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Proceedings Paper

Deep learning-aided CBCT image reconstruction of interventional material from four x-ray projections
Author(s): Elias Eulig; Joscha Maier; N. Robert Bennett; Michael Knaup; Klaus Hörndler; Adam Wang; Marc Kachelrieß
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Paper Abstract

Interventional guidance aims at providing the radiologist with detailed information about the location and orientation of interventional tools such as guide wires and stents. Most commonly, this is done by acquiring fluoroscopic images using an interventional C-arm system. Due to its projective nature, fluoroscopy is restricted to provide information from two spatial dimensions, preventing an exact 3D localization of the interventional tools. Analogous to computed tomography for diagnostic imaging, four-dimensional (three spatial dimensions plus the temporal dimension) interventional guidance has the potential to drastically improve both the speed and accuracy of such interventions, but is currently impractical due to the excessively high dose that would be necessary for continuous cone-beam CT (CBCT) scanning at high frame rates.

In this work we develop a novel deep learning-based approach to reconstruct interventional tools from only four x-ray projections. We train and test this deep tool reconstruction (DTR) network on simulated data. Only small deviations from the ground truth (GT) reconstruction of the tools were observed, both quantitatively and qualitatively, showing that deep learning-based four-dimensional interventional guidance has the potential to overcome the drawbacks of conventional interventional guidance in the future.

Paper Details

Date Published: 16 March 2020
PDF: 7 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113121L (16 March 2020); doi: 10.1117/12.2548662
Show Author Affiliations
Elias Eulig, German Cancer Research Ctr. (Germany)
Stanford Univ. (United States)
Joscha Maier, German Cancer Research Ctr. (Germany)
N. Robert Bennett, Stanford Univ. (United States)
Michael Knaup, German Cancer Research Ctr. (Germany)
Klaus Hörndler, Ziehm Imaging GmbH (Germany)
Adam Wang, Stanford Univ. (United States)
Marc Kachelrieß, German Cancer Research Ctr. (Germany)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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