Hybrid Learning Imaging Systems for Accelerated MRI
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Solution Overview
Problem
Conventional medical imaging systems rely heavily on human-designed sensors, procedures, and interpretation methods, which can be inefficient and require extensive data and time for high-quality results, especially in complex imaging tasks.
Innovation Solution
Hybrid learning systems that integrate human learning elements, such as scientific models and problem-solving experiences, with machine learning elements, like neural networks, to optimize sensor configurations, measurement procedures, and inference operations, allowing for adaptive and autonomous data-driven improvements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional human-designed sensors and procedures are used, then system reliability is maintained, but productivity is low and measurement requirements are high
Solution Approach 1:
The system employs machine learning models that autonomously optimize sensor configurations and measurement procedures without requiring extensive human intervention. The neural networks automatically adjust acquisition parameters and reconstruct images, enabling the system to serve itself in optimizing its own operation and reducing dependency on manual tuning and extensive measurement data.
Solution Approach 2:
The patent utilizes adaptive parameter adjustment through machine learning, where acquisition parameters such as sampling rates, sensor positions, and measurement sequences are dynamically optimized based on real-time data analysis. This allows the system to achieve high-quality imaging with reduced measurement requirements by intelligently adjusting parameters rather than relying on fixed conventional settings.
2Productivity
If conventional human-designed procedures are used, then ease of operation is maintained, but productivity is low
Solution Approach 1:
The patent replaces traditional mechanical and manual image reconstruction procedures with machine learning-based computational methods. Neural networks perform inference operations that automatically optimize image reconstruction from raw sensor data, substituting conventional step-by-step processing algorithms with intelligent models that learn optimal reconstruction paths, thereby increasing inference speed while managing complexity through automated computation.
3Manufacturing precision
If extensive measurement data is collected, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system implements partial measurement strategies where machine learning models are trained to reconstruct high-quality images from incomplete or undersampled measurement data. Rather than requiring full extensive measurements, the neural networks learn to infer missing information from partial data, achieving near-full precision with significantly reduced measurement requirements and shorter scan times.
Solution Approach 2:
The patent employs pre-trained machine learning models that have been previously trained on extensive datasets to perform rapid inference on new data. This preliminary training phase allows the system to achieve high image quality during actual scanning by applying learned patterns directly to new measurements, eliminating the need to perform extensive analysis during the scanning process itself.
Data Source
AI summary
Techniques are presented that exploit human learning and machine learning in the acquiring of and reasoning with sensor data. To reveal quantities of interest of the physical world, instrument-based sensing or probing can particularly use, in a hybrid fashion, elements such as scientific models and problem solving experiences originated from human learning of the operation principles of the physical world, together with elements such as adaptive compute units or neural networks constructed for machine learning of patterns in high-dimensional space and in massive data. Integration and autonomous improvement are through numerical computations and schemed updates, which can benefit development or deployment of algorithms, procedures and sensors, as well as interpretation of results.