研究目的
To improve the quality of PCA based beamformers both in the imaging contrast and resolution by proposing a novel beamforming method that utilizes a kernel to select neighbor points and adjusts the number of selected eigenvectors based on comparisons with neighbor points.
研究成果
The proposed method provides significant enhancement in the imaging contrast of PCA based beamformers while keeping the quality of the imaging resolution similar to the ESBMV beamformer. It suppresses the interface component of echo signals and saves the signal component by using echo signals of neighbor points.
研究不足
The performance of the ESBMV beamformer is achieved at the cost of high computational complexity. The proposed method aims to reduce this complexity while maintaining or improving imaging quality.
1：Experimental Design and Method Selection:
The study proposes a novel beamforming method that utilizes a kernel to select neighbor points and adjusts the number of selected eigenvectors based on comparisons with neighbor points to improve imaging contrast and resolution.
2：Sample Selection and Data Sources:
Simulation and phantom study data were used, including point target simulation and cyst phantom simulation.
3：List of Experimental Equipment and Materials:
Field II software for simulation, a 19 mm linear array with 128 elements, central and sampling frequencies set to 5 MHz and 100 MHz respectively.
4：Experimental Procedures and Operational Workflow:
The MV covariance matrix was calculated, and its eigenvalue and eigenvectors were estimated. The number of selected eigenvectors was compared with its relevant kernel points to calculate a new number of eigenvalues.
5：Data Analysis Methods:
Contrast ratio (CR) and contrast-to-noise ratio (CNR) were estimated to compare the performance of the proposed beamformer with the MV and ESBMV beamformers.
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