3D Spectral Tomosynthesis for Lesion Characterization
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Solution Overview
Problem
Current spectral X-ray imaging techniques face challenges in differentiating between cystic and solid lesions, as well as malignant and benign micro-calcifications, due to overlapping spectral information, which hampers accurate tissue characterization in mammography.
Innovation Solution
An apparatus and method that utilize tomosynthesis medical data with spectral information from multiple photon energy levels to determine the material composition inside and outside a delineated feature boundary, allowing for precise characterization by combining depth information with spectral data to differentiate between features with overlapping spectral data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If spectral X-ray imaging is used to differentiate tissue types, then tissue characterization capability is improved, but measurement precision deteriorates due to overlapping spectral information
Solution Approach 1:
The patent transitions from 2D spectral mammography to 3D spectral tomosynthesis by acquiring images at multiple angles and reconstructing volumetric data. This additional spatial dimension allows separation of overlapping spectral signals from different tissue depths, improving measurement precision while maintaining tissue characterization capability
Solution Approach 2:
The patent segments the breast tissue into multiple volumetric slices or layers through tomosynthesis reconstruction. This segmentation allows independent spectral analysis of each layer, reducing spectral overlap from adjacent tissues and improving the precision of material composition determination
2Device complexity
If assumptions about body part characteristics are made for spectral discrimination, then analysis complexity is reduced, but characterization accuracy deteriorates
Solution Approach 1:
The patent determines material composition by solving systems of equations using spectral data from multiple energy levels without requiring priori assumptions about tissue properties. The system dynamically adapts to actual tissue characteristics present in the image data, improving characterization accuracy while maintaining manageable complexity through automated computational methods
3Adaptability or versatility
If spectral data from multiple energy levels is used, then tissue differentiation capability is improved, but device complexity increases
Solution Approach 1:
The patent employs a dual-energy or multi-energy X-ray system that can operate in multiple modes (spectral mammography, spectral tomosynthesis, and combined modes). This multi-functional approach enables tissue differentiation using the same hardware platform, improving versatility without proportionally increasing device complexity
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
This approach enables more accurate differentiation between tissue types, reducing the need for additional assumptions and improving the characterization of features embedded in other tissue, particularly in breast tomosynthesis imaging.
Implementation Method 1
image data associated with a plurality of rays of radiation that have passed through the body part, wherein the image data comprises spectral data associated with at least two photon energy levels
Implementation Method 2
spectral absorption characteristics differ measurably
Data Source
AI summary
The present invention relates to an apparatus for characterization of a feature in a body part. It is describe to provide (210) tomosynthesis medical data comprising a plurality of images of the body part, wherein the plurality of images comprise image data associated with a plurality of rays of radiation that have passed through the body part, wherein the image data comprises spectral data associated with at least two photon energy levels of the plurality of rays of radiation, wherein the medical data comprises data of the feature. A delineated boundary of the feature is determined (220). At least one material composition of the body part inside the delineated boundary is determined (240) comprising a function of the spectral data inside the delineated boundary. The feature is characterised (250) as a function of the at least one material composition inside the delineated boundary of the feature. Data representative of the feature is output (260).