DICOM Metadata Retrieval via Common Value Segmentation
Solution Overview
Problem
Existing systems face challenges in efficiently retrieving and processing large amounts of DICOM metadata from storage mediums, especially when data is stored on third-party servers, leading to significant delays and high computational burdens due to the need to generate pre-combined metadata blobs, which is resource-intensive and time-consuming.
Innovation Solution
A method that loads a subset of DICOM instances, compares metadata to generate a common set of values and per-instance data, streams these values to the client, and continues loading and transmitting data in parallel, allowing for efficient metadata retrieval and reduction of redundant data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all DICOM instances are loaded and processed to generate complete metadata blobs, then metadata completeness is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the metadata into two distinct parts: common metadata that is identical across multiple DICOM instances, and instance-specific metadata that varies between instances. This segmentation allows the system to process and transmit only the necessary portions of metadata, reducing overall processing time while maintaining completeness.
Solution Approach 2:
The system performs partial processing by generating common metadata without requiring all DICOM instances to be fully loaded and processed first. It uses available instances to create the common portion and adds instance-specific metadata as needed, rather than waiting for complete data availability.
2Productivity
If pre-combined metadata blobs are generated for all DICOM instances, then metadata retrieval efficiency is improved, but computational burden and resource consumption increase
Solution Approach 1:
The metadata is divided into common and instance-specific portions, allowing the system to pre-process and store only the common metadata for efficient retrieval. Instance-specific metadata is processed on-demand, reducing the computational burden of pre-processing while maintaining retrieval efficiency for the common portion.
Solution Approach 2:
The common metadata serves multiple instances simultaneously, functioning as a universal template that can be applied to any instance in the series. This multi-functionality reduces the need to process and store separate complete metadata for each instance, lowering computational burden.
3Loss of information
If complete metadata is transmitted for all DICOM instances, then data accuracy is improved, but transmission time and network bandwidth consumption increase
Solution Approach 1:
The transmitted metadata is segmented into common metadata (transmitted once and applicable to all instances) and instance-specific metadata (transmitted separately for each instance). This segmentation reduces the total volume of data transmitted while ensuring that all necessary information for data accuracy is preserved.
Solution Approach 2:
Instead of transmitting complete metadata for each instance, the system transmits a copy of the common metadata once and then transmits only the unique instance-specific portions. This copying approach maintains data accuracy while significantly reducing transmission time and bandwidth consumption.
4Measurement precision
If metadata is processed sequentially for all DICOM instances, then processing accuracy is improved, but overall processing speed decreases
Solution Approach 1:
The processing is segmented into two parallel tracks: common metadata processing that can proceed with partial data availability, and instance-specific metadata processing that occurs sequentially for each instance. This segmentation allows the common portion to be processed faster while maintaining accuracy through subsequent instance-specific processing.
Solution Approach 2:
The system performs preliminary processing of common metadata using available instances before all instances are fully loaded. This preliminary action maintains processing accuracy by establishing a correct foundation that is later refined with instance-specific data, while improving overall processing speed by not waiting for complete data availability.
Data Source
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AI summary
A system and method of retrieving metadata, the method comprising: in response to a request from a client for metadata for a set of DICOM instances, loading n DICOM instances of the set of requested DICOM instances; comparing the metadata of the n loaded DICOM instances; generating a common set of values based on the comparison of the n loaded DICOM instances; streaming the common set of values to the client; determining perinstance data for each of the n loaded DICOM instances, the per-instance data being a difference between the metadata of a DICOM instance and the common set of values; transmitting the perinstance data of the loaded DICOM instances to the client; loading additional DICOM instances of the set of requested DICOM instances; determining perinstance data for each of the additional DICOM instances; and transmitting the perinstance data of the additional DICOM instances to the client.