Medical Image Processing Device Time Component Correlation
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Solution Overview
Problem
Current tissue analysis systems face difficulties in confirming long-term changes in deformable tissues, such as the heart, before and after treatment, making it challenging for doctors to determine the effectiveness of surgical operations.
Innovation Solution
A medical image processing device and method that acquires and classifies two-dimensional or three-dimensional image data based on defined time components, correlating image data from different time intervals to display changes over time, allowing for easier observation of deformable tissues before and after treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is acquired at multiple imaging times to analyze long-term changes in deformable tissues, then the ability to confirm long-term changes is improved, but the difficulty of comparing images at different time points increases due to short-term deformation
Solution Approach 1:
The patent segments the time component into two distinct parts: a first time component representing long-term changes (e.g., before and after treatment) and a second time component representing short-term periodic changes (e.g., cardiac cycle phases). This segmentation allows independent processing and correlation of long-term trends while accounting for short-term deformations, thereby improving the ability to confirm long-term changes without increasing comparison difficulty.
Solution Approach 2:
The patent introduces a two-dimensional time coordinate system where the first time component (long-term) and second time component (short-term periodic) form separate axes. Image data is organized and correlated along these two dimensions, allowing simultaneous visualization of both long-term progression and short-term cyclic variations. This dimensional approach transforms the complex comparison problem into a structured multi-dimensional analysis.
2Measurement precision
If image data is correlated based only on phase position, then short-term periodic changes are accounted for, but actual time differences between imaging sessions are ignored leading to inaccurate comparisons
Solution Approach 1:
The patent changes the time parameter representation by introducing dual time components: the first time component captures actual time intervals between imaging sessions (e.g., days or weeks apart), while the second time component captures phase positions within periodic cycles. This parameter transformation preserves both actual time information and periodic phase information, enabling accurate correlation that accounts for both temporal dimensions without losing critical time data.
3Measurement precision
If the first time interval between imaging sessions is reduced, then actual time consistency is improved, but the ability to observe long-term changes deteriorates
Solution Approach 1:
The patent implements a dynamic time correlation approach where the system adaptively handles variable time intervals between imaging sessions. The first time component flexibly accommodates any interval duration (from days to months), while the second time component dynamically adjusts to synchronize periodic phases regardless of the actual time elapsed. This dynamic structure allows long observation periods with varying interval lengths without sacrificing time consistency or long-term change detection capability.
Data Source
AI summary
A medical image processing device includes a port, a processor and a display. The port acquires a plurality of image data from a living body. The processor classifies the plurality of image dam to genes ate a plurality of image groups based OH a first time component. The first time component is defined by a first time interval among imaging times at which the plurality of image data are generated. The processor correlates each image data in one image groups with each image data in another image group, based on both an actual time and a time ratio of a second time component. The second time component is defined by a second time interval among the imaging times being shorter than the first time interval. The display displays images based on the plurality of image data based on the correlation of the image data in the image groups.