Normalized Spatial Autocorrelation in Ultrasound B-Mode Imaging for Point-Scatterer Detection

OBJECTIVE

Point-scatterer detection plays a key role in medical ultrasound B-mode imaging. Speckle noise and insufficient spatial resolution are important factors affecting point-scatterer detection. To address this issue, normalized spatial autocorrelation in ultrasound B-mode imaging (NSACB) is proposed.

METHODS

First, the acquired data are pre-processed by adding Gaussian white noise (GWN) with a certain signal-to-Gaussian white noise ratio (SGWNR). Next, normalized spatial autocorrelation is applied to the pre-processed data, and the data are divided into several new signals with different spatial lags. Then, the new signals are performed unsigned delay multiply and sum. Finally, the NSACB beamformed data are bandpass filtered by extracting the frequency component around twice the center frequency. Simulated and in vitro experiments were designed for validation.

RESULTS

Simulations revealed that the lateral resolution of NSACB measured by the -6-dB mainlobe width can reach as high as 11.11% of delay and sum (DAS), 25.01% of filtered delay multiply and sum (F-DMAS) and 50% of LAG-FDMAS-SCF. The sidelobe level of the NSACB can be reduced at most by 28 dB. Experimental results of simple and complex scatterer phantoms indicate the image resolution of the proposed NSACB can even reach up to 18.76% of DAS, 27.28% of F-DMAS and 14.29% of LAG-FDMAS-SCF. Compared with these methods, the proposed NSACB can reduce the sidelobe level at least by 18 dB.

CONCLUSION

Although the proposed method causes loss of the ability to observe hypo-echoic structures, these results suggest future work to determine the ability to detect breast microcalcifications, kidney stones, biopsy needle tracking and other scenarios requiring scatterer detection.

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Unified pixel-based (PB) beamforming has been implemented for ultrasound imaging, offering significant enhancements in lateral resolution compared to the conventional dynamic focusing. However, it still suffers from clutter and off-axis artifacts, limiting the contrast resolution. This paper proposes an efficient method to improve image quality by integrating filtered delay multiply and sum (F-DMAS) into the framework. This hybrid strategy incorporates the spatial coherence of the received data into the beamforming process to improve contrast resolution and clutter rejection in the generated image. We also integrate a Wiener filter to suppress the spatiotemporