Mahdi Shabany

In pulse-echo ultrasound (US) imaging, achieving higher lateral resolution requires increasing the transmit frequency, which results in penetration depth reduction. Given this trade-off between penetration depth and spatial resolution, we propose a method to resolve sub-diffraction scale structures in synthetic aperture (SA) US imaging. While structured illumination (SI) has previously been employed in US imaging, we introduce its application in SA US imaging to achieve superior resolution. In our approach, grating patterns are superimposed on the object to exploit high-frequency information, enhancing spatial resolution beyond conventional diffraction limits. However, a key challenge arises in applying SI to SA imaging: structured patterns cannot be directly generated using the single-element or multi-element transmissions inherent to SA. To overcome this, we introduce a novel method where pseudo-random plane wave (PW) illuminations are applied, followed by the retrieval of SA data from the acquired dataset. Five SA images corresponding to different SI pattern shifts are reconstructed and post-processed to generate a high-resolution structured illumination synthetic aperture (SISA) image. In-silico point spread function results reveal a 122% and 47% enhancement in lateral resolution when compared to conventional PW imaging (one-way focusing) and SA imaging (two-way focusing) for a grating pattern with a spatial frequency of 0.91 mm⁻¹. Furthermore, two additional in-silico studies were conducted to evaluate contrast and resolution under realistic conditions, demonstrating the superior performance of our proposed method compared to the previously reported acoustic SI technique. Additionally, two experiments validated the contrast and resolution performance of the proposed method in in-vitro conditions. The method possesses a wide range of applications, particularly in the detection of lesions demanding high spatial resolution.

Monitoring the security of important and busy places such as airports and train stations is very important. Millimeter-wave (MMW) security scanning systems are among the most prominent technologies used for this purpose. One of the key advantages of these systems is the automatic detection of objects hidden under clothing from millimeter wave images using deep neural networks, which increases the speed and accuracy of object identification and preserves people’s privacy. In this article, three different deep neural networks are investigated for automatic object detection in millimeter wave images. Still, due to the variety of humans’ body structures, objects and materials, a huge dataset gathered in a variety of conditions is a must. Furthermore, materials with low reflection coefficients have a very low contrast on human body, which makes their detection with any automatic detection algorithm a real challenge. To overcome these challenges, in this article two new data augmentation methods; i.e., the spectral data enhancement and geometrical data augmentation; are proposed, tailored based on the characteristics of the millimeter wave imaging system. The proposed geometrical data augmentation method increases the number and diversity of the training dataset required for training deep neural networks in the targeted subject. While, the proposed spectral data enhancement adds an extra dimension to the input data to overcome the low contrast of some materials on the body. The presented results in Section V show that the use of these data augmentation methods increases the accuracy of deep neural networks significantly, which ultimately help to detect objects of variant reflectivity level millimeter wave images with a limited dataset.

This article focuses on dealing with aliasing artifacts in millimeter-wave imaging systems. To address the challenges and complexities of designing multistatic arrays, sparse arrangements are utilized in practical imaging systems. Depending on the arrangement of antennas, such arrays may present severe aliasing artifacts. Some techniques have already been introduced to tackle such artifacts and enhance the image quality. These techniques may distort the target image by causing some loss in parts of the image, impose high computational/system costs, or suffer from the lack of generalizability. One of these techniques uses nonuniform arrays. Such arrays are designed by optimizing the point spread function (PSF). The PSF is a suitable representative of the system’s performance provided that the system is shift-invariant. Since nonuniform arrays are shift-variant, these techniques are unreliable, especially in the case of off-center targets. As a consequence, there are no frameworks to design nonuniform arrays on a reliable basis to suppress aliasing artifacts in the whole region of interest (ROI). To address this issue, based on the k-space analysis of aliasing in multistatic arrays and nonuniform Fourier transform concept, we propose a small random displacement to be applied to antennas’ location in sparse periodic arrays (SPAs). This displacement prevents the aliasing components from coherently accumulating. Nevertheless, due to the imposed nonuniformity, it is impossible to use FFT-based algorithms in this case. A subsequent resampling or the use of nonuniform FFT can address this problem. The provided simulations and experimental measurements show that the proposed method can efficiently lower the peak level of aliasing artifacts by around 3 dB compared with SPAs. So, the proposed method can be used as an effective way to suppress aliasing artifacts in real-time multistatic millimeter-wave imaging systems.

