Pre-operative Planning for Reorientation Surgery Using Simulated X-rays
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Solution Overview
Problem
Existing pre-operative planning software for reorientation surgery, such as peri-acetabular osteotomy, requires the definition of 3D surface models of the acetabulum, which is time-consuming, costly, and complex, involving tedious manual segmentation and large datasets for algorithmic techniques.
Innovation Solution
The method reorients portions of the 3D medical image itself and simulates x-ray images using the reoriented data, allowing users to measure acetabular metrics on simulated x-rays to determine optimal reorientation plans without defining acetabular boundaries, leveraging digitally reconstructed radiographs (DRRs) for image simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a boundary-based approach with 3D surface models is used for pre-operative planning, then the ability to measure acetabular metrics is improved, but the complexity and time required for segmentation and model generation increases
Solution Approach 1:
The patent extracts only the necessary volumetric region (acetabulum) from the full 3D medical image through automatic segmentation, rather than requiring complete boundary definition of the entire pelvis. This selective extraction maintains measurement precision for acetabular metrics while reducing the complexity and time required for overall model generation and processing
Solution Approach 2:
The patent creates a simplified volumetric representation (point cloud or voxel model) of the acetabulum from the 3D medical image, serving as a computational copy that suffices for metric measurement without requiring complete anatomical boundary models. This copying approach maintains measurement capability while significantly reducing segmentation complexity
2Measurement precision
If manual segmentation is performed to define acetabular boundaries, then the accuracy of the surface model is improved, but the time and labor required increases
Solution Approach 1:
The patent implements automatic segmentation algorithms that self-process the 3D medical image to extract the acetabular volumetric region without requiring manual intervention. The system automatically identifies and segments the relevant anatomical structure, maintaining measurement accuracy while eliminating the time-consuming manual segmentation process
Solution Approach 2:
The patent performs preliminary automatic segmentation and volumetric region extraction before the user begins measurement tasks. By pre-processing the image to identify and isolate the acetabular region automatically, the system prepares the data in advance, eliminating the need for manual segmentation and reducing overall processing time
3Productivity
If algorithmic segmentation techniques are used, then the speed of boundary definition is improved, but the development and validation complexity increases
Solution Approach 1:
The patent divides the complex task of 3D segmentation into simpler, manageable stages: initial automatic volumetric region extraction, followed by user-defined region refinement. This segmentation of the segmentation process maintains high productivity while reducing the complexity of algorithm development and validation by working with simpler data representations
4Measurement precision
If statistical modelling techniques are used to generate surface models, then the accuracy of acetabular representation is improved, but the requirement for large labelled datasets increases
Solution Approach 1:
The patent creates a direct volumetric copy of the patient's specific acetabulum from their individual 3D medical image, rather than relying on statistical models that require large datasets of labelled anatomy. This patient-specific copying approach maintains representation accuracy while eliminating the need for large labelled datasets
Solution Approach 2:
The patent focuses on extracting and representing only the local acetabular region with high accuracy from the patient's specific imaging data, rather than attempting to create general statistical models of acetabular anatomy. This local quality approach maintains measurement precision for the specific patient while avoiding the need for large labelled datasets
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach simplifies the planning process by eliminating the need for boundary definition, reducing development and usage complexity, and enabling efficient determination of optimal reorientation plans through iterative user input and simulated x-ray analysis.
Implementation Method 1
generating a change image comprising a digitally reconstructed radiograph (DRR) from the second volumetric data set
Data Source
AI summary
Preoperative planning techniques are described such as for hip surgery. Rather than pre-operatively planning by reorienting a model of the boundaries of the acetabulum derived from a 3D medical image, the proposed solution reorients portions of the 3D medical image itself and simulates one or more x-ray images using the reoriented 3D data. Optionally, simulated x-ray(s) of the un-modified CT scan may also be generated for comparison purposes. The user then measures acetabular metrics on the simulated x-ray(s) in order to determine the radiographic outcomes that a given magnitude and direction of reorientation would achieve. By iteratively selecting a reorientation and measuring the simulated x-ray(s), an optimal reorientation plan is determined by the user.