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Objective. Synthetic transmit aperture (STA) ultrasound imaging is well known for ideal focusing in the full field of view. However, it suffers from low signal-to-noise ratio (SNR) and low frame rate, because each transducer element must be activated individually. In our previous study, we encoded all the transducer elements with partial Hadamard matrix and reconstructed the complete STA dataset with compressed sensing (CS) algorithm (CS-STA). As all the elements are activated in each transmission and the number of transmissions is smaller than that of STA, this method can achieve higher SNR and higher frame rate. Its main drawback is the time-consuming CS reconstruction (∼hours). In this study, we propose to accelerate the complete STA dataset reconstruction with minimal l 2-norm least squares method. Approach. Partial Hadamard apodized plane wave (PW) transmissions were performed to acquire the PW dataset. Thereafter, the complete STA dataset can be reconstructed from the PW dataset with minimal l 2-norm least squares method. Due to the orthogonality of partial Hadamard matrix, the minimal l 2-norm least squares solution can be easily calculated. Main results. The proposed method is tested with simulation data and experimental phantom and in-vivo data. The results demonstrate that the proposed method achieves ∼5 × 103 times faster reconstruction speed than CS algorithm. The simulation results demonstrate that the proposed method is capable of achieving the same accuracy as the conventional CS-STA method for the STA dataset reconstruction. The simulations, phantom and in-vivo experiments show that the proposed method is capable of improving the generalized contrast-to-noise ratio (gCNR) and SNR with maintained spatial resolution and fewer transmissions, compared with STA. Significance. In conclusion, the improved image quality and reduced computational time of LS-STA pave the way for its real-time applications in the clinics. [ABSTRACT FROM AUTHOR]

Purpose: As a typical chronic kidney disease (CKD), hypertensive nephrosclerosis (HN) is a common syndrome of hypertension, characterized by chronic kidney microvascular damage. Early diagnosis of microvascular damage using conventional ultrasound imaging encounters challenges in sensitivity and specificity owing to the inherent diffraction limit. Ultrasound localization microscopy (ULM) has been developed to obtain microvasculature and microvascular hemodynamics within the kidney, and would be a promising tool for the early diagnosis of CKD. Methods: In this study, the advantage of quantitative indexes obtained by using ULM (mean arterial blood flow speeds of different segments of interlobular arteries) over indexes obtained using conventional clinical serum (β2‐microglobulin, serum urea nitrogen, and creatinine) and urine (24‐h urine volume and urine protein) tests and ultrasound Doppler imaging (peak systolic velocity [PSV], end‐diastolic velocity [EDV], and resistance index [RI]) and contrast‐enhanced ultrasound imaging (CEUS; rise time [RT], peak intensity [IMAX], mean transit time [mTT], and area under the time‐intensity curve [AUC]) for early diagnosis of HN were investigated. Examinations were carried out on six spontaneously hypertensive rats (SHR) and five normal Wistar–Kyoto (WKY) rats at the age of 10 weeks. Results: The experimental results show that the indicators derived from conventional clinical inspections (serum and urine tests) and ultrasound imaging (PSV, EDV, RI, RT, IMAX, mTT, and AUC) do not show significant difference between hypertensive and healthy rats (p > 0.05), while the TTP of the SHR group (28.52 ± 5.52 s) derived from CEUS is significantly higher than that of the WKY group (18.68 ± 7.32 s; p < 0.05). The mean blood flow speed in interlobular artery of SHR (12.47 ± 1.06 mm/s) derived from ULM is significantly higher than that of WKY rats (10.13 ± 1.17 mm/s; p < 0.01). Conclusion: The advantages of ULM over conventional clinical inspections and ultrasound imaging methods for early diagnosis of HN were validated. The quantitative results show that ULM can effectively diagnose HN at the early stage by detecting the blood flow speed changes of interlobular arteries. ULM may promise a reliable technique for early diagnosis of HN in the future. [ABSTRACT FROM AUTHOR]

• In-phase/quadrature (IQ) data were used to accelerate CS-STA. • IQ-domain CS-STA maintains high image quality of conventional RF-domain CS-STA. • CS-STA obtains similar resolution, higher contrast and higher frame rate, compared with STA. • More compatible with clinical ultrasound system than RF-domain CS-STA. Compressed sensing-based synthetic transmit aperture (CS-STA) was previously proposed to recover the full radio-frequency (RF) channel dataset of synthetic transmit aperture (STA) from that of a smaller number of randomly apodized plane wave (PW) transmissions. In this way, the imaging frame rate (FR) and contrast are improved with maintained spatial resolution, compared with those of STA. Because CS-STA reconstruction is repeated for all receive elements and RF samples (with a high sampling frequency), the recovery of STA dataset in RF domain is time-consuming. In the meantime, a large amount of RF data needs to be transferred and stored, resulting in an increase of system complexity and required memory space. In this study, CS-STA is extended to in-phase/quadrature (IQ) domain (with lower sampling frequency) for the recovery of baseband STA IQ dataset to accelerate the CS-STA reconstruction by reducing the amount of data to be processed. More importantly, CS-STA reconstruction using IQ data is of practical importance, as clinical ultrasound systems typically record baseband IQ signal instead of RF signal. Simulations, phantom and in vivo experiments verify the feasibility of CS-STA in IQ domain for the recovery of STA dataset. More specifically, CS-STA using IQ data achieves similar image quality and appreciably improves reconstruction speed (by ∼3 times) compared with that using RF data. These findings demonstrate that IQ-domain CS-STA is capable of relieving the computational and storage burdens, which may facilitate the implementation of CS-STA in practical ultrasound systems. [ABSTRACT FROM AUTHOR]

• A novel self-supervised US reconstruction method based on deep neural network. • An encoder-decoder architecture produces high-quality US images. • Various regularizations are used to improve the reconstruction quality. • Thousand times faster than the other sparse regularization methods. • Coherent compounding is applied to further enhance the image quality. Image reconstruction from radio-frequency (RF) data is crucial for ultrafast plane wave ultrasound (PWUS) imaging. Compared with the traditional delay-and-sum (DAS) method based on relatively imprecise assumptions, sparse regularization (SR) method directly solves the inverse problem of image reconstruction and has presented significant improvement in the image quality when the frame rate remains high. However, the computational complexity of SR is too high for practical implementation, which is inherently associated with its iterative process. In this work, a deep neural network (DNN), which is trained with an incorporated loss function including sparse regularization terms, is proposed to reconstruct PWUS images from RF data with significantly reduced computational time. It is remarkable that, a self-supervised learning scheme, in which the RF data are utilized as both the inputs and the labels during the training process, is employed to overcome the lack of the "ideal" ultrasound images as the labels for DNN. In addition, it has been also verified that the trained network can be used on the RF data obtained with steered plane waves (PWs), and thus the image quality can be further improved with coherent compounding. Using simulation data, the proposed method has significantly shorter reconstruction time (∼10 ms) than the conventional SR method (∼1-5 mins), with comparable spatial resolution and 1.5-dB higher contrast-to-noise ratio (CNR). Besides, the proposed method with single PW can achieve higher CNR than DAS with 75 PWs in reconstruction of in-vivo images of human carotid arteries. [Display omitted] [ABSTRACT FROM AUTHOR]