Registrierung von intraoperativem 3D-Ultraschall mit präoperativen MRT-Daten für die computergestützte orthopädische Chirurgie

Winter, Susanne; Dekomien, Claudia; Hensel, Karin; Hold, Stephanie; Schmitz, Georg; Teske, Wolfram

doi:10.1055/s-2007-965689

Cited by 7 publications

(1 citation statement)

References 10 publications

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“…It was already demonstrated, both in phantoms and in vivo, that the algorithm achieves high registration success rates with acceptable computation times using realistic starting deviations [23]. The ultrasound-based registration was performed in previous work with CT data of the spine, knee, or shoulder [6,23] as well as with MRI data of, e.g., the head or the knee [8,21,22].…”

Section: Introductionmentioning

confidence: 99%

System architecture for intraoperative ultrasound registration in image-based medical navigation

Dekomien¹,

Roeschies²,

Winter³

2012

Biomedizinische Technik/Biomedical Engineering

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Medical navigation systems for orthopedic surgery are becoming more and more important with the increasing proportion of older people in the population, and hence the increasing incidence of diseases of the musculoskeletal system. The central problem for such systems is the exact transformation of the preoperatively acquired datasets to the coordinate system of the patient's body, which is crucial for the accuracy of navigation. Our approach, based on the use of intraoperative ultrasound for image registration, is capable of robustly registering bone structures for different applications, e.g., at the spine or the knee. Nevertheless, this new procedure demands additional steps of preparation of preoperative data. To increase the clinical acceptance of this procedure, it is useful to automate most of the data processing steps. In this article, we present the architecture of our system with focus on the automation of the data processing steps. In terms of accuracy, a mean target registration error of 0.68 mm was achieved for automatically segmented and registered phantom data where the reference transformation was obtained by performing point-based registration using artificial structures. As the overall accuracy for subject data cannot be determined non-invasively, automatic segmentation and registration were judged by visual inspection and precision, which showed a promising result of 1.76 mm standard deviation for 100 registration trials based on automatic segmentation of magnetic resonance imaging data of the spine.

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