Evidence map›Paper›PMID 37261890›Full record

ArticleJournal of applied clinical medical physics2023

Reducing scan time in

Ching-Ching Yang, Kuan-Yin Ko, Pei-Yao Lin

Open access · goldAbstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2023. 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
0.5field-weighted citation impact, top 35% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Reducing scan time inJournal of applied clinical medical physics · 2023
    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

3 authors at 2 institutions in 1 country.

Ching-Ching YangDepartment of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan.
Kuan-Yin KoDepartment of Nuclear Medicine, National Taiwan University Cancer Center, Taipei, Taiwan.
Pei-Yao LinDepartment of Nuclear Medicine, National Taiwan University Cancer Center, Taipei, Taiwan.
National Taiwan University · TWKaohsiung Medical University · TW

Funding

Kaohsiung Medical University Research Foundation KMU-M112023
6 · The paper itself

Abstract

purposeThe aim of this study was to reduce scan time in

methodsThe CNN model used in this work was based on DenseNet, and the training and testing datasets were generated from Monte Carlo simulation. The CNN input images (IMG

resultsNo difference was found in IMG

conclusionOur results showed that the proposed method can reduce scan time by up to 70% for objects larger than 13 mm, making it a useful tool for personalized dosimetry in

Indexed as

Neural Networks, ComputerRadioisotopesHumansMonte Carlo MethodRadionuclide ImagingReceptors, PeptideRadioisotopesReceptors, Peptide177Lu planar scintigraphyconvolutional neural networkMonte Carlo simulationpersonalized dosimetryscan time reduction

Identifiers

PMID37261890
PMCPMC10562044
OpenAlexW4378952868

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.