Medical Image Data Retrieval Interface Using Hierarchical Segmentation
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Solution Overview
Problem
Current medical imaging technologies face challenges in rapidly visualizing large multi-dimensional data sets, leading to information overload and inefficiencies in data retrieval, particularly with conventional processing techniques struggling to handle the increasing sizes of medical volume data sets.
Innovation Solution
A data retrieval interface system that allows for the selection of arbitrary extent and resolution in multiple dimensions, using a Physical region object to define a multi-dimensional region of interest and dynamically determine a sampling grid, enabling efficient retrieval and transmission of relevant image data, independent of client hardware and network bandwidth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional processing techniques are used to handle large medical volume data sets, then data retrieval can be performed using standard methods, but visualization speed becomes unacceptably slow and information overload occurs
Solution Approach 1:
The patent segments the large medical volume data set into multiple smaller data blocks organized in a hierarchical structure. This segmentation allows the system to retrieve and process only the specific blocks needed for current visualization, rather than handling the entire data set, thereby improving visualization speed while managing large data volumes efficiently.
Solution Approach 2:
The patent introduces a hierarchical organization dimension to the data structure, creating multiple levels of data representation from coarse to fine detail. This additional organizational dimension enables progressive loading and rendering of data at appropriate resolution levels, significantly improving visualization performance on large data sets without overwhelming the system.
2Measurement precision
If full resolution data is retrieved for diagnostic purposes, then image quality is sufficient for diagnosis, but data transmission time and network bandwidth requirements increase
Solution Approach 1:
The patent implements local quality by retrieving data at different resolution levels for different regions of the volume data set. Regions of interest that require diagnostic precision are retrieved at full resolution, while other regions are retrieved at lower resolutions or aggregated forms, thereby reducing overall data transmission time while maintaining sufficient image quality for diagnostic purposes.
Solution Approach 2:
The patent applies partial action by retrieving only the specific portions of the data set needed for current diagnostic tasks rather than the entire data set at full resolution. The hierarchical structure enables selective retrieval of relevant data blocks at appropriate resolution levels, reducing transmission time while providing sufficient detail where needed.
3Adaptability or versatility
If interactive visualization of multi-dimensional data sets is enabled, then diagnostic flexibility is improved, but processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-organizing the volume data into a hierarchical block structure with pre-computed aggregation levels during data ingestion. This preliminary organization enables fast interactive querying and visualization by allowing the system to quickly assemble relevant data blocks at appropriate resolution levels without performing complex processing during interactive sessions, thereby improving diagnostic flexibility while managing processing complexity.
Data Source
AI summary
Data retrieval systems for retrieving data from a multidimensional medical data set include: (a) a client configured to electronically request image data of a patient; (b) a server in communication with a plurality of electronically stored multidimensional patient medical image data sets; and (c) a data retrieval interface in communication with the client and the server. The data retrieval interface is configured to retrieve image data from the multidimensional data sets. The respective data sets have a number of grid points in G dimensions and a number of values V for each grid point. Some of the data sets have different G dimensions and V values than others. The data retrieval interface is configured to employ an object oriented retrieval process. The client can employ and Image region object to request data and the interface can employ a Physical region object that defines a multi-dimensional region extent associated with a client request for data on a region of interest to retrieve relevant image data from a respective patient data set. Related signal processor circuits, computer programs and data structures for data retrievals are also described.