MRI Image Fusion Processor for Tissue Differentiation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for fusing and synthesizing medical images acquired with multiple scanning parameters lack flexibility and intuitiveness, as they apply preset solutions regardless of the clinician's preferences, failing to effectively differentiate tissue types.
Innovation Solution
A system and method that allows for the selective combination of visual enhancements from multiple MRI datasets using user-defined parameters, incorporating registration, segmentation, and intensity homogeneity correction processes to generate a fused response image that can include color- and intensity-enhanced regions of interest, enabling personalized image fusion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If preset fusion parameters are applied to all datasets, then automation is improved, but adaptability to clinician needs deteriorates
Solution Approach 1:
The system implements dynamic adaptability by allowing clinicians to modify fusion parameters interactively. The fusion process transitions from static preset parameters to dynamic user-adjustable parameters, enabling the system to adapt to different clinician preferences and clinical scenarios while maintaining automated processing capabilities.
Solution Approach 2:
The system enables clinicians to self-customize fusion parameters according to their specific needs and preferences. By providing intuitive interfaces for parameter adjustment, the system allows users to tailor the fusion output without requiring complex programming or technical expertise, thus balancing automation with adaptability.
2Loss of information
If multiple imaging parameters are combined, then information completeness is improved, but image complexity deteriorates
Solution Approach 1:
The system segments different imaging parameters (e.g., ADC values, T2-weighted images, DCE-MRI) into distinct input datasets that are processed separately before fusion. This segmentation allows comprehensive information to be integrated while maintaining organized, manageable processing streams that reduce overall system complexity.
Solution Approach 2:
The fusion processor is designed as a universal system that can handle multiple types of imaging parameters through standardized processing routines. This multi-functionality approach allows diverse data types to be combined without requiring separate complex processing paths for each parameter type.
3Measurement precision
If comprehensive tissue differentiation is achieved, then diagnostic accuracy is improved, but processing time deteriorates
Solution Approach 1:
The system performs preliminary processing of individual imaging parameters before fusion, including normalization, registration, and pre-computation of tissue characteristics. This preliminary action prepares the data in advance, enabling faster final fusion processing while maintaining comprehensive tissue differentiation capabilities.
Solution Approach 2:
The fusion process applies different processing strategies to different regions of interest within the images. By focusing computational resources on areas with pathological significance and using optimized algorithms for specific tissue types, the system achieves high diagnostic accuracy without uniformly processing entire datasets, thus reducing overall processing time.
Data Source
AI summary
This invention provides a system and method for fusing and synthesizing a plurality of medical images defined by a plurality of imaging parameters allowing visual enhancements of each image data set to be combined. The system provides an image fusion process/processor that fuses a plurality of magnetic resonance imaging datasets. A first image dataset of the datasets is defined by apparent diffusion coefficient (ADC) values. A second image dataset of the MRI datasets is defined by at least one parameter other than the ADC values. The image fusion processor generates a fused response image that visually displays a combination of image features generated by the ADC values combined with image features generated by the at least one parameter other than the ADC values. The fused response image can illustratively include at least one of color-enhanced regions of interest and intensity-enhanced regions of interest.