id: 05979152
dt: a
an: 05979152
au: Demirci, Stefanie; Bigdelou, Ali; Wang, Lejing; Wachinger, Christian;
    Baust, Maximilian; Tibrewal, Radhika; Ghotbi, Reza; Eckstein,
    Hans-Henning; Navab, Nassir
ti: 3D stent recovery from one X-ray projection.
so: Fichtinger, Gabor (ed.) et al., Medical image computing and
    computer-assisted intervention ‒ MICCAI 2011. 14th international
    conference, Toronto, Canada, September 18‒22, 2011. Proceedings, Part
    I. Berlin: Springer (ISBN 978-3-642-23622-8/pbk). Lecture Notes in
    Computer Science 6891, 178-185 (2011).
py: 2011
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-23623-5_23
ab: Summary: In the current clinical workflow of endovascular abdominal aortic
    repairs (EVAR) a stent graft is inserted into the aneurysmatic aorta
    under 2D angiographic imaging. Due to the missing depth information in
    the X-ray visualization, it is highly difficult in particular for
    junior physicians to place the stent graft in the preoperatively
    defined position within the aorta. Therefore, advanced 3D visualization
    of stent grafts is highly required. In this paper, we present a novel
    algorithm to automatically match a 3D model of the stent graft to an
    intraoperative 2D image showing the device. By automatic preprocessing
    and a global-to-local registration approach, we are able to abandon
    user interaction and still meet the desired robustness. The complexity
    of our registration scheme is reduced by a semi-simultaneous
    optimization strategy incorporating constraints that correspond to the
    geometric model of the stent graft. Via experiments on synthetic,
    phantom, and real interventional data, we are able to show that the
    presented method matches the stent graft model to the 2D image data
    with good accuracy.
rv: