Ultrasound Doppler Image Gamma Curve Selection
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Solution Overview
Problem
Existing ultrasound image processing systems struggle to automatically set an appropriate gamma curve for forming Doppler images, leading to suboptimal expression of blood flow signals and increased clutter noise.
Innovation Solution
An ultrasound image processing apparatus that includes a texture parameter specifying unit, an appropriate gamma curve decision unit, and a Doppler image formation unit. The apparatus uses a learning model trained with power distribution information and texture parameters to predict appropriate texture parameters for the power distribution, which are then used to decide an appropriate gamma curve for forming Doppler images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If a gamma curve is used to transform Doppler signal power to pixel values, then the visibility of blood flow signals is improved, but clutter noise becomes more prominent and reduces image quality
Solution Approach 1:
The patent segments the Doppler signal processing into two distinct pathways: one for blood flow signals and one for clutter signals. By separating the processing of these signal types, the system can apply different gamma curve transformations to each, optimizing visibility for blood flow while suppressing clutter noise independently.
Solution Approach 2:
The patent applies different gamma curve characteristics to different signal components based on their local properties. Blood flow signals receive a gamma transformation that enhances visibility, while clutter signals receive a different transformation that suppresses their prominence. This local differentiation resolves the contradiction between enhancing desired signals and suppressing unwanted noise.
2Adaptability or versatility
If manual setting of gamma curve parameters is used, then flexibility in adjusting image characteristics is improved, but operation complexity and time consumption increase
Solution Approach 1:
The patent implements an automatic gamma curve selection mechanism that analyzes the input Doppler signal characteristics and autonomously determines the optimal gamma curve parameters. This self-service approach eliminates the need for manual parameter adjustment while maintaining adaptability to different signal types and imaging conditions.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor the Doppler signal properties and automatically adjust gamma curve parameters based on the detected signal characteristics. This closed-loop control provides both adaptability and ease of operation by using real-time signal analysis to optimize image quality without user intervention.
3Object-affected harmful factors
If clutter signal suppression is applied before gamma transformation, then noise reduction is improved, but the natural distribution of pixel values and image contrast are degraded
Solution Approach 1:
The patent employs dynamic gamma curve transformation that adapts to the statistical distribution of the input signal. By making the gamma transformation dynamic and signal-dependent, the system can suppress clutter noise while preserving the natural contrast relationships in the blood flow signals, avoiding the degradation that occurs with fixed suppression methods.
Solution Approach 2:
The patent combines multiple processing operations into a composite transformation function that integrates clutter suppression and gamma transformation into a unified process. This composite approach allows the system to achieve both noise reduction and contrast preservation by coordinating the effects of different processing stages rather than applying them sequentially and independently.
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 system automatically sets an appropriate gamma curve, enhancing the visibility of blood flow signals by reducing clutter noise and allowing for accurate expression of power values in Doppler images.
Implementation Method 1
a Doppler signal is formed from a reception signal by using a Doppler effect in a received wave from a subject
Data Source
AI summary
A Doppler signal processing unit acquires target power distribution information indicating a power distribution of a Doppler signal. A texture parameter specifying unit inputs the target power distribution information to a learning model. The trained learning model outputs a texture parameter appropriate for the target power distribution information. The texture parameter is a parameter indicating a feature of a gamma curve. An appropriate gamma curve decision unit decides an appropriate gamma curve that is appropriate for the target power distribution information, based on the texture parameter appropriate for the target power distribution information. An image formation unit forms a power Doppler image based on the target power distribution information and the appropriate gamma curve.