Automatic Study Linking for Hybrid Medical Imaging
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Solution Overview
Problem
Current medical imaging systems require manual combination of images from different modalities, leading to error-prone associations and potential diagnostic failures, especially in hybrid imaging where automatic association of studies from different types like CT and PET is not possible.
Innovation Solution
A computer-based automation tool that uses a classification scheme and rule database to automatically link medical studies from different modalities by detecting linking information from the study data itself, allowing for automatic association and storage of linking information for later processing, without user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual combination of studies is used, then physician can access images from different modalities, but error prone and leads to severe failures with respect to diagnosis
Solution Approach 1:
The system performs automatic linking of studies without requiring physician intervention. The automated linking process uses classification schemes and rule databases to match studies based on patient identity, acquisition date, and modality types, eliminating manual combination operations while ensuring accurate pairing of CT, PET, MRI, and other modalities.
2Reliability
If automatic linking is implemented, then diagnostic accuracy improves, but system complexity increases due to classification schemes and rule databases
Solution Approach 1:
The system divides the linking process into distinct segments: classification of studies by modality type, retrieval of linking rules from a database, matching of studies based on patient identity and acquisition date, and final linking of paired studies. This segmentation makes the complex automatic linking process manageable and maintainable through modular components.
Solution Approach 2:
A rule database serves as an intermediary between the classification scheme and the actual linking process. The rule database stores pre-defined criteria for matching studies from different modalities, acting as a mediator that translates classification information into specific linking decisions without requiring complex real-time computation.
3Ease of operation
If manual combination is used, then physician has control over study selection, but requires specific knowledge with respect to data retrieval and unique identifiers
Solution Approach 1:
The system automatically performs study selection and pairing without requiring physician knowledge of unique identifiers or data retrieval protocols. The automated process extracts patient identity and acquisition date information from study metadata, matches studies according to pre-defined rules, and creates links between complementary modalities without human intervention in the technical details.
Data Source
AI summary
A method and an apparatus are disclosed for automatically linking at least two medical studies which are associated to different acquisition modalities (CT/PET) and which are subject of post-processing in the context of hybrid imaging. In a preparation phase of at least one embodiment, there is defined a classification scheme according to pre-definable rules, conditions and attributes. In an execution phase for a selected source study of a first modality there is looked for at least one target study of a second modality of the same type according to the rules. Then, the source study is automatically linked to the at least one target study.