Spectro-Mechanical Tumor Imaging for Boundary and Depth Detection
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Solution Overview
Problem
Existing imaging systems for characterizing tumors within a solid body lack a systematic method for detecting the boundary between the tumor and surrounding tissue, leading to inaccuracies in size and depth estimation, particularly when the medium is thick or opaque, and do not effectively incorporate both mechanical and spectral imaging modalities.
Innovation Solution
A spectro-mechanical imaging (SMI) system that utilizes an inhomogeneity variance guided boundary detection method to measure mechanical properties such as size, depth, and stiffness, and spectral properties like absorption and scattering coefficients, using a combination of mechanical and spectral imaging modes with a multimodal sensing unit and compression-induced techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing imaging systems are used to characterize tumors, then basic imaging functionality is provided, but accurate boundary detection and size/depth estimation fail particularly when the medium is thick or opaque
Solution Approach 1:
The patent combines mechanical imaging and spectral imaging modalities into a unified spectro-mechanical imaging system. The mechanical imaging component captures tissue deformation characteristics while the spectral imaging component captures optical absorption and scattering properties. By merging these two modalities and applying inhomogeneity variance analysis to their combined data, the system achieves accurate tumor boundary detection even in thick or opaque media where individual modalities would fail.
Solution Approach 2:
The patent introduces inhomogeneity variance analysis as an intermediary computational method that processes imaging data to detect tumor boundaries. This intermediary approach transforms raw imaging data from multiple modalities into enhanced boundary information by analyzing spatial variations in tissue properties, enabling accurate size and depth estimation without requiring direct visualization of deep or opaque tumor regions.
2Reliability
If multi-modal imaging techniques are used to characterize tumors, then better tumor characterization is achieved, but systematic boundary detection method is lacking
Solution Approach 1:
The patent implements inhomogeneity variance analysis as a feedback mechanism that systematically processes imaging data to identify tumor boundaries. The method calculates variance in tissue inhomogeneity across the imaging field, uses this variance information to locate boundary regions, and iteratively refines boundary detection. This systematic feedback approach transforms multi-modal imaging data into reliable boundary detection results, solving the operational challenge of systematically determining tumor size and depth.
3Measurement precision
If measurement quality is maintained in thick or opaque media, then accurate tumor characterization is achieved, but existing systems fail when the medium is too thick or opaque
Solution Approach 1:
The patent creates a universal spectro-mechanical imaging system that functions effectively across varying tissue depths and optical properties. By integrating multiple imaging modalities (mechanical and spectral) and applying inhomogeneity variance analysis, the system adapts to different tissue conditions including thick and opaque media. The multi-functional approach allows the same system to reliably characterize tumors at various depths and in various tissue types, overcoming the limitation of existing systems that fail in thick or opaque media.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate estimation of tumor size, depth, and stiffness by systematically detecting tumor boundaries, improving characterization and extending capabilities for deeper tumors through mechanical and spectral imaging integration.
Implementation Method 1
compression-induced techniques
Implementation Method 2
elastic modulus of the target
Implementation Method 3
absorption and scattering coefficients
Implementation Method 4
scattering coefficients
Data Source
AI summary
A method, implemented on a machine having at least one processor, storage, and a communication platform capable of connecting to a network for characterizing a tumor embedded inside a medium, comprising: measuring at least one of a mechanical property and a spectral property of the tumor.