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Organ-specific context-sensitive CT image reconstruction and display
Author(s): Sabrina Dorn; Shuqing Chen; Stefan Sawall; David Simons; Matthias May; Joscha Maier; Michael Knaup; Heinz-Peter Schlemmer; Andreas Maier; Micheal M. Lell; Marc Kachelrieß
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Paper Abstract

In this work, we present a novel method to combine mutually exclusive CT image properties that emerge from different reconstruction kernels and display settings into a single organ-specific image reconstruction and display. We propose a context-sensitive reconstruction that locally emphasizes desired image properties by exploiting prior anatomical knowledge. Furthermore, we introduce an organ-specific windowing and display method that aims at providing a superior image visualization. Using a coarse-to-fine hierarchical 3D fully convolutional network (3D U-Net), the CT data set is segmented and classified into different organs, e.g. the heart, vasculature, liver, kidney, spleen and lung, as well as into the tissue types bone, fat, soft tissue and vessels. Reconstruction and display parameters most suitable for the organ, tissue type, and clinical indication are chosen automatically from a predefined set of reconstruction parameters on a per-voxel basis. The approach is evaluated using patient data acquired with a dual source CT system. The final context-sensitive images simultaneously link the indication-specific advantages of different parameter settings and result in images joining tissue-related desired image properties. A comparison with conventionally reconstructed and displayed images reveals an improved spatial resolution in highly attenuating objects and air while maintaining a low noise level in soft tissue in the compound image. The images present significantly more information to the reader simultaneously and dealing with multiple volumes may no longer be necessary. The presented method is useful for the clinical workflow and bears the potential to increase the rate of incidental findings.

Paper Details

Date Published: 9 March 2018
PDF: 7 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057326 (9 March 2018); doi: 10.1117/12.2291897
Show Author Affiliations
Sabrina Dorn, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)
Shuqing Chen, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Stefan Sawall, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)
David Simons, German Cancer Research Ctr. (DKFZ) (Germany)
Matthias May, Univ. Hospital of Erlangen (Germany)
Joscha Maier, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)
Michael Knaup, German Cancer Research Ctr. (DKFZ) (Germany)
Heinz-Peter Schlemmer, German Cancer Research Ctr. (DKFZ) (Germany)
Andreas Maier, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Micheal M. Lell, Klinikum Nürnberg, Paracelsus Medical Univ. (Germany)
Marc Kachelrieß, German Cancer Research Ctr. (DKFZ) (Germany)
Ruprecht-Karls-Univ. (Germany)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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