Thermoacoustic Image Reconstruction Correction Kernels
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Solution Overview
Problem
Conventional image reconstruction algorithms for thermoacoustic imaging are inefficient and produce artifacts due to assumptions that do not hold for thermoacoustic data, leading to degraded image quality, as they are derived from other imaging modalities like CT and assume complete data sets and homogeneous tissue properties.
Innovation Solution
A method and system that apply correction kernels specific to transducer elements, views, and spatial sensitivities to thermoacoustic data, generated through calibration procedures, to correct for individual transducer properties, relative relationships, and spatial variations, improving image reconstruction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional CT reconstruction algorithms are used for thermoacoustic imaging, then the imaging process can be simplified by using existing algorithms, but the image quality deteriorates due to artifacts and inefficiency
Solution Approach 1:
The patent changes the parameters of the reconstruction algorithm by introducing correction kernels with specific mathematical forms (e.g., Gaussian kernels, rational functions) that are optimized for thermoacoustic data characteristics. These kernels adjust the reconstruction process to account for the specific physics of thermoacoustic imaging, thereby improving image quality while maintaining algorithmic simplicity.
Solution Approach 2:
The patent applies different correction kernels to different regions or aspects of the thermoacoustic data. For example, separate kernels may be applied to correct for transducer-specific characteristics versus spatial sensitivity variations. This localized approach allows optimization of image quality in specific areas without compromising the overall simplicity of the reconstruction process.
2Manufacturing precision
If time-domain filters like Shepp-Logan filter are applied to thermoacoustic data, then blurring effects can be reduced, but severe artifacts are introduced when data is incomplete
Solution Approach 1:
The patent replaces traditional time-domain filters with correction kernels that have specific mathematical forms optimized for thermoacoustic data. These kernels (such as Gaussian or rational function-based corrections) are designed to work effectively with the limited data available in thermoacoustic imaging, reducing artifacts while maintaining image clarity. The kernels adjust parameters like bandwidth and amplitude to match the specific characteristics of thermoacoustic signals.
Solution Approach 2:
The correction kernels are derived through calibration procedures that analyze the actual thermoacoustic data characteristics. This feedback mechanism allows the reconstruction algorithm to adapt to the specific conditions of the imaging data, adjusting the kernel parameters based on observed signal properties to minimize artifacts while maximizing image quality.
3Manufacturing precision
If a large number of CT views are used for reconstruction, then the Fourier space sampling becomes dense and reconstruction accuracy improves, but the computational complexity and data requirements increase significantly
Solution Approach 1:
The patent changes the approach by using correction kernels that can process thermoacoustic data with fewer views. The kernels are designed to compensate for the limited angular sampling by applying spatial frequency corrections that effectively fill in the gaps, achieving accurate reconstruction without requiring dense Fourier space sampling. This reduces the number of required views while maintaining reconstruction quality.
Solution Approach 2:
The patent substitutes the mechanical/data-intensive approach of collecting many CT views with a computational approach using correction kernels. Instead of physically acquiring more data through additional views, the system uses mathematical corrections processed through the reconstruction algorithm to achieve the same accuracy, thereby reducing data collection 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 application of correction kernels enhances the quality of thermoacoustic image reconstruction by addressing the inefficiencies and artifacts in existing algorithms, providing more accurate and robust images by accounting for unique transducer properties and spatial sensitivities.
Implementation Method 1
directing radio frequency (RF) energy pulses generated by an RF source into a tissue region of interest
Implementation Method 2
heat an object (region) of interest within the subject rapidly, which causes the object to expand and then contract
Implementation Method 3
acoustic pressure waves (signals) being induced in the subject that are detected using an acoustic receiver such as an ultrasound or thermoacoustic transducer array
Implementation Method 4
ultrasound or thermoacoustic transducer array
Data Source
AI summary
A method and system for reconstructing a thermoacoustic image that utilizes the steps of directing radio frequency (RF) energy pulses generated by an RF source into a tissue region of interest; detecting, at each of a plurality of views along a scanning trajectory of a transducer element array about the region of interest, acoustic signals generated within the region of interest in response to the RF energy pulses and generating thermoacoustic data; applying at least one correction kernel to the thermoacoustic data; and after the at least one correction kernel has been applied to the thermoacoustic data, reconstructing a thermoacoustic image therefrom.