Acetabular Registration Using CT and Point Cloud Isolation
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Solution Overview
Problem
Conventional surgical navigation systems face challenges in effectively isolating the acetabulum from non-target regions such as the femur during total hip arthroplasty procedures, particularly in imaging and registration processes.
Innovation Solution
A system that utilizes pre-operative CT images and intra-operative fluoroscopy or point cloud data to isolate the pelvic operating area (acetabulum) by applying merge rules to exclude non-target regions, and employs a navigated ball tip stylus for surface painting and palpation to define bone landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional surgical navigation systems use standard imaging and registration processes, then the overall surgical workflow is maintained, but the ability to effectively isolate the acetabulum from non-target regions such as the femur is compromised
Solution Approach 1:
The patent segments the pelvic region imaging into target surgical areas (acetabulum) and non-target areas (femur, sacrum, other bones). The image processing system automatically identifies and segments these regions using anatomical knowledge and image analysis algorithms, allowing precise isolation of the acetabulum for registration while excluding interfering structures.
Solution Approach 2:
The patent extracts and removes non-target regions from the imaging data before registration. By identifying bones other than the pelvis (such as femur, sacrum, and other visible bones) and excluding them from the registration process, the system isolates the acetabulum and improves registration accuracy without requiring complex manual intervention.
2Quantity of substance
If pre-operative CT images are used to capture the pelvic region, then comprehensive anatomical data is obtained, but non-target regions such as the femur are included which interfere with acetabulum isolation
Solution Approach 1:
The system segments the comprehensive CT data into distinct anatomical components, identifying the pelvis and its acetabulum versus other bones like the femur and sacrum. This automatic segmentation allows the system to utilize all available anatomical data while selectively processing only the relevant acetabular regions for registration.
Solution Approach 2:
The system extracts and excludes non-target bones from the registration process. By automatically identifying bones other than the pelvis in the CT images and removing them from consideration, the system maintains comprehensive anatomical data coverage while eliminating interference from non-target regions during acetabulum localization.
3Measurement precision
If manual methods are used to identify and exclude non-target regions, then registration accuracy can be improved, but the surgical workflow time and complexity increase
Solution Approach 1:
The system performs automatic identification and exclusion of non-target regions without requiring manual intervention from the surgical team. The image processing algorithms autonomously analyze the CT images, identify the pelvis and acetabulum, exclude other bones, and prepare the registration data, thereby maintaining high accuracy while preserving surgical workflow efficiency.
Solution Approach 2:
The system performs the complex task of identifying and excluding non-target regions as a preliminary step before the actual registration process. By automatically completing this preparation work beforehand, the system eliminates the need for time-consuming manual intervention during surgery while ensuring accurate acetabulum isolation for subsequent navigation.
Data Source
AI summary
A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.