Burton Ma

Robust registration for computer-integrated orthopedic surgery: Laboratory validation and clinical experience

In order to provide navigational guidance during computer-integrated orthopedic surgery, the anatomy of the patient must first be registered to a medical image or model. A common registration approach is to digitize points from the surface of a bone and then find the rigid transformation that best matches the points to the model by constrained optimization. Many optimization criteria, including a least-squares objective function, perform poorly if the data include spurious data points (outliers). This paper describes a statistically robust, surface-based registration algorithm that we have developed for orthopedic surgery. To find an initial estimate, the user digitizes points from predefined regions of bone that are large enough to reliably locate even in the absence of anatomic landmarks. Outliers are automatically detected and managed by integrating a statistically robust M-estimator with the iterative-closest-point algorithm. Our in vitro validation method simulated the registration process by drawing registration data points from several sets of densely digitized surface points. The method has been used clinically in computer-integrated surgery for high tibial osteotomy, distal radius osteotomy, and excision of osteoid osteoma.

Image-Guided Distal Radius Osteotomy Using Patient-Specific Instrument Guides

In this article, we describe a method for computer-assisted distal radius osteotomies in which computer-generated, patient-specific plastic guides are used for intraoperative guidance. Before surgery, the correction and plate location are planned using computed tomography scans for both radii and ulnae, and the planned locations of the distal and proximal drill holes for the plate are saved. A plastic, patient-specific instrument guide is created using a rapid prototyping machine into which a mirror image of intraoperative, accessible bone structure of the distal radius is integrated. This allows for unique positioning of the guide during surgery. For each planned drill location, a guidance hole is incorporated into the guide. During surgery, a conventional incision is made, and the guide is positioned on the radius. The surgeon drills the holes for the plate screws into the intact radius and performs the osteotomy using the conventional technique. Using the predrilled holes, the surgeon affixes the plate to the radius fragments. The guides are easy to integrate into the surgical workflow and minimize the need for intraoperative fluoroscopy for guidance of the procedure.

Surface-Based Registration with a Particle Filter

We propose the use of a particle filter as a solution to the rigid shape-based registration problem commonly found in computer-assisted surgery. This approach is especially useful where there are only a few registration points corresponding to only a fraction of the surface model. Tests performed on patient models, with registration points collected during surgery, suggest that particle filters perform well and also provide novel quality measures to the surgeon.

Comparison Study of Intraoperative Surface Acquisition Methods for Surgical Navigation

Soft-tissue image-guided interventions often require the digitization of organ surfaces for providing correspondence from medical images to the physical patient in the operating room. In this paper, the effect of several inexpensive surface acquisition techniques on target registration error and surface registration error (SRE) for soft tissue is investigated. A systematic approach is provided to compare image-to-physical registrations using three different methods of organ spatial digitization: 1) a tracked laser-range scanner (LRS), 2) a tracked pointer, and 3) a tracked conoscopic holography sensor (called a conoprobe). For each digitization method, surfaces of phantoms and biological tissues were acquired and registered to CT image volume counterparts. A comparison among these alignments demonstrated that registration errors were statistically smaller with the conoprobe than the tracked pointer and LRS ( p <; 0.01). In all acquisitions, the conoprobe outperformed the LRS and tracked pointer: for example, the arithmetic means of the SRE over all data acquisitions with a porcine liver were 1.73 ±0.77 mm, 3.25 ±0.78 mm, and 4.44 ±1.19 mm for the conoprobe, LRS, and tracked pointer, respectively. In a cadaveric kidney specimen, the arithmetic means of the SRE over all trials of the conoprobe and tracked pointer were 1.50 ±0.50 mm and 3.51 ±0.82 mm, respectively. Our results suggest that tissue displacements due to contact force and attempts to maintain contact with tissue, compromise registrations that are dependent on data acquired from a tracked surgical instrument and we provide an alternative method (tracked conoscopic holography) of digitizing surfaces for clinical usage. The tracked conoscopic holography device outperforms LRS acquisitions with respect to registration accuracy.

Estimation of Optimal Fiducial Target Registration Error in the Presence of Heteroscedastic Noise

We study the effect of point dependent (heteroscedastic) and identically distributed anisotropic fiducial localization noise on fiducial target registration error (TRE). We derive an analytic expression, based on the concept of mechanism spatial stiffness, for predicting TRE. The accuracy of the predicted TRE is compared to simulated values where the optimal registration transformation is computed using the heteroscedastic errors in variables algorithm. The predicted values are shown to be contained by the 95% confidence intervals of the root mean square TRE obtained from the simulations.

Characterization of texture and residual stress in a section of 610 mm pipeline steel

Abstract Gas pipelines are inspected for defects such as corrosion. The most commonly used nondestructive inspection tool uses the magnetic flux leakage (MFL) technique. The MFL signals depend on the magnetic behaviour of the pipe, which is sensitive to its microstructure and crystallographic texture as well as both residual and applied stresses. Here a section of commercial X70 pipeline is characterized using microstructural examination, X-ray diffraction (to determine crystallographic texture) and neutron diffraction (for residual stress measurement). The results correlate well with the manufacturing steps used for this type of pipe. Magnetic characterization is also performed using magnetic Barkhausen noise measurements, which reflect the magnetic anisotropy in the pipe and thus the MFL signal. These results do not correlate simply with crystallographic texture and residual strain results, but this is not unexpected given the complex nature of the material and its stress state.

On fiducial target registration error in the presence of anisotropic noise

We study the effect of anisotropic noise on target registration error (TRE) by using a tracked and calibrated stylus tip as the fiducial registration application. We present a simple, efficient unscented Kalman filter algorithm that is suitable for fiducial registration even with a small number of fiducials. We also derive an equation that predicts TRE under anisotropic noise. The predicted TRE values are shown to closely match the simulated TRE values achieved using our UKF-based algorithm.

A Theoretical Comparison of Different Target Registration Error Estimators

Estimation of target registration error (TRE), a common measure of the registration accuracy, is an important issue in computer assisted surgeries. Within the last decade, several new approaches have been developed to estimate either the mean squared value of TRE or the distribution of TRE under different noise conditions. In this paper, we theoretically demonstrate that all the proposed algorithms converge to a general Maximum Likelihood (ML) solution. Numerical simulations are performed to validate our derivations. Using experimentally measured fiducial localization error, we provide an example of TRE prediction in the presence of anisotropic noise.

Using registration uncertainty visualization in a user study of a simple surgical task

We present a novel method to visualize registration uncertainty and a simple study to motivate the use of uncertainty visualization in computer-assisted surgery. Our visualization method resulted in a statistically significant reduction in the number of attempts required to localize a target, and a statistically significant reduction in the number of targets that our subjects failed to localize. Most notably, our work addresses the existence of uncertainty in guidance and offers a first step towards helping surgeons make informed decisions in the presence of imperfect data.

Unified point selection and surface-based registration using a particle filter

We propose an algorithm for jointly performing registration point selection and interactive, rigid, surface-based registration. The registration is computed using a particle filter that outputs a sampled representation of the distribution of the registration parameters. The distribution is propagated through a point selection algorithm derived from a stiffness model of surface-based registration, allowing the selection algorithm to incorporate knowledge of the uncertainties in the registration parameters. We show that the behavior of target registration error improves as the quality measure of the registration points increases.