3D Osteo-Articular Model Reconstruction Using Bulk Data Simulation
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Solution Overview
Problem
The existing methods for reconstructing three-dimensional osteo-articular structures from two-dimensional patient-specific data face challenges due to low accuracy in matching and biased registration, especially when dealing with blurred data, leading to suboptimal reconstructions.
Innovation Solution
The method involves using a preliminary solution model that includes bulk data characteristics such as density and thickness, simulating radiography data to match patient-specific data, and iteratively processing and modifying the model to achieve concordance, utilizing techniques like segmentation and intensity-based methods for accurate reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If geometric projection of surface data is used to obtain model data, then the model reconstruction process is simple and fast, but the matching accuracy with blurred patient-specific data is low and registration is biased
Solution Approach 1:
The patent changes the parameter representation from surface geometry only to include bulk internal characteristics (density, thickness, composition). By simulating radiographic attenuation based on these bulk parameters, the model data better represents the actual physical properties that affect X-ray absorption, thereby improving matching accuracy with patient-specific radiographic data while maintaining computational efficiency through parameterized models.
2Measurement precision
If bulk data characteristics (density, thickness) are incorporated into the preliminary solution, then matching accuracy with patient-specific data improves, but the complexity of the model increases
Solution Approach 1:
The patent performs preliminary action by pre-defining parameterized bulk characteristics (density, thickness, composition) in the preliminary solution model before actual reconstruction. These parameters are prepared in advance based on typical anatomical knowledge and can be quickly adjusted during iterative refinement, avoiding the need to compute complex internal structures from scratch and thus limiting the increase in model complexity.
Solution Approach 2:
The patent uses parameterized representations for bulk characteristics where density, thickness, and composition are defined as adjustable parameters rather than requiring detailed volumetric modeling. This parameterization approach allows the model to capture essential internal characteristics that improve matching accuracy while keeping the computational model relatively simple and manageable.
3Measurement precision
If iterative processing and modification are performed to achieve concordance between model data and detection data, then reconstruction accuracy improves, but the processing time increases
Solution Approach 1:
The patent employs parameterized bulk characteristics that can be efficiently adjusted during iterative processing. By representing complex internal structures through a limited set of parameters (density, thickness, composition), the optimization process becomes computationally lighter, allowing multiple iterations to achieve high accuracy without excessive time consumption.
Solution Approach 2:
The patent implements feedback through iterative comparison between simulated radiographic data (based on current model parameters) and actual patient-specific detection data. The differences are used to update the bulk parameters in the next iteration, progressively improving reconstruction accuracy. This feedback loop is made efficient by using parameterized models that can be quickly updated based on error signals.
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 enhances the accuracy and robustness of the reconstruction process, allowing for more precise and reliable three-dimensional models of osteo-articular structures, suitable for diagnostic and preoperative planning, and enabling real-time reconstruction during surgery.
Implementation Method 1
The simulation may comprise subtracting a first simulated image from a second simulated image, the first simulated image and the second simulated image being respectively obtained from equations of low energy and high energy X-rays respectively
Data Source
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AI summary
Method for reconstruction of a three-dimensional model of an osteo-articular structure of a patient, wherein a) bi-dimensional patient-specific data (41) of said structure is provided; (d) a preliminary model, corresponding to a previously established model of the structure, is provided (42) from a database (21), said preliminary model comprising surface data describing the coordinates of the surface of the model, and bulk data describing at least one characteristic of the inside of the model; the preliminary model is modified (42',43,44,46,47,48) to be brought in concordance with said patient-specific data.