Rule-Based Image Display System for Multi-Modality Diagnostic Integration
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Solution Overview
Problem
The integration of diverse image data from advanced imaging modalities like CT, MRI, and PET into a form usable by diagnosticians is challenging due to format and display issues, leading to increased confusion and potential misdiagnosis.
Innovation Solution
A computer-based analytic framework that uses rule-derived methods to select, display, and integrate image data from various sources, employing DICOM metadata and Convolutional Neural Networks (CNNs) to enhance image content-based parameters for accurate anatomical and disease-based comparisons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple advanced imaging modalities (CT, MRI, PET) are integrated to provide comprehensive diagnostic information, then the completeness and accuracy of diagnostic data is improved, but the complexity of data integration and display increases
Solution Approach 1:
The patent segments the complex task of multi-modality image integration by creating separate functional modules: a study selection module that identifies relevant prior studies, an image retrieval module that fetches appropriate images, and a display module that presents them in organized hanging protocols. This segmentation allows each module to handle specific aspects of the integration challenge independently, reducing overall system complexity while maintaining comprehensive diagnostic capabilities
Solution Approach 2:
The patent introduces an intermediary computer system that acts as a mediator between the multiple imaging modalities and the diagnostician. This intermediary automatically retrieves, organizes, and displays images from different modalities (CT, MRI, PET, X-ray) according to predefined hanging protocols, eliminating the need for manual integration and reducing the complexity burden on the diagnostic workflow
2Reliability
If images from multiple studies are displayed simultaneously for comparison, then diagnostic accuracy is improved, but the likelihood of confusion between different patients' images increases
Solution Approach 1:
The patent applies local quality by organizing displayed images according to specific hanging protocols that are tailored to each diagnostic scenario and body region. Each protocol assigns specific images to specific display locations with clear labeling, ensuring that while multiple images are shown simultaneously, each image's origin and relevance are locally distinguished through standardized positioning and identification, preventing confusion between different patients' images
Solution Approach 2:
The system incorporates feedback mechanisms where the computer automatically selects and displays only those images that are relevant to the current diagnostic task based on the primary study being reviewed. The hanging protocol selection and image retrieval processes provide feedback loops that verify image relevance before display, reducing the risk of showing incorrect or unrelated images from different patients
3Adaptability or versatility
If traditional manual hanging protocols are used for organizing images, then flexibility in customization is maintained, but time consumption and efficiency are reduced
Solution Approach 1:
The patent implements preliminary action by pre-defining multiple hanging protocols that cover common diagnostic scenarios (chest, abdomen, spine, mammography, etc.). These protocols are prepared in advance with optimized image arrangements and can be automatically applied when a primary study is selected, eliminating the need for manual organization and significantly improving efficiency while maintaining adaptability through the availability of multiple pre-configured options
Solution Approach 2:
The system provides dynamic adaptability where hanging protocols can be automatically selected and adjusted based on the type of primary study being reviewed. The computer system dynamically retrieves appropriate protocols and modifies the display configuration to match the diagnostic needs, allowing flexibility to adapt to different clinical scenarios without requiring manual reconfiguration each time
Data Source
AI summary
The invention provides, in some aspects, a system for implementing a rule derived basis to display image sets. In various embodiments of the invention, the selection of the images to be displayed, the layout of the images, as well as the rendering parameters and styles can be determined using a rule derived basis. The rules are based on meta data of the examination as well as image content that is being analyzed by neuronal networks. In an embodiment of the present invention, the user is presented with images displayed based on their preferences without having to first manually adjust parameters.