Predictor Algorithm for MRI Field of View Alignment
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Solution Overview
Problem
Automating the positioning of a field of view in Magnetic Resonance Imaging (MRI) scans is challenging due to variability in patient anatomy and operator inconsistency, leading to suboptimal results.
Innovation Solution
A predictor algorithm with a trainable machine learning component uses localizer MRI images and subject metadata to generate predicted field of view alignment data, leveraging deep-learning models that incorporate image and text embeddings to improve accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual positioning by operators is used, then flexibility and adaptability to individual patients is maintained, but consistency and reproducibility deteriorate due to operator variability
Solution Approach 1:
The patent replaces manual mechanical positioning operations with an automated computational system that uses machine learning algorithms. The predictor algorithm processes localizer images and patient metadata to automatically calculate optimal field of view positioning, eliminating operator variability while maintaining positioning accuracy.
Solution Approach 2:
The system performs self-service by automatically generating field of view positioning recommendations without requiring operator intervention. The predictor algorithm independently processes input data (localizer images and metadata) and produces positioning outputs, making the system autonomous and reproducible across different operators and patients.
2Reliability
If automated positioning algorithms are implemented, then consistency and reproducibility improve, but adaptability to individual patient anatomy deteriorates
Solution Approach 1:
The predictor algorithm dynamically adjusts positioning parameters based on input data characteristics. It processes varying patient metadata (age, weight, anatomy type) and localizer image characteristics to generate customized field of view positioning for each patient, maintaining adaptability while ensuring reproducibility through consistent algorithmic processing.
Solution Approach 2:
The system incorporates feedback mechanisms where the predictor algorithm learns from historical data and continuously improves its positioning accuracy. The algorithm compares predicted positioning with actual outcomes and refines its parameters, enabling it to adapt to different patient anatomies while maintaining high reproducibility across repeated measurements.
3Measurement precision
If deep-learning models with image and text embeddings are used, then positioning accuracy improves, but computational complexity and training requirements worsen
Solution Approach 1:
The system performs preliminary action by pre-training the predictor algorithm on historical data before clinical use. The deep-learning model is trained in advance on labeled examples of localizer images, patient metadata, and correct positioning outcomes, enabling it to make accurate predictions during actual scanning without requiring complex real-time computation during patient exams.
Solution Approach 2:
The patent uses an intermediary approach by introducing a pre-trained predictor algorithm as a mediator between raw input data (localizer images and metadata) and final positioning decisions. This intermediary model encapsulates the complex deep-learning computations, providing accurate positioning recommendations while shielding users from the underlying computational complexity.
Data Source
AI summary
Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a predictor algorithm (122) configured for outputting predicted field of view alignment data (128) for a magnetic resonance imaging system (502) in response to inputting one or more localizer magnetic resonance images (124) and subject metadata (126). The predictor algorithm comprises a trainable machine learning algorithm. The medical system further comprises a processor (104) configured for controlling the medical system. Execution of the machine executable instructions causes the processor to: receive (200) the one or more localizer magnetic resonance images and the subject metadata; and receive (202) the predicted field of view alignment data from the predictor algorithm in response to inputting the one or more localizer magnetic resonance images into the predictor algorithm and in response to inputting the subject metadata.