Zahra Kavehvash

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.

Coherent plane wave compounding ultrasound imaging combines low-resolution frames acquired from multiple angles to generate a high-quality image. However, achieving an optimal balance between frame rate and image quality is challenging, as increasing the number of emission angles leads to a reduction in frame rate. In scenarios with sub-sampled angles, selecting an optimal subset of angles becomes crucial to minimizing data acquisition time while preserving image quality comparable to all-angle transmission. To address this challenge, we propose two methods: Coprime sub-sampled angle (CSA) and semi sub-sampled angle (SSA). These approaches strategically select two subsets of angles to effectively suppress grating lobes resulting from down-sampling in the transmission angle intervals. Unlike traditional methods like random or periodic subsampling, CSA and SSA offer controlled and robust suppression of grating lobes while maintaining image quality, making them ideal for sub-sampled angle configurations. Our validation with the Plane-wave Imaging Challenge in Medical Ultrasound dataset shows that CSA achieves comparable resolution with an 8.4% contrast ratio improvement using only 25.3% of all available angles. Meanwhile, SSA achieves similar resolution with a 7.7% contrast gain, using 36% of the angles, while effectively preserving speckle quality.

Synthetic transmit aperture (STA) ultrasound (US) imaging, despite its notable advantages, suffers from hardware and computational complexity leading to a high system cost, especially for an array with an excessive number of elements. Sub-array beamforming (SAB) or micro-beamforming has received attention for its ability to reduce the number of channels and cabling between the US probe and the back-end console. However, studies have shown that channel reduction in SAB (1) leads to severe imaging artifacts and loss of quality due to grating lobes and (2) reduces the spatial resolution and size of the field of view (FOV). This work proposes a method to reduce the number of acquisition channels while maintaining image quality compared to the standard STA imaging. To achieve this, we first utilize k-space analysis to evaluate the resolution in the STA US imaging system. This analysis determines the contribution of each transmitter and receiver in collecting spatial frequency domain data from each pixel within the FOV. Then two solutions based on sub-aperture beam steering in reception and beam diverging in transmission are presented to improve spatial resolution and FOV in SAB US images. In-silico, in-vitro, and in-vivo experimental results validated the efficacy of the proposed method. In comparison to delay-and-sum SAB and adaptive beamformer versions of the SAB, the proposed Diverged Transmitting beams with Steered Reception beams (DTSR) SAB method exhibits improvements by providing more resolved images with a larger FOV comparable to the standard STA imaging, while utilizing a reduced number of transmit events and acquisition channels.

Circular-view (circular scan) photoacoustic computed tomography (PACT) with low-density detection points (DPs) is an efficient, high-speed, and inexpensive modality with numerous (pre-) clinical applications. However, as the number of DPs decreases (decrease in A-lines), some unwanted streak artifacts appear in the reconstructed PACT image. Here, we present an approach to address the mentioned challenge and enhance image contrast. In this method, several low-resolution-images (LRIs) are reconstructed by employing a few DPs’ data with randomized locations. These LRIs are used in computing an artifact score matrix (ASM) to identify the location of artifacts. Three numerical (two vasculatures and human brain), two experimental (triangle and complex leaf), and an in vivo (a rat brain) studies were conducted to evaluate the efficacy of the proposed method (applying the computed ASM to the final reconstructed PA image). Our findings show that the proposed method outperforms conventional methods and offers better image quality. The signal-to-noise ratio and structural similarity index values of the proposed method are quantitatively 20 dB and 25% better than the conventional method, respectively. Furthermore, compared to the conventional method, the proposed method has an artifact standard deviation that is 50 times lower.

This article presents a direct time-of-flight (d-ToF) pulsed light detection and ranging (LiDAR) sensor with interference suppression and a linear-mode receiver implemented using discrete components. The sensor achieves 0.3⁰ (horizontal) × 1⁰ (vertical) angular resolution with 60 (H) × 8 (V) pixels using 2D mechanical scanning, covering 18⁰ (H) × 8⁰ (V) field-of-view (FoV). Measurements up to the range of 4.5m using 1D line scanning and a 100kHz laser source, show a frame rate of 16 fps and a precision of 0.2%. Furthermore, a novel coding mechanism is introduced that effectively suppresses interference signals, while adding minimal complexity to the regular time-correlated single-photon counting (TCSPC) processing and without compromising the sensor specifications, such as the maximum range and frame rate. The proposed technique can effectively suppress even very strong interference signals. In general, the proposed technique can provide up to 2 ^N times interference suppression using N-bit pseudo-random codes. The measurement results show an interference suppression of 15.29 dB using 3-bit pseudo-random coding, which follows the claimed 2 ^N rule with a good approximation.

This article proposes a new nonisolated, interleaved, high step-up, soft-switching converter for renewable energy applications. The proposed converter is a boost topology designed to have a continuous input current with low ripple. It employs no auxiliary circuit or extra switches to achieve soft-switching, and the voltage stress on the power switches is reduced. By using the transformer's leakage inductance, the power diodes’ reverse recovery current problem is tackled without the need for an extra snubber circuit. The proposed converter has been analyzed and compared with similar topologies. Experimental results from a 400-W prototype with a 30–300 V conversion ratio are presented, demonstrating the effectiveness of the proposed converter, which is well-suited for use with renewable energy sources.

In a photoacoustic (PA) imaging system, the detectors are bandwidth-limited. Therefore, they capture PA signals with some unwanted ripples. This limitation degrades the resolution/contrast and induces sidelobes and artifacts in the reconstructed images along the axial direction. To compensate for the limited bandwidth effect, we present a PA signal restoration algorithm, where a mask is designed to extract the signals at the absorber positions and remove the unwanted ripples. This restoration improves the axial resolution and contrast in the reconstructed image. The restored PA signals can be considered as the input of the conventional reconstruction algorithms (e.g., Delay-and-sum (DAS) and Delay-multiply-and-sum (DMAS)). To compare the performance of the proposed method, DAS and DMAS reconstruction algorithms were performed with both the initial and restored PA signals on numerical and experimental studies (numerical targets, tungsten wires, and human forearm). The results show that, compared with the initial PA signals, the restored PA signals can improve the axial resolution and contrast by 45% and 16.1 dB, respectively, and suppress background artifacts by 80%.

Circular full-view configuration of photoacoustic imaging systems (C-PAI) has many applications in biomedicine (e.g., breast and brain imaging). To obtain a high-quality reconstructed image, dense spatial sampling (a large number of acoustic detectors) is needed, which makes the system expensive and challenging. Unfortunately, by reducing the number of spatial samples, streak artifacts appear, which degrade the quality of the reconstructed image. In this article, we propose a spatial-domain factor to suppress the streak artifacts and enhance the reconstructed image quality in a sparse sampling C-PAI system. Numerical and experimental studies are conducted to evaluate the proposed method. The results show that by reducing the number of spatial samples by one-fifth of the minimum required to meet the Nyquist criteria, the proposed method provides a higher quality reconstructed image in terms of artifacts suppression and resolution improvement compared to the conventional method with dense spatial sampling. The proposed method improves the structural similarity index measure (SSIM), generalized contrast-to-noise ratio (gCNR), CNR, and tangential resolution values up to 100%, 9%, 38.6 dB, and ∼45%, respectively. Based on the advantages of the proposed method, a low-cost version of a C-PAI system for clinical applications can be developed.