This article focuses on analyzing the aliasing artifact in millimeter-wave imaging systems, with a special focus on multistatic arrays. The current framework to analyze the behavior of multistatic structures is based on the effective aperture concept. Based on this framework, an equivalent monostatic array, approximating the position of each transmitter-receiver pair by its midpoint, is used to quantify the response and efficiency of the system. Although this framework helps to simplify the study of the complex characteristics of multistatic arrays, it suffers from vital deficiencies. Especially, it fails to describe the aliasing artifacts, seen in the image of some sparse multistatic configurations, where the study of their equivalent monostatic arrays proves that the Nyquist sampling rate is satisfied. In order to explain such behaviors, we propose a k-space-based framework to evaluate multistatic imaging systems. This framework not only explains the reason behind the aliasing artifacts but also estimates the locations where the aliasing happens. Moreover, this analysis can be beneficial to design multistatic configurations with reduced aliasing artifacts. The provided simulations and the experimental results prove the accuracy of the proposed framework that can effectively estimate the occurrence and the place of aliasing artifacts.

This paper focuses on an ef?cient approach of designing multi-static arrays for millimeter-wave imaging, based on the k-space or Fourier-spatial domain characteristic of imaging systems. Our goal is to decrease the redundancy of the data measured by each antenna, and to improve the resolution of the reconstructed image. The proposed technique is based on determining the role of each transmitter and receiver, in collecting the data from each voxel of the target in k-space domain and then rotating transmitters’ beams to measure the desirable information. The effect of non-uniform redundant k-space domain frequency samples that act as an undesirable ?lter, is compensated using a modi?ed SAR back-projection algorithm. Experimental and simulation results are presented and compared with that of a sparse multi-static array with the same number of transmitters and receivers. Our simulations and measurements show signi?cant improvement in terms of overall quality and edge preservation in the reconstructed images. Also, the obtained results demonstrate that using the proposed structure and algorithm, the average improvement in peak-signalto-noise ratio (PSNR), structural similarity index measure (SSIM) and digital image correlation (DIC) metrics of 3.03 dB, 0.22 and 0.173, are achieved, respectively.

This brief introduces a modified backprojection algorithm to compensate the effect of redundancy of captured data in spatial-fourier or k-space domain for multistatic millimeter-wave imaging. The data measured by each transmitter-receiver pair is explained in k-space domain and the redundancies are determined. Such redundancies act as an undesirable filter that distorts the appearance of the resulting image. Our goal is to modify SAR backprojection algorithm to address this problem, where extensive simulation and experimental results are also provided. Our measurements show a significant improvement in the overall quality and edge preservation in the reconstructed image of objects under the test through the proposed reconstruction algorithm.

This paper presents a novel blood pressure (BP) estimation method based on pulse transit time (PTT) and pulse arrival time (PAT) to estimate the systolic BP (SBP) and the diastolic BP (DBP). A data acquisition hardware is designed for high-resolution sampling of phonocardiogram (PCG), photoplethysmogram, and electrocardiogram (ECG). PCG and ECG perform as the proximal timing reference to obtain PTT and PAT indices, respectively. In order to derive a BP estimator model, a calibration procedure, including a supervised physical exercise, is conducted for each individual, which causes changes in their BP, and then, a number of reference BPs are measured alongside the acquisition of the signals per subject. It is suggested to use a force-sensing resistor that is placed under the cuff of the BP reference device to mark the exact moments of reference BP measurements, which are corresponding to the inflation of the cuff. Additionally, a novel BP estimator nonlinear model, based on the theory of elastic tubes, is introduced to estimate the BP using PTT/PAT values precisely. The proposed method is evaluated on 32 subjects. Using the PTT index, the correlation coefficients for SBP and DBP estimation are 0.89 and 0.84, respectively. Using the PAT index, the correlation coefficients for SBP and DBP estimation are 0.95 and 0.84, respectively. The results show that the proposed method, exploiting the introduced nonlinear model with the use of PAT index or PTT index, provides a reliable estimation of SBP and DBP.