Preoperative Simulation Model Generation with Lymphatic and Membrane Integration
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Solution Overview
Problem
Current methods for generating preoperative simulation models from medical images, such as CT or MRI, are limited as they only provide geometrical information, fail to capture dynamic conditions, and cannot model lymphatic vessels or membranes, making them unsuitable for accurately simulating actual surgical conditions.
Innovation Solution
A method that involves constructing volume data from medical images, repositioning and reorienting organs, generating blood-vessel and fat models, meshing the data, and using template models to include lymphatic and membrane representations, allowing for more accurate simulation of surgical conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical image data such as CT or MRI is used to acquire information of each individual patient, then geometrical information can be acquired, but physical and dynamical conditions cannot be acquired
Solution Approach 1:
The model construction process is segmented into multiple steps: first constructing basic organ models from medical images, then separately adding blood vessel models, fat models, lymphatic vessel models, and membrane models. This segmentation allows each component to be modeled with appropriate detail while maintaining overall system manageability.
Solution Approach 2:
The patent performs preliminary actions by pre-modeling various anatomical structures (blood vessels, fat, lymphatic vessels, membranes) before the actual surgical simulation. These pre-modeled components are then integrated into the final simulation model, allowing realistic surgical planning without requiring complex real-time calculations.
2Ease of manufacture
If a simulation model is created using only medical image data, then the model construction is simplified, but the model does not match actual surgical conditions
Solution Approach 1:
The patent merges multiple data sources and model types: medical image data, blood vessel models, fat models, lymphatic vessel models, and membrane models are all combined into a single integrated simulation model. This merging preserves anatomical accuracy while maintaining ease of construction through systematic integration.
Solution Approach 2:
The simulation model uses composite structures by combining different types of anatomical representations (organs from CT/MRI, blood vessels as tubular structures, fat as volumetric data, lymphatic vessels and membranes as surface models) into a unified composite model that reflects actual surgical conditions.
3Device complexity
If lymphatic vessels and membranes are not modeled, then the model construction is simpler, but the model is unsuitable for preoperative simulation
Solution Approach 1:
The patent introduces dynamic elements by modeling lymphatic vessels and membranes that can deform and interact during surgical procedures. These dynamic components respond to surgical manipulations, providing realistic feedback for preoperative simulation while maintaining manageable complexity through efficient modeling techniques.
Data Source
AI summary
The invention is directed to the provision of a method for generating a model for a preoperative simulation, wherein the method includes: a first step of constructing volume data for necessary organs by acquiring geometrical information from a medical image; a second step of manipulating the volume data to reposition and reorient an operator-designated organ to achieve a position and orientation appropriate for a surgical operation; a third step of generating a blood-vessel model, depicting a blood vessel to be joined to the designated organ, so as to match the position and orientation of the designated organ; a fourth step of generating volume data by forming a fat model of prescribed thickness around a prescribed organ contained in the earlier constructed volume data, after the blood-vessel model has been joined to the designated organ; a fifth step of thereafter meshing the organ represented by the generated volume data; a sixth step of manipulating a template model of a prescribed shape by using a template, and arranging the template model around the generated blood-vessel model; and a seventh step of generating a line-segment model based on the thus arranged template model.