Eigenspace-Based Minimum Variance Adaptive Beamformer Combined with Delay Multiply and Sum: Experimental Study

Abstract

Delay and sum (DAS) is the most common beamforming algorithm in linear-array photoacoustic imaging (PAI) as a result of its simple implementation. However, it leads to a low resolution and high sidelobes. Delay multiply and sum (DMAS) was used to address the incapabilities of DAS, providing a higher image quality. However, the resolution improvement is not well enough compared to eigenspace-based minimum variance (EIBMV). In this paper, the EIBMV beamformer has been combined with DMAS algebra, called EIBMV-DMAS, using the expansion of DMAS algorithm. The proposed method is used as the reconstruction algorithm in linear-array PAI. EIBMV-DMAS is experimentally evaluated where the quantitative and qualitative results show that it outperforms DAS, DMAS and EIBMV. The proposed method degrades the sidelobes for about 365 %, 221 % and 40 %, compared to DAS, DMAS and EIBMV, respectively. Moreover, EIBMV-DMAS improves the SNR about 158 %, 63 % and 20 %, respectively.

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Cited by in corpus (7)

• Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm
• Enhanced Linear-array Photoacoustic Beamforming using Modified Coherence Factor
• Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging
• An Efficient Nonlinear Beamformer Based on P^{th} Root of Detected Signals for Linear-Array Photoacoustic Tomography: Application to Sentinel Lymph Node Imaging
• Model-Based Photoacoustic Image Reconstruction using Compressed Sensing and Smoothed L0 Norm
• Effects of Important Parameters Variations on Computing Eigenspace-Based Minimum Variance Weights for Ultrasound Tissue Harmonic Imaging
• Three-Dimensional Photoacoustic Tomography using Delay Multiply and Sum Beamforming Algorithm