Ultrasound Blood Flow Imaging With Angle-Correlation Noise Suppression
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Solution Overview
Problem
High frame-rate ultrasound imaging suffers from rapid deterioration of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), particularly in mid-to-deep ranges, due to the use of unfocused or weakly-focused ultrasound waves, which necessitates effective noise-induced bias suppression for improved small vessel image quality and microvessel imaging.
Innovation Solution
A method involving a computer system that divides ultrasound frame data acquired at different transmitting angles into two non-overlapping groups, performs clutter filtering on each group, and computes a correlation between the resulting blood flow signals to suppress noise-induced bias, enhancing the SNR and CNR of the reconstructed blood flow image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high frame-rate ultrasound imaging is achieved using unfocused or weakly-focused ultrasound waves, then the Doppler sensitivity and spatial-temporal information for blood flow imaging are improved, but the signal-to-noise ratio and contrast-to-noise ratio deteriorate rapidly, especially in mid-to-deep ranges
Solution Approach 1:
The patent segments the ultrasound frame data into multiple groups based on different transmitting angles. Each group is processed separately through compounding and clutter filtering, then combined through correlation computation. This segmentation allows the system to maintain high Doppler sensitivity while managing noise through group-specific processing, resolving the contradiction between sensitivity and signal quality.
Solution Approach 2:
The patent introduces an intermediary correlation computation step between the grouped and filtered blood flow signals. This intermediary process computes correlation between first and second blood flow signal data, effectively suppressing noise-induced bias while preserving true blood flow signals. The correlation computation acts as a mediator that reconciles the high sensitivity data with noise suppression requirements.
2Reliability
If high frame-rate ultrasound imaging is achieved using unfocused or weakly-focused ultrasound waves, then the spatial-temporal information for separating blood and tissue signals is improved, but the contrast-to-noise ratio deteriorates rapidly in mid-to-deep ranges
Solution Approach 1:
The patent segments frame data by transmitting angles into distinct groups, allowing each group to preserve spatial-temporal information while being processed independently. This segmentation enables the system to maintain the rich spatial-temporal data needed for blood-tissue separation while managing contrast degradation through targeted processing of each segment.
Solution Approach 2:
The patent extracts and removes noise-induced bias through the correlation computation process. By computing correlation between blood flow signals from different angle groups and subtracting the correlated noise components, the system extracts true blood flow information while removing the degrading noise, thereby improving contrast-to-noise ratio while preserving spatial-temporal information.
3Measurement precision
If noise-induced bias is suppressed through correlation computation of blood flow signals, then the signal-to-noise ratio and contrast-to-noise ratio are improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex processing task into manageable stages: grouping frame data by angle, compounding within groups, clutter filtering, and final correlation computation. This segmentation reduces processing complexity by breaking down the overall complex operation into simpler, sequential steps that can be implemented efficiently.
Solution Approach 2:
The patent performs preliminary actions of compounding and clutter filtering on each group of frame data before the final correlation computation. These preliminary processing steps simplify the subsequent correlation operation by pre-processing the data, thereby reducing the overall computational complexity while still achieving effective noise suppression.
Data Source
AI summary
Systems and methods for removing the bias induced by noise from power Doppler images to achieve improvements of microvessel image contrast are provided. In one example, the noise-induced bias can be suppressed by utilizing the characteristics of uncorrelated noise in the ultrasound image from data acquired or compounded at different transmitting angles. In another example, the noise-induced bias can be suppressed due to the lack of correlation between adjacent ultrasound images. These example implementations may also be combined, as will be described below.