Date Published: June 13, 2018
Publisher: Public Library of Science
Author(s): Kenan Niu, Jasper Homminga, Victor I. Sluiter, André Sprengers, Nico Verdonschot, Olivier Cartiaux.
A 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.
We 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.
The 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%.
The 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.
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) . 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 [4–6]. 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. During surgery, the preoperative image data then 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.
In this study, we compared our proposed ICP-PS method with standard ICP in both numerical simulation and cadaveric experiment. The simulation approach was used to assess the effects of two basic factors on the registration accuracy: (1) the number of registration points; (2) the level of UPLE. Based on simulation and experimental results, our method outperformed standard ICP registration. Our method provided an effective solution for registering a known shape model to a small number of points, which demonstrated the potential for A-mode US based intraoperative registration in upper and lower extremity orthopedic surgeries, e.g. skeletal positioning in TKA, THA and orthopedic surgeries in elbow and should joint. Furthermore, we have provided a framework for estimating the number of points for a required accuracy at a known level of error, which gives valuable information for experimental assessments in orthopedic surgery.
This study has presented a simulation approach to investigate the effects of the number of registration points and the Ultrasound Point Localization Error on the registration accuracy for intraoperative registration in CAOS. The simulation results were then verified in the cadaver experiment. A registration algorithm using less points to achieve a high accuracy was established and validated. The addition of a perturbation search and feedback to ICP resulted in significant improvement of accuracy compared to standard ICP. Furthermore, the simulation approach provides a well-defined framework for estimating the minimally required number of registration points for point cloud registration, once the required levels of accuracy and time efficiency have been set and the UPLE can be estimated. With the high potential to implement further improvements associated with the localization accuracy of acquired registration points, the proposed ICP-PS method would be suitable to accurately estimate bone position and orientation.