Evidence map›Paper›PMID 33949078›Full record

ArticleJournal of applied clinical medical physics2021

A calibration CT mini-lung-phantom created by 3-D printing and subtractive manufacturing.

H Henry Guo, Mats Persson, Oliver Weinheimer, Jarrett Rosenberg, Terry E Robinson, Jia Wang

Open access · goldAbstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 47% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

6 authors at 5 institutions in 3 countries.

H Henry GuoDepartment of Radiology, Stanford Medical Center, Stanford, CA, USA.
Mats PerssonDepartment of Physics, KTH Royal Institute of Technology, Stockholm, Sweden.
Oliver WeinheimerDepartment of Radiology, University of Heidelberg, Heidelberg, Germany.
Jarrett RosenbergDepartment of Radiology, Stanford University, Stanford, CA, USA.
Terry E RobinsonEmeritus, Department of Pediatrics, Stanford Medical Center, Stanford, CA, USA.
Jia WangEnvironmental Health and Safety, Stanford University, Stanford, CA, USA.
Stanford Medicine · USHeidelberg University · DEKTH Royal Institute of Technology · SEStanford Health Care · USStanford University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We describe the creation and characterization of a calibration CT mini-lung-phantom incorporating simulated airways and ground-glass densities. Ten duplicate mini-lung-phantoms with Three-Dimensional (3-D) printed tubes simulating airways and gradated density polyurethane foam blocks were designed and built. Dimensional accuracy and CT numbers were measured using micro-CT and clinical CT scanners. Micro-CT images of airway tubes demonstrated an average dimensional variation of 0.038 mm from nominal values. The five different densities of incorporated foam blocks, simulating ground-glass, showed mean CT numbers (±standard deviation) of -897.0 ± 1.5, -844.1 ± 1.5, -774.1 ± 2.6, -695.3 ± 1.6, and -351.0 ± 3.7 HU, respectively. Three-Dimensional printing and subtractive manufacturing enabled rapid, cost-effective production of ground-truth calibration mini-lung-phantoms with low inter-sample variation that can be scanned simultaneously with the patient undergoing lung quantitative CT.

Indexed as

Printing, Three-DimensionalTomography, X-Ray ComputedCalibrationHumansLungPhantoms, Imaging3D printingairway measurementscalibrationphantomsquantitative CT

Identifiers

PMID33949078
PMCPMC8200432
OpenAlexW3157404080

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.