Deep Neural Network for Medical Image Motion Estimation
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Solution Overview
Problem
Existing methods for motion modeling in medical images rely on hand-crafted algorithms that are not robust and lack generalizability to changes in image quality, modality, and organs, making them inefficient for estimating organ deformation and motion prediction.
Innovation Solution
A deep neural network is trained to learn motion features and predict organ motion by generating diffeomorphic deformation fields, allowing for the estimation of observed and non-observed motion in medical image sequences, and generating predicted frames using convolutional encoder-decoders with variational autoencoders and memory modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-crafted algorithms with strong priors are used for motion modeling, then the algorithms can provide structured motion estimation, but they lack robustness and generalizability to changes in image quality, modality, and organs
Solution Approach 1:
The patent replaces hand-crafted algorithms with a deep learning-based system. Specifically, a neural network is trained to learn motion patterns directly from image data, substituting the mechanical, rule-based approach with a data-driven, adaptive system that can generalize across different modalities and organs without requiring re-engineering of priors
Solution Approach 2:
The patent transforms the motion estimation problem from using fixed, hand-crafted parameters to learning parameters dynamically from data. The system learns to predict motion parameters (such as displacement vectors, velocity fields) directly from image sequences, allowing the parameters to adapt to different imaging conditions, modalities, and organ types based on the learned patterns
2Stability of the object's composition
If diffeomorphic spatiotemporal B-spline parameterized velocity fields are used to compute trajectories, then temporal consistency is ensured, but the computational complexity and time consumption increase significantly
Solution Approach 1:
The patent performs preliminary training of the neural network on large datasets containing various motion patterns and temporal sequences. During this offline training phase, the system learns to maintain temporal consistency through learned regularizations and architectural constraints. Once trained, the model can rapidly infer temporally consistent trajectories without performing computationally intensive optimizations during actual motion estimation
Solution Approach 2:
The patent replaces the iterative optimization process required for diffeomorphic B-spline velocity fields with a direct neural network prediction. Instead of performing time-consuming gradient-based optimizations to find optimal velocity fields, the trained network directly predicts motion parameters that inherently satisfy temporal consistency, substituting the mechanical optimization process with a learned direct mapping
3Ease of manufacture
If manual construction of parameterized motion models is performed, then the models can be tailored to specific applications, but they lack generalizability to various medical imaging tasks
Solution Approach 1:
The patent creates a universal motion estimation system based on deep learning that can handle multiple medical imaging tasks and modalities with a single trained model. The neural network is designed to learn general motion patterns that apply across different organs, imaging modalities (such as MRI, CT, ultrasound), and clinical applications, eliminating the need to manually construct separate parameterized models for each specific task
Solution Approach 2:
The patent substitutes manual model construction and parameter tuning with automated learning from data. Instead of requiring experts to manually design and adjust motion models for different applications, the system automatically learns appropriate motion representations from training data, making the model construction process effortless and the resulting models broadly generalizable
Data Source
AI summary
A method and system for computer-based motion estimation and modeling in a medical image sequence of a patient is disclosed. A medical image sequence of a patient is received. A plurality of frames of the medical image sequence are input to a trained deep neural network. Diffeomorphic deformation fields representing estimated motion between the frames of the medical image sequence input to the trained deep neural network are generated. Future motion, or motion between frames, is predicted from the medical image sequence and at least one predicted next frame is generated using the trained deep neural network. An encoding of the observed motion in the medical image sequence is also generated, which is used for motion classification (e.g., normal or abnormal) or motion synthesis to generate synthetic data.