2,307 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
Accelerated High-Resolution Photoacoustic Tomography via Compressed Sensing
Current 3D photoacoustic tomography (PAT) systems offer either high image
quality or high frame rates but are not able to deliver high spatial and
temporal resolution simultaneously, which limits their ability to image dynamic
processes in living tissue. A particular example is the planar Fabry-Perot (FP)
scanner, which yields high-resolution images but takes several minutes to
sequentially map the photoacoustic field on the sensor plane, point-by-point.
However, as the spatio-temporal complexity of many absorbing tissue structures
is rather low, the data recorded in such a conventional, regularly sampled
fashion is often highly redundant. We demonstrate that combining variational
image reconstruction methods using spatial sparsity constraints with the
development of novel PAT acquisition systems capable of sub-sampling the
acoustic wave field can dramatically increase the acquisition speed while
maintaining a good spatial resolution: First, we describe and model two general
spatial sub-sampling schemes. Then, we discuss how to implement them using the
FP scanner and demonstrate the potential of these novel compressed sensing PAT
devices through simulated data from a realistic numerical phantom and through
measured data from a dynamic experimental phantom as well as from in-vivo
experiments. Our results show that images with good spatial resolution and
contrast can be obtained from highly sub-sampled PAT data if variational image
reconstruction methods that describe the tissues structures with suitable
sparsity-constraints are used. In particular, we examine the use of total
variation regularization enhanced by Bregman iterations. These novel
reconstruction strategies offer new opportunities to dramatically increase the
acquisition speed of PAT scanners that employ point-by-point sequential
scanning as well as reducing the channel count of parallelized schemes that use
detector arrays.Comment: submitted to "Physics in Medicine and Biology
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