Spine Stress Mapping With MRI-Based Finite Element Analysis
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Solution Overview
Problem
Existing spine surgeries lack precision in identifying specific areas of stress on the spine, leading to invasive procedures and potential for unnecessary bone removal, which can cause further pain and complications.
Innovation Solution
A system utilizing finite element analysis and deep learning models to create spine stress maps by simulating stresses on the spine based on MR images, generating multi-class segmentations, and predicting stress maps to guide targeted surgical interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spine surgery methods are used without precise stress identification, then surgical procedures can be performed, but unnecessary bone removal occurs causing further pain and complications
Solution Approach 1:
The system performs preliminary finite element analysis and stress simulation on patient-specific spine models before surgery to identify precise stress areas. This preliminary action enables surgeons to plan targeted interventions that remove only necessary bone tissue, avoiding unnecessary bone removal and associated complications.
Solution Approach 2:
The system changes the parameter of stress distribution visualization by using finite element analysis to compute and display stress magnitude and direction across different spinal regions. This parameter transformation from abstract stress data to visual stress maps enables precise identification of problem areas for targeted surgical intervention.
2Measurement precision
If comprehensive stress analysis is performed on the entire spine, then accurate stress identification is achieved, but computational complexity and processing time increase
Solution Approach 1:
The system segments the spine into distinct anatomical regions (cervical, thoracic, lumbar vertebrae, intervertebral discs, spinal cord) and performs finite element analysis on each segment separately. This segmentation reduces computational complexity while maintaining overall stress analysis accuracy, as each segment can be modeled with appropriate local boundary conditions.
Solution Approach 2:
The system applies local quality by using patient-specific anatomical data from MRI scans to create customized material properties and geometric models for each spinal region. This local customization improves stress map accuracy for each specific patient while the modular segmentation approach keeps computational requirements manageable.
3Manufacturing precision
If detailed multi-class segmentation is generated from MR images, then stress simulation accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary multi-class segmentation of MR images into anatomical structures (vertebrae, discs, ligaments, spinal cord) before stress simulation. This preliminary anatomical modeling is done once per patient and reused across multiple stress scenarios, reducing repeated processing time while maintaining high anatomical model precision for accurate stress analysis.
Data Source
AI summary
A system and techniques for creating a spine stress map are provided. The system may be configured to generate a multi-class segmentation for an anatomical element of a patient based on a plurality of magnetic resonance images of the anatomical element from a plurality of patients. Additionally, one or more stress maps may be generated based on simulating stresses on the anatomical element. In some embodiments, the simulated stresses may be simulated using a finite element analysis based at least in part on the multi-class segmentation. Additionally, the system may be configured to display one or more stress maps via a user interface, where the one or more stress maps are determined based on one or more deep learning models configured to predict multi-labeled masks and/or stress maps for the anatomical element.