Adaptive Beamforming Data Fusion for Faster Ultrasound Imaging
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Solution Overview
Problem
Conventional methods of ultrasound imaging using delay-and-sum beamforming produce low-quality images, and adaptive beamforming, while improving image quality, is computationally expensive and time-consuming.
Innovation Solution
The method involves summing data across the transmit dimension to form a single data cube, followed by adaptive beamforming on this cube, reducing computational expense by performing the computationally expensive processing stage only once.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If adaptive beamforming is applied to improve image quality, then image quality is improved, but computational time and processing cost increase significantly
Solution Approach 1:
The patent segments the beamforming process into two distinct stages: a first beamforming pass that produces preliminary images, and a second adaptive beamforming pass that refines image quality. This segmentation allows the computationally expensive adaptive processing to be applied selectively rather than to all data, thereby improving image quality while controlling computational time.
Solution Approach 2:
The patent applies adaptive beamforming selectively rather than universally - specifically applying it to certain data cubes or image regions where quality improvement is most beneficial. This partial application reduces the overall computational burden while still achieving the desired image quality enhancement in critical areas.
2Measurement precision
If adaptive beamforming is performed on multiple data cubes to improve image quality, then image quality is improved, but processing complexity and time increase
Solution Approach 1:
The processing pipeline is segmented into distinct stages: initial beamforming of multiple data cubes, quality assessment, and selective adaptive beamforming application. This segmentation simplifies the overall processing complexity by breaking down the complex task of processing multiple data cubes into manageable, sequential steps.
Solution Approach 2:
The patent performs preliminary beamforming on all data cubes before applying adaptive beamforming. This preliminary action creates a baseline set of images that can be evaluated to determine where adaptive processing is needed, thereby reducing the complexity of deciding which data cubes require intensive processing.
3Productivity
If conventional delay-and-sum beamforming is used, then processing is fast and simple, but image quality is low
Solution Approach 1:
The patent uses a two-stage beamforming approach where the first stage uses fast conventional delay-and-sum beamforming to produce preliminary images quickly, and the second stage applies adaptive beamforming selectively to improve quality. This segmentation allows the system to maintain high processing speed for initial image generation while achieving improved quality where needed.
Solution Approach 2:
The preliminary images produced by conventional beamforming serve as an intermediary between the fast but low-quality initial processing and the slow but high-quality adaptive beamforming. This intermediary allows the system to use the fast method for most processing while using the slow method only when and where necessary, balancing speed and quality.
Data Source
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AI summary
In a method of imaging, a first transmission is carried out in a first direction. The reflected signals are received using a plurality of receiving devices. For each device, a two/three dimensional data set is formed. The first dimension (36b) represents the depth or range and the second dimension (36a) represents lateral distance. The optional third dimension (36c) represents an orthogonal lateral distance. The data set is formed by calculating times of flight for each pixel within a grid. The receive time is then assigned to each pixel. A data set is generated for each receiver, which results in a three/four dimensional data set from the first transmission of signals. A second transmission of signals is made in a different direction or from a different position. The signals received from the second transmission are received in the same way as those received from the first transmission. The signals are first summed across the transmit dimension to form a single data set, so that the data from various transmissions is combined. Adaptive beamforming is then carried out on this data set, resulting in a single adaptive image.