Fiber optical shape sensing of flexible instruments for endovascular navigation

• Published in: International journal for computer assisted radiology and surgery 2019-12, Vol.14 (12), p.2137-2145
• Main Authors: Jäckle, Sonja, Eixmann, Tim, Schulz-Hildebrandt, Hinnerk, Hüttmann, Gereon, Pätz, Torben
• Format: Article
• Language: English
• Subjects: Blood vessels; Bragg gratings; Computed tomography; Computer Imaging; Computer Science; Contrast agents; Detection; Endovascular Procedures - instrumentation; Endovascular Procedures - methods; Fiber Optic Technology; Fiber optics; Flexible structures; Fluoroscopy; Health Informatics; Humans; Imaging; Medicine; Medicine & Public Health; Model accuracy; Model testing; Models, Theoretical; New technology; Optical fibers; Original; Original Article; Pattern Recognition and Graphics; Position sensing; Radiology; Surgery; Three dimensional models; Three dimensional printing; Tomography, X-Ray Computed; Tracking systems; Vision
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-019-02059-0

Purpose Endovascular aortic repair procedures are currently conducted with 2D fluoroscopy imaging. Tracking systems based on fiber Bragg gratings are an emerging technology for the navigation of minimally invasive instruments which can reduce the X-ray exposure and the used contrast agent. Shape sensing of flexible structures is challenging and includes many calculations steps which are prone to different errors. To reduce this errors, we present an optimized shape sensing model. Methods We analyzed for every step of the shape sensing process, which errors can occur, how the error affects the shape and how it can be compensated or minimized. Experiments were done with one multicore fiber system with 38 cm sensing length, and the effects of different methods and parameters were analyzed. Furthermore, we compared 3D shape reconstructions with the segmented shape of the corresponding CT scans of the fiber to evaluate the accuracy of our optimized shape sensing model. Finally, we tested our model in a realistic endovascular scenario by using a 3D printed vessel system created from patient data. Results Depending on the complexity of the shape, we reached an average error of 0.35–1.15 mm and maximal error of 0.75–7.53 mm over the whole 38 cm sensing length. In the endovascular scenario, we obtained an average and maximal error of 1.13 mm and 2.11 mm, respectively. Conclusion The accuracies of the 3D shape sensing model are promising, and we plan to combine the shape sensing based on fiber Bragg gratings with the position and orientation of an electromagnetic tracking to obtain the located catheter shape.