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Stack transition artifact removal for cardiac CT using patch-based similarities
Author(s): Sergej Lebedev; Eric Fournie; Karl Stierstorfer; Marc Kachelrieß
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Paper Abstract

Cardiac CT can be achieved by performing short scans with prospective gating. As the collimation of multi–slice CT scanners generally does not allow for a coverage of the entire heart, sequence scans, also known as step-andshoot, can be used, where irradiation is performed multiple times for varying positions. Each of these short scans yields data, generally with a longitudinal overlap, that can be reconstructed into a sub-volume, or stack. The latter ideally corresponds to the same phase. The issue addressed in this work is irregular motion, such as irregular heart motion. It leads to stacks that do not represent exactly the same volume, resulting in discontinuities at stack transitions when assembling the complete CT volume. We propose a stack transition artifact removal method including a simple symmetric registration approach. Originating from a set of control points in overlap regions between adjacent stacks, the algorithm symmetrically searches for matching sub volumes in the two neighboring stacks, respectively. The offsets to the respective control points of matching sub volumes is used to compute two deformation vector fields that match the two stacks to each other. The deformation vector fields are extended from the overlapping regions in order to maintain smooth and anatomically meaningful images. We validated the method using clinical data sets. By applying a straightforward symmetric registration method to cardiac data, we show that the stack transition artifacts can be addressed in this fashion. The artifact removal was able to considerably improve image quality and constitutes a framework that can be enhanced and expanded on in future.

Paper Details

Date Published: 9 March 2018
PDF: 6 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731X (9 March 2018); doi: 10.1117/12.2293232
Show Author Affiliations
Sergej Lebedev, German Cancer Research Ctr. (DKFZ) (Germany)
Siemens Healthineers (Germany)
Heidelberg Univ. (Germany)
Eric Fournie, Siemens Healthineers (Germany)
Karl Stierstorfer, Siemens Healthineers (Germany)
Marc Kachelrieß, German Cancer Research Ctr. (DKFZ) (Germany)
Heidelberg 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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