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Prototype system for interventional dual-energy subtraction angiography
Author(s): Michael A. Speidel; Christiane S. Burton; Ethan P. Nikolau; Sebastian Schafer; Paul F. Laeseke
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Paper Abstract

Dual-energy subtraction angiography (DESA) using fast kV switching has received attention for its potential to reduce misregistration artifacts in thoracic and abdominal imaging where patient motion is difficult to control; however, commercial interventional solutions are not currently available. The purpose of this work was to adapt an x-ray angiography system for 2D and 3D DESA. The platform for the dual-energy prototype was a commercially available xray angiography system with a flat panel detector and an 80 kW x-ray tube. Fast kV switching was implemented using custom x-ray tube control software that follows a user-defined switching program during a rotational acquisition. Measurements made with a high temporal resolution kV meter were used to calibrate the relationship between the requested and achieved kV and pulse width. To enable practical 2D and 3D imaging experiments, an automatic exposure control algorithm was developed to estimate patient thickness and select a dual-energy switching technique (kV and ms switching) that delivers a user-specified task CNR at the minimum air kerma to the interventional reference point. An XCAT-based simulation study conducted to evaluate low and high energy image registration for the scenario of 30-60 frame/s pulmonary angiography with respiratory motion found normalized RMSE values ranging from 0.16% to 1.06% in tissue-subtracted DESA images, depending on respiratory phase and frame rate. Initial imaging in a porcine model with a 60 kV, 10 ms, 325 mA / 120 kV, 3.2 ms, 325 mA switching technique demonstrated an ability to form tissuesubtracted images from a single contrast-enhanced acquisition.

Paper Details

Date Published: 8 March 2019
PDF: 10 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109511U (8 March 2019); doi: 10.1117/12.2512956
Show Author Affiliations
Michael A. Speidel, Univ. of Wisconsin-Madison (United States)
Christiane S. Burton, Univ. of Wisconsin-Madison (United States)
Ethan P. Nikolau, Univ. of Wisconsin-Madison (United States)
Sebastian Schafer, Siemens Healthineers (United States)
Paul F. Laeseke, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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