Personalized Statistical Breast Model Generation
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Solution Overview
Problem
Current biomechanical simulations of the breast, which require elaborate geometric models generated from costly and time-consuming MRI procedures, are limited in their ability to provide personalized models for pre-surgical decision aids due to the high variability in breast shape and composition, and the lack of standardization in tissue distribution.
Innovation Solution
An image processing system and method that uses input images from various modalities to create a personalized statistical model of breast tissue distribution by correlating meta-data with spatial coordinates, allowing for the generation of realistic, personalized virtual breast models without relying on expensive MRI for personalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MRI imaging is used to generate elaborate geometric models of the breast, then the accuracy and detail of the geometric model is improved, but the cost and time required for the procedure increases significantly
Solution Approach 1:
The patent uses statistical models and algorithms to create virtual copies of breast geometry from limited input data (such as surface images or simple scans), replacing the need for time-consuming MRI procedures. The system generates personalized 3D breast models by correlating statistical variations with minimal imaging data, achieving accurate geometric representation without requiring extensive MRI scanning time.
Solution Approach 2:
The patent pre-computes statistical variations and geometric templates from a population database before individual patient modeling. By preparing reference models and statistical parameters in advance, the system can rapidly generate personalized breast geometries without requiring time-consuming real-time MRI analysis for each patient.
2Measurement precision
If MRI imaging is used to generate elaborate geometric models of the breast, then the accuracy and detail of the geometric model is improved, but the cost of the procedure increases
Solution Approach 1:
The patent creates virtual copies of breast anatomy using statistical modeling techniques that require minimal actual imaging data. Instead of consuming expensive MRI resources to capture detailed internal geometry, the system generates accurate virtual models by correlating statistical variations with limited input images, significantly reducing imaging resource consumption while maintaining model accuracy.
Solution Approach 2:
The patent transforms the approach from direct imaging-based geometry capture to parameter-based generation. By using statistical parameters and variation models derived from population data, the system can generate accurate breast geometries through mathematical transformations rather than consuming expensive imaging resources for each individual patient.
3Ease of manufacture
If generic heuristic assumptions about internal tissue distribution are used, then the modeling process is simplified, but the realism and accuracy of the model decreases
Solution Approach 1:
The patent incorporates feedback mechanisms where the system continuously refines tissue distribution estimates by comparing generated models with actual patient imaging data. Statistical variations are adjusted based on observed deviations from expected patterns, allowing the system to maintain simplicity while improving accuracy through iterative refinement and validation against real imaging data.
Solution Approach 2:
The patent uses statistical parameters and probability distributions to model tissue distribution, replacing simple heuristic assumptions with data-driven parameter variations. By correlating tissue characteristics with statistical variations from population data and adjusting parameters based on individual patient characteristics, the system achieves both simplicity in the modeling process and high accuracy in tissue distribution representation.
Data Source
AI summary
An image processing system, comprising an input interface (IN) for receiving a plurality of input images acquired of test objects. The system further comprises a material type analyzer (MTA) configured to produce material type readings at corresponding locations across said input images (IM(CH)). A statistical module (SM) of the system is configured to determine based on said readings an estimate for a probability distribution of material type for said corresponding locations.