Domain-Specific LLM Pulse Sequence Generation for MRI Configuration
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Solution Overview
Problem
Configuring magnetic resonance imaging systems requires extensive training and experience, and existing methods lack automation in generating pulse sequence data efficiently.
Innovation Solution
A domain-specific large language model is trained to output tokenized pulse sequence data, using a pulse sequence token library, to automate the generation of magnetic resonance imaging protocols, assisted by user input and fine-tuning for specific clinical settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration methods are used for MRI systems, then the system can be configured with high precision and reliability, but the process requires extensive training and experience, reducing productivity and increasing time consumption
Solution Approach 1:
The patent introduces a domain-specific large language model as an intermediary between the user and the MRI system configuration. The LLM translates natural language clinical requirements into precise pulse sequence commands, acting as a mediator that bridges the gap between manual configuration accuracy and automated efficiency. This intermediary maintains configuration reliability by ensuring accurate translation of clinical intent into technical parameters while eliminating the need for extensive operator training.
Solution Approach 2:
The patent replaces the manual mechanical process of configuring MRI pulse sequences with an intelligent software system. Instead of requiring operators to manually navigate complex configuration interfaces and understand detailed pulse sequence parameters, the system uses a domain-specific LLM to automatically generate the configuration based on natural language input, substituting the manual mechanical configuration process with an automated intelligent system.
2Productivity
If automated methods are used to generate pulse sequence data, then productivity and efficiency are improved, but the complexity of the system increases and may reduce measurement precision
Solution Approach 1:
The patent changes the parameter representation from raw pulse sequence commands to a tokenized vocabulary derived from actual clinical usage. By analyzing the statistical parameters of pulse sequence commands in real MRI protocols and creating a token library based on frequent patterns, the system simplifies the complexity while maintaining automation capability. The domain-specific LLM is trained on these parameter patterns, enabling it to generate accurate configurations without requiring the full complexity of manual pulse sequence design.
3Ease of manufacture
If a general large language model is used, then the system is easier to implement, but it lacks domain-specific knowledge and cannot generate clinically relevant pulse sequence data
Solution Approach 1:
The patent applies local quality by creating a domain-specific version of the large language model tailored to MRI pulse sequence generation. Instead of using a generic LLM for all purposes, the system fine-tunes the model with domain-specific training data consisting of actual MRI pulse sequence protocols and clinical requirements. This localized adaptation ensures the model possesses the specific knowledge needed to generate clinically relevant configurations while maintaining the architectural advantages of large language models.
Solution Approach 2:
The patent performs preliminary action by pre-training the domain-specific LLM on a comprehensive dataset of MRI pulse sequence commands and clinical protocols before deployment. The model is trained in advance on tokenized pulse sequence data and site-specific medical records, enabling it to generate accurate, clinically relevant configurations from the outset rather than requiring post-deployment adjustments or validation.
4Reliability
If extensive training is provided to operators, then configuration precision and reliability are maintained, but the time required for operator preparation increases and productivity decreases
Solution Approach 1:
The patent enables the system to serve itself by using the domain-specific LLM to automatically generate and validate pulse sequence configurations without human intervention. The model independently translates clinical requirements into accurate technical configurations, eliminating the need for human operators to undergo extensive training. The system self-corrects and self-optimizes based on the training data it has been provided, maintaining high configuration accuracy without requiring trained personnel.
Data Source
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AI summary
Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a domain-specific large language model (122). The large language model is configured to output tokenized pulse sequence data (126) in response to receiving subject data (124). The tokenized pulse sequence data is configured as tokens chosen from a pulse sequence token library (322). The medical system further comprises a computational system (104). Execution of the machine executable instructions causes the computational system to receive (200) the tokenized pulse sequence data in response to inputting the subject data into the domain-specific large language model.