Tissue Structure Measurement via Light Imaging and Raman Spectroscopy
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Solution Overview
Problem
Current methods for intra-operative diagnosis of tumour margins during tissue-conserving surgery, such as Mohs micrographic surgery and breast conserving surgery, face challenges with low accuracy and high costs due to the time-consuming nature of traditional histopathological examination and limited availability of skilled technicians, especially for detecting small or recurrent tumours.
Innovation Solution
A method combining ultraviolet light imaging and Raman spectroscopy to rapidly identify specific regions of interest within tissue samples, allowing for focused spectroscopic analysis and reducing the overall time required for diagnosis while maintaining accuracy, using auto-fluorescence imaging to prioritize sampling points for Raman spectroscopy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional histopathological examination methods are used for intra-operative diagnosis, then measurement precision and reliability are improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The tissue sample analysis is segmented into two distinct phases: first, rapid light imaging scans the entire sample to identify regions of interest; second, Raman spectroscopy is applied only to these specific regions for detailed molecular characterization. This segmentation allows the system to maintain high diagnostic accuracy through spectroscopic analysis while dramatically reducing overall diagnosis time by limiting spectroscopy to only necessary areas.
Solution Approach 2:
Light imaging is performed as a preliminary action before Raman spectroscopy. The light imaging step pre-identifies suspicious regions that require further analysis, preparing the sample for targeted spectroscopic examination. This preliminary screening eliminates the need for time-consuming full-sample spectroscopy while ensuring that all potentially problematic areas are captured for detailed analysis.
2Measurement precision
If traditional histopathological examination methods are used for intra-operative diagnosis, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The diagnostic process is divided into a rapid light imaging stage that covers the entire tissue sample, followed by a targeted Raman spectroscopy stage applied only to identified regions of interest. This segmentation enables the system to maintain the high diagnostic accuracy of traditional methods while significantly improving surgical throughput by reducing the time required for complete tissue analysis.
Solution Approach 2:
Light imaging serves as an intermediary technique between the surgeon and the final diagnosis. It provides a rapid overview of the tissue sample, guiding subsequent spectroscopic analysis to only the most suspicious areas. This intermediary step acts as a bridge that preserves diagnostic accuracy while enhancing surgical productivity by eliminating unnecessary spectroscopic measurements in normal tissue areas.
3Measurement precision
If comprehensive spectroscopic analysis is performed on entire tissue samples, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The tissue sample is conceptually segmented into normal and suspicious regions based on light imaging characteristics. Raman spectroscopy is then applied only to the segmented suspicious regions rather than the entire sample. This approach ensures that tumour detection accuracy is maintained by thoroughly analyzing all potentially problematic areas while avoiding time-consuming spectroscopy of clearly normal tissue.
Solution Approach 2:
Different analytical methods are applied to different regions of the tissue sample based on their suspected characteristics. Light imaging is applied globally to the entire sample for rapid overview, while Raman spectroscopy is applied locally only to regions identified as suspicious. This local quality approach ensures high detection accuracy in critical areas while minimizing overall analysis time by excluding normal regions from detailed spectroscopic examination.
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 fast and objective diagnosis of tumours like basal cell carcinoma and breast tumours, significantly reducing diagnosis time from hours to minutes, improving surgical efficiency and reducing healthcare costs by minimizing the need for extensive tissue preparation and skilled personnel.
Implementation Method 1
The sample area is illuminated with ultraviolet light and one or more images are taken of the sample area
Implementation Method 2
spectroscopic analysis, which typically takes an extended amount of time for each location, can be restricted to only locations that need to be measured
Data Source
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AI summary
The disclosure relates to measurement and classification of tissue structures in samples using a combination of light imaging and spectroscopy, in particular although not necessarily exclusively for detection of tumors such as basal cell carcinoma or breast tumors in tissue samples. Embodiments disclosed include a method of automatically identifying tissue structures in a sample, the method comprising the steps of: measuring (1702, 1703) a response of an area of the sample to illumination with light; identifying (1704) regions within the area having a measured response within a predetermined range; determining (1705) locations within the identified regions; performing (1706) spectroscopic analysis of the sample at the determined locations; and identifying (1707) a tissue structure for each region from the spectroscopic analysis performed on one or more locations therein.