International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015 pp 339-346

Adaption of 3D Models to 2D X-Ray Images during Endovascular Abdominal Aneurysm Repair

  • Daniel Toth
  • Marcus Pfister
  • Andreas Maier
  • Markus Kowarschik
  • Joachim Hornegger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9349)

Abstract

Endovascular aneurysm repair (EVAR) has been gaining popularity over open repair of abdominal aortic aneurysms (AAAs) in the recent years. This paper describes a distortion correction approach to be applied during the EVAR cases. In a novel workflow, models (meshes) of the aorta and its branching arteries generated from preoperatively acquired computed tomography (CT) scans are overlayed with interventionally acquired fluoroscopic images. The overlay provides an arterial roadmap for the operator, with landmarks (LMs) marking the ostia, which are critical for stent placement. As several endovascular devices, such as angiographic catheters, are inserted, the anatomy may be distorted. The distortion reduces the accuracy of the overlay. To overcome the mismatch, the aortic and the iliac meshes are adapted to a device seen in uncontrasted intraoperative fluoroscopic images using the skeletonbased as-rigid-as-possible (ARAP) method. The deformation was evaluated by comparing the distance between an ostium and the corresponding LM prior to and after the deformation. The central positions of the ostia were marked in digital subtraction angiography (DSA) images as ground truth. The mean Euclidean distance in the image plane was reduced from 19.81±17.14mm to 4.56±2.81 mm.

Keywords

computational geometry as-rigid-as-possible mesh deformation abdominal aortic aneurysm EVAR 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Daniel Toth
    • 1
    • 2
  • Marcus Pfister
    • 1
  • Andreas Maier
    • 2
  • Markus Kowarschik
    • 1
  • Joachim Hornegger
    • 2
  1. 1.Siemens Healthcare GmbHForchheimGermany
  2. 2.Pattern Recognition Lab.Friedrich-Alexander-University Erlangen-NurembergErlangenGermany

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