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.

A new frequency-domain implementation of a synthetic aperture focusing technique is presented in the paper. The concept is based on synthetic aperture radar (SAR) and sonar that is a developed version of the convolution model in the frequency domain. Compared with conventional line-by-line imaging, synthetic aperture imaging has a better resolution and contrast at the cost of more computational load. To overcome this problem, point-by-point reconstruction methods have been replaced by block-processing algorithms in radar and sonar; however, these techniques are relatively unknown in medical imaging. In this paper, we extended one of these methods called wavenumber to medical ultrasound imaging using a simple model of synthetic aperture focus. The model, derived here for monostatic mode, can be generalized to multistatic as well. The method consists of 4 steps: a 2D fast Fourier transform of the data, frequency shift of the data to baseband, interpolation to convert polar coordinates to rectangular ones, and returning the data to the spatial-domain using a 2D inverse Fourier transform. We have also used chirp pulse excitation followed by matched filtering and spotlighting algorithm to compensate the effect of differences in parameters between radar and medical imaging. Computational complexities of the two methods, wavenumber and delay-and-sum (DAS), have been calculated. Field II simulated point data have been used to evaluate the results in terms of resolution and contrast. Evaluations with simulated data show that for typical phantoms, reconstruction by the wavenumber algorithm is almost 20 times faster than classical DAS while retaining the resolution.

Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that regions with high degree values could be detected as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in PD.