Radiology Report Image Context Mapping for Automatic Window/Level
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manually setting image viewing context in radiology reports is time-consuming and inefficient, hindering the navigation to referenced images and potentially compromising the quality of patient diagnosis.
Innovation Solution
A system and method that automatically sets image viewing context by extracting image references and body parts from radiology reports, mapping them to appropriate viewing settings, and displaying images accordingly based on user selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually setting image viewing context is performed, then image viewing quality can be optimized, but time consumption increases and efficiency decreases
Solution Approach 1:
The system performs preliminary action by automatically extracting image references and body parts from the radiology report before the user needs to view the images. The NLP module processes the report text in advance to identify relevant information, and the mapping module pre-determines the appropriate window width/level settings based on the extracted body part, eliminating the need for manual setting during the viewing process.
Solution Approach 2:
The system implements self-service by automatically determining and applying the appropriate image viewing context without requiring user intervention. The automated pipeline including NLP extraction, body part mapping, and window width/level assignment operates autonomously based on the radiology report content, allowing the system to serve itself rather than requiring manual configuration by the radiologist.
2Measurement precision
If manually setting image viewing context is performed, then viewing accuracy can be maintained, but workflow efficiency deteriorates
Solution Approach 1:
The patent replaces the mechanical manual process of setting image viewing context with an automated information processing system. The NLP module uses computational linguistics to extract image references and body parts from text, and the mapping module uses pre-established knowledge bases to automatically determine window width/level settings, substituting manual mechanical actions with automated computational processes.
3Productivity
If automated image viewing context setting is implemented, then workflow efficiency improves, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the automated image viewing context setting process into distinct functional modules: an NLP extraction module for processing report text, a body part mapping module for translating anatomical terms to viewing parameters, and a window width/level assignment module for applying the settings. This modular segmentation makes the complex system more manageable and maintainable.
Solution Approach 2:
The patent introduces an intermediary mapping module that serves as a mediator between the radiology report text and the image viewing parameters. This intermediary layer translates the unstructured text information into structured viewing context settings, facilitating the automation process while isolating the complexity of the translation logic from the user interface.
Data Source
AI summary
A system and method for automatically setting image viewing context. The system and method perform the steps of extracting image references and body parts associated with the image references from a report, mapping each of the body parts to an image viewing context so that image references associated are also associated with the image viewing context, receiving a user selection indicating an image to be viewed, determining whether the user selection is one of the image references associated with the image viewing context and displaying the image of the user selection.