Evidence map›Paper›PMID 40781832›Full record

ArticleMedical physics2025

Hybrid phantom for lung CT: Design and validation.

Paulo Roberto Costa, Gisell Ruiz Boiset, Elsa Bifano Pimenta, Raphael Moratta Vieira Rocha, Raissa Aline Santos Moura, Wagner Henrique Marques, Luuk J Oostveen, Bram Geurts, Marcio Valente Yamada Sawamura, Denise Yanikian Nersissian and 2 more

Abstract readValidation Study
In one paragraph

Article in Medical physics, 2025. 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Paulo Roberto CostaInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Gisell Ruiz BoisetInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Elsa Bifano PimentaInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Raphael Moratta Vieira RochaInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Raissa Aline Santos MouraInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Wagner Henrique MarquesInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Luuk J OostveenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherland.
Bram GeurtsDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherland.
Marcio Valente Yamada SawamuraHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Denise Yanikian NersissianInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Elisabeth Mateus YoshimuraInstituto de Física, Universidade de São Paulo (USP), São Paulo, São Paulo, Brazil.
Ioannis SechopoulosDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherland.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 131691/2021-0Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 138533/2022-9Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 141335/2021-1Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 302986/2023-5Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 311657/2021-4Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 2018/05982-0Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 2021/14688-0Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 2022/11457-0Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 2023/03945-8
6 · The paper itself

Abstract

backgroundCT lung imaging protocols need to be optimized. This claim is especially important due to the possible introduction of low-dose CT (LDCT) for lung cancer screening. Given the incorporation of non-linear reconstructions and post-processing, the use of phantoms that consider task-based evaluation is needed. This is also true for acceptance and continuous QC use. PURPOSE: To present and validate a lung-CT hybrid phantom composed of two setups, one for task-based image quality metrics and the other anthropomorphic.

methodsThe task-based metrics setup was based on the well-known Mercury phantom and the anthropomorphic setup named Freddie (from Figure of Merit Performance evaluation of Detectability in Diagnostic CT Imaging Equipment) was designed with the same basic dimensions of the Mercury phantom, but including pieces and materials for mimicking chest structures, such as tracheobronchial tree and lung parenchyma. This setup allows the inclusion of pieces of different sizes to mimic ground-glass opacities, and sub-solid and solid lung nodules. The validation of the phantom adopted three methods: comparative evaluation of the attenuation properties and the corresponding Hounsfield Units (HU) values of the selected materials; image assessment according to five chest radiologists and eight non-radiologists' observations (reader study), and measurement of task-based metrics. Images of both setups were acquired using two clinical thorax protocols, both using automatic tube current modulation (TCM). Two x-ray filter combinations were adopted. The images were reconstructed using a deep learning-based algorithm.

resultsThe agreement of nominal and observed HU values in the task-based setup was within 15%, except for three (TangoBlack+, VeroClear, and HIPS) of the materials employed in the phantom construction, at some beam energies. In the reader study, synthetic solid nodules printed in VeroClear received average Likert scores 4.0 (range 3.0-4.0) from radiologists and 3 (range 2.6-3.8) from non-radiologists, printed in TangoBlack+ received an average Likert score of 3.9 (range 3.8-4.2) from radiologists and 4.0 (range 3.8-4.4) from non-radiologists, while those printed in HIPS scored an average Likert of 3.8 (range 3.3-3.9) from radiologists and 3.3 (range 3.1-3.3) from non-radiologists. The synthetic ground-glass opacities (GGO) nodules manufactured in EVA received an average Likert score of 3.8 (range 2.8-4.6) from radiologists and 4.3 (range 3.6-4.8) from non-radiologists. The task-based setup demonstrated detectability index variations across protocols influenced by the dose levels, voltage, and x-ray beam filtration used.

conclusionsThe novelty of the proposed design is concentrated on the possibility of associating the response of the task-based setup (Mercury) with a patient-based setup (Freddie) in a unique phantom. This hybrid design enhances the potential to apply the detectability index for optimizing CT protocols in clinical scenarios.

Indexed as

Early Detection of CancerLungLung NeoplasmsPhantoms, ImagingTomography, X-Ray ComputedEquipment DesignHumans3D printinganthropomorphic phantomLDCTlung imaging

Identifiers

PMID40781832
PMCPMC12334903

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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.