Objective and subjective comparison of virtual monoenergetic vs. polychromatic images in patients with pancreatic ductal adenocarcinoma.


Journal

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

Informations de publication

Date de publication:
Jul 2019
Historique:
received: 02 10 2018
accepted: 15 02 2019
revised: 15 01 2019
pubmed: 20 3 2019
medline: 27 8 2019
entrez: 20 3 2019
Statut: ppublish

Résumé

The aim of this study was to assess the objective and subjective image characteristics of monoenergetic images (MEI[+]), using a noise-optimized algorithm at different kiloelectron volts (keV) compared to polyenergetic images (PEI), in patients with pancreatic ductal adenocarcinoma (PDAC). This retrospective, institutional review board-approved study included 45 patients (18 male, 27 female; mean age 66 years; range, 42-96 years) with PDAC who had undergone a dual-energy CT (DECT) of the abdomen for staging. One standard polyenergetic image (PEI) and five MEI(+) images in 10-keV intervals, ranging from 40 to 80 keV, were reconstructed. Line-density profile analysis, as well as the contrast-to-noise ratio (CNR) of the tumor, the signal-to-noise ratio (SNR) of the regular pancreas parenchyma and the tumor, and the CNR of the three main peripancreatic vessels, was calculated. For subjective quality assessment, two readers independently assessed the images using a 5-point Likert scale. Reader reliability was evaluated using an intraclass correlation coefficient. Line-density profile analysis revealed the largest gradient in attenuation between PDAC and regular tissue in MEI(+) at 40 keV. Low-keV MEI(+)reconstructions at 40 and 50 keV increased CNR and SNR compared to PEI (40 keV: CNR 46.8 vs. 7.5; SNR MEI(+) reconstructions at 40 keV and 50 keV provide better objective and subjective image quality compared to conventional PEI of DECT in patients with PDAC. • Low-keV MEI(+) reconstructions at 40 and 50 keV increase tumor-to-pancreas contrast compared to PEI. • Low-keV MEI(+) reconstructions improve objective and subjective image quality parameters compared to PEI. • Dual-energy post-processing might be a valuable tool in the diagnostic workup of patients with PDAC.

Identifiants

pubmed: 30888484
doi: 10.1007/s00330-019-06116-9
pii: 10.1007/s00330-019-06116-9
pmc: PMC6554239
doi:

Types de publication

Comparative Study Journal Article

Langues

eng

Pagination

3617-3625

Subventions

Organisme : Oesterreichische Nationalbank
ID : 16886

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Auteurs

Lucian Beer (L)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Michael Toepker (M)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Ahmed Ba-Ssalamah (A)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Christian Schestak (C)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Anja Dutschke (A)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Martin Schindl (M)

Department of Surgery, Medical University of Vienna, Vienna, Austria.

Alexander Wressnegger (A)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Helmut Ringl (H)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.

Paul Apfaltrer (P)

Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria. paul.apfaltrer@meduniwien.ac.at.
Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. paul.apfaltrer@meduniwien.ac.at.

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Classifications MeSH