Spatiotemporal Image Subregion Mapping for Vascular Change Detection
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Solution Overview
Problem
Existing time-resolved imaging methods, such as digital subtraction angiography (DSA), struggle to accurately map changes in a region of interest due to the entire spatiotemporal mapping region being included, making it difficult to visually detect changes in complex anatomical objects like vasculature.
Innovation Solution
A method for providing a result data set that identifies a spatiotemporal subregion within the image data set, delimited by the region of interest and the start and end of the change, using manual, semi-automatic, or automatic techniques, enabling precise mapping of changes in the region of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the entire spatiotemporal mapping region is included in the difference image, then complete mapping of the object is achieved, but visual detection of changes in the region of interest becomes more difficult
Solution Approach 1:
The patent applies segmentation by dividing the entire spatiotemporal mapping region into multiple subregions, each corresponding to a specific region of interest. This allows the system to focus on and highlight changes within specific anatomical areas rather than displaying the entire field of view, thereby improving visual detection of changes while maintaining complete mapping capability.
Solution Approach 2:
The patent implements local quality by applying different processing or display characteristics to different subregions. Specifically, regions showing changes are highlighted or emphasized differently from static regions, allowing medical operatives to quickly identify areas of interest without being overwhelmed by the entire mapping region.
2Duration of action of moving object
If multiple filling images are acquired as a sequence in time and combined with the mask image, then dynamic changes over time are mapped, but the complexity of the image data increases
Solution Approach 1:
The patent extracts only the relevant temporal information for each region of interest by identifying the earliest start and latest end times of changes within each subregion. This extraction approach reduces the complexity of processing multiple time-resolved images while preserving the essential dynamic information needed for medical analysis.
Data Source
AI summary
A method for providing a result data set includes: acquiring a medical image data set of an object under examination that maps a change in the object under examination on a time-resolved basis, wherein the change starts and/or ends at different locations and at least partially different points in time within the object under examination. The method further includes identifying a spatiotemporal subregion in the image data set that maps the change in a region of interest of the object under examination, wherein the subregion is delimited spatially by the mapping of the region of interest and temporally by the earliest start and/or the latest end of the change mapped at the locations within the region of interest. The method further includes providing a result data set based on the subregion of the image data set, wherein the result data set maps the region of interest on a time-resolved basis.