Abstract

Background

Photon counting CT (PCCT) is a promising technique for neuroradiological CT examinations. In initial studies on non-contrast PCCT of the head (NCCT), however, artifacts close to the calvarium were noticed, which lead to an inhomogeneous representation of the brain tissue. In this study, a new software for image reconstruction to reduce artifacts is evaluated.

Methods

In the new CT software developed by the manufacturer, off-focal radiation was remodeled and is mathematically corrected in the NCCT in data processing during image formation. For the evaluation, 60 patients with an NCCT in the currently used software and 44 patients in the new software were included retrospectively. A detailed quantitative analysis using multiple regions of interest and a qualitative analysis with a reading by experienced radiologists was performed to evaluate image quality and tissue homogeneity below the calvarium.

Results

The new software reduced the inhomogeneity of the cortical brain tissue near the calvarium. As a quantitative measure, there is a clear reduction of the signal difference of the gray and white matter at different distances from the calvarium (p < 0.001). In the qualitative analysis, the inhomogeneity of the brain tissue was reduced, and the gray-white differentiation improved (p < 0.001) in the clinically used virtual monoenergetic image at 65 keV.

Conclusions

Optimized modelling and mathematical correction of the off-focal radiation in the new software led to an effective reduction of the inhomogeneity of the cortical brain tissue and thus improved image quality.

Details

Title
Non-contrast photon counting computed tomography of the head: optimized modeling of off-focal radiation to reduce calvarium-related tissue inhomogeneity
Author
Arwed Elias Michael; Petersilka, Martin; Schoenbeck, Denise; Woeltjen, Matthias Michael; Julius Henning Niehoff; Moenninghoff, Christoph; Kurzendorfer, Tanja; Borggrefe, Jan; Goertz, Lukas; Kroeger, Jan Robert
Pages
1-8
Section
Research
Publication year
2025
Publication date
2025
Publisher
Springer Nature B.V.
e-ISSN
14712342
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3236996975
Copyright
© 2025. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.