systems (Switzerland) · CHBrigham and Women's Hospital · USHarvard University · USVarian Medical Systems (United States) · US

Funding

A novel flat-panel detector for advanced on-board radiation therapy imagingR01CA188446 · NCI · DANA-FARBER CANCER INST · PI BERBECO, ROSS I., FUEGLISTALLER, RONY · 2014 to 2024
$7.2M
NCI NIH HHS R01 CA188446
6 · The paper itself

Abstract

Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900-2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.

Indexed as

Computer SimulationCone-Beam Computed TomographyMonte Carlo MethodComputer GraphicsPhantoms, ImagingPhotons

Identifiers

PMID33263311
PMCPMC9100851
OpenAlexW3048382340

What OpenQuestion holds

Textmetadata
LicenceTDM
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.