Edge-Preserving Smoothing for Tissue State Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current spatially resolved mass spectrometry techniques for tissue analysis have limited spatial resolution and suffer from low signal-to-noise ratios, leading to inaccurate classification of tissue states due to the limitations of the matrix layer application and ionization processes in MALDI imaging, resulting in poor quality status images that fail to discern structures smaller than 5 micrometers and exhibit high classification errors.
Innovation Solution
The method involves acquiring spatially resolved mass spectra, generating multiple mass images from predetermined intervals, applying an edge-preserving smoothing algorithm to these images, and using a classification algorithm to calculate a status image that incorporates information from the environment, thereby enhancing signal quality and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spatially resolved mass spectra are acquired using MALDI imaging, then molecular information can be obtained from tissue sections, but the spatial resolution is limited and structures smaller than 5 micrometers cannot be discerned
Solution Approach 1:
The patent applies preliminary smoothing actions to the mass images before classification. By smoothing the mass images in advance using algorithms that suppress noise and enhance signals, the method prepares the data to overcome the inherent spatial resolution limitations of MALDI imaging, enabling discernment of structures at the limits of existing resolution capabilities
Solution Approach 2:
The patent introduces smoothing algorithms as intermediary processing steps between raw mass spectrum acquisition and tissue state classification. These algorithms act as mediators that process the raw data to enhance signal quality and suppress noise, thereby improving the effective spatial resolution and structure discernment capability without changing the physical measurement process
2Measurement precision
If mass spectra are measured with high signal requirements, then classification accuracy can be improved, but the signal-to-noise ratio remains low leading to high classification errors
Solution Approach 1:
The patent converts the harmful noise present in low signal-to-noise ratio mass spectra into beneficial enhanced signals through smoothing algorithms. By applying algorithms that suppress noise and enhance information-carrying signals, the method transforms the problematic low signal-to-noise condition into improved classification accuracy, turning the weakness into a strength
Solution Approach 2:
The patent changes the parameters of the mass images through smoothing processing. By adjusting smoothing parameters and applying edge-preserving smoothing algorithms, the method modifies the signal characteristics to enhance the signal-to-noise ratio while maintaining classification accuracy, effectively changing the data parameters to overcome the original measurement limitations
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 improves the spatial resolution and classification accuracy by suppressing noise and enhancing information-carrying signals, allowing for the discernment of histologically relevant structures and improved differentiation between tissue states, even at the limits of existing spatial resolution.
Implementation Method 1
spatially resolved mass spectra of a tissue section (1), preferably with MALDI time-of-flight mass spectrometers (MALDI=ionization by matrix assisted laser desorption)
Implementation Method 2
MALDI time-of-flight mass spectrometers
Data Source
AI summary
Spatially resolved tissue states (status image) are determined from spectrally resolved mass spectra of a tissue section by (a) acquiring a plurality of spatially resolved mass spectra of the tissue section, (b) generating at least two mass images from the spatially resolved mass spectra, (c) smoothing the mass images using an edge-preserving smoothing algorithm and (d) calculating a status image from the smoothed mass images by means of a classification algorithm derived from mathematical statistics.