Paper
16 March 2020 Evaluation of geometric and exposure parameters used in fluoroscopically-guided neuro-interventional procedures
Author Affiliations +
Abstract
The imaging parameters used in neurointerventional procedures were evaluated to better understand the exposure techniques used clinically and their impact on patient dose. All parameters are available on the imaging system’s network bus in real time for each exposure pulse during a procedure. The Canon Dose Tracking System (DTS), which we developed, records the parameters of each exposure event in a raw data log file of controller area network (CAN) packets. We have collected such log files for 120 neurointerventional cases. Parameters are extracted by converting the raw data log file to a reformatted text file using a MATLAB script. The text file is input into a Microsoft Visual Studio project which outputs a new text file, which, with a reference table, allows the parameters to be identified. A written Python script is used to extract the specific parameters that were to be evaluated and output a .csv file. These were then input into MATLAB for analysis. The parameters extracted were the kVp, beam filter type, mAs, and the cranial/caudal angle as well as the RAO/LAO angle for the frontal and lateral gantries for DA and pulsed fluoroscopy (PF) modes. The gantry angles ranged from 34⁰ CRA to 42⁰ CAU and from 114⁰ RAO to 91⁰ LAO for DA and PF, respectively. The median kVp was 84 and 73 and the average per frame mAs was about 11 and 1.8 for DA and PF, respectively. This analysis should allow a better understanding of clinical practice in order to relate technique to patient and staff dose.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Collins, J. Troville, C. Guo, S. Rudin, and D. R. Bednarek "Evaluation of geometric and exposure parameters used in fluoroscopically-guided neuro-interventional procedures", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113123B (16 March 2020); https://doi.org/10.1117/12.2549026
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KEYWORDS
Data conversion

MATLAB

Imaging systems

Fluoroscopy

Visualization

Digital filtering

Medical research

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