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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:1709.07965

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 level 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., This paper is accepted in "Ultrasound in Medicine and Biology" journal

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. However, 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., Comment: This paper is accepted in "IEEE Transaction on Biomedical Engineering"

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.

One of the common algorithms used to reconstruct photoacoustic (PA) images is the non-adaptive Delay-and-Sum (DAS) beamformer. However, the quality of the reconstructed PA images obtained by DAS is not satisfying due to its high level of sidelobes and wide mainlobe. In contrast, adaptive beamformers, such as minimum variance (MV), result in an improved image compared to DAS. In this paper, a novel beamforming method, called Double MV (D-MV) is proposed to enhance the image quality compared to the MV. It is shown that there is a summation procedure between the weighted subarrays in the output of the MV beamformer. This summation can be interpreted as the non-adaptive DAS beamformer. It is proposed to replace the existing DAS with the MV algorithm to reduce the contribution of the off-axis signals caused by the DAS beamformer between the weighted subarrays. The numerical results show that the proposed technique improves the full-width-half-maximum (FWHM) and signal-to-noise ratio (SNR) for about 28.83 \mu m and 4.8 dB in average, respectively, compared to MV beamformer. Also, quantitative evaluation of the experimental results indicates that the proposed D-MV leads to 0.15 mm and 1.96 dB improvement in FWHM and SNR, in comparison with MV beamformer.

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 evaluation

Delay and Sum (DAS), as the most common beamforming algorithm in Photoacoustic Imaging (PAI), having a simple implementation, results in a low-quality image. Delay Multiply and Sum (DMAS) was introduced to improve the quality of the reconstructed images using DAS. However, the resolution improvement is now well enough compared to high resolution adaptive reconstruction methods such as Eigenspace- Based Minimum Variance (EIBMV). We proposed to integrate the EIBMV inside the DMAS formula by replacing the existing DAS algebra inside the expansion of DMAS, called EIBMV-DMAS. It is shown that EIBMV-DMAS outperforms DMAS in the terms of levels of sidelobes and width of mainlobe significantly. For instance, at the depth of 35 mm, EIBMV-DMAS outperforms DMAS and EIBMV in the term of sidelobes for about 108 dB, 98 dB and 44 dB compared to DAS, DMAS, and EIBMV, respectively. The quantitative comparison has been conducted using Full-Width-Half-Maximum (FWHM) and Signal-to-Noise Ratio (SNR), and it was shown that EIBMV-DMAS reduces the FWHM about 1.65 mm and improves the SNR about 15 dB, compared to DMAS., Submitted in 24th Iranian Conference on Biomedical Engineering (ICBME 2017)

Delay-and-Sum (DAS) beamformer is the most common beamforming algorithm in Photoacoustic imaging (PAI) due to its simple implementation and real time imaging. However, it provides poor resolution and high levels of sidelobe. A new algorithm named Delay-Multiply-and-Sum (DMAS) was introduced. Using DMAS leads to lower levels of sidelobe compared to DAS, but resolution is not satisfying yet. In this paper, a novel beamformer is introduced based on the combination of Minimum Variance (MV) adaptive beamforming and DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation leads to some terms which contain a DAS equation. It is proposed to use MV adaptive beamformer instead of existing DAS inside the DMAS algebra expansion. MVB-DMAS is evaluated numerically compared to DAS, DMAS and MV and Signal-to-noise ratio (SNR) metric is presented. It is shown that MVB-DMAS leads to higher image quality and SNR for about 13 dB, 3 dB and 2 dB in comparison with DAS, DMAS and MV, respectively., Comment: SPIE Digital Optical Technologies, 2017, Munich, Germany. arXiv admin note: substantial text overlap with arXiv:1709.07965

Spectrum sensing is a key function of cognitive radios and is used to determine whether a primary user is present in the channel or not. Many approaches have been proposed when both primary user and secondary user employ a single antenna. Recently several techniques have also been proposed assuming that the the secondary user employs multiple antennas. In this paper, we formulate and solve the generalized likelihood ratio test (GLRT) for spectrum sensing when both primary user transmitter and the secondary user receiver are equipped with multiple antennas. We do not assume any prior information about the channel statistics or the primary user's signal structure. Two cases are considered when the secondary user is aware of the energy of the noise and when it is not. The final test statistics derived from GLRT are based on the eigenvalues of the sample covariance matrix. Through analysis we exhibit the role of the eigenvalues in characterizing the signal ${+}$ noise and noise subspaces in the received data. Simulation results are presented in terms of the receiver operating characteristics and detection probabilities for several cases of interest.

This paper presents Ipris (Intra-perinodular textural transition), a new radiomic method, to automatically distinguish between benign and malignant nodules on routine lung CT scans. Ipris represents a minimal set of quantitative measurements which attempt to capture the transition in textural appearance going from the inside to the outside of the nodule. Briefly the approach involves partitioning the 3D volume and interface of the nodule into K nested shells. Then, a set of 48 Ipris features from 2D slices of the shells are extracted. The features pertain to the spiculations, intensity and gradient sharpness obtained from intensity differences between inner and outer voxels of an interface voxel. The Ipris features were used to train a support vector machine classifier in order to distinguish between benign (granulomas) from malignant (adenocarcinomas) nodules on non-contrast CT scans. We used CT scans of 290 patients from multiple institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. Independent validation of the Ipris approach yielded an AUC of 0.83 whereas, the established textural and shape radiomic features yielded a corresponding AUC of 0.75, while the AUCs for two human experts (1 pulmonologist, 1 radiologist) yielded corresponding AUCs of 0.69 and 0.73.

In this paper, we consider the problem of spectrum sensing in cognitive radios (CRs) when the receiver of the secondary user (SU) is equipped with a multiantenna system. Using an estimate of the cross correlation among the signals received at different antenna elements, we propose a blind detection method, which assumes no prior knowledge of the signaling scheme used by the PU, the noise power, or the channel path coefficients. The cross correlation among the received signals is a result of the correlation among the channel path coefficients from the primary user (PU) transmitter to different antenna elements of the secondary receiver. The detection and false alarm probabilities of the proposed algorithms are evaluated using an asymptotic analysis, and the results are compared with simulation results. It is shown that the proposed methods outperform several recently proposed blind-sensing techniques for CRs using multiple antennas.

Distributed detection in wireless sensor networks in the presence of one or more classes of misbehaving nodes is considered. Misbehavior may arise from Byzantine attacks, or may be caused by other factors such as node failure due to hardware or software degradation. We consider binary hypotheses testing where the honest nodes transmit their binary decisions to the fusion center (FC), while the misbehaving nodes transmit fictitious decisions. The FC must identify the misbehaving nodes in order to remove their deleterious effect. We show that each class of nodes can be identified with its operating point on the ROC (receiver operating characteristic) curve. Maximum likelihood estimation of the nodes' operating points is then formulated and solved as an expectation maximization (EM) problem with the nodes' identities as latent variables. The solution from the EM algorithm is then used to classify the nodes and to solve the distributed hypothesis testing problem. Numerical results compared with those from the reputation-based schemes show a significant improvement in both classification of the nodes and hypothesis testing results. We also discuss an inherent ambiguity in the node classification problem and explain how it can be resolved.

A simple and practical algorithm to design an irregular Low Density Parity Check (LDPC) codes for finite length applications is introduced. The simulation result will be proved the advantages of designed codes in compare with conventional regular and irregular finite length LDPC codes. Due to the semi-random structure of proposed codes, it will be encoded simpler than the other LDPC codes. Finally, a modified Extended Bit Filling (EBF) is proposed to control rows weight.