Effective speckle reduction in ultrasound B-mode imaging is important for enhancing the image quality and improving the accuracy in image analysis and interpretation. In this paper, a new feature-enhanced speckle reduction (FESR) method based on multiscale analysis and feature enhancement filtering is proposed for ultrasound B-mode imaging. In FESR, clinical features (e.g., boundaries and borders of lesions) are selectively emphasized by edge, coherence, and contrast enhancement filtering from fine to coarse scales while simultaneously suppressing speckle development via robust diffusion filtering. In the simulation study, the proposed FESR method showed statistically significant improvements in edge preservation, mean structure similarity, speckle signal-to-noise ratio, and contrast-to-noise ratio (CNR) compared with other speckle reduction methods, e.g., oriented speckle reducing anisotropic diffusion (OSRAD), nonlinear multiscale wavelet diffusion (NMWD), the Laplacian pyramid-based nonlinear diffusion and shock filter (LPNDSF), and the Bayesian nonlocal means filter (OBNLM). Similarly, the FESR method outperformed the OSRAD, NMWD, LPNDSF, and OBNLM methods in terms of CNR, i.e., 10.70 ± 0.06 versus 9.00 ± 0.06, 9.78 ± 0.06, 8.67 ± 0.04, and 9.22 ± 0.06 in the phantom study, respectively. Reconstructed B-mode images that were developed using the five speckle reduction methods were reviewed by three radiologists for evaluation based on each radiologist's diagnostic preferences. All three radiologists showed a significant preference for the abdominal liver images obtained using the FESR methods in terms of conspicuity, margin sharpness, artificiality, and contrast, p<0.0001. For the kidney and thyroid images, the FESR method showed similar improvement over other methods. However, the FESR method did not show statistically significant improvement compared with the OBNLM method in margin sharpness for the kidney and thyroid images. These results demonstrate that the proposed FESR method can improve the image quality of ultrasound B-mode imaging by enhancing the visualization of lesion features while effectively suppressing speckle noise.

Noninvasive monitoring of blood flow in the carotid artery is important for evaluating not only cerebrovascular but also cardiovascular diseases. In this paper, a wireless neckband ultrasound Doppler system, in which two 2.5-MHz ultrasonic sensors are utilized for acquiring Doppler signals from both carotid arteries, is presented for continuously evaluating blood flow dynamics. In the developed wireless neckband Doppler system, the acquired Doppler signals are quantized by 14-bit analog-to-digital-converters running at 40 MHz, and pre-processing operations (i.e., demodulation and clutter filtering) are performed in an embedded field programmable gate array chip. Then, these data are transferred to an external smartphone (i.e., Galaxy S7, Samsung Electronics Co., Suwon, Korea) via Bluetooth 2.0. Post-processing (i.e., Fourier transform and image processing) is performed using an embedded application processor in the smartphone. The developed carotid neckband Doppler system was evaluated with phantom and in vivo studies. In a phantom study, the neckband Doppler system showed comparable results with a commercial ultrasound machine in terms of peak systolic velocity and resistive index, i.e., 131.49 ± 3.97 and 0.75 ± 0.02 vs. 131.89 ± 2.06 and 0.74 ± 0.02, respectively. In addition, in the in vivo study, the neckband Doppler system successfully demonstrated its capability to continuously evaluate hemodynamics in both common carotid arteries. These results indicate that the developed wireless neckband Doppler system can be used for continuous monitoring of blood flow dynamics in the common carotid arteries in point-of-care settings.

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Chirp-coded excitation can increase the signal-to-noise ratio (SNR) without degrading the axial resolution. Effective pulse compression (PC) is important to maintain the axial resolution and can be achieved with radio frequency (RF) and complex baseband (CBB) data (i.e., and , respectively). can further reduce the computational complexity compared to ; however, suffers from a degraded SNR due to tissue attenuation. In this paper, we propose a new dynamic CBB PC method ( that can improve the SNR while compensating for tissue attenuation. The compression filter coefficients in the method are generated by dynamically changing the demodulation frequencies along with the depth. For PC, the obtained coefficients are independently applied to the in-phase and quadrature components of the CBB data. To evaluate the performance of the proposed method, simulation, phantom, and in vivo studies were conducted, and all three studies showed improved SNR, i.e., maximally 3.87, 7.41, and 5.75 dB, respectively. In addition, the measured peak range sidelobe level of the proposed method yielded lower values than the and , and it also derived a suitable target location, i.e., a <0.07-mm target location error, while maintaining the axial resolution. In an in vivo abdominal experiment, the method depicted brighter and clearer features in the hyperechoic region because highly correlated signals were produced by compensating for tissue attenuation. These results demonstrated that the proposed method can improve the SNR of chirp-coded excitation while preserving the axial resolution and the target location and reducing the computational complexity.