Holographic Wavefield Imaging Decimation for Living Tissue
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Solution Overview
Problem
Existing digital imaging methods for living tissue and geophysical volumes face limitations in sampling efficiency and image quality, failing to optimize data processing and sampling ratios, which affects the resolution and cost-effectiveness of imaging processes.
Innovation Solution
The method involves obtaining wavefield data, selecting a holographic computational method such as Kirchhoff diffraction stacking or wavefield synthesis, decimating data subsets based on technical and business parameters, and generating new digital images with improved quality, while determining quantitative differences in sampling and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional digital imaging methods are used, then imaging can be performed, but sampling efficiency is low and image quality is limited
Solution Approach 1:
The patent applies parameter changes by systematically varying the sampling ratio (decimation factor) to optimize the balance between image quality and processing efficiency. Different decimation levels are tested to find the optimal parameter setting that achieves acceptable image quality with reduced data processing requirements.
Solution Approach 2:
The patent uses partial action by selecting data subsets at different decimation levels rather than processing complete datasets. This allows the system to achieve sufficient image quality with a portion of the available data, improving sampling efficiency while maintaining acceptable diagnostic standards.
2Manufacturing precision
If higher sampling ratios are used, then image resolution improves, but processing costs and time increase
Solution Approach 1:
The patent implements partial action by processing data subsets with different decimation factors rather than analyzing complete datasets. This approach achieves sufficient image resolution for diagnostic purposes while significantly reducing the computational time and resources required.
Solution Approach 2:
The system dynamically adjusts the decimation factor based on diagnostic requirements and processing constraints. The sampling ratio is not fixed but can be optimized in real-time to balance image resolution quality with processing speed, allowing flexible adaptation to different diagnostic scenarios.
3Measurement precision
If complete data sets are processed, then image quality is maximized, but computational resources and costs increase
Solution Approach 1:
The patent extracts essential diagnostic information by processing selected data subsets rather than complete datasets. By applying decimation to remove redundant data while preserving critical signal components, the system achieves acceptable image quality with reduced computational resource consumption and lower processing costs.
Solution Approach 2:
The system changes the processing parameter (decimation factor) to optimize the trade-off between image quality and computational cost. By systematically evaluating different decimation levels, the patent identifies the parameter setting that provides sufficient diagnostic quality with minimal computational resource expenditure.
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 enhances image quality and reduces costs by optimizing sampling ratios and processing efficiency, leading to improved resolution and cost savings in digital imaging applications.
Implementation Method 1
obtaining wavefield data representing recordings of a propagating wavefield through living tissue
Implementation Method 2
selecting a holographic computational method of imaging the wavefield data from a group consisting of the Kirchhoff diffraction stacking method
Implementation Method 3
selecting a holographic computational method of imaging the wavefield data from a group consisting of the Kirchhoff diffraction stacking method, the Kirchhoff wave front 'smear' method, wavefield synthesis
Data Source
AI summary
Methods of providing digital images of living tissue that may include: obtaining data of a propagating wavefield through living tissue; obtaining a reference digital image of the living tissue; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; and determining a quantitative difference measure between the reference digital image and the new digital image based on the changing of one or more parameters selected from the group consisting of field sampling, imaging sampling, and image quality.