Evidence map›Paper›PMID 34910309›Full record

ArticleMedical physics2022

Three-dimensional printing of patient-specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures.

Kai Mei, Michael Geagan, Leonid Roshkovan, Harold I Litt, Grace J Gang, Nadav Shapira, J Webster Stayman, Peter B Noël

Abstract readLetter
In one paragraph

Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 citing papers in PubMed.

  1. Article
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  3. Obese patient imaging: Potential dose reduction with photon-counting CT.Journal of applied clinical medical physics · 2026
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  12. Obese patient imaging: improved dose efficiency with photon-counting CT.medRxiv : the preprint server for health sciences · 2025
    Article
  13. Article
  14. Article
  15. Article
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  20. Lifelike and Deformable Lung Phantoms for 4DCT Imaging: A Three-Dimensional Printing Approach.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2024
    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.

Kai MeiDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Michael GeaganDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Leonid RoshkovanDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Harold I LittDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Grace J GangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Nadav ShapiraDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
J Webster StaymanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Peter B NoëlDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Patient-specific, high-sensitivity spectral CT for assessment of pancreatic cancerR01EB030494 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI GANG, JIANAN GRACE, NOËL, PETER B · 2021 to 2024
$2.4M
Nonlinear performance analysis and prediction for robust low dose lung CTR01CA249538 · NCI · UNIVERSITY OF PENNSYLVANIA · PI GANG, JIANAN GRACE · 2021 to 2024
$2.0M
NCI NIH HHS R01 CA249538NIBIB NIH HHS R01 EB030494NIH HHS R01CA249538NIH HHS R01EB030494
6 · The paper itself

Abstract

purposePhantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures.

methodsPixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the patient scan. Density and geometrical accuracy between phantom and patient CT data were evaluated for various anatomical features in the lung.

resultsFor the calibration phantom, measured mean HU show a very high level of linear correlation with respect to the utilized filament line widths, (r > 0.999). Qualitatively, the CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels (from -800 to 0 HU), with clearly visible vascular and parenchymal structures. Regions of interest comparing attenuation illustrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist reveal a high degree of geometrical correlation of details between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the scans.

conclusionThe present study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate organ geometry, image texture, and attenuation profiles. PixelPrint will enable applications in the research and development of CT technology, including further development in radiomics.

Indexed as

Printing, Three-DimensionalTomography, X-Ray ComputedCalibrationHumansLungPhantoms, Imaging3D printingcomputed tomographyimage qualitylungquality assuranceradiomics

Identifiers

PMID34910309
PMCPMC8828694

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.