Medical Image ROI Assembly for Unified Multi-Phase Rendering
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Solution Overview
Problem
Existing standard volume rendering algorithms are unable to calculate a usable rendering of different sets of medical images covering the same area in a single view, making it difficult to visualize all anatomical structures revealed in multiple image sets simultaneously.
Innovation Solution
A computer-implemented method for single rendering of multiple medical image sets, involving segmentation, intersection, and assembly of regions of interest to create a unified image set, using formulas for mask computation and voxel assignment to integrate distinct image sets into a single view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard volume rendering algorithms are used to render each set of medical images separately, then each set can be visualized with appropriate contrast and detail, but it becomes impossible to view all anatomical structures from multiple sets in a single view
Solution Approach 1:
The patent segments each set of medical images into regions of interest (ROIs) by extracting anatomical structures that are specifically visible in each phase. This segmentation allows the system to process and combine only the relevant portions from different image sets, enabling multi-phase visualization without overwhelming complexity.
Solution Approach 2:
The patent merges the segmented regions of interest from multiple medical image sets into a unified single set of images. By combining the extracted ROIs from different phases (e.g., pre-contrast, arterial, venous) into one integrated dataset, the system enables simultaneous visualization of all anatomical structures in a single view.
2Productivity
If regions of interest are extracted and assembled from multiple image sets, then a unified view of all anatomical structures is achieved, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary extraction and segmentation of regions of interest from each medical image set before the final assembly. By pre-processing and identifying ROIs in advance, the system reduces the computational burden during the final rendering step, improving overall processing efficiency.
Solution Approach 2:
The patent extracts only the necessary regions of interest from each complete medical image set, removing unnecessary data before assembly. This extraction approach minimizes the amount of data that needs to be processed and combined, reducing computational time while preserving all essential anatomical information.
3Loss of information
If all medical image sets are combined into a single rendering, then comprehensive anatomical visualization is achieved, but the ability to distinguish structures from different phases is lost
Solution Approach 1:
The patent applies local quality by maintaining phase-specific characteristics within the unified rendering. Each region of interest retains its original phase information and visual properties, allowing the system to distinguish between structures from different phases (e.g., arterial vs. venous) while presenting them in a single integrated view.
Solution Approach 2:
The patent uses phase labels or markers as intermediary elements that track the origin of each region of interest from different image sets. These intermediaries enable the system to maintain phase-specific identification within the unified rendering, preventing loss of phase information while achieving comprehensive visualization.
Data Source
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AI summary
The disclosure notably relates to a computer-implemented method for single rendering at least two sets of medical images of a patient. The at least two sets of medical images cover an area of the patient. The method comprises obtaining (S10) the at least two sets of medical images. Each set of medical images covers one or more respective regions of interest. The method comprises extracting (S20) the one or more respective regions of interest of each of the obtained at least two sets of medical images. The method comprises assembling (S30) the extracted respective regions of interest into a single set of images. Such a method forms an improved solution for single rendering sets of medical images.