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Medical Physics
Article . 2022 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
Medical Physics
Article . 2022
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Empirical scatter correction: CBCT scatter artifact reduction without prior information

Authors: Philip, Trapp; Joscha, Maier; Markus, Susenburger; Stefan, Sawall; Marc, Kachelrieß;

Empirical scatter correction: CBCT scatter artifact reduction without prior information

Abstract

AbstractBackgroundThe image quality of cone beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software‐based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of the aforementioned.PurposeThis study proposes a simple and effective postprocessing software‐based correction method of scatter artifacts in CBCT scans without specific prior knowledge.MethodsWe propose the empirical scatter correction (ESC), which generates scatter‐like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table‐top CBCT, and a pelvis scan from a Varian Edge CBCT scanner.ResultsESC is able to improve the image quality of CBCT scans, which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter‐free reference image was as low as −6 HU after using ESC, whereas the uncorrected data deviated by more than −200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement, where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts, the CT values agree well with those of the slit scan (difference to slit scan: 35 HU corrected and −289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed ESC. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system.ConclusionsESC allows to reduce artifacts caused by patient scatter solely based on the projection data.

Keywords

Phantoms, Imaging, Image Processing, Computer-Assisted, Humans, Scattering, Radiation, Spiral Cone-Beam Computed Tomography, Cone-Beam Computed Tomography, Artifacts, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
hybrid