Doppler Ultrasound Video Denoising for Artifact Persistence Filtering
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Solution Overview
Problem
Existing ultrasound imaging systems, particularly Doppler ultrasound, suffer from artifacts due to misconfigurations, human error, anatomical factors, and technological limitations, leading to misleading or confusing flow information.
Innovation Solution
A computer-implemented denoising method for Doppler ultrasound videos that analyzes individual frames to identify and remove artifacts by categorizing pixels into real Doppler signal and artifacts using temporal persistence and connected components, adjusting thresholds to distinguish between them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If Doppler ultrasound imaging is performed to detect fluid flow velocity, then flow information can be obtained, but artifacts are generated that lead to inaccurate representation and potential loss of clinically relevant data
Solution Approach 1:
The patent segments the Doppler ultrasound signal into multiple frequency components using Fast Fourier Transform (FFT). This segmentation allows individual analysis of each frequency component to identify and remove artifacts while preserving genuine flow information, thereby resolving the contradiction between maintaining reliability and preventing information loss.
Solution Approach 2:
The patent introduces an intermediary processing stage that analyzes the spectral content of the Doppler signal. This intermediary layer acts as a mediator between the raw signal and the final output, filtering out artifacts through spectral analysis while preserving clinically relevant data, thus improving both reliability and information retention.
2Measurement precision
If signal processing is applied to remove artifacts, then accuracy of Doppler signals is improved, but complexity of the imaging system increases
Solution Approach 1:
The patent replaces complex mechanical or hardware-based artifact removal mechanisms with software-based spectral analysis and digital signal processing. By using FFT and algorithmic filtering, the system achieves high measurement precision without requiring additional physical components, thereby improving accuracy while minimizing increases in device complexity.
3Reliability
If artifact removal processing is performed on ultrasound video, then diagnostic accuracy is improved, but processing time and computational resources are consumed
Solution Approach 1:
The patent performs preliminary spectral analysis on the Doppler signal to identify artifact characteristics before final image reconstruction. By pre-processing the signal to characterize and flag artifact components, the system can efficiently remove them during reconstruction, improving diagnostic accuracy while minimizing additional processing time through optimized computational sequencing.
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 method effectively reduces or eliminates artifacts, providing a clearer and more accurate Doppler ultrasound image for diagnostic purposes.
Implementation Method 1
a piezoelectric transducer driven to perform both duties as source and receiver. As a source, the transducer is responsible for sending ultrasonic acoustic signals generated by converting electrical pulses into a wave. The acoustic wave pulse travelling through the tissue is partially reflected by materials with different acoustic impedance. Therefore, as a receiver, the transducer is responsible for detecting the reflected pulses and converting them into radio frequency (RF) electrical signals
Implementation Method 2
A particular application of diagnostic ultrasound imaging uses Doppler measurement to detect and display fluid flow velocity. This is the physical phenomenon whereby a sound wave frequency that encounters a moving body undergoes a variation that is directly proportional to the speed of the body's movement itself. The Doppler effect is therefore based on measurement of frequency variations between an incident beam and a reflected beam from a moving body
Implementation Method 3
The contact between ultrasound and various anatomical structures marks the beginning of various physical phenomena such as reflection, dispersion, diffraction and absorption. This first two of the above phenomena generate the echo signal that returns to the transducer
Data Source
AI summary
A denoising method for removing artefacts acquiring a plurality of frames composing an ultrasound video; identifying pixels containing doppler activation. The method further includes generating for each frame, a first set of image data to associate to each pixel position of the corresponding frame at least a first or a second category to categorize the pixels of the corresponding frame and identifying for each frame the pixels containing doppler activation, generating at least a persistence sequence associated to a frame portion containing identification data ordered according to the frame sequence such that adjacent identification data in the sequence refers to consecutive frames, and in the persistence sequence, calculating the length of each persistence sub-sequence comprising consecutive identification data having the first value. In addition, automatically calculating a reference threshold is automatically calculated and the doppler pixels are eliminated if the latter belong to a persistence sequence below the threshold.