Medical Image Set Assembly for Unified Anatomical Rendering
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Solution Overview
Problem
Existing standard volume rendering algorithms are unable to effectively combine different sets of medical images covering the same area of a patient, acquired at different phases, to provide a unified view of anatomical structures.
Innovation Solution
A computer-implemented method for single rendering of multiple medical image sets, involving segmentation, intersection, and assembly of regions of interest, using binary masks and voxel grids to create a unified image set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If standard volume rendering algorithms are used to render multiple sets of medical images, then each set can be visualized individually, but different anatomical structures are revealed in separate views making it difficult to see all structures in a single view
Solution Approach 1:
The method segments different sets of medical images by creating separate binary masks for each set, identifying regions of interest in each image set independently. This segmentation allows the system to track and combine anatomical structures from multiple phases while maintaining their individual identities, ultimately enabling all structures to be visualized in a single integrated view.
Solution Approach 2:
The method merges multiple binary masks representing different anatomical structures from different image sets into a single combined mask. This merging process integrates regions of interest from all input image sets, allowing the volume rendering algorithm to display all anatomical structures from multiple phases in a single unified view rather than requiring separate views for each set.
2Loss of information
If multiple sets of medical images are acquired at different phases to cover the same area, then more anatomical structures can be observed, but the complexity of processing and rendering these multiple sets increases
Solution Approach 1:
The method extracts regions of interest from each medical image set by generating binary masks that isolate anatomical structures from background and other tissues. This extraction process simplifies the data by focusing only on relevant anatomical features, reducing the complexity of processing multiple full-volume image sets while maintaining complete coverage of all anatomical structures across different phases.
Solution Approach 2:
The method changes the parameter representation of medical images by converting continuous grayscale image data into binary mask format (0 or 1 values). This parameter transformation simplifies the data structure, making it easier to process and combine multiple image sets. The binary representation allows for efficient logical operations (intersection, union, difference) when merging masks from different phases, reducing overall processing complexity.
3Ease of manufacture
If standard volume rendering is applied to each image set separately, then rendering computation is straightforward, but the resulting multiple views do not provide a unified representation of all anatomical structures
Solution Approach 1:
The method performs preliminary actions by pre-processing each image set to generate binary masks that identify and label regions of interest before the final rendering step. This preliminary segmentation and mask creation simplifies the subsequent rendering process, as the volume rendering algorithm only needs to process the already-segmented masks rather than analyzing raw image data from multiple phases. This preliminary preparation enables the unified representation of all anatomical structures in a single view.
Data Source
AI summary
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 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 the one or more respective regions of interest of each of the obtained at least two sets of medical images. The method comprises assembling 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.