Evidence map›Paper›PMID 32320971›Full record

ArticlePhysics in medicine and biology2020

Computational model for detector timing effects in Compton-camera based prompt-gamma imaging for proton radiotherapy.

Paul Maggi, Steve Peterson, Rajesh Panthi, Dennis Mackin, Hao Yang, Zhong He, Sam Beddar, Jerimy Polf

Abstract 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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  7. Secondary Particle Interactions in a Compton Camera Designed forIEEE transactions on radiation and plasma medical sciences · 2021
    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

8 authors.

Paul MaggiMaryland Proton Treatment Center, Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States of America.
Steve PetersonDepartment of Physics, University of Cape Town, Rondebosch, South Africa.
Rajesh PanthiDepartment of Radiation Physics, University of Texas M D Anderson Cancer Center, Houston, TX, United States of America.
Dennis MackinDepartment of Radiation Physics, University of Texas M D Anderson Cancer Center, Houston, TX, United States of America.
Hao YangH3D Inc, Ann Arbor, MI, United States of America.
Zhong HeH3D Inc, Ann Arbor, MI, United States of America.
Sam BeddarDepartment of Radiation Physics, University of Texas M D Anderson Cancer Center, Houston, TX, United States of America.ORCID 0000-0001-5825-110X
Jerimy PolfMaryland Proton Treatment Center, Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, United States of America.

Funding

Prompt Gamma Imaging for the in-vivo range verification during proton radiotherapyR01CA187416 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI BEDDAR, SAM, KAYE, WILLIAM ROBERT · 2015 to 2019
$2.7M
NCI NIH HHS R01 CA187416
6 · The paper itself

Abstract

This paper describes a realistic simulation of a Compton-camera (CC) based prompt-gamma (PG) imaging system for proton range verification for a range of clinical dose rates, and its comparison to PG measured data with a pre-clinical CC. We used a Monte Carlo plus Detector Effects (MCDE) model to simulate the production of prompt gamma-rays (PG) and their energy depositions in the CC. With Monte Carlo, we simulated PG emission resulting from irradiation of a high density polyethylene phantom with a 150 MeV proton pencil beam at dose rates of 5.0 × 10

Indexed as

Computer SimulationProton TherapyHumansImage Processing, Computer-AssistedMonte Carlo MethodPhantoms, ImagingRadionuclide ImagingTime Factors

Identifiers

PMID32320971
PMCPMC8808425

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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.