Projective biomechanical depth matching for soft tissue registration in laparoscopic surgery

• Verfasst von: Reichard, Daniel [VerfasserIn]
• Verfasst von: Wagner, Martin [VerfasserIn]
• Verfasst von: Kenngott, Hannes Götz [VerfasserIn]
• Verfasst von: Müller, Beat P. [VerfasserIn]
• Titel: Projective biomechanical depth matching for soft tissue registration in laparoscopic surgery
• Verf.angabe: Daniel Reichard, Dominik Häntsch, Sebastian Bodenstedt, Stefan Suwelack, Martin Wagner, Hannes Kenngott, Beat Müller-Stich, Lena Maier-Hein, Rüdiger Dillmann, Stefanie Speidel
• E-Jahr: 2017
• Jahr: 26 May 2017
• Umfang: 10 S.
• Fussnoten: Gesehen am 02.05.2018
• Titel Quelle: Enthalten in: International journal of computer assisted radiology and surgery
• Ort Quelle: Berlin : Springer, 2006
• Jahr Quelle: 2017
• Band/Heft Quelle: 12(2017), 7, Seite 1101-1110
• ISSN Quelle: 1861-6429
• DOI: doi:10.1007/s11548-017-1613-6
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1572523107

Abstract

PurposeA key component of computer- assisted surgery systems is the accurate and robust registration of preoperative planning data with intraoperative sensor data. In laparoscopic surgery, this image-based registration remains challenging due to soft tissue deformations. This paper presents a novel approach for biomechanical soft tissue registration of preoperative CT data with stereo endoscopic image data.MethodsThe proposed method consists of two registrations steps. First, we use a 3D surface mosaic from partial surfaces reconstructed from stereo endoscopic images to initially align the biomechanical model with the intraoperative position and shape of the organ. After this initialization, the biomechanical model is projected onto newly captured surfaces, resulting in displacement boundary conditions, which in turn are used to update the biomechanical model.ResultsThe method is evaluated in silico, using a human liver model, and in vivo, using porcine data. The quantitative in silico data shows a stable behaviour of the biomechanical model and root-mean-square deviation of volume vertices of under 3 mm with adjusted biomechanical parameters.ConclusionThis work contributes a fully automatic featureless non-rigid registration approach. The results of the in silico and in vivo experiments suggest that our method is able to handle dynamic deformations during surgery. Additional experiments, especially regarding human tissue behaviour, are an important next step towards clinical applications.