Medical Imaging Parameter Recommendation System for Radiology Training
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Solution Overview
Problem
Inexperienced radiology technologists face challenges in setting and adjusting medical imaging parameters correctly, leading to potential errors in image quality and diagnosis, due to limited support from current software and the need for timely guidance.
Innovation Solution
A system that utilizes a non-transitory computer-readable medium to store instructions for an operator assistance method, which includes obtaining data from reference imaging examinations, generating training materials, and providing a user interface to display suggested scan parameters for current or upcoming medical imaging examinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If current software provides only valid parameter ranges without task-specific optimization, then device complexity is reduced, but manufacturing precision (image quality) deteriorates
Solution Approach 1:
The system enables self-service by automatically selecting and recommending optimal imaging parameters based on the examination type and patient characteristics. The parameter selection is performed autonomously without requiring manual intervention or complex user input, allowing the system to serve itself in determining appropriate parameters while maintaining high image quality standards.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting imaging parameters based on specific examination types, patient age, weight, and other characteristics. The system transforms static parameter ranges into dynamic, context-specific parameter recommendations, enabling optimized image quality for different clinical scenarios without increasing user-facing complexity.
2Manufacturing precision
If experienced technologists manually guide junior technologists through parameter settings, then manufacturing precision (parameter accuracy) is improved, but loss of time (training duration) increases
Solution Approach 1:
The system incorporates feedback mechanisms by providing real-time recommendations based on the examination type and patient characteristics. The parameter recommendations are continuously adjusted based on feedback from the examination protocol requirements and patient-specific factors, enabling junior technologists to achieve accurate parameter settings independently without prolonged mentorship.
Solution Approach 2:
The patent applies preliminary action by pre-configuring and pre-recommending optimal parameters based on examination type and patient characteristics before the actual imaging procedure. This advance preparation eliminates the need for real-time manual guidance during critical imaging moments, significantly reducing training time while maintaining parameter accuracy.
3Manufacturing precision
If comprehensive training materials are provided for all imaging modalities, then manufacturing precision (parameter knowledge) is improved, but loss of time (training duration) increases
Solution Approach 1:
The system segments comprehensive training materials into modular, examination-type-specific modules. Instead of presenting all imaging modalities simultaneously, the training system divides content into discrete segments based on examination type, patient characteristics, and difficulty level, allowing learners to progress through targeted content without being overwhelmed by the entirety of available knowledge.
Solution Approach 2:
The patent implements dynamics by making the training content adaptive and flexible rather than static and comprehensive. The training materials dynamically adjust based on the learner's progress, examination type, and specific needs, allowing trainees to access only the relevant parameter knowledge for their current task while maintaining the option to review comprehensive materials when needed.
Data Source
AI summary
A non-transitory computer readable medium (26s) stores instructions executable by at least one electronic processor (14s) to perform an operator assistance method (100) including: obtaining data related to reference imaging examinations during performance of the medical imaging examinations; generating training materials (32) from the obtained data; the training materials being related to the performance of the reference medical imaging examinations; and providing training user interface (UI) (44) on a display device (36), the training UI displaying a visualization (36) of a selected portion of the training materials which is selected based on information about a current or upcoming medical imaging examination.