Time-domain Estimator for Ultrasound Image Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ultrasound imaging faces challenges in achieving high spatial resolution due to limitations in aperture size and phase aberration, particularly in cardiac and breast imaging, where diffraction effects and high operating frequencies lead to decreased signal-to-noise ratio and increased costs with 2D array systems.
Innovation Solution
The method involves using a Time-domain, Optimized, Near-field Estimator (TONE) and its variants, such as Quick TONE (qTONE) and Diffuse TONE (dTONE), which determine weights of candidate targets in a region of interest using singular value decomposition and probability distribution modeling to reconstruct images, while reducing computational burden and image artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If aperture size is increased to improve spatial resolution, then resolution improves, but device complexity and cost increase
Solution Approach 1:
The imaging problem is segmented into multiple discrete target locations within a region of interest. Each target position is independently estimated using the estimator, allowing the system to achieve high resolution without requiring a physically large aperture. The segmentation of the imaging space into candidate target positions enables computational resolution enhancement.
Solution Approach 2:
The patent transitions from physical aperture dimension to computational dimension by using an estimator that processes received signals through mathematical operations (singular value decomposition, probability distribution modeling). This dimensional shift allows resolution improvement without increasing physical aperture size, as the resolution enhancement is achieved through signal processing in the computational domain rather than physical expansion.
2Measurement precision
If operating frequency is increased to improve spatial resolution, then resolution improves, but signal-to-noise ratio decreases
Solution Approach 1:
The estimator uses feedback mechanisms by iteratively processing received signals and refining target location estimates. The system receives signals, processes them through the estimator to determine target positions, and uses this information to improve subsequent measurements. This feedback loop allows the system to maintain reliability by continuously optimizing signal interpretation despite high-frequency noise challenges.
Solution Approach 2:
The patent introduces an estimator as an intermediary between the received high-frequency signals and the final image reconstruction. This intermediary component (the time-domain estimator with singular value decomposition) acts as a mediator that filters and processes the noisy high-frequency signals, extracting useful target location information while suppressing noise, thereby maintaining signal-to-noise ratio while enabling high-resolution imaging.
3Measurement precision
If 2D array systems are used to improve spatial resolution, then resolution improves, but cost increases
Solution Approach 1:
Instead of using expensive 2D array systems, the patent creates a computational copy of the aperture functionality through the estimator. The estimator mathematically replicates the signal processing capabilities that would require physical 2D arrays, using singular value decomposition and probability distribution modeling to achieve similar resolution results at lower cost. This computational copying avoids the need for expensive hardware while maintaining imaging performance.
Solution Approach 2:
The patent changes the fundamental parameter from physical array configuration to computational processing parameters. Rather than increasing physical aperture dimensions or using 2D arrays, the system modifies signal processing parameters (using time-domain estimation, singular value decomposition, and probability distribution analysis) to achieve high resolution. This parameter transformation allows resolution improvement without the associated cost increase of 2D array systems.
4Productivity
If computational methods are simplified to reduce burden, then processing speed improves, but image quality may deteriorate
Solution Approach 1:
The estimator implements partial action by focusing computational resources on estimating target locations at discrete candidate positions rather than processing the entire continuous signal space. This selective approach (using probability distribution modeling at specific points) reduces computational burden while maintaining image quality, as the system performs sufficient processing at critical locations without the excessive computational cost of full-spectrum analysis.
Data Source
AI summary
One or more systems, methods, or computer program products can include receiving an echo data set including information representative of an echo sensed by a transducer, provided at least in part by one or more actual targets included in a region of interest. The region of interest can be modeled, including selecting or generating an array manifold matrix including information corresponding to any one or more candidate targets. The weights of the candidate targets can be determined using the array manifold matrix and the echo data set, including minimizing an argument of a function modeling the weights of the candidate targets. In an example, the echo data set can be dithered, such as by adding a specified dithering signal.