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Open Access 07-06-2023 | Computed Tomography | Computed Tomography

Combining virtual monoenergetic imaging and iterative metal artifact reduction in first-generation photon-counting computed tomography of patients with dental implants

Authors: Theresa Sophie Patzer, Andreas Steven Kunz, Henner Huflage, Philipp Gruschwitz, Pauline Pannenbecker, Saif Afat, Judith Herrmann, Bernhard Petritsch, Thorsten Alexander Bley, Jan-Peter Grunz

Published in: European Radiology | Issue 11/2023

Abstract

Objectives

While established for energy-integrating detector computed tomography (CT), the effect of virtual monoenergetic imaging (VMI) and iterative metal artifact reduction (iMAR) in photon-counting detector (PCD) CT lacks thorough investigation. This study evaluates VMI, iMAR, and combinations thereof in PCD-CT of patients with dental implants.

Material and methods

In 50 patients (25 women; mean age 62.0 ± 9.9 years), polychromatic 120 kVp imaging (T3D), VMI, T3D_iMAR, and VMI_iMAR were compared. VMIs were reconstructed at 40, 70, 110, 150, and 190 keV. Artifact reduction was assessed by attenuation and noise measurements in the most hyper- and hypodense artifacts, as well as in artifact-impaired soft tissue of the mouth floor. Three readers subjectively evaluated artifact extent and soft tissue interpretability. Furthermore, new artifacts through overcorrection were assessed.

Results

iMAR reduced hyper-/hypodense artifacts (T3D 1305.0/−1418.4 versus T3D_iMAR 103.2/−46.9 HU), soft tissue impairment (106.7 versus 39.7 HU), and image noise (16.9 versus 5.2 HU) compared to non-iMAR datasets (p ≤ 0.001). VMI_iMAR ≥ 110 keV subjectively enhanced artifact reduction over T3D_iMAR (p ≤ 0.023). Without iMAR, VMI displayed no measurable artifact reduction (p ≥ 0.186) and facilitated no significant denoising over T3D (p ≥ 0.366). However, VMI ≥ 110 keV reduced soft tissue impairment (p ≤ 0.009). VMI_iMAR ≥ 110 keV resulted in less overcorrection than T3D_iMAR (p ≤ 0.001). Inter-reader reliability was moderate/good for hyperdense (0.707), hypodense (0.802), and soft tissue artifacts (0.804).

Conclusion

While VMI alone holds minimal metal artifact reduction potential, iMAR post-processing enabled substantial reduction of hyperdense and hypodense artifacts. The combination of VMI ≥ 110 keV and iMAR resulted in the least extensive metal artifacts.

Clinical relevance

Combining iMAR with VMI represents a potent tool for maxillofacial PCD-CT with dental implants achieving substantial artifact reduction and high image quality.

Key Points

• Post-processing of photon-counting CT scans with an iterative metal artifact reduction algorithm substantially reduces hyperdense and hypodense artifacts arising from dental implants.

• Virtual monoenergetic images presented only minimal metal artifact reduction potential.

• The combination of both provided a considerable benefit in subjective analysis compared to iterative metal artifact reduction alone.

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Title: Combining virtual monoenergetic imaging and iterative metal artifact reduction in first-generation photon-counting computed tomography of patients with dental implants
Authors: Theresa Sophie Patzer
Andreas Steven Kunz
Henner Huflage
Philipp Gruschwitz
Pauline Pannenbecker
Saif Afat
Judith Herrmann
Bernhard Petritsch
Thorsten Alexander Bley
Jan-Peter Grunz
Publication date: 07-06-2023
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Published in: European Radiology / Issue 11/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-023-09790-y

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Combining virtual monoenergetic imaging and iterative metal artifact reduction in first-generation photon-counting computed tomography of patients with dental implants

Abstract

Objectives

Material and methods

Results

Conclusion

Clinical relevance

Key Points

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objectives

Material and methods

Results

Conclusion

Clinical relevance

Key Points

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