Linking Graph for Synchronized Medical Image Series Comparison
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Solution Overview
Problem
Radiologists face difficulties in efficiently organizing and comparing large volumes of medical images from multiple series in a DICOM study, which can be time-consuming and cumbersome, hindering effective diagnosis and treatment.
Innovation Solution
A linking graph system that automatically or manually links medical image series based on position coordinates, allowing simultaneous display of linked images across multiple viewports, enhancing the ability to compare images of the same plane and coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologists manually organize and compare thousands of image instances per patient per study, then diagnostic accuracy can be maintained, but time consumption and operational difficulty increase significantly
Solution Approach 1:
The system automatically performs the organization and comparison of image instances without requiring manual intervention from radiologists. The linking graph data structure automatically associates image instances across series based on position coordinates, and the system automatically synchronizes scrolling and selection across multiple series, making the system self-organizing rather than requiring radiologist-driven organization.
2Productivity
If multiple image series are displayed and compared simultaneously, then diagnostic efficiency improves, but system complexity and computational requirements increase
Solution Approach 1:
The system divides the large dataset of thousands of image instances into organized series groups, with each series displayed in separate viewports. The linking graph segments the data structure into manageable nodes and edges, allowing the system to handle complexity through structured division rather than monolithic processing.
Solution Approach 2:
The linking graph serves as an intermediary data structure that manages the relationships between image instances across different series. Rather than directly comparing all possible image pairs (which would be computationally overwhelming), the linking graph pre-establishes relationships based on position coordinates, acting as a mediator that simplifies the comparison process.
3Measurement precision
If image series are manually organized and linked, then diagnostic precision can be maintained, but operational ease and speed decrease
Solution Approach 1:
The system automatically establishes links between image instances across series by comparing position coordinates without requiring manual organization by radiologists. The linking graph data structure self-organizes the relationships, and the automatic scrolling synchronization eliminates the need for manual alignment, dramatically improving ease of operation while maintaining diagnostic precision through accurate position-based matching.
Data Source
AI summary
Methods, systems, and computer-readable media are disclosed for organizing and linking image series for a patient in a linking graph. The organizing and linking of image series for a patient can be performed automatically and based on manual selection of a user. As a user scrolls through image instances of for a primary image series, image instances from linked image series that share position coordinates (or image instances closest thereto) are automatically and simultaneously presented to the user viewing the primary image series.