Anatomical Deformation Prediction Across Variable Patient Poses
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Solution Overview
Problem
Existing medical imaging technologies face challenges in accurately determining organ deformation due to variations in patient pose and body shape during different scans, leading to cumbersome and costly procedures that expose patients to unnecessary radiation.
Innovation Solution
A system utilizing machine learning models to predict anatomical structure deformation by generating a condition-agnostic feature descriptor and a signed distance function, allowing for the generation of anatomical structure representations under varying conditions without additional scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scanning is performed more frequently to minimize conditional changes between scans, then measurement precision is improved, but productivity deteriorates due to increased cost and time
Solution Approach 1:
The system performs preliminary action by acquiring a reference scan at an initial time point and using machine learning models to predict organ deformations at future time points. This eliminates the need for frequent repeated scans, as the prediction model proactively generates deformation information based on the reference scan and predicted poses, thereby maintaining measurement precision while improving productivity.
Solution Approach 2:
The system creates a virtual copy of the organ's deformation state through machine learning prediction rather than physical repeated scanning. The prediction model generates synthetic deformation data that replicates what would be obtained from actual scans, allowing frequent measurement assessment without the costs and time associated with repeated physical scanning.
2Measurement precision
If the patient strictly maintains the same position and pose during different scan procedures, then measurement precision is improved, but ease of operation deteriorates due to procedural complexity
Solution Approach 1:
The system changes the approach from controlling patient position (a physical constraint) to accounting for position variations through parameters. The machine learning model incorporates pose information as input parameters and learns to compensate for position changes, allowing patients to move naturally while maintaining measurement accuracy through computational correction rather than physical constraint.
Solution Approach 2:
The system introduces a machine learning model as an intermediary between the patient's natural movement and the deformation measurement. This model translates variations in patient pose into corrected deformation predictions, mediating the relationship between position changes and measurement accuracy, thereby eliminating the need for strict position control while maintaining precision.
3Measurement precision
If frequent scanning is performed to capture organ deformation, then measurement precision is improved, but object-affected harmful factors worsen due to increased radiation exposure
Solution Approach 1:
The system creates virtual copies of deformation data through machine learning prediction instead of obtaining them through repeated physical scanning. The prediction model generates synthetic images and deformation measurements that replicate the information from actual scans, allowing frequent assessment of organ deformation without subjecting patients to repeated radiation exposure.
Solution Approach 2:
The system replaces the mechanical/physical scanning process with a computational prediction system. Instead of using physical imaging equipment to repeatedly capture organ states, the system uses machine learning models to compute and predict deformation, substituting radiation-based physical measurement with non-invasive computational analysis.
Data Source
AI summary
The physical characteristics of one or more anatomical structures of a person may change in accordance with conditions surrounding the determination of such physical characteristics. Machine learning based techniques may be used to determine a template representation of the one or more anatomical structures that may indicate the physical characteristics of the one or more anatomical structures free of the impact imposed by changing conditions. The template representation may then be used to predict the physical characteristics of the one or more anatomical structures under a new set of conditions, without subjecting the person to additional medical scans.