CT-based automated planning of acetabular cup for total hip arthroplasty (THA) based on hybrid use of two statistical atlases.

PURPOSE: This study describes the use of CT images in atlas-based automated planning methods for acetabular cup implants in total hip arthroplasty (THA). The objective of this study is to develop an automated cup planning method considering the statistical distribution of the residual thickness. METHODS: From a number of past THA planning datasets, we construct two statistical atlases that represent the surgeon's expertise. The first atlas is a pelvis-cup merged statistical shape model (PC-SSM), which encodes global spatial relationships between the patient anatomy and implant. The other is a statistical residual thickness map (SRTM) of the implant surface, which encodes local spatial constraints of the anatomy and implant. In addition to PC-SSM and SRTM, we utilized the minimum thickness as a threshold constraint to prevent penetration. RESULTS: The proposed method was applied to the pelvis shapes segmented from CT images of 37 datasets of osteoarthritis patients. Automated planning results with manual segmentation were compared to the plans prepared by an experienced surgeon. There was no significant difference in the average cup size error between the two methods (1.1 and 1.2 mm, respectively). The average positional error obtained by the proposed method, which integrates the two atlases, was significantly smaller (3.2 mm) than the previous method, which uses single atlas (3.9 mm). In the proposed method with automated segmentation, the size error of the proposed method for automated segmentation was comparable (1.1 mm) to that for manual segmentation (1.1 mm). The average positional error was significantly worse (4.2 mm) than that using manual segmentation (3.2 mm). If we only consider mildly diseased cases, however, there was no significance between them (3.2 mm in automated and 2.6 mm in manual segmentation). CONCLUSION: We infer that integrating PC-SSM and SRTM is a useful approach for modeling experienced surgeon's preference during cup planning.

Automated preoperative planning of femoral stem in total hip arthroplasty from 3D CT data: atlas-based approach and comparative study.

Atlas-based methods for automated preoperative planning of the femoral stem implant in total hip arthroplasty are described. Statistical atlases are constructed from a number of past preoperative plans prepared by experienced surgeons in order to represent the surgeon's expertise of the planning. Two types of atlases are considered. One is a statistical distance map atlas, which represents surgeon's preference of the contact pattern between the femoral canal (host bone) and stem (implant) surfaces. The other is an optimal reference plan, which is selected as the best representative plan expected to minimize the deviation from the surgeon's preferred contact pattern. These atlases are fitted to the patient data to automatically generate the preoperative plan of the femoral stem. In this paper, we formulate a general framework of atlas-based implant planning, and then describe the methods for construction and utilization of the two proposed atlases. In the experiments, we used 40 cases to evaluate the proposed methods and compare them with previous methods by defining the errors as differences between automated and surgeon's plans. By using the proposed methods, the positional and orientation errors were significantly reduced compared with the previous methods and the size error was superior to inter-surgeon variability in size selection using 2D templates on an X-ray image reported in previous work.

Expertise modeling for automated planning of acetabular cup in total hip arthroplasty using combined bone and implant statistical atlases.

Intraoperative robotic and computer-guided assistances are now commonly used in total hip arthroplasty (THA) for accurate execution of the preoperative plan. Although the preoperative plan to be accurately executed is critical, it is still interactively prepared in a time-consuming and subjective manner. In this paper, atlas-based approach to automated surgical planning of the acetabular cup in THA is described to stabilize its quality as well as reduce its time-consuming nature. Surgeon's expertise is embedded in two types of statistical atlases, which are constructed from training datasets of CT-based 3D plans prepared by experienced surgeons. One is a statistical shape model which encodes global spatial relationships between the patient anatomy and implant. The other is the statistical map of residual bone thickness on the implant surface, which encodes local spatial constraints of the anatomy and implant. Given the 3D pelvis shape of the patient, we formulate a procedure to determine the best size and position of the acetabular cup which satisfy the constraints derived from the two statistical atlases. We validated the proposed planning method by retrospective study using the datasets which were actually used in the THA surgery.

Construction of a statistical surgical plan atlas for automated 3D planning of femoral component in total hip arthroplasty.

The problem of automating preoperative planning of the femoral component (stem) for total hip arthroplasty (THA) is addressed. In our previous method, time-consuming trial-and-error processes were involved in parameter tuning of the objective function. This problem prevents application in different stem systems. To overcome this problem, a statistical surgical plan atlas (SSPA) is constructed from training datasets of stem planning. The SSPA represents the average and variance of the distance distribution on the stem surface to the femoral canal surface. That is, it encodes the distribution of the degree of contact preferred by the surgeon. Automated planning is performed by minimizing the squared difference between distributions of the SSPA and planning solution. The proposed method involves no parameter tuning to define the objective function that evaluates differences from the planning the surgeon prefers. Experimental evaluations showed that the proposed method renders parameter tuning unnecessary while it still provides comparable accuracy to the previous method.