Literature DB >> 30738531

Development of a 3D printed anthropomorphic lung phantom for image quality assessment in CT.

Irene Hernandez-Giron1, Johan Michiel den Harder2, Geert J Streekstra2, Jacob Geleijns3, Wouter J H Veldkamp3.   

Abstract

PURPOSE: To design a 3D printed anthropomorphic lung vessel phantom for CT image quality assessment and to evaluate the phantom image and dose characteristics.
METHODS: An in-house algorithm generated a vessel tree model, based on human lungs anatomy, which was 3D printed using a multi jet modeling printer (0.25 mm ≤ vessel diameters ≤ 8.25 mm) and inserted in an elliptical holder (thorax surrogate). The phantom was scanned (Toshiba Aquilion Genesis CT) and compared in terms of attenuation (Hounsfield units, HU) and dose characteristics with studies of five patients (normal BMI) and a commercial torso phantom, performed with the same thorax protocol. The pixel value distribution in the lung area was assessed with histograms. To investigate the adjustment of tube current modulation, tube load and CTDI were compared.
RESULTS: The histogram peaks for respectively vessels and surrounding tissue were at 105 HU and -985 HU (3D printed phantom), at -25 HU and -1000 HU (torso phantom) and at 25 HU and -875 HU (average patient). The contrast between vessels and surrounding was -1090 HU (3D printed), -975 HU (torso phantom), and -900 HU (average patient). The measured HU values (soft tissue and vertebra) were (32 ± 15) HU and (210 ± 71) HU (average patient); (4 ± 4) HU, (390 ± 39) HU (torso phantom) and (119 ± 5) HU, (951 ± 31) HU (3D printed phantom and holder). CTDIvol was (1.9 ± 4.7 mGy) for patients, 1.9 mGy for the torso phantom and 2.1 mGy for the 3D printed lung phantom.
CONCLUSIONS: An anthropomorphic 3D printed lung phantom was developed and its CT image and dose characteristics evaluated. The phantom has the potential to provide clinically relevant and reproducible measures of CT image quality.
Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  3D printed phantom; Computed tomography; Image quality; Lung

Mesh:

Year:  2018        PMID: 30738531     DOI: 10.1016/j.ejmp.2018.11.015

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  9 in total

1.  Simulating Tissues with 3D-Printed and Castable Materials.

Authors:  Michael O'Reilly; Michael Hoff; Seth D Friedman; James F X Jones; Nathan M Cross
Journal:  J Digit Imaging       Date:  2020-10       Impact factor: 4.056

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

Authors:  Kai Mei; Michael Geagan; Leonid Roshkovan; Harold I Litt; Grace J Gang; Nadav Shapira; J Webster Stayman; Peter B Noël
Journal:  Med Phys       Date:  2021-12-23       Impact factor: 4.071

3.  PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for CT imaging.

Authors:  Nadav Shapira; Kevin Donovan; Kai Mei; Michael Geagan; Leonid Roshkovan; Harold I Litt; Grace J Gang; J Webster Stayman; Russell T Shinohara; Peter B Noël
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

4.  Additively Manufactured Patient-Specific Anthropomorphic Thorax Phantom With Realistic Radiation Attenuation Properties.

Authors:  Sepideh Hatamikia; Gunpreet Oberoi; Ewald Unger; Gernot Kronreif; Joachim Kettenbach; Martin Buschmann; Michael Figl; Barbara Knäusl; Francesco Moscato; Wolfgang Birkfellner
Journal:  Front Bioeng Biotechnol       Date:  2020-05-08

5.  Automatic quantitative analysis of pulmonary vascular morphology in CT images.

Authors:  Zhiwei Zhai; Marius Staring; Irene Hernández Girón; Wouter J H Veldkamp; Lucia J Kroft; Maarten K Ninaber; Berend C Stoel
Journal:  Med Phys       Date:  2019-07-09       Impact factor: 4.071

6.  X-ray attenuation of bone, soft and adipose tissue in CT from 70 to 140 kV and comparison with 3D printable additive manufacturing materials.

Authors:  Xiangjie Ma; Michael Figl; Ewald Unger; Martin Buschmann; Peter Homolka
Journal:  Sci Rep       Date:  2022-08-26       Impact factor: 4.996

7.  Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

Authors:  Joël Greffier; Salim Si-Mohamed; Julien Frandon; Maeliss Loisy; Fabien de Oliveira; Jean Paul Beregi; Djamel Dabli
Journal:  Med Phys       Date:  2022-06-24       Impact factor: 4.506

8.  Development of a CT imaging phantom of anthromorphic lung using fused deposition modeling 3D printing.

Authors:  Dayeong Hong; Sangwook Lee; Guk Bae Kim; Sang Min Lee; Namkug Kim; Joon Beom Seo
Journal:  Medicine (Baltimore)       Date:  2020-01       Impact factor: 1.817

Review 9.  3D Printing of Physical Organ Models: Recent Developments and Challenges.

Authors:  Zhongboyu Jin; Yuanrong Li; Kang Yu; Linxiang Liu; Jianzhong Fu; Xinhua Yao; Aiguo Zhang; Yong He
Journal:  Adv Sci (Weinh)       Date:  2021-07-08       Impact factor: 16.806

  9 in total

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