Evidence map›Paper›PMID 37840044›Full record

ArticleScientific reports2023

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging.

Kai Mei, Pouyan Pasyar, Michael Geagan, Leening P Liu, Nadav Shapira, Grace J Gang, J Webster Stayman, Peter B Noël

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Article
  2. Obese patient imaging: Potential dose reduction with photon-counting CT.Journal of applied clinical medical physics · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Bone Organoids: A Novel Tool for Modeling and Managing Skeletal Disorders in Diabetes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Obese patient imaging: improved dose efficiency with photon-counting CT.medRxiv : the preprint server for health sciences · 2025
    Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. A novel method to simulate radiographs of 3D printed objects.Journal of applied clinical medical physics · 2025
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Kai MeiDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. kai.mei@pennmedicine.upenn.edu.
Pouyan PasyarDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Michael GeaganDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Leening P LiuDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Nadav ShapiraDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Grace J GangDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
J Webster StaymanDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Peter B NoëlDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Funding

Framework for radiomics standardization with application in pulmonary CT scansR01EB031592 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI GANG, JIANAN GRACE, STAYMAN, JOSEPH WEBSTER · 2022 to 2025
$2.6M
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 EB030494NIBIB NIH HHS R01 EB031592
6 · The paper itself

Abstract

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Indexed as

CalciumTomography, X-Ray ComputedCervical VertebraeHumansPhantoms, ImagingPrinting, Three-DimensionalReproducibility of ResultsCalcium

Identifiers

PMID37840044
PMCPMC10577126

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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.