Optoacoustic Data Preprocessing for Parametric Map Accuracy
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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 variations in instrument and tissue characteristics, which affect the quality of optoacoustic maps and images.
Innovation Solution
The system employs preprocessing techniques such as sinogram processing, including bad transducer detection, common mode stripe filtering, band pass filtering, normalization, and fluence compensation to enhance the accuracy and reliability of optoacoustic data, allowing for the creation of parametric maps that co-register with ultrasound images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If preprocessing techniques such as sinogram processing, filtering, and normalization are applied, then the accuracy and reliability of optoacoustic data improve, but the complexity of the data processing system increases
Solution Approach 1:
The preprocessing system is divided into distinct functional modules: bad transducer detection module, common mode stripe filtering module, band pass filtering module, normalization module, and fluence compensation module. Each module handles a specific aspect of data processing, making the complex preprocessing task manageable and systematic while improving overall data accuracy.
Solution Approach 2:
The system performs preliminary detection and removal of bad transducers before main image reconstruction, applies fluence compensation to correct systematic errors before parametric map generation, and uses preprocessing filters to eliminate unwanted information early in the processing chain. This preliminary action prevents propagation of errors through subsequent processing steps.
2Reliability
If multiple preprocessing steps including bad transducer detection and fluence compensation are implemented, then the quality of parametric maps improves, but the processing time increases
Solution Approach 1:
Bad transducer detection is performed as a preliminary step before main processing to identify and exclude defective transducers early, preventing wasted computation on invalid data. Fluence compensation is applied in advance of parametric map generation to correct systematic errors, ensuring higher quality results without requiring iterative recalibration during final processing.
Solution Approach 2:
The system extracts and removes unwanted information from sinogram data through dedicated filtering operations. Common mode stripe filtering extracts and removes systematic artifacts, while band pass filtering extracts only the relevant frequency components, discarding noise and irrelevant signals. This selective extraction improves quality by focusing computational resources on meaningful data.
3Measurement precision
If band pass filtering and normalization are applied to sinogram data, then unwanted information is removed and data accuracy improves, but the device complexity increases
Solution Approach 1:
The filtering process is segmented into distinct frequency bands using band pass filters, each targeting specific frequency ranges relevant to optoacoustic signals. This segmentation allows selective enhancement of useful frequency components while suppressing noise and artifacts, improving data accuracy through systematic frequency-domain processing.
Solution Approach 2:
The system applies normalization to change the scale and distribution of sinogram data parameters, transforming raw amplitude values into standardized metrics that facilitate accurate parametric map generation. This parameter transformation improves measurement precision by ensuring consistent data ranges across different transducers and measurement conditions.
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 quality and accuracy of optoacoustic data processing, enabling the generation of reliable oxygenation, hemoglobin, and masked oxygenation maps, enhancing the diagnostic capabilities of optoacoustic imaging systems.
Implementation Method 1
system and method for producing parametric maps of optoacoustic data
Data Source
AI summary
A method is disclosed for creating and outputting a masked parametric map (e.g., hemoglobin oxygenation) that reflects parameters in a first parametric map (e.g., relative oxygenation) and second parametric map (e.g., relative hemoglobin). In an illustrative embodiment, the method comprises steps of generating a first parametric map, generating a second parametric map, and then generating a masked parametric map that reflects parameters in the first and second parametric maps. The masked map may present information not readily apparent from the first parametric map and the second parametric map, and not obtainable from the first and second parametric maps independently. The first parametric map may be based upon portions of two optoacoustic images created using differing wavelengths of light. The first parametric map is reflective of areas within the volume of tissue that have a differing response to the longer wavelength light event compared to the shorter one. The second parametric map is reflective of areas within the volume of tissue that have a stronger response to the longer and shorter wavelength light events than the surrounding areas. A masked parametric map is output which is reflective of a combination of information in the first and second parametric maps. In an embodiment, the masked parametric map is generated by generating a mask reflective of a combination of information in the first and second parametric maps, and applying the mask to one of the first or second parametric maps to form the masked parametric map. In an embodiment, one or more of the parametric maps is coregistered with, and overlayed on an ultrasound image of the same volume of tissue before being output.