Can virtual non-contrast imaging replace true non-contrast imaging in multiphase scanning of the neck region?

computed tomography diagnostic dual-energy CT head and neck virtual non-contrast imaging

Journal

Acta radiologica open
ISSN: 2058-4601
Titre abrégé: Acta Radiol Open
Pays: England
ID NLM: 101651010

Informations de publication

Date de publication:
Aug 2023
Historique:
received: 17 07 2023
accepted: 18 09 2023
medline: 28 9 2023
pubmed: 28 9 2023
entrez: 28 9 2023
Statut: epublish

Résumé

Dual-energy computed tomography (DECT) is an advanced imaging method that enables reconstruction of virtual non-contrast (VNC) images from a contrast-enhanced acquisition. This has the potential to reduce radiation exposure by eliminating the need for a true non-contrast (TNC) phase. The purpose is to evaluate the feasibility of VNC images in the neck region. A total of 100 patients underwent a DECT scan as part of diagnostic workup of primary hyperparathyroidism. VNC images were reconstructed from 30 s (arterial) and 50 s (venous) post-contrast scans. Regions of interest (ROIs) were placed in thyroid tissue, lymph node, carotid artery, jugular vein, fat, and sternocleidomastoid muscle. Mean densities of all anatomical structures were compared between VNC and TNC images. For all anatomical structures except the thyroid gland, the difference in mean density between TNC and VNC images was less than 15 HU. The mean difference in density between TNC and VNC images of the thyroid was 53.2 HU (95% CI 46.8; 59.6, This study demonstrated an acceptable agreement in density between true non-contrast and virtual non-contrast images for most anatomical structures in the neck region. Therefore, VNC images may have the potential to replace TNC images in the neck. However, due to significant differences in CT density of thyroid tissue, true non-contrast imaging cannot be directly substituted by virtual non-contrast imaging when examining the thyroid and its surrounding tissue.

Sections du résumé

Background UNASSIGNED
Dual-energy computed tomography (DECT) is an advanced imaging method that enables reconstruction of virtual non-contrast (VNC) images from a contrast-enhanced acquisition. This has the potential to reduce radiation exposure by eliminating the need for a true non-contrast (TNC) phase.
Purpose UNASSIGNED
The purpose is to evaluate the feasibility of VNC images in the neck region.
Materials and methods UNASSIGNED
A total of 100 patients underwent a DECT scan as part of diagnostic workup of primary hyperparathyroidism. VNC images were reconstructed from 30 s (arterial) and 50 s (venous) post-contrast scans. Regions of interest (ROIs) were placed in thyroid tissue, lymph node, carotid artery, jugular vein, fat, and sternocleidomastoid muscle. Mean densities of all anatomical structures were compared between VNC and TNC images.
Results UNASSIGNED
For all anatomical structures except the thyroid gland, the difference in mean density between TNC and VNC images was less than 15 HU. The mean difference in density between TNC and VNC images of the thyroid was 53.2 HU (95% CI 46.8; 59.6,
Conclusion UNASSIGNED
This study demonstrated an acceptable agreement in density between true non-contrast and virtual non-contrast images for most anatomical structures in the neck region. Therefore, VNC images may have the potential to replace TNC images in the neck. However, due to significant differences in CT density of thyroid tissue, true non-contrast imaging cannot be directly substituted by virtual non-contrast imaging when examining the thyroid and its surrounding tissue.

Identifiants

pubmed: 37767056
doi: 10.1177/20584601231205159
pii: 10.1177_20584601231205159
pmc: PMC10521284
doi:

Types de publication

Journal Article

Langues

eng

Pagination

20584601231205159

Informations de copyright

© The Author(s) 2023.

Déclaration de conflit d'intérêts

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Auteurs

Zaid Al-Difaie (Z)

GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.

Max Hmc Scheepers (MH)

GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.

Nicole D Bouvy (ND)

Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Sanne Engelen (S)

Department of Surgery, Maastricht University Medical Center, Maastricht, The Netherlands.

Bas Havekes (B)

Division of Endocrinology and Metabolic Disease, Department of Internal Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.

Alida A Postma (AA)

Department of Radiology and Nuclear Medicine, School for Mental Health and Neuroscience, Neuroradiology, Maastricht University Medical Center, Maastricht, The Netherlands.

Classifications MeSH