Interframe Energy Normalization in Optoacoustic Imaging
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Solution Overview
Problem
Current medical imaging technologies face challenges in effectively processing optoacoustic data to produce accurate and reliable images of tissue oxygenation and hemoglobin levels, particularly due to issues like unwanted data in sinograms, which can result from instrument characteristics, tissue interactions, and external factors.
Innovation Solution
The system employs a combination of preprocessing techniques such as bad transducer detection, common mode stripe filtering, band pass filtering, and fluence compensation to refine sinogram data, followed by image reconstruction and post-processing to generate parametric maps of oxygenation and hemoglobin levels, which are then co-registered with ultrasound images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple preprocessing techniques are applied to remove unwanted data and artifacts, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the optoacoustic data processing into distinct preprocessing steps: bad transducer detection, common mode stripe filtering, band pass filtering, and fluence compensation. Each step addresses specific types of artifacts independently, allowing systematic removal of unwanted data while maintaining measurement precision.
Solution Approach 2:
The patent applies preprocessing techniques before image reconstruction to eliminate artifacts and unwanted data in advance. By performing bad transducer detection, filtering, and compensation operations on the raw sinogram data prior to reconstruction, the system ensures higher measurement precision in the final images without requiring complex post-processing corrections.
2Reliability
If preprocessing and post-processing steps are added to refine sinogram data, then reliability is improved, but loss of time occurs
Solution Approach 1:
The patent performs critical preprocessing operations including bad transducer detection, common mode filtering, and fluence compensation before image reconstruction. By addressing data quality issues and applying corrections in advance, the system ensures reliable images while avoiding time-consuming iterative corrections after reconstruction.
Solution Approach 2:
The patent replaces manual or iterative artifact removal processes with automated digital signal processing techniques. Algorithms for bad transducer detection, stripe filtering, and fluence compensation are applied computationally to sinogram data, significantly reducing processing time compared to manual methods while maintaining high reliability.
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 the accuracy and reliability of optoacoustic imaging by removing unwanted data and artifacts, leading to improved visualization of tissue composition and functional parameters like oxygenation and hemoglobin levels.
Implementation Method 1
a handheld probe and first and second pulsed light sources... generate pulses of light... produce optoacoustic return signal data
Implementation Method 2
A sensor measures a portion of the light transmitted along the light path
Data Source
AI summary
An optoacoustic imaging system includes a handheld probe and first and second pulsed light sources having a common output. The first and second light sources generate pulses of light at first and second predominant wavelengths, respectively. The handheld probe includes an ultrasound transducer array having an active end located at the distal end of the handheld probe for receiving an optoacoustic return signal. A sensor measures a portion of the light transmitted along the light path. A data acquisition samples the ultrasound transducer array during a predetermined period of time after a pulse of light from the first light source and during a predetermined period of time after a pulse of light from the second light source. The sampled data is stored. An image processing unit reconstructs a first optoacoustic image based on the sampled data corresponding to a pulse of light from the first light source and reconstructs a second optoacoustic image based on the sampled data corresponding to a pulse of light from the second light source. An energy normalizing unit computes a normalization factor based on a measurement from each sensor in the array and applies the normalization factor to data corresponding to the pulse of light.