Optoacoustic Imaging Parametric Map Fluence Compensation
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Solution Overview
Problem
Current optoacoustic imaging systems face challenges in accurately processing and presenting data due to issues like unwanted information, inaccurate scaling, and variability in light sources, which affect the quality of oxygenation and hemoglobin maps.
Innovation Solution
The system employs preprocessing techniques such as sinogram processing, fluence compensation, and image reconstruction methods to remove unwanted data, normalize energy, and compensate for variations, ultimately generating accurate parametric maps of oxygenation and hemoglobin levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional optoacoustic imaging methods are used, then imaging speed is maintained, but measurement precision and data accuracy deteriorate due to unwanted information and inaccurate scaling
Solution Approach 1:
The patent extracts and removes unwanted information from the optoacoustic data through preprocessing techniques. Specifically, it separates and eliminates artifacts, noise, and irrelevant signals from the raw data before further processing, thereby improving the accuracy of the final parametric maps without losing useful information.
Solution Approach 2:
The patent applies preliminary preprocessing actions to the optoacoustic data before main processing. This includes normalization, filtering, and compensation techniques performed in advance to correct scaling inaccuracies and remove unwanted information, ensuring that subsequent processing operates on cleaned and calibrated data.
2Manufacturing precision
If simple processing methods are used, then processing speed is maintained, but manufacturing precision and data quality deteriorate due to variability in light sources
Solution Approach 1:
The patent changes key processing parameters to improve data quality. It implements dynamic adjustment of normalization factors, filtering thresholds, and compensation parameters based on the specific characteristics of each dataset and light source variability, thereby achieving consistent results across different imaging conditions without requiring overly complex systematic changes.
Solution Approach 2:
The patent incorporates feedback mechanisms where processing parameters are adjusted based on the observed quality and characteristics of the input data. The system monitors data quality metrics and automatically adjusts preprocessing parameters to optimize the output, creating a self-correcting process that maintains precision without manual intervention.
3Measurement precision
If comprehensive preprocessing is applied, then measurement precision improves, but loss of time in data processing increases
Solution Approach 1:
The patent applies partial preprocessing actions selectively based on the specific needs of each dataset. Instead of applying all possible preprocessing techniques uniformly, it identifies and applies only the necessary corrections and filters required for each particular case, thereby reducing unnecessary processing time while maintaining the precision needed for accurate measurements.
4Reliability
If normalization techniques are applied, then reliability of data is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent implements universal normalization techniques that serve multiple functions simultaneously. The same preprocessing module performs normalization, scaling correction, and artifact removal in an integrated manner, rather than requiring separate dedicated systems for each function. This multi-functional approach improves reliability while minimizing the increase in overall system 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
The solution enhances the accuracy and reliability of optoacoustic imaging by improving data quality and consistency, leading to better representation of tissue composition and reducing artifacts.
Implementation Method 1
optoacoustic imaging system
Data Source
AI summary
Electromagnetic energy is deposited into a volume, an acoustic return signal from energy deposited in the volume is measured, and a parametric map that estimates values of at least one parameter as spatially represented in the volume is computed. A reference level of a region of interest is determined, and upper and lower color map limits are specified, with at least one of them being determined in relation to the reference level. The parametric map is then rendered in the palette of a color map by mapping the estimated values of the parametric map onto the color map according to the color map limits. Two wavelengths of energy can be applied to the volume, and the parametric map computation can be adapted by applying an implicit or explicit model of, or theoretical basis for, distribution of electromagnetic energy fluence within the volume pertaining to the two wavelengths. The actual electromagnetic energy fluence caused by each wavelength has a propensity, due to variability within the volume, to differ from the modeled or theoretical electromagnetic energy fluence.