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Purpose: Utilizing Electroencephalogram (EEG) is more than at any time in history, therefore we have introduced an open-source MATLAB function to provide simulated EEG which is as equivalent as viable to empirical EEG in a user-friendly way with ground truth that is not accessible in real EEG records. This function should be versatile due to the requirements such as the number and orientation of sources, various noises, mode of activation function, and different anatomical structures. Materials and Methods: We indicate all phases, modes, and formulas which constitute EEGg, EEG generator. This function supports selecting main sources locations and orientation, choosing SNR with white Gaussian noise, electrode numbers, and mode of activation functions. Also, users have the option to use automatic or partly automatic, or fully automatic EEG construction in EEGg. This function is ready to use at https://github.com/Avayekta/EEG. Results: EEGg is designed with several parameters that users have chosen. Hence, users can choose different variables to inspect the time and frequency aspects of synthetic EEG. Conclusion: EEGg is a multi-purpose and comprehensive function to mimic EEG but with ground-truth EEG data and adjustable parameters.

Purpose: A new code based on Helmholtz decomposition is presented to separate longitudinal (pressure) and transverse (shear) components of a mixed wave field. This algorithm will help isolate shear or pressure components of an elastic wave to further concentrate on each specific wave and its physical characteristics, particularly in medical imaging instrument development and image processing techniques. Materials and Methods: Using the combination of Fourier transform and Helmholtz decomposition, first, the mathematical basis of the work is prepared. After reaching a usable formula, this basis is embedded in the Code written in MATLAB program. Then, various test data containing shear and pressure waves were created and fed to the Code to evaluate its ability to decompose the displacements into the shear and pressure waves. Results: This new algorithm successfully isolated the transverse and longitudinal wavefront of the mixed wavefield. The Code demonstrated 100% accuracy for separating the shear wave and more than 99% for the pressure wave. Moreover, the background noise was kept under 0.03% in every step. Conclusion: The results show that using Helmholtz decomposition in Fourier space on 3D data can help decompose a displacement field into its irrotational and solenoidal components with high accuracy. A weak dependency on wave thickness and contrast was observed, but the algorithm's accuracy never fell below 99%.

Elastography is an imaging technique with the ability to determine low quantities of some of the mechanical properties of tissues. The aim of our research is to design a new 3D algorithm using the Shifted Fourier Transform (SFT) to perform a quasi-static elastography. Our innovative idea is implementation of a 3D convolution instead of using three 2D convulsions. At first, we collected the raw data from Abaqus engineering software in the form of breast tissue with a coefficient of elasticity of healthy tissue and tumor tissue with a coefficient of elasticity of tumor tissue. The primary raw data consists of a number of points with x, y and z specified for tumor and healthy breast tissue. At this step, we simulated the displacements in directions of x, y and z at each point of the prescribed tissues for 15 ​mm displacement of probe in –Y direction then we collected 1831 points for tumor and 4186 points for breast before and after pressure. After applying a novel reconstruction algorithm, we convolved all images with the 3D Gabor filters to obtain phases, represented displacements of the breast and tumor images for before and after pressure. To reach this goal, we designed a Gabor filter bank based on the dimensions of the input images in different scales, directions, and deviations. Using the 3D SFT, we calculated the displacements of the breast and tumor tissues followed by 3D elastogram representation of the images. Finally, we implemented a 2D analysis of SFT in order to investigate validation of the 3D SFT. In 2D algorithm, we used three two-dimensional convulsions in XY, YZ and XZ planes. The results obtained from the small displacements marked by circles, confirmed the accuracy of the 3D SFT algorithm. These areas of interest are the tumor areas in the 2D analysis.

