View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT – An Investigation of Potential Improvement

Tristan M. GottschalkFriedrich-Alexander-Universität Erlangen-Nürnberg
Siemens Healthcare GmbH
, Andreas MaierFriedrich-Alexander-Universität Erlangen-Nürnberg, Florian KordonFriedrich-Alexander-Universität Erlangen-Nürnberg
Siemens Healthcare GmbH
, Björn W. KreherSiemens Healthcare GmbH
December 2021 issue
Publication date: 2021/12/15
PDF · arXiv

Abstract

The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-based, view-consistent 2D projection-based method on the downstream MAR's outcome is investigated. For segmenting the present metal, a rather simple learning-based 2D projection-wise segmentation network that is trained using real data acquired during cadaver studies, is examined. To overcome the disadvantages that come along with a 2D projection-wise segmentation, a Consistency Filter is proposed. The influence of the shifted segmentation domain is investigated by comparing the results of the standard fsMAR with a modified fsMAR version using the new segmentation masks. With a quantitative and qualitative evaluation on real cadaver data, the investigated approach showed an increased MAR performance and a high insensitivity against metal artifacts. For cases with metal outside the reconstruction's FoV or cases with vanishing metal, a significant reduction in artifacts could be shown. Thus, increases of up to roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for single slices were achieved. The shown results reveal a beneficial influence of the shift to a 2D-based segmentation method on real data for downstream use with a MAR method, like the fsMAR. The nature of the method further suggests the same beneficial behavior for all (also recent data-driven) MAR methods, that for now comprise a 3D-volume-based segmentation step for subsequent inpainting.

Keywords

trauma intervention · cone-beam computed tomography · metal artifact reduction · metal segmentation

Bibtex @article{melba:2021:018:gottschalk, title = "View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT – An Investigation of Potential Improvement", authors = "Gottschalk, Tristan M. and Maier, Andreas and Kordon, Florian and Kreher, Björn W.", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "December 2021 issue", year = "2021" }

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