  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 minimal-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 a 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 to 1.15 mm and maximal error of 0.75 to 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. Conclusions:
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 sensor system to obtain the located shape of
the catheter.
