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Interventional endovascular procedures simulation with patient-specific
carotid arteries models generated from CTA scans
Moti Freiman1, Einav Nammer2, Ofek Shilon2, Leo Joskowicz1, Jacob Sosna3
1. School of Eng. and Computer Science, The Hebrew Univ. of Jerusalem, Israel.
2. Simbionix LTD, Israel.
3. Dept of Radiology, School of Medicine, Hadassah Hebrew U, Medical Center, Israel.
Purpose
Minimally invasive endovascular surgeries such as carotid, coronary, and cardiac angiographic
procedures are frequent in interventional radiology. They require experienced physicians and
involve time-consuming trial and error with repeated contrast agent injection and X-ray imaging.
This leads to outcome variability and non-negligible complication rates. Training simulators such
the ANGIO MentorTM (Simbionix LTD. Israel, 2008) have the potential to significantly reduce
the physicians’ learning curve, improve their performance, and reduce the outcome variability
[1]. A key limitation is the simulators’ reliance on hand-tailored anatomical models generated by
a technician from CTA scans, which are impractical to produce for each patient in a clinical
environment.
Patient specific simulation requires the segmentation of the entire vascular anatomy, including the
Common Carotid Artery (CCA), the extracranial Internal Carotid Artery (ICA) and External
Carotid Artery (ECA), the ECA branches, the Carotid Bifurcation (CB), the Subclavian Arteries
(SA), and the Aortic Arch (AA). The vertebral arteries are desired but not required for the
simulation. These vessel structures usually have a very large intra and inter-patient intensity and
geometrical shape variability, are near bone structures with similar intensity values, and suffer
from imaging artifacts caused by metallic objects such as dental implants. In addition, in many
pathological cases, severe stenosis around the carotid bifurcation frequently causes segmentation
failure [2]. Therefore the generation of patient specific models for simulation is a challenging
task.
Materials and Method
In this study, we conducted a preliminary evaluation of the resulting segmentation models using
our new method for patient-specific carotid interventional radiology simulations [3]. The method
starts with the morphological-based segmentation of the aorta and the construction of a prior
intensity probability distribution function for arteries. The carotid arteries are then segmented
with a graph min-cut method based on a new edge weights function that adaptively couples voxel
intensity, intensity prior, and geometric vesselness shape prior. Finally, the same graph-cut
optimization framework is used to interactively remove a few vessel segments and to fill minor
vessel discontinuities caused by intensity variations.
In the study, we used a Simbionix ANGIO MentorTM (Simbionix LTD. Israel, 2008) station. The
ANGIO MentorTM is an integrated software and hardware endovascular simulation platform (Fig.
1a). It simulates interventional vascular procedures based on a diagnostic CTA and a vasculature
simulation model. It supports realistic haptic catheter insertion and manipulation feedback (Fig.
1b) and creates continuous fluoroscopic X-ray imaging, fluoroscopic C-arm positioning, and
simulated contrast agent injection (Fig. 2a-c). For more details, see http://www.simbionix.com
Four CTA datasets acquired with administrated 100cc of non-iodinated of contrast agent with a
rapid injection aid at 3-4cc per sec. The CTA scans, acquired on a Sensation 16 Siemens Medical
Solutions scanner (Forchheim, Germany) have in-plane pixel size 0.5x0.5mm2, matrix size
512x512, 0.55mm slice spacing, and 750 slices. The patient specific models were generated from
the CTA images as follows. First, the carotid arteries systems were segmented using the nearlyautomatic method described in [3]. Next, 3D mesh with centerlines, bifurcation points, and
vascular radiuses were computed with the VMTK software library automatic meshing and
centerline generation modules [4]. The entire simulation model generation required less than 10
minutes for each on a standard PC, most of it computation time without interaction.
Results
The simulation models were then directly transferred to the Simbionix ANGIO MentorTM
simulator platform. We then performed common interventional radiology procedures, such as
catheter insertion and manipulation, balloon positioning and dilation, and stent placement on the
patient-specific models. Fig. 2a-c shows sample snapshots of the simulation with the patientspecific models.
A movie showing the simulation with
http://www.cs.huji.ac.il/~freiman/vessels-cut.
our
3D
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The simulations ran flawlessly and successfully in real time for over an hour. The users reported
great realism and an excellent overall experience, which was significantly better than similar
experiences with the previous manually generated models. While this simulation experiment is
qualitative and preliminary, it constitutes a proof-of-concept of practical patient specific carotid
interventional radiology simulations from clinical CTA scans.
Conclusion
We have presented a proof-of-concept of practical patient specific carotid interventional
radiology simulations from clinical CTA scans. Patient specific models were generated using
previously presented techniques, and used for clinical simulation successfully. Our results
indicate that the generated models are accurate, robust, and provide useful information for patient
specific simulation.
We are currently expanding the using of patient specific models for intra-operative mode guided
interventional radiology procedures.
References
[1] Stern, J., Zeltser, I., Pearle, M., 2007. Percutaneous renal access simulators. J. of Endourology
21 (3), 270–273.
[2] Manniesing, R., Viergever, M., Niessen, W., 2007. Vessel axis tracking using topology
constrained surface evolution. IEEE Trans. Med. Imaging 26 (3), 309–316.
[3] Freiman, M., Broide, N., Natanzon, M., Weizman, L., Nammer, E., Shilon, O., Frank, J.,
Joskowicz, L., Sosna, J., 2009. Vessels-Cut: A Graph Based Approach to Patient-Specific
Carotid Arteries Modeling. In: Proc. of the 2nd 3D Physiological Human workshop,
3DPH’09. Vol. 5903 of LNCS. pp. 1–12.
[4] Antiga, L., Steinman, D., 2004. Robust and objective decomposition and mapping of
bifurcating vessels. IEEE Trans. Med. Imaging 23 (6), 704–713, http://www.vmtk.org.
(a) Simbionix ANGIO MentorTM station
(b) Haptic catheter manipulation
Fig. 1: (a) Simbionix ANGIO MentorTM, (b) Haptic catheter manipulation.
(a) lateral view
(b) AP view
(c) Bifurcation with stent
Fig. 2: Patient-specific simulation experiment: (a) Lateral simulated
angiogram, (b) AP simulated angiogram, (c) bifurcation with stent – detail.