Four Quadrant Mapping for MRI Cancer Detection
Solution Overview
Problem
Current methods for analyzing multi-dimensional MRI data lack the ability to effectively identify specific signals for prostate cancer and correlate with Gleason scores, limiting their diagnostic accuracy and ability to differentiate between cancerous and benign tissues.
Innovation Solution
The proposed system employs a four quadrant and matrix analysis technique that combines T2-weighted and diffusion-weighted imaging data to generate novel quantitative MR features, using vector representations of image voxels in a 3-dimensional or higher parameter space to identify cancerous tissues and determine Gleason grades, by analyzing the interdependence of T2 and apparent diffusion coefficient (ADC) through a hybrid multidimensional MRI (HM-MRI) approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional ADC or T2 imaging methods are used, then the imaging process is simple, but the diagnostic accuracy and ability to differentiate cancerous from benign tissues is limited
Solution Approach 1:
The patent applies four quadrant mapping to transform multi-dimensional MRI data into a two-dimensional plot where each voxel is represented by angle and distance values. This dimensional transformation enables differentiation of cancerous from benign tissues by visualizing data distribution patterns in quadrants, thereby improving diagnostic accuracy while maintaining manageable complexity through graphical representation
Solution Approach 2:
The patent transforms conventional imaging parameters (ADC and T2 values) into new derived parameters (angle values and distance values) through mathematical relationships. This parameter transformation creates novel quantitative features that enhance the ability to identify cancerous tissues and correlate with Gleason scores, resolving the contradiction between measurement precision and complexity
2Loss of information
If multi-dimensional MRI data analysis is performed, then more information is obtained, but the ability to identify specific cancer signals is insufficient
Solution Approach 1:
The patent segments the continuous multi-dimensional data space into four distinct quadrants based on angle values. This segmentation allows specific cancer-related signals to be isolated and identified within particular quadrants, improving cancer detection precision while retaining comprehensive information through the structured division of the data space
Solution Approach 2:
By transforming ADC and T2 parameters into angle and distance parameters, the patent creates a new parameter space where cancerous tissues exhibit distinct distributions. This parameter transformation enhances signal specificity for cancer detection while preserving the underlying information through bijective mathematical relationships
3Measurement precision
If traditional Gleason scoring is used, then the grading process is straightforward, but the differentiation between cancer grades is limited
Solution Approach 1:
The patent adds a graphical dimension to Gleason scoring by plotting voxels in four quadrants and analyzing distance value distributions. This visual representation correlates distance metrics with Gleason grades, improving grading precision while maintaining ease of operation through intuitive graphical analysis rather than complex calculations
Data Source
AI summary
A diagnostic system to analyze imaging data includes a memory configured to store hybrid imaging data of a tissue sample. The system also includes a processor operatively coupled to the memory and configured to generate a four quadrant plot based on the hybrid imaging data. Each point in the four quadrant plot corresponds to an image voxel of the tissue sample. The processor is also configured to determine one or more angle values and one or more distance values for image voxels in the four quadrant plot. The processor is further configured to identify one or more characteristics of the tissue sample based at least in part on the one or more angle values and the one or more distance values. The processor is further configured to perform a matrix analysis of the data, which can be used to identify the one or more characteristics of the tissue sample.