Super-resolution biomarker map generation via 3D matrix convolution
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Solution Overview
Problem
Current clinical imaging methods are unable to reliably characterize tumor heterogeneity, which is a major factor in cancer treatment failure, as they lack the capability to effectively differentiate between inter-tumor and intra-tumor variations in cellular morphology, gene expression, metabolism, proliferation, and metastatic potential.
Innovation Solution
A method and system that utilize image computing units to process medical image data, creating three-dimensional matrices, applying matrix operations, and employing convolution algorithms to generate super-resolution biomarker maps (SRBMs) that provide enhanced resolution and detailed biomarker information, facilitating the characterization of tumor heterogeneity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional clinical imaging methods are used, then imaging acquisition is simple and fast, but the ability to characterize tumor heterogeneity and differentiate between inter-tumor and intra-tumor variations is insufficient
Solution Approach 1:
The patent segments the tumor into multiple regions of interest (ROIs) based on biomarker expression patterns. By dividing the tumor into distinct spatial zones with different molecular characteristics, the system enables precise characterization of intra-tumor heterogeneity while maintaining manageable processing complexity through automated segmentation algorithms
Solution Approach 2:
The patent transitions from conventional two-dimensional imaging to three-dimensional volumetric analysis by integrating multiple imaging modalities (MRI, CT, PET) and adding temporal dimension through longitudinal tracking. This multi-dimensional approach enables comprehensive characterization of tumor heterogeneity across space and time without proportionally increasing operational complexity
2Measurement precision
If high-resolution biomarker mapping is achieved through multi-modal image processing, then tumor heterogeneity characterization is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary processing steps including automated image registration, segmentation, and feature extraction before final biomarker map generation. By completing preparatory computations in advance and using pre-defined processing pipelines, the system reduces real-time processing requirements and accelerates final analysis without compromising resolution
Solution Approach 2:
The patent implements continuous iterative refinement where biomarker maps are progressively improved through multiple processing stages. The system continuously adjusts and refines results based on feedback from different imaging modalities, maintaining productive computation flow throughout to minimize idle time while achieving high-resolution output
3Measurement precision
If invasive biopsies are performed to obtain detailed tissue information, then diagnostic accuracy is high, but patient morbidity and treatment disruption increase
Solution Approach 1:
The patent uses non-invasive imaging modalities (MRI, CT, PET) as intermediaries to indirectly detect tissue biomarkers. By employing contrast agents and advanced image processing algorithms, the system translates imaging signals into accurate biomarker maps without requiring physical tissue sampling, thus eliminating biopsy-related morbidity while maintaining diagnostic precision
Solution Approach 2:
The patent replaces the mechanical biopsy procedure with a computational imaging system that uses mathematical models and algorithmic analysis to extract biomarker information from imaging data. This substitution eliminates the invasive mechanical process of tissue extraction while achieving equivalent or superior diagnostic accuracy through multi-modal image fusion and machine learning techniques
Data Source
AI summary
A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.