Soft Tissue Surface Deformation for Target Motion Estimation
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Solution Overview
Problem
Existing methods for estimating target motion in soft tissues, such as those used in medical imaging, face challenges with accuracy due to reliance on gray scale measurements and time-consuming optimization processes, which can lead to blurred images and reduced treatment accuracy.
Innovation Solution
A system and method utilizing medical imaging equipment to capture and process images, extracting deformation features from soft tissue surfaces using neural networks, allowing for timely and accurate estimation of target motion based on surface deformation without repeated calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deformation registration based on gray scale is used, then motion estimation can be performed, but measurement precision deteriorates due to sensitivity to gray scale changes and noise
Solution Approach 1:
The patent extracts and utilizes only the surface deformation information from the soft tissue, ignoring the internal gray scale variations. By taking out the surface feature as the key indicator, the method avoids the pitfall of relying on noisy gray scale measurements inside the tissue volume.
Solution Approach 2:
The patent introduces surface deformation as an intermediary parameter to bridge the gap between image acquisition and target motion estimation. Instead of directly measuring target position through noisy gray scale images, the surface deformation serves as a stable mediator that reflects target motion without being directly affected by gray scale changes.
2Measurement precision
If optimization iteration process is used for registration, then motion estimation can be achieved, but productivity deteriorates due to time consumption
Solution Approach 1:
The patent performs preliminary extraction of surface deformation features from the images before the motion estimation process. By pre-processing to obtain the surface deformation field, the subsequent motion estimation can be performed much faster without requiring time-consuming optimization iterations.
Solution Approach 2:
The patent replaces the mechanical optimization iteration process with a direct calculation method based on surface deformation. Instead of using iterative numerical optimization to find the best match, the method directly computes motion from surface deformation measurements, substituting a complex computational process with a simpler, faster calculation.
3Measurement precision
If biomechanical model combined registration is used, then registration accuracy is improved, but device complexity increases and it is difficult to accurately describe biomechanical characteristics
Solution Approach 1:
The patent extracts only the essential surface deformation information from the complex biomechanical model, separating the useful signal from the model complexity. By taking out just the surface feature extraction capability, the method achieves accurate registration without incorporating the complex biomechanical modeling machinery.
Solution Approach 2:
The patent uses a simple, computationally inexpensive surface extraction approach instead of complex biomechanical models. The method employs straightforward image processing techniques to extract surface deformation, using simple tools rather than complex computational models to achieve the desired accuracy.
Data Source
AI summary
Provided is a system and method for estimating the motion of a target inside a tissue based on surface deformation of the soft tissue. The system consists of an acquisition unit, a reference input unit, two surface extraction units, a target position extraction unit, a feature calculation unit, and a target motion estimation unit. The method includes: the acquisition unit acquires an image Ii of the soft tissue; the surface extraction unit extracts a surface fi of the soft tissue from Ii; the reference input unit acquires a reference image Iref of the soft tissue; the surface extraction unit and the target position extraction unit respectively extract a reference surface fref of the soft tissue and a target reference position tref from Iref, the feature calculation unit calculates deformation feature Ψi of fi relative to fref, the target motion estimation unit estimates the target displacement based on Ψi and tref.