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Proceedings Paper

Feasibility of intra-acquisition motion correction for 4D DSA reconstruction for applications in the thorax and abdomen
Author(s): Martin Wagner; Paul Laeseke; Colin Harari; Sebastian Schafer; Michael Speidel; Charles Mistretta
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Paper Abstract

The recently proposed 4D DSA technique enables reconstruction of time resolved 3D volumes from two C-arm CT acquisitions. This provides information on the blood flow in neurovascular applications and can be used for the diagnosis and treatment of vascular diseases. For applications in the thorax and abdomen, respiratory motion can prevent successful 4D DSA reconstruction and cause severe artifacts. The purpose of this work is to propose a novel technique for motion compensated 4D DSA reconstruction to enable applications in the thorax and abdomen. The approach uses deformable 2D registration to align the projection images of a non-contrast and a contrast enhanced scan. A subset of projection images is then selected, which are acquired in a similar respiratory state and an iterative simultaneous multiplicative algebraic reconstruction is applied to determine a 3D constraint volume. A 2D-3D registration step then aligns the remaining projection images with the 3D constraint volume. Finally, a constrained back-projection is performed to create a 3D volume for each projection image. A pig study has been performed, where 4D DSA acquisitions were performed with and without respiratory motion to evaluate the feasibility of the approach. The dice similarity coefficient between the reference 3D constraint volume and the motion compensated reconstruction was 51.12 % compared to 35.99 % without motion compensation. This technique could improve the workflow for procedures in interventional radiology, e.g. liver embolizations, where changes in blood flow have to be monitored carefully.

Paper Details

Date Published: 2 March 2018
PDF: 8 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057415 (2 March 2018); doi: 10.1117/12.2293812
Show Author Affiliations
Martin Wagner, Univ. of Wisconsin-Madison (United States)
Paul Laeseke, Univ. of Wisconsin-Madison (United States)
Colin Harari, Univ. of Wisconsin-Madison (United States)
Sebastian Schafer, Siemens Healthineers (United States)
Michael Speidel, Univ. of Wisconsin-Madison (United States)
Charles Mistretta, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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