Organ Positioning Assessment With Patient-Specific Imaging Feedback
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Solution Overview
Problem
Current medical imaging systems lack patient-specific and radiographer-specific support for optimal organ positioning during examinations, leading to potential mispositioning issues that cannot be avoided post-acquisition.
Innovation Solution
A device and method for organ positioning assessment that includes data interfaces for retrieving patient data and examination images, a pre-assessment unit for generating personalized positioning advice, and a post-assessment unit for providing feedback on image quality, using machine learning to analyze patient-specific and radiographer-specific factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general guideline-based positioning assessment is used, then positioning assessment can be performed, but patient-specific constraints and radiographer-specific factors are not considered
Solution Approach 1:
The system performs preliminary assessment before image acquisition by analyzing patient data (weight, height, age, disabilities) and radiographer experience level to generate personalized positioning advice in advance. This allows the positioning guidance to be tailored to individual constraints before the actual examination begins, rather than assessing after the fact.
Solution Approach 2:
The system provides feedback to radiographers about positioning quality based on analyzed image characteristics and compares them against patient-specific constraints and radiographer experience levels. This feedback loop enables continuous improvement of positioning accuracy by considering individual factors that affect positioning capability.
2Measurement precision
If positioning assessment is performed after image acquisition, then assessment can be conducted, but mispositioning cannot be avoided
Solution Approach 1:
The system performs preliminary positioning assessment and generates personalized positioning advice before image acquisition. By analyzing patient characteristics (weight, height, age, disabilities) and radiographer experience beforehand, the system provides guidance that can prevent mispositioning from occurring in the first place, rather than detecting it after acquisition.
3Manufacturing precision
If patient-specific positioning advice is provided, then positioning accuracy can be improved, but system complexity increases
Solution Approach 1:
The system uses machine learning models that have been trained on existing data to generate positioning advice. Instead of requiring complex real-time analysis algorithms, the system leverages pre-trained models that can provide patient-specific recommendations based on input data about patient characteristics and radiographer experience, simplifying the computational requirements.
Solution Approach 2:
The system introduces an intermediary layer of analysis that processes patient data and radiographer information to generate positioning advice. This intermediary assessment unit acts as a mediator between the raw data and the final positioning guidance, simplifying the overall system architecture by breaking down the complex task into manageable steps.
Data Source
AI summary
A device for organ positioning assessment for acquiring images in the course of a medical examination of a patient, comprises: a data-interface configured to receive and/or automatically retrieve physical patient-data; a pre-assessment-unit configured to generate a positioning-advice for the medical examination based on the type of medical examination and the patient-data; a data-interface configured to output the positioning-advice; a data-interface configured to receive and/or automatically retrieve a number of examination-images of the patient; a post-assessment-unit configured to generate patient-specific feedback-data based at least on the number of examination-images and the positioning-advice; and a data-interface configured to output the feedback-data.