Medical Image Viewing Server Reproducing Reading Context
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Solution Overview
Problem
Medical imaging study reports often lack the ability to accurately reproduce the original data processing context, making it difficult for referring physicians to replicate findings due to variations in database, image orientation, and tools used during the original analysis.
Innovation Solution
A medical image processing and retrieval system that processes medical image data using interpretation tools, stores interpretation information in a database, and allows users to retrieve this information via a user interface, enabling the reproduction of the original reading context through a viewing server with embedded hyperlinks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the referring physician manually retrieves and scrolls through image slices from the archival system to match the original report images, then the physician can access the original imaging data, but the process is time-consuming and cannot guarantee reproduction of the same reading context
Solution Approach 1:
The system performs preliminary actions by automatically retrieving the exact same image slices that were used to generate the original report and pre-processing them with the same interpretation tools before the referring physician needs to review them. This eliminates the manual scrolling and matching process while ensuring the reading context is reproduced accurately.
Solution Approach 2:
The system creates a copy of the original reading context by retrieving identical image slices from the archival system and applying the same interpretation tools and processing parameters. This digital copying approach preserves the exact reading conditions without requiring the physician to manually recreate them through time-consuming manual processes.
2Measurement precision
If the physician attempts to reproduce measurements using interpretation tools on archived images, then some measurement capability is available, but there is no guarantee that the same database, image orientation, or processing parameters will be used
Solution Approach 1:
The system uses feedback from the original report metadata to automatically retrieve and apply the exact same interpretation tools, database parameters, and processing settings that were used to generate the original findings. This feedback loop ensures measurement precision is maintained without requiring the physician to manually configure complex processing parameters.
Solution Approach 2:
The system performs self-service by automatically retrieving the appropriate image slices and applying the correct interpretation tools and processing parameters based on the report information, without requiring the physician to manually configure the complex processing context. The system serves itself by autonomously recreating the reading environment.
3Loss of information
If the system stores and retrieves detailed interpretation information and processing parameters for each medical image study, then the reading context can be accurately reproduced, but the database storage and retrieval complexity increases
Solution Approach 1:
The system uses a universal database structure that stores interpretation information and processing parameters in a standardized format that can be applied across multiple medical image studies and interpretation tools. This multi-functional approach preserves processing context information efficiently without requiring separate complex storage systems for each study type.
Data Source
AI summary
In a medical image processing and retrieval system or method, an image processing system is configured to obtain a set of medical image data and to process the medical image data using at least one interpretation tool. A database is configured to store interpretation information generated in processing the medical image data using the interpretation tool. A viewing server is configured in response to a user interface instruction to retrieve from the database the interpretation information generated in processing the medical image data and to identity the at least one interpretation tool to the user interface.


