Evaluation of methods to produce an image library for automatic patient model localization for dose mapping during fluoroscopically guided procedures

Josh Kilian-Meneghin; Z. Xiong; S. Rudin; A. Oines; D. R. Bednarek

doi:10.1117/12.2254693

9 March 2017 Evaluation of methods to produce an image library for automatic patient model localization for dose mapping during fluoroscopically guided procedures

Josh Kilian-Meneghin, Z. Xiong, S. Rudin, A. Oines, D. R. Bednarek

Author Affiliations +

Proceedings Volume 10132, Medical Imaging 2017: Physics of Medical Imaging; 1013245 (2017) https://doi.org/10.1117/12.2254693
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

The purpose of this work is to evaluate methods for producing a library of 2D-radiographic images to be correlated to clinical images obtained during a fluoroscopically-guided procedure for automated patient-model localization. The localization algorithm will be used to improve the accuracy of the skin-dose map superimposed on the 3D patient- model of the real-time Dose-Tracking-System (DTS). For the library, 2D images were generated from CT datasets of the SK-150 anthropomorphic phantom using two methods: Schmid’s 3D-visualization tool and Plastimatch’s digitally-reconstructed-radiograph (DRR) code. Those images, as well as a standard 2D-radiographic image, were correlated to a 2D-fluoroscopic image of a phantom, which represented the clinical-fluoroscopic image, using the Corr2 function in Matlab. The Corr2 function takes two images and outputs the relative correlation between them, which is fed into the localization algorithm. Higher correlation means better alignment of the 3D patient-model with the patient image. In this instance, it was determined that the localization algorithm will succeed when Corr2 returns a correlation of at least 50%. The 3D-visualization tool images returned 55-80% correlation relative to the fluoroscopic-image, which was comparable to the correlation for the radiograph. The DRR images returned 61-90% correlation, again comparable to the radiograph. Both methods prove to be sufficient for the localization algorithm and can be produced quickly; however, the DRR method produces more accurate grey-levels. Using the DRR code, a library at varying angles can be produced for the localization algorithm.

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Josh Kilian-Meneghin, Z. Xiong, S. Rudin, A. Oines, and D. R. Bednarek "Evaluation of methods to produce an image library for automatic patient model localization for dose mapping during fluoroscopically guided procedures", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 1013245 (9 March 2017); https://doi.org/10.1117/12.2254693

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KEYWORDS

Visualization

Radiography

Image visualization

Gaussian filters

Image quality

Computed tomography

Image filtering

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