Large Image Manager for Centralized PACS Data Access
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Solution Overview
Problem
Current medical image management systems face challenges in integrating and managing large medical image data items generated by different imagers, often resulting in isolated workflows and limited access to holistic patient images, leading to inefficiencies and potential oversights in clinical practices.
Innovation Solution
A system comprising a Picture Archiving and Communication System (PACS) with a large image manager component that identifies, stores, and transmits representative data objects for large medical image data items, allowing for centralized access and management across various imaging formats and protocols, thereby facilitating integration and efficient data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large medical image data items are stored and managed in a centralized PACS system, then data accessibility and integration are improved, but system complexity and processing burden increase
Solution Approach 1:
The system segments large medical image data items into multiple smaller data sets that can be individually managed and processed. Each data set represents a portion of the complete image, allowing the system to handle large images without overwhelming the PACS infrastructure while maintaining centralized access and integration capabilities.
Solution Approach 2:
The system introduces a new dimensional approach by implementing a hierarchical storage architecture where complete large images are stored in a specialized large image storage facility while the PACS maintains references and metadata. This dimensional separation allows centralized management without concentrating all data in the core PACS system.
2Measurement precision
If complete large medical images are transmitted to viewing stations, then image quality and diagnostic accuracy are improved, but data transmission time and network bandwidth consumption increase
Solution Approach 1:
The system implements partial action by transmitting only the necessary portions of large medical images to viewing stations. Based on diagnostic requirements and user interactions, the system selectively transmits relevant data sets rather than complete images, reducing transmission time and network bandwidth consumption while maintaining diagnostic accuracy when full images are ultimately required.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing large medical images into structured data sets with metadata before transmission. This preliminary organization allows for efficient selective transmission of only the necessary portions, reducing actual transmission time while ensuring diagnostic quality is maintained when complete images are needed.
3Ease of manufacture
If multiple independent workflows are used for different imaging procedures, then workflow specialization is improved, but system integration and holistic patient view are worsened
Solution Approach 1:
The system implements universality by creating a unified large image storage and management framework that serves multiple imaging workflows simultaneously. Different imaging procedures (pathology, radiology, cardiology) can all access and integrate their respective large images through this common infrastructure, maintaining workflow specialization while achieving system-level integration and holistic patient view.
Solution Approach 2:
The system merges multiple independent workflows by implementing a centralized large image storage facility that all workflows can access. While each workflow maintains its operational independence and specialization, the unified storage layer combines data from all sources, enabling integrated patient care and holistic imaging views across different medical disciplines.
Data Source
AI summary
A system and method for managing medical image data items. Pixel data and metadata are identified in the medical image data items. A plurality of pixel objects are generated using the pixel data. The plurality of pixel objects are stored in a first storage memory. A plurality of representative objects are generated using the metadata. The representative objects are stored in a second storage memory. The representative objects may include overview objects and representative metadata objects. The representative objects can be generated in a DICOM-compatible format suitable for PACS workflows.