3D printing of anatomically realistic phantoms with detection tasks to assess the diagnostic performance of CT images.


Journal

European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774

Informations de publication

Date de publication:
Aug 2020
Historique:
received: 03 11 2019
accepted: 12 03 2020
revised: 28 02 2020
pubmed: 30 3 2020
medline: 5 1 2021
entrez: 30 3 2020
Statut: ppublish

Résumé

Detectability experiments performed to assess the diagnostic performance of computed tomography (CT) images should represent the clinical situation realistically. The purpose was to develop anatomically realistic phantoms with low-contrast lesions for detectability experiments. Low-contrast lesions were digitally inserted into a neck CT image of a patient. The original and the manipulated CT images were used to create five phantoms: four phantoms with lesions of 10, 20, 30, and 40 HU contrast and one phantom without any lesion. Radiopaque 3D printing with potassium-iodide-doped ink (600 mg/mL) was used. The phantoms were scanned with different CT settings. Lesion contrast was analyzed using HU measurement. A 2-alternative forced choice experiment was performed with seven radiologists to study the impact of lesion contrast on detection accuracy and reader confidence (1 = lowest, 5 = highest). The phantoms reproduced patient size, shape, and anatomy. Mean ± SD contrast values of the low-contrast lesions were 9.7 ± 1.2, 18.2 ± 2, 30.2 ± 2.7, and 37.7 ± 3.1 HU for the 10, 20, 30, and 40 HU contrast lesions, respectively. Mean ± SD detection accuracy and confidence values were not significantly different for 10 and 20 HU lesion contrast (82.1 ± 6.3% vs. 83.9 ± 9.4%, p = 0.863 and 1.7 ± 0.4 vs. 1.8 ± 0.5, p = 0.159). They increased to 95 ± 5.7% and 2.6 ± 0.7 for 30 HU lesion contrast and 99.5 ± 0.9% and 3.8 ± 0.7 for 40 HU lesion contrast (p < 0.005). A CT image was manipulated to produce anatomically realistic phantoms for low-contrast detectability experiments. The phantoms and our initial experiments provide a groundwork for the assessment of CT image quality in a clinical context. • Phantoms generated from manipulated CT images provide patient anatomy and can be used for detection tasks to evaluate the diagnostic performance of CT images. • Radiologists are unconfident and unreliable in detecting hypodense lesions of 20 HU contrast and less in an anatomical neck background. • Detectability experiments with anatomically realistic phantoms can assess CT image quality in a clinical context.

Identifiants

pubmed: 32221686
doi: 10.1007/s00330-020-06808-7
pii: 10.1007/s00330-020-06808-7
pmc: PMC7338819
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

4557-4563

Subventions

Organisme : Bundesministerium für Wirtschaft und Energie
ID : 03EFHBE093

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Auteurs

Gracia Lana Ardila Pardo (GL)

Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Chariteplatz 1, 10117, Berlin, Germany.

Juliane Conzelmann (J)

Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Chariteplatz 1, 10117, Berlin, Germany.

Ulrich Genske (U)

Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Chariteplatz 1, 10117, Berlin, Germany.

Bernd Hamm (B)

Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Chariteplatz 1, 10117, Berlin, Germany.

Michael Scheel (M)

Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Chariteplatz 1, 10117, Berlin, Germany.

Paul Jahnke (P)

Department of Radiology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Chariteplatz 1, 10117, Berlin, Germany. paul.jahnke@charite.de.

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