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    <title language="eng">Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT</title>
    <abstract language="deu">Purpose: &#13;
The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain.&#13;
&#13;
Methods: &#13;
3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability.&#13;
&#13;
Results: &#13;
The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the processing (from 1336 to 150 s).&#13;
&#13;
Conclusions: &#13;
Adaptive anisotropic filtering has the potential to substantially improve image quality and/or reduce the radiation dose required for obtaining 3D image data using cone beam CT.</abstract>
    <parentTitle language="eng">Medical Physic</parentTitle>
    <identifier type="doi">10.1118/1.3633901</identifier>
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    <author>Lars Wigstrom</author>
    <author>Hannes G Hofmann</author>
    <author>Joachim Hornegger</author>
    <author>Lei Zhu</author>
    <author>Norbert Strobel</author>
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    <language>eng</language>
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    <title language="eng">Simultaneous estimation of X-ray back-scatter and forward-scatter using multi-task learning</title>
    <parentTitle language="eng">Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23</parentTitle>
    <identifier type="doi">10.1007/978-3-030-59713-9_20</identifier>
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    <author>Philipp Roser</author>
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    <author>Annette Birkhold</author>
    <author>Alexander Preuhs</author>
    <author>Christopher Syben</author>
    <author>Elisabeth Hoppe</author>
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    <collection role="institutes" number="fe">Fakultät Elektrotechnik</collection>
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    <publishedYear>2019</publishedYear>
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    <title language="eng">Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation</title>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2019: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck</parentTitle>
    <identifier type="doi">10.1007/978-3-658-25326-4_27</identifier>
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    <author>Xia Zhong</author>
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    <author>Siming Bayer</author>
    <author>Nishant Ravikumar</author>
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    <title language="eng">Deep scatter splines: Learning-based medical X-ray scatter estimation using B-splines</title>
    <parentTitle language="eng">The 6th International Conference on Image Formation in X-Ray Computed Tompgraphy</parentTitle>
    <identifier type="url">https://arxiv.org/pdf/2005.03470.pdf</identifier>
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    <author>Philipp Roser</author>
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    <title language="eng">Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering</title>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9</parentTitle>
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    <author>Karthik Shetty</author>
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    <title language="eng">PLIKS - A Pseudo-Linear Inverse Kinematic Solver for3D Human Body Estimation</title>
    <parentTitle language="eng">Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition</parentTitle>
    <identifier type="url">https://arxiv.org/pdf/2211.11734.pdf</identifier>
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    <title language="eng">Learning-based occupational x-ray scatter estimation</title>
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    <title language="eng">Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images</title>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="doi">10.1038/s41598-021-82370-6</identifier>
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    <title language="eng">Fully-automatic CT data preparation for interventional X-ray skin dose simulation</title>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2020: Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin</parentTitle>
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    <title language="eng">XDose: toward online cross-validation of experimental and computational X-ray dose estimation</title>
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