Imageless versus image-based registration in navigated arthroscopy of the hip

Ghent University Hospital, Department of Orthopedic Surgery and Traumatology, De Pintelaan 185, 9000 Ghent, Belgium.
The Bone & Joint Journal (Impact Factor: 3.31). 05/2012; 94(5):624-9. DOI: 10.1302/0301-620X.94B5.28627
Source: PubMed


The aim of this study was to determine the accuracy of registration and the precision of the resection volume in navigated hip arthroscopy for cam-type femoroacetabular impingement, using imageless and image-based registration. A virtual cam lesion was defined in 12 paired cadaver hips and randomly assigned to either imageless or image-based (three-dimensional (3D) fluoroscopy) navigated arthroscopic head-neck osteochondroplasty. The accuracy of patient-image registration for both protocols was evaluated and post-operative imaging was performed to evaluate the accuracy of the surgical resection. We found that the estimated accuracy of imageless registration in the arthroscopic setting was poor, with a mean error of 5.6 mm (standard deviation (SD) 4.08; 95% confidence interval (CI) 4.14 to 7.19). Because of the significant mismatch between the actual position of the probe during surgery and the position of that probe as displayed on the navigation platform screen, navigated femoral osteochondroplasty was physically impossible. The estimated accuracy of image-based registration by means of 3D fluoroscopy had a mean error of 0.8 mm (SD 0.51; 95% CI 0.56 to 0.94). In terms of the volume of bony resection, a mean of 17% (SD 11; -6% to 28%) more bone was resected than with the virtual plan (p = 0.02). The resection was a mean of 1 mm deeper (SD 0.7; -0.3 to 1.6) larger than on the original virtual plan (p = 0.02).
In conclusion, given the limited femoral surface that can be reached and digitised during arthroscopy of the hip, imageless registration is inaccurate and does not allow for reliable surgical navigation. However, image-based registration does acceptably allow for guided femoral osteochondroplasty in the arthroscopic management of femoroacetabular impingement.

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    • "The main problem with the latter is that these models are often created based on a normal anatomy dataset, and using them for pathological subjects can be problematic [12]. Audenaert et al. described the estimated accuracy of imageless surgery as poor because of the significant difference between the actual location of the probe during surgery and what is displayed on the navigation platform screen [13]. The Acrobot Sculptor (Stanmore Implants Worldwide Ltd.) is a semiactive robot, uses the computer tomography (CT) data as input, and could assist with bone resection for UKA surgery in a consistent manner to minimise variability [14]; however, the repeatability and accuracy of this robot in clinical settings are yet to be determined. "
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    ABSTRACT: In recent years, robots have become commonplace in surgical procedures due to their high accuracy and repeatability. The Acrobot Sculptor is an example of such a robot that can assist with unicompartmental knee replacement. In this study, we aim to evaluate the accuracy of the robot (software and hardware) in a clinical setting. We looked at (1) segmentation by comparing the segmented data from Sculptor software to other commercial software, (2) registration by checking the inter- and intraobserver repeatability of selecting set points, and finally (3) sculpting (n = 9 cases) by evaluating the achieved implant position and orientation relative to that planned. The results from segmentation and registration were found to be accurate. The highest error was observed in flexion extension orientation of femoral implant (0.4 ± 3.7°). Mean compound rotational and translational errors for both components were 2.1 ± 0.6 mm and 3 ± 0.8° for tibia and 2.4 ± 1.2 mm and 4.3 ± 1.4° for the femur. The results from all processes used in Acrobot were small. Validation of robot in clinical settings is highly vital to ensure a good outcome for patients. It is therefore recommended to follow the protocol used here on other available similar products.
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    ABSTRACT: This article describes a novel method for image-based, minimally invasive registration of the femur, for application to computer-assisted unicompartmental knee arthroplasty. The method is adapted from the well-known iterative closest point algorithm. By utilising an estimate of the hip centre on both the preoperative model and intraoperative patient anatomy, the proposed 'bounded' iterative closest point algorithm robustly produces accurate varus-valgus and anterior-posterior femoral alignment with minimal distal access requirements. Similar to the original iterative closest point implementation, the bounded iterative closest point algorithm converges monotonically to the closest minimum, and the presented case includes a common method for global minimum identification. The bounded iterative closest point method has shown to have exceptional resistance to noise during feature acquisition through simulations and in vitro plastic bone trials, where its performance is compared to a standard form of the iterative closest point algorithm.
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