1,046 research outputs found
Linear-Array Photoacoustic Imaging Using Minimum Variance-Based Delay Multiply and Sum Adaptive Beamforming Algorithm
In Photoacoustic imaging (PA), Delay-and-Sum (DAS) beamformer is a common
beamforming algorithm having a simple implementation. However, it results in a
poor resolution and high sidelobes. To address these challenges, a new
algorithm namely Delay-Multiply-and-Sum (DMAS) was introduced having lower
sidelobes compared to DAS. To improve the resolution of DMAS, a novel
beamformer is introduced using Minimum Variance (MV) adaptive beamforming
combined with DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is
shown that expanding the DMAS equation results in multiple terms representing a
DAS algebra. It is proposed to use the MV adaptive beamformer instead of the
existing DAS. MVB-DMAS is evaluated numerically and experimentally. In
particular, at the depth of 45 mm MVB-DMAS results in about 31 dB, 18 dB and 8
dB sidelobes reduction compared to DAS, MV and DMAS, respectively. The
quantitative results of the simulations show that MVB-DMAS leads to improvement
in full-width-half-maximum about 96 %, 94 % and 45 % and signal-to-noise ratio
about 89 %, 15 % and 35 % compared to DAS, DMAS, MV, respectively. In
particular, at the depth of 33 mm of the experimental images, MVB-DMAS results
in about 20 dB sidelobes reduction in comparison with other beamformers.Comment: This is the final version of this paper, which is accepted in the
"Journal of Biomedical Optics". Compared to previous versions, this version
contains more experiments and evaluatio
Eigenspace-Based Minimum Variance Combined with Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging
In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly
used beamformer. However, it leads to a low resolution and high level of
sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower
sidelobes compared to DAS. In this paper, to improve the resolution and
sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based
Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is
shown that expanding the DMAS algebra leads to several terms which can be
interpreted as DAS. Using the EIBMV adaptive beamforming instead of the
existing DAS (inside the DMAS algebra expansion) is proposed to improve the
image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is
shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution
and sidelobes. In particular, at the depth of 11 mm of the experimental images,
EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to
DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental
images, the quantitative results indicate that EIBMV-DMAS leads to improvement
in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and
EIBMV, respectively.Comment: arXiv admin note: substantial text overlap with arXiv:1709.0796
Semi-blind adaptive beamforming for high-throughput quadrature amplitude modulation systems
A semi-blind adaptive beamforming scheme is proposed for wireless systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, equal to the number of receiver antenna arrays elements, are first utilised to provide a rough initial least squares estimate of the beamformer's weight vector. A concurrent constant modulus algorithm and soft decision-directed scheme is then applied to adapt the beamformer. This semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution, as demonstrated in our simulation study
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