One of the most common algorithms used in linear-array photoacoustic imaging, is Delay-and-Sum (DAS) beamformer due to its simple implementation. The results show that this algorithm results in a low resolution and high sidelobes. In this paper, it is proposed to use the sparse-based algorithm in order to suppress the noise level efficiently and improve the image quality. The forward problem of the beamforming is defined through a Least square (LS) method, and a ℓ1-norm regularization term is added to the problem which forces the sparsity of the output to the existing minimization problem. The new robust method, named sparse beamforming (SB) method, significantly suppresses the sidelobes and reduces the noise level due to the sparse added term. Numerical results show that SB leads to signal-to-noise-ratio improvement about 98.69 dB and 82.26 dB, in average, compared to DAS and Delay-Multiply-and-Sum (DMAS), respectively. Also, the full-width-half-maximum is improved about 396 μm and 123 μm, in average, compared to DAS and DMAS algorithms, respectively, using the proposed SB method, which indicates the good performance of SB method in image enhancement.
In linear-array photoacoustic imaging, different types of algorithms and beamformers are used to construct the images. Delay-and-Sum (DAS), as a non-adaptive algorithm, is one of the most popular algorithms used due to its low complexity. However, the results obtained from this algorithm contain high sidelobes and wide mainlobe. The adaptive Minimum Variance (MV) beamformer can address these limitations and improve the images in terms of resolution and contrast. In this paper, it is proposed to suppress the sidelobes more efficiently compared to MV by eliminating the effect of the samples caused by noise and interference. This would be achieved by zeroing the samples corresponding to the lower values of the calculated weights. In the other words, in the proposed MV-based-sparse subarray (MVB-S) method, the subarrays are considered to be sparse. The results show that MVB-S method leads to signal-to-noise-ratio improvement about 39.72 dB and 18.92 dB in average, compared to DAS and MV, respectively, which indicates the good performance of MVB-S method in noise reduction and sidelobe suppression.
One of the most common algorithms used in Photoacoustic and ultrasound image reconstruction, is the nonadaptive Delay-and-Sum (DAS) beamformer. The results show that this algorithm suffers from low resolution and high level of sidelobes. In this paper, it is suggested to weight the DAS beamformed signals to address these limitations and improve the image quality. The new weighting factor, named Delay-Multiply-and-StandardDeviation (DMASD) is designed in the way that the standard deviation of the mutual coupled and multiplied delayed signals is calculated, normalized and multiplied to the DAS formula. Quantitative results obtained from the numerical study show that the proposed DMASD weighting factor improves the Signal-to-Noise-Ratio for about 48.62 dB and 46.53 dB, compared to DAS and the Delay-and-Standard-Deviation (DASD) weighting factor, respectively, at the depth of 35 mm. Also, the Full-Width-Half-Maximum is improved about 0.78 mm and 0.84 mm, compared to DAS and DASD weighting factor, respectively, at the same depth using the proposed DMASD weighting factor, which indicates the improvement of resolution.
Photoacoustic imaging (PAI), is a promising medical imaging technique that provides the high contrast of the optical imaging and the resolution of ultrasound (US) imaging. Among all the methods, Three-dimensional (3D) PAI provides a high resolution and accuracy. One of the most common algorithms for 3D PA image reconstruction is delay-and-sum (DAS). However, the quality of the reconstructed image obtained from this algorithm is not satisfying, having high level of sidelobes and a wide mainlobe. In this paper, delay-multiply-andsum (DMAS) algorithm is suggested to overcome these limitations in 3D PAI. It is shown that DMAS algorithm is an appropriate reconstruction technique for 3D PAI and the reconstructed images using this algorithm are improved in the terms of the width of mainlobe and sidelobes, compared to DAS. Also, the quantitative results show that DMAS improves signal-to-noise ratio (SNR) and full-width-half-maximum (FW HM) for about 25 dB and 0.2 mm, respectively, compared to DAS.
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