Multimodal Specimen Imaging with X-ray and Optical Classification
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Solution Overview
Problem
Current breast-conserving surgery techniques face challenges in accurately assessing tissue margins due to the lack of comprehensive and practical methods for wide-field detection of malignant tissue, leading to high rates of second surgeries and slow turnaround times, as existing methods are either time-consuming or resource-intensive.
Innovation Solution
A multimodal imaging system combining micro-X-ray computed tomography (CT) and structured light imaging (SLI) with machine-learning-based classifiers to provide voxel-based CT images and optical images, enabling the creation of tissue-type maps for precise tissue classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If point-sampling methods (electrical impedance, diffuse reflectance, Raman spectroscopy) are used for margin assessment, then resource consumption is reduced, but detection coverage and speed are insufficient for wide field-of-view detection
Solution Approach 1:
The patent combines multiple imaging modalities (optical imaging, micro-CT, and surface imaging) into a unified system that processes the entire specimen simultaneously. This merging of techniques allows wide field-of-view detection without requiring multiple separate point-sampling operations, thus maintaining low resource consumption while achieving comprehensive coverage.
Solution Approach 2:
The system transitions from two-dimensional surface sampling to three-dimensional volumetric imaging through micro-CT scanning. This dimensional change enables comprehensive detection of the entire specimen volume, including deep tissue structures, without increasing resource consumption proportionally.
2Measurement precision
If touch-prep cytology or frozen section pathology is used for margin assessment, then diagnostic accuracy is improved, but processing time and resource intensity increase significantly
Solution Approach 1:
The system performs imaging and preliminary analysis of the entire specimen immediately after excision, before formal pathology processing. This preliminary action provides rapid margin assessment results that can guide surgical decisions without waiting for time-consuming histopathological processing.
Solution Approach 2:
The patent replaces mechanical and chemical pathology processing methods with non-invasive imaging techniques (optical imaging and micro-CT). This substitution eliminates the need for time-consuming tissue sectioning, staining, and microscopic examination while providing comparable or superior diagnostic information.
3Device complexity
If broad tissue categorization (normal vs. malignant) is used for analysis, then classification complexity is reduced, but tissue subtype discrimination capability is lost
Solution Approach 1:
The system applies different analysis methods and features to different tissue regions and subtypes. By preserving and analyzing local tissue characteristics (optical properties, density, texture) rather than averaging them into broad categories, the system maintains the ability to distinguish between adipose, fibroglandular, benign, and malignant tissue subtypes.
Solution Approach 2:
The patent creates a composite classification system that integrates multiple types of information (optical imaging data, micro-CT density data, texture analysis) to differentiate tissue subtypes. This composite approach provides rich tissue characterization without requiring simple binary categorization.
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 allows for rapid and accurate intraoperative assessment of breast tissue specimens, improving diagnostic accuracy and reducing the need for second surgeries by distinguishing between benign and malignant tissue subtypes with high spatial resolution and depth contrast.
Implementation Method 1
a micro-X-ray computed tomography (CT) unit adapted to provide voxel-based CT images of the specimen
Implementation Method 2
a structured light imaging (SLI) unit adapted to provide optical images obtained at a plurality of wavelengths, a plurality of structured light phases
Data Source
AI summary
A surgical specimen imaging system includes a micro-X-ray computed tomography (CT) unit for CT imaging of the specimen and a structured light imaging (SLI) unit for optical imaging at multiple wavelengths, multiple phase offsets, and multiple structured-light pattern periods including unstructured light. The system's image processing unit receives CT and optical images and is configured by firmware in memory to co-register the images and process the optical images to determine texture at multiple subimages of the optical images, determined textures forming a texture map. The texture map is processed by a machine-learning-based classifier to determine a tissue type map of the specimen, and the tissue type map is processed with the CT images to give a 3D tissue-type map. In embodiments, the firmware extracts optical properties including scattering and absorption at multiple wavelengths and the classifier also uses these properties in generating the tissue type map.