Automated Brain Tissue Viability Classification via Normalized Perfusion Maps
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Solution Overview
Problem
Current systems for generating perfusion-based tissue viability images in medical imaging, particularly for brain tissue, lack accuracy and efficiency in quickly identifying tissue classes, which hinders effective diagnosis and treatment in conditions like strokes.
Innovation Solution
A method that computes normalized perfusion maps using identified normal brain reference regions and perfusion parameters, followed by tissue classification using linguistic rules and fuzzy logic to generate synthetic brain tissue viability maps, enabling rapid and accurate identification of infarct, ischemic, and normal tissue classes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to generate perfusion-based tissue viability images, then the images can be generated, but the accuracy in identifying tissue classes is insufficient and the process is too slow for effective diagnosis
Solution Approach 1:
The patent replaces manual radiologist interpretation of multiple perfusion maps with an automated computer-based classification system. The system uses algorithmic processing of perfusion parameters (rCBF, CBV, MTT, PWI, TTP) to automatically categorize tissue into infarct, ischemic, and normal classes, eliminating the mechanical process of manual review while maintaining or improving accuracy and significantly increasing speed.
Solution Approach 2:
The patent transforms raw perfusion parameter values into normalized perfusion maps by comparing against patient-specific normal reference regions. This parameter transformation enables consistent tissue classification across different patients and imaging protocols. The normalization process adjusts perfusion values relative to individual patient baselines, creating standardized metrics that improve both accuracy and reproducibility of tissue viability assessment.
2Reliability
If multiple perfusion parameters are used to improve tissue viability assessment, then the diagnostic information is more comprehensive, but the complexity of processing and interpreting the data increases
Solution Approach 1:
The patent segments the complex perfusion data into distinct tissue classes (infarct, ischemic, normal) based on patterns across multiple perfusion parameters. Each parameter (rCBF, CBV, MTT, PWI, TTP) contributes specific information that helps differentiate tissue types. The segmentation approach organizes the complexity by creating clear, discrete categories that are easier to interpret while maintaining comprehensive diagnostic information.
Solution Approach 2:
The patent introduces normalized perfusion maps as an intermediary representation that simplifies the relationship between raw perfusion parameters and tissue classification. The normalization process creates a standardized intermediate form of the data that mediates between the complexity of multiple raw parameters and the need for clear tissue viability assessment. This intermediary layer reduces processing complexity while preserving diagnostic reliability.
3Measurement precision
If patient-specific normal reference regions are used to normalize perfusion values, then the accuracy is improved by accounting for individual variability, but the processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary identification and characterization of normal reference regions before the main tissue classification process. By pre-establishing the normal baseline for each patient using automated algorithms, the system prepares the normalization data in advance. This preliminary action reduces the computational burden during the main classification phase, as the normalization factors are already calculated, thereby decreasing overall processing time while maintaining patient-specific accuracy.
Data Source
AI summary
A computing system and method for generating, displaying and manipulating synthetic 2D and 3D brain tissue viability images and associated metrics from multiple perfusion maps derived from CT or other imaging system input image datasets. Stored linguistic tissue classification rules characterizing infarct, ischemic and/or normal or other tissue classes as a function of one or more perfusion parameters are used. The perfusion maps are normalized to normal brain tissue regions. Elements of the input image dataset are classified into one of the tissue classes as a function of the normalized perfusion maps and the classification rules. The classification includes ranking each of the plurality of tissue classes for elements of the input image dataset, and assigning one of the classes to elements of the dataset as a function of the ranks.