Purpose: For over three decades, various researchers have aimed to construct a model of breast cancer. Most of them have used an infrared thermal model to stimulate breast cancer, but in this study, a novel estimation methodology is presented to detect the breast cancer tumor using the surface measurement obtained by Passive Acoustic Thermometer (PAT). PAT is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Materials and Methods: This article uses a simulation framework for breast tissue simulation and tumor detection using the PAT methodologies in different scenarios. This framework supports the generation of acoustic radiation, tissue modelling, signal processing, parameter estimation, and temperature reconstruction processes. The proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Using the proposed framework, PAT has been evaluated in breast cancer detection. Results: According to the results, obtained from the temperature estimation in scenario 3, the sub-band estimation method, which is utilized in practical experiments in this field, shows different errors in each sub-band, making it difficult to select the true estimation. Therefore, a novel formulation is proposed that provides only one estimated temperature for breast tissue with a reasonable error (1.28 degrees) for tumor detection. Conclusion: The results show that it is possible to use this framework to evaluate the PAT in different scenarios for tumor detection. In fact, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiments.

This article summarizes the first and second Iranian brain–computer interface competitions held in 2017 and 2018 by the National Brain Mapping Lab. Two 64-channel electroencephalography (EEG) datasets were contributed, including motor imagery as well as motor execution by three limbs. The competitors were asked to classify the type of motor imagination or execution based on EEG signals in the first competition and the type of executed motion as well as the movement onset in the second competition. Here, we provide an overview of the datasets, the tasks, the evaluation criteria, and the methods proposed by the top-ranked teams. We also report the results achieved with the submitted algorithms and discuss the organizational strategies for future campaigns.

Abstract Background In Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough, compared to the high resolution beamformers such as minimum variance (MV). In this paper, it is proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF. Results The higher performance of HRCF is proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91% and 15% and the signal-to-noise ratio about 40% and 14%, respectively. Conclusion Proposed method provides a high resolution along with a low level of sidelobes for PAI.

Purpose: Internal temperature is a significant factor for medical diagnosis. There are several thermometric methods, including IR, MRI, and active ultrasonic thermometry, which have limitations for clinical applications. The new method in this field called Passive Acoustic Thermometry (PAT), which enhanced some of this limitation. PAT is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Several experimental studies have been carried out so far in the field of PAT. While, to the best of our knowledge, there is no simulation-based research for nonhomogeneous materials reported yet. In this article (for the first time) we proposed a simulation framework for evaluating the PAT methodologies in nonhomogeneous materials; also we proposed a new formulation for temperature estimation in PAT algorithm. Materials and Methods: This framework supports the generation of acoustic radiation, signal processing, parameter estimation, and temperature reconstruction processes. At the moment the proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Using the proposed framework, we tried to implement the previously practical experiments and the results of the simulation are consistent with those of the practical experiments. Also, we proposed the formulation that improves the error of temperature estimation. Results: We study 6 scenarios, including 2 environments with a target at 3 different temperatures. The average error of the proposed formulation in two different nonhomogeneous materials for three different temperatures is less than 0.25°C. Conclusion: The results show that the proposed formulation is the best estimation in the formula that has been introduced until now and compare with the previous study the accuracy is enhanced 54% (from 0.79 to 0.36 deg.). Therefore, the proposed formula enhanced PAT accuracy for temperature estimation. Also, the results show that it is possible to use this framework to evaluate the PAT in different scenarios. Therefore, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiment.

Acoustic thermometry is one of non-invasive methods to measure the temperature inside tissue area. especially when using the thermal therapy techniques such as High Intensity Focused Ultrasound (HIFU) and other thermal based methods, thermal assessment of heated area seems to be necessary. Some of acoustic properties of medium are temperature dependent, therefore, evaluation of temperature dependent parameters will be an indirect and non-invasive approach in thermometry. In this paper some of thermometry methods based on changes in acoustic properties of medium has been reviewed. The published methods are classified in two main categories: passive and active thermometry. In the passive thermometry, the thermal measurement probes, induced no acoustic signals to the medium, but they receive the radiated signals from the heated medium. In active method, the thermometry probe transmits a signal into the heated region and receive the echoes, then the received RF signals are processed in order to measure the temperature.

