Optoacoustic Sinogram Preprocessing for Tissue Differentiation
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Solution Overview
Problem
Current medical imaging technologies face challenges in effectively processing and presenting optoacoustic data, particularly in removing unwanted information and enhancing image quality for accurate tissue characterization, especially in differentiating between malignant and benign breast tumors.
Innovation Solution
The system employs a combined optoacoustic and ultrasound platform that processes sinograms through preprocessing steps such as bad transducer detection, common mode stripe filtering, band pass filtering, and fluence compensation to produce high-quality images, including oxygenation and hemoglobin maps, which are then coregistered with ultrasound images for enhanced diagnostic capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optoacoustic data is processed through multiple preprocessing steps (bad transducer detection, common mode stripe filtering, band pass filtering, fluence compensation), then image quality and tissue characterization accuracy are improved, but processing complexity and computational time increase
Solution Approach 1:
The preprocessing pipeline is segmented into distinct modular steps: bad transducer detection, common mode stripe filtering, band pass filtering, and fluence compensation. Each module handles a specific aspect of data cleaning and enhancement, making the complex processing manageable and systematic while maintaining high image quality through targeted interventions at each stage.
Solution Approach 2:
Bad transducer detection and common mode stripe filtering are performed as preliminary actions before main image reconstruction. This removes unwanted information and artifacts early in the processing chain, preventing them from propagating through subsequent steps and reducing the computational burden on later processing stages.
2Reliability
If multiple preprocessing filters and compensation techniques are applied to optoacoustic data, then diagnostic accuracy for differentiating malignant and benign tumors is improved, but processing time and computational resources increase
Solution Approach 1:
The preprocessing operations are designed to flow continuously through the data stream without interruption. Each filtering and compensation step processes the output of the previous step in a continuous manner, maintaining diagnostic information integrity while optimizing computational efficiency through streamlined data flow and avoiding redundant processing cycles.
3Loss of information
If sinograms are processed through band pass filtering and fluence compensation to produce high-quality images, then tissue differentiation capability is improved, but device complexity and processing requirements increase
Solution Approach 1:
Band pass filtering selectively passes specific frequency ranges while attenuating others, and fluence compensation adjusts signal intensities based on depth and tissue optical properties. These parameter-based transformations enhance tissue characterization by emphasizing diagnostically relevant signal components while suppressing noise and artifacts, achieving high image quality through controlled parameter modulation rather than complex structural additions.
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 the generation of accurate, high-resolution images that improve tissue characterization and differentiation between malignant and benign tissues, providing better diagnostic accuracy and clarity in breast tumor assessment.
Implementation Method 1
a device that can obtain a sinogram corresponding to a single light event by sampling a plurality of transducer elements for a period of time after the light event
Data Source
AI summary
A method is disclosed for generating sinograms by sampling a plurality of transducers acoustically coupled with the surface of a volume of tissue over a period of time after a light pulse at one wavelength, and after another light pulse at a different wavelength, and for processing those sinograms, reconstructing at least two optoacoustic images from the two sinograms, processing the two optoacoustic images to generate two envelope images and generating a parametric map from information in the two envelope images. In an embodiment, motion and tracking are determined to align the envelope images. In an embodiment, at least a second parametric map is produced from information in the same two envelope images. In an embodiment an ultrasound image is also acquired, and the parametric map is coregistered with and overlayed upon the ultrasound image, and then displayed.