Probabilistic Motion Model for Medical Image Deformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional motion analysis in medical imaging struggles to extract consistent temporal deformations and intrinsic motion parameters, leading to difficulties in modeling and understanding organ motion patterns, with existing registration algorithms lacking realism and personalization.
Innovation Solution
A machine learning-based probabilistic motion model is employed, comprising an encoding network, temporal convolutional network, and decoding network, to learn a motion representation that captures underlying motion features in medical image sequences, enabling deformation field calculation, motion simulation, interpolation, and extrapolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional registration algorithms with temporal regularization are used, then accurate deformation fields are produced, but intrinsic motion parameters cannot be extracted
Solution Approach 1:
The patent extracts intrinsic motion parameters from the deformation field by introducing a motion parameter extraction module that identifies and isolates meaningful motion characteristics (such as organ contraction, expansion, or translation) from the computed deformation field, separating essential motion information from noise and artifacts
Solution Approach 2:
The patent introduces an intermediate motion parameter representation layer between the image registration process and the final deformation field output. This intermediate layer captures intrinsic motion parameters that serve as a bridge, allowing both accurate deformation computation and meaningful motion characterization to coexist
2Adaptability or versatility
If biomechanical models are used to model motion, then motion patterns can be characterized, but realism and personalization are compromised
Solution Approach 1:
The patent employs a dynamic, data-driven motion model that adapts to individual subjects based on their specific image sequences. Rather than using fixed biomechanical assumptions, the model learns motion patterns directly from the subject's own imaging data, enabling personalization while maintaining physical plausibility through regularization constraints
Solution Approach 2:
The patent changes the fundamental parameters of the motion model from fixed biomechanical constants to subject-specific parameters learned from imaging data. This allows the model to capture individual anatomical variations and motion characteristics while maintaining the mathematical framework needed for realistic motion simulation
3Loss of information
If machine learning based motion model is used, then intrinsic motion parameters are extracted and temporal consistency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into distinct modular components: image registration module, deformation field computation module, motion parameter extraction module, and synthesis module. Each module handles a specific aspect of the computation, allowing for optimized processing and reducing overall computational complexity through specialized algorithms in each segment
Data Source
AI summary
Systems and methods for performing a medical imaging analysis task using a machine learning based motion model are provided. One or more medical images of an anatomical structure are received. One or more feature vectors are determined. The one or more feature vectors are mapped to one or more motion vectors using the machine learning based motion model. One or more deformation fields representing motion of the anatomical structure are determined based on the one or more motion vectors and at least one of the one or more medical images. A medical imaging analysis task is performed using the one or more deformation fields.