Purpose: Passive Acoustic Thermometer (PAT) is a safe method for internal temperature estimation that works based on acoustic radiation of materials with a specific temperature. Several experimental studies have been carried out so far in the field of PAT. While, to the best of our knowledge, there is no simulation-based research reported yet. Materials and Methods: In this article (for the first time) we proposed a simulation framework for evaluating the PAT methodologies. This framework supports the generation of acoustic radiation, signal processing, parameter estimation, and temperature reconstruction processes. At the moment, the proposed framework estimates the temperature in the frequency domain and uses the frequency spectrum of the acquired ultrasound signals captured by a single transducer. Results: Using the proposed framework, we tried to implement previously practical experiments and the results of the simulation are consistent with those of the practical experiment. The mean error of temperature estimation was below 0.45 °C. The results show that it is possible to use this framework to evaluate the PAT in different scenarios. Conclusion: Therefore, this method enhances the possibility of examination of different conditions and algorithms. It also reduces the cost of practical experiment.

Purpose: Event-Related Potentials (ERPs) have been used in addiction studies to evaluate cognitive performance and craving in individuals with Substance Use Dependence (SUD). This paper reviews studies that used ERPs to investigate cue reactivity, inhibitory control and error processing in SUDs to integrate new findings. Materials and Methods: Five abused substances are included in the investigation, i.e. alcohol, nicotine, cannabis, cocaine, and methamphetamine. For each substance, the main recent findings related to the ERPs are specifically discussed, according to the latency of ERPs. Results: Individuals with SUD allocate more attention resources to the cognitive processing of substance related cues, indexed by increased amplitude of middle and late latency ERPs. Laboratory observations also show amplitude enlargement for early latency ERPs. Individuals with SUD reveal a deficiency in the inhibitory control and conscious error processing, indexed by attenuated N2 and Pe amplitude. Conclusion: This study expands the findings of previous related reviews implying that substance abusers allocate more attention resources to drug cues indexed by enlarged P3 and LPP amplitude. Regarding P3 elicited in inhibitory control tasks, there is not still convergent results, while N2 and Pe become attenuated as reported in previous reviews. The cognitive and motor inhibitory component (P3) changes show a controversial result.

Purpose: The mechanical map of liver tissue like stiffness is as important as its anatomical image for clinical purposes like staging the liver fibrosis. Acoustic radiation force-based shear waves interference patterns elastography is an interesting independent imaging rate technique which can generate shear waves in the liver tissue in any desired depth by means of the high intensity focused, long duration push beams. Because of wave attenuation and absorption process the sound wave energy is dissipated in the tissue and due to energy conservation law is turned into heat thus like any other ultrasound imaging modality, shear waves interference patterns elastography carries the risk of tissue heating and thermal ablation specially at the focal spot. Therefore, particular attention must be paid to the thermal safety assessment to shear waves interference patterns elastography. The aim of the present simulation study is the thermal safety evaluation in the liver tissue. Materials and Methods: The liver tissue has been simulated in the presence of its adjacent tissues like skin, muscle, ribs and intercostal muscles in 3 dimensions during shear waves interference patterns elastography. With 4 seconds exposure time and 2 MPa focal pressure. Results:Temperature at the focal point increases from normal body temperature (37˚C) to 47˚C. Conclusion: Thermal effects appraisal, indicates that the general tissue heating stays within the safe region.

Purpose: Variations in the mechanical properties of soft tissues may be a sign of a disease. Since some disease like fibrosis or cancer change the stiffness of related tissues, we can assess the disease of a soft tissue with its elasticity. The elastic stiffness properties of soft tissues can be estimated using locally induced displacements and shear waves. Materials and Methods: A two-dimensional plane finite element model has been created as soft tissue. The soft tissue has been exposed to two Amplitude Modulated High Intensity Focused Ultrasound transducer (AMHIFU), hence shear wave interference patterns which can be captured by lower frame rate imaging are generated. The acoustic radiation force created by a self-focusing ultrasound transducer has been determined from an ultrasound pressure field simulation. A Gaussian function was fitted to the resulting Acoustic Radiation Force (ARF) field and implemented in the form of a body force in the finite element model. Results: The effect of different excitation parameters for their optimization in the elasticity estimation has been investigated. Conclusion: In the result section, the effect of ARF excitation parameters on shear wave elasticity measurements has been represented. Shear wave interference pattern elastography which does not need high frame rate imaging with optimized parameters can be used as a non-invasive method for measuring the elastic stiffness of soft tissues.