Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography

In linear-array photoacoustic imaging (PAI), beamforming methods can be used to reconstruct the images. Delay-and-sum (DAS) beamformer is extensively used due to its simple implementation. However, this algorithm results in high level of sidelobes and low resolution. In this paper, it is proposed to form the photoacoustic (PA) images through a regularized inverse problem to address these limitations and improve the image quality. We define a forward/backward problem of the beamforming and solve the inverse problem using a sparse constraint added to the model which forces the sparsity of the output beamformed data. It is shown that the proposed Sparse beamforming (SB) method is robust against noise due to the sparsity nature of the problem. Numerical results show that the SB method improves the signal-to-noise ratio (SNR) for about 98.69 dB, 82.26 dB and 74.73 dB, in average, compared to DAS, delay-multiply-and-sum (DMAS) and double stage-DMAS (DS-DMAS), respectively. Also, quantitative evaluation of the experimental results shows a significant noise reduction using SB algorithm. In particular, the contrast ratio of the wire phantom at the depth of 30 mm is improved about 103.97 dB, 82.16 dB and 65.77 dB compared to DAS, DMAS and DS-DMAS algorithms, respectively, indicating a better performance of the proposed SB in terms of noise reduction.Accepted versio

Validation of delay-multiply-and-standard-deviation weighting factor for improved photoacoustic imaging of sentinel lymph node

Delay-and-sum (DAS) is one of the most common algorithms used to construct the photoacoustic images due to its low complexity. However, it results in images with high sidelobes and low resolution. Delay-and-standard-deviation (DASD) weighting factor can improve the contrast of the images compared to DAS. However, it still suffers from high sidelobes. In this work, a new weighting factor, named delay-multiply-and-standard-deviation (DMASD) is introduced to enhance the contrast of the reconstructed images compared to other mentioned methods. In the proposed method, the standard deviation of the mutual multiplied delayed signals are calculated, normalized and multiplied to DAS beamformed data. The results show that DMASD improves the signal-to-noise-ratio about 19.29 dB and 7.3 dB compared to DAS and DASD, respectively, for in vivo imaging of the sentinel lymph node. Moreover, the contrast-ratio is improve by the DMASD about 23.61 dB and 10.81 dB compared to DAS and DASD, respectively.Accepted versio

Regularized Capon Beamformer Using ℓ1-Norm Applied to Photoacoustic Imaging

Delay-and-Sum (DAS), as a non-adaptive beamforming method, is one of the most common algorithms used in Photoacoustic imaging due to its simple implementation. The results obtained from this algorithm suffer from low resolution and high sidelobes. The adaptive Minimum variance (MV) method improves the image quality compared to DAS in terms of resolution and contrast. In this paper, it is proposed to add a ℓ1-norm regularization term to the conventional MV minimization problem and create a new sparse beamforming method, named Modified-Sparse-Mv (ms-Mv)algorithm. In fact, the sparsity of the output is forced to the beampattern by adding this new sparse added term, which results in more noise reduction and sidelobe suppression compared to MV. The minimization problem is convex, and therefore, it can be solved using an iterative algorithm. The results show that the proposed MS-MV method improves the signal-to-noise-ratio for about 5.36 dB and 6.44 dB compared to DAS and MV, respectively, for the designed wire phantom.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Acoustical Wavefield Imagin