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تسجيل مستخدم جديدThree-Dimensional Photoacoustic Tomography using Delay Multiply and Sum Beamforming Algorithm
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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-and-sum (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 (FWHM) for about 25 dB and 0.2 mm, respectively, compared to DAS.
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Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. How
ever, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely Delay-Multiply-and-Sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, Double Stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.
In Ultrasound (US) imaging, Delay and Sum (DAS) is the most common beamformer, but it leads to low quality images. Delay Multiply and Sum (DMAS) was introduced to address this problem. However, the reconstructed images using DMAS still suffer from le
vel of sidelobes and low noise suppression. In this paper, a novel beamforming algorithm is introduced based on the expansion of DMAS formula. It is shown that there is a DAS algebra inside the expansion, and it is proposed to use DMAS instead of the DAS algebra. The introduced method, namely Double Stage DMAS (DS-DMAS), is evaluated numerically and experimentally. The quantitative results indicate that DS-DMAS results in about 25% lower level of sidelobes compared to DMAS. Moreover, the introduced method leads to 23%, 22% and 43% improvement in Signal-to-Noise Ratio, Full-Width-Half-Maximum and Contrast Ratio, respectively, in comparison with DMAS beamformer.
Photoacoustic imaging (PAI) is an emerging biomedical imaging modality capable of providing both high contrast and high resolution of optical and UltraSound (US) imaging. When a short duration laser pulse illuminates the tissue as a target of imaging
, tissue induces US waves and detected waves can be used to reconstruct optical absorption distribution. Since receiving part of PA consists of US waves, a large number of beamforming algorithms in US imaging can be applied on PA imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in US imaging. However, make use of DAS beamformer leads to low resolution images and large scale of off-axis signals contribution. To address these problems a new paradigm namely Delay-Multiply-and-Sum (DMAS), which was used as a reconstruction algorithm in confocal microwave imaging for breast cancer detection, was introduced for US imaging. Consequently, DMAS was used in PA imaging systems and it was shown this algorithm results in resolution enhancement and sidelobe degrading. However, in presence of high level of noise the reconstructed image still suffers from high contribution of noise. In this paper, a modified version of DMAS beamforming algorithm is proposed based on DAS inside DMAS formula expansion. The quantitative and qualitative results show that proposed method results in more noise reduction and resolution enhancement in expense of contrast degrading. For the simulation, two-point target, along with lateral variation in two depths of imaging are employed and it is evaluated under high level of noise in imaging medium. Proposed algorithm in compare to DMAS, results in reduction of lateral valley for about 19 dB followed by more distinguished two-point target. Moreover, levels of sidelobe are reduced for about 25 dB.
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.
Downlink beamforming is a key technology for cellular networks. However, computing the transmit beamformer that maximizes the weighted sum rate subject to a power constraint is an NP-hard problem. As a result, iterative algorithms that converge to a
local optimum are used in practice. Among them, the weighted minimum mean square error (WMMSE) algorithm has gained popularity, but its computational complexity and consequent latency has motivated the need for lower-complexity approximations at the expense of performance. Motivated by the recent success of deep unfolding in the trade-off between complexity and performance, we propose the novel application of deep unfolding to the WMMSE algorithm for a MISO downlink channel. The main idea consists of mapping a fixed number of iterations of the WMMSE algorithm into trainable neural network layers, whose architecture reflects the structure of the original algorithm. With respect to traditional end-to-end learning, deep unfolding naturally incorporates expert knowledge, with the benefits of immediate and well-grounded architecture selection, fewer trainable parameters, and better explainability. However, the formulation of the WMMSE algorithm, as described in Shi et al., is not amenable to be unfolded due to a matrix inversion, an eigendecomposition, and a bisection search performed at each iteration. Therefore, we present an alternative formulation that circumvents these operations by resorting to projected gradient descent. By means of simulations, we show that, in most of the settings, the unfolded WMMSE outperforms or performs equally to the WMMSE for a fixed number of iterations, with the advantage of a lower computational load.
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