Radiological Image Analysis Using Quantitative Feature Extraction
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Solution Overview
Problem
Current radiological practices are primarily qualitative and lack comprehensive quantitative analysis, failing to fully capture tumor morphology and behavior, with existing methods limited to dimensional measurements and averaging values over entire regions of interest, which are not predictive of therapeutic benefits.
Innovation Solution
A method for analyzing quantitative information from radiological images by identifying regions of interest, segmenting them, extracting a range of quantitative features, and creating radiological image records that include imaging and clinical parameters, enabling the generation of patient reports with diagnoses, prognoses, and treatment recommendations based on statistical relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If dimensional measurements (RECIST or 2D) are used for quantitative analysis, then measurement simplicity is improved, but measurement precision and ability to reflect tumor morphology complexity deteriorates
Solution Approach 1:
The patent segments the tumor region into multiple sub-regions or habitats based on texture, intensity, or other imaging features. This segmentation allows extraction of quantitative features from each sub-region, capturing the heterogeneity and complexity of tumor morphology while maintaining automated processing capability.
Solution Approach 2:
The patent transitions from one-dimensional (RECIST) or two-dimensional (2D) measurements to three-dimensional volumetric analysis. By extracting quantitative features throughout the entire tumor volume, the system captures morphological complexity in multiple dimensions, improving measurement precision without sacrificing operational feasibility through automated algorithms.
2Ease of operation
If averaging values over entire region of interest is used, then ease of analysis is improved, but loss of information about local tumor characteristics increases
Solution Approach 1:
Instead of averaging over the entire region of interest, the patent segments the ROI into multiple sub-regions and extracts quantitative features from each segment. This preserves local tumor characteristics while maintaining automated analysis through systematic processing of segmented regions.
Solution Approach 2:
The patent applies local quality by extracting and analyzing quantitative features from specific sub-regions or habitats within the tumor rather than treating the entire ROI uniformly. This allows different parts of the tumor to be characterized by their own specific features, preserving local information while enabling comprehensive analysis.
3Measurement precision
If comprehensive quantitative feature extraction is implemented, then measurement precision and predictive capability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary segmentation of the tumor into sub-regions or habitats before feature extraction. This preliminary action organizes the complex data structure in advance, making subsequent quantitative feature extraction more systematic and manageable, thereby reducing overall computational complexity while maintaining comprehensive analysis.
Solution Approach 2:
The patent extracts multiple quantitative features (such as texture, intensity, shape, and habitat characteristics) from segmented tumor regions. By changing from single-parameter to multi-parameter analysis, the system achieves improved measurement precision and predictive capability while using standardized computational methods to manage complexity.
4Ease of operation
If radiological practice remains qualitative, then ease of interpretation is improved, but loss of quantitative information for predictive analysis increases
Solution Approach 1:
The patent introduces quantitative feature extraction algorithms as an intermediary between qualitative radiological interpretation and predictive analysis. These algorithms automatically extract quantitative features from images, providing both quantitative data for prediction and maintaining the ability for qualitative interpretation by radiologists.
Solution Approach 2:
The patent replaces manual qualitative assessment with automated quantitative feature extraction using computational algorithms. This substitution preserves the interpretability needed for clinical decision-making while capturing quantitative information that enhances predictive capability for treatment response and prognosis.
Data Source
AI summary
An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.