Predicting detailed inner ear anatomy from pre-operational CT for cochlear implant surgery

  • Authors
  • Kjer, Hans Martin; Vera, Sergio; Fagertun, Jens; Pérez, Frederic; Herrero Jover, Javier; González Ballester, Miguel Ángel; Paulsen, Rasmus R
  • UPF authors
  • GONZALEZ BALLESTER, MIGUEL ANGEL;
  • Type
  • Scholarly articles
  • Journal títle
  • International Journal of Computer Assisted Radiology and Surgery
  • Publication year
  • 2015
  • Volume
  • 10
  • Number
  • S1
  • Pages
  • 98-99
  • ISSN
  • 1861-6410
  • Publication State
  • Published
  • Abstract
  • Purpose A Cochlear Implant is a surgically inserted prosthetic device for restoration of hearing given to persons who are profoundly deaf or severely hard of hearing. Pre- and post-operational CT scans are routinely used in planning and assessment of Cochlear Implant surgeries. However, due to the small size of the implant and cochlea, the images contain only very gross anatomical information about the inner ear. Providing additional patient-specific anatomical information about the inner ear is very valuable. It allows surgeons and manufacturers to make decisions about the design and programming of the inserted implant, in a manner that optimizes the restored hearing capabilities of the recipient. A promising way of achieving this is to use statistical shape models from high-resolution imaging techniques such as lCT. Previous work already [1] shows the potential and the interesting clinical implications/applications. In this study we present an alternative image registration approach for predicting detailed inner ear anatomy in pre-operative CTs using Statistical Deformation Model (SDM) regularization. Further, we present some preliminary evaluation of the clinically predictive accuracy.
  • Complete citation
  • Kjer, Hans Martin; Vera, Sergio; Fagertun, Jens; Pérez, Frederic; Herrero Jover, Javier; González Ballester, Miguel Ángel; Paulsen, Rasmus R. Predicting detailed inner ear anatomy from pre-operational CT for cochlear implant surgery. International Journal of Computer Assisted Radiology and Surgery 2015; 10(S1): 98-99.
Bibliometric indicators
  • Índex Scimago de 0.524(2015)