MRI Workflow Assistance Using LLMs for Parameter Error Prevention
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Solution Overview
Problem
Magnetic resonance examinations require significant expert knowledge for parameter adjustments, and small changes can lead to errors or artifacts, posing challenges for inexperienced operators.
Innovation Solution
A computer-implemented method using a large language model (LLM) to assist users in magnetic resonance examinations by receiving queries, determining output information, and providing assistance through text or voice, incorporating context and historical data to improve decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a magnetic resonance examination is adapted to specific medical and diagnostic issues with numerous options, then the diagnostic capability and versatility are improved, but the complexity of operation and expert knowledge required increase significantly
Solution Approach 1:
The patent introduces an intermediary system (computing unit with LLM) that mediates between the user and the complex magnetic resonance examination parameters. The LLM processes natural language queries and translates them into appropriate technical parameters and settings, shielding users from the underlying complexity while maintaining full adaptability of the examination options.
Solution Approach 2:
The system enables self-service operation where the magnetic resonance apparatus automatically retrieves relevant patient data, determines appropriate examination parameters, and configures settings based on the clinical question without requiring manual expert intervention. The computing unit autonomously processes the examination workflow from query to parameter configuration.
2Measurement precision
If parameter settings are adjusted to optimize examination quality, then the diagnostic precision is improved, but the risk of errors and artifacts increases due to sensitivity to small changes
Solution Approach 1:
The system implements feedback mechanisms where the LLM continuously monitors and evaluates parameter settings against the clinical question and patient data. The computing unit provides feedback on potential errors, validates parameter combinations, and adjusts settings to maintain optimal quality while avoiding error-prone configurations through iterative refinement.
Solution Approach 2:
The computing unit performs preliminary actions by pre-configuring and validating parameter settings before the actual examination begins. The system retrieves relevant data, determines appropriate parameters in advance, and performs error checking on parameter combinations before execution, preventing errors rather than detecting them during the examination.
3Adaptability or versatility
If sequence parameters are changed to adapt to different diagnostic needs, then the examination versatility is improved, but the difficulty of detecting and measuring the correct settings increases
Solution Approach 1:
The patent replaces the mechanical/manual system of parameter selection with an intelligent information processing system. The LLM substitutes manual parameter detection and verification with automated natural language processing and reasoning, where the system understands the clinical question and automatically determines the correct parameters without requiring users to manually detect or measure appropriate settings.
Solution Approach 2:
The computing unit acts as an intermediary that translates between the clinical question (natural language) and the technical parameters. This intermediary layer makes the parameter settings transparent and understandable by explaining the rationale behind each parameter choice in terms of the original clinical question, eliminating the difficulty of detecting and verifying correct settings.
Data Source
AI summary
The disclosure relates to a method for assisting a user in implementing a workflow of a magnetic resonance examination on a patient. The method may include receiving a query by the user, wherein the input is made in text form or as voice input; determining output information corresponding to the query by means of a large language model (LLM), and providing the output information; and outputting the output information in text form or as voice output.