Abstract
PurposeA fast and accurate intraoperative registration method is important for Computer-Aided Orthopedic Surgery (CAOS). A-mode ultrasound (US) is able to acquire bone surface data in a non-invasive manner. To utilize A-mode US in CAOS, a suitable registration algorithm is necessary with a small number of registration points and the presence of measurement errors. Therefore, we investigated the effects of (1) the number of registration points and (2) the Ultrasound Point Localization Error (UPLE) on the overall registration accuracy.MethodsWe proposed a new registration method (ICP-PS), including the Iterative Closest Points (ICP) algorithm and a Perturbation Search algorithm. This method enables to avoid getting stuck in the local minimum of ICP iterations and to find the adjacent global minimum. This registration method was subsequently validated in a numerical simulation and a cadaveric experiment using a 3D-tracked A-mode US system.ResultsThe results showed that ICP-PS outperformed the standard ICP algorithm. The registration accuracy improved with the addition of ultrasound registration points. In the numerical simulation, for 25 sample points with zero UPLE, the averaged registration error of ICP-PS reached 0.25 mm, while 1.71 mm for ICP, decreasing by 85.38%. In the cadaver experiment, using 25 registration points, ICP-PS achieved an RMSE of 2.81 mm relative to 5.84 mm for the ICP, decreasing by 51.88%.ConclusionsThe simulation approach provided a well-defined framework for estimating the necessary number of ultrasound registration points and acceptable level of UPLE for a given required level of accuracy for intraoperative registration in CAOS. ICP-PS method is suitable for A-mode US based intraoperative registration. This study would facilitate the application of A-mode US probe in registering the point cloud to a known shape model, which also has the potential for accurately estimating bone position and orientation for skeletal motion tracking and surgical navigation.
Highlights
Computer-Aided Orthopedic Surgery (CAOS) systems have been developed, validated and used for surgeries in the lower extremity, such as total knee arthroplasty (TKA) [1, 2] and total hip arthroplasty (THA) [3]
The results showed that Iterative Closest Points (ICP)-PS outperformed the standard ICP algorithm
In the cadaver experiment, using 25 registration points, ICP-PS achieved an root mean square error (RMSE) of 2.81 mm relative to 5.84 mm for the ICP, decreasing by 51.88%
Summary
Computer-Aided Orthopedic Surgery (CAOS) systems have been developed, validated and used for surgeries in the lower extremity, such as total knee arthroplasty (TKA) [1, 2] and total hip arthroplasty (THA) [3]. CAOS systems offer several advantages over traditional surgery: improving guidance of the surgical instruments, reducing complication rates, minimizing trauma from instrument access and allowing preview and measurement of anatomical regions in a virtual environment [4,5,6]. In some of CAOS scenarios, medical images of a patient are acquired preoperatively, for example from Computed Tomography (CT) or Magnetic Resonance Imaging (MRI), and used to plan the surgical steps. The preoperative image data need to be registered to the actual patient. The first step is to acquire intraoperative data (e.g. digitized points, lines, curves or surfaces) from the anatomy of the actual patient in the operating room. The second step is to use an appropriate registration algorithm to determine the transformation that matches the preoperative data to intraoperative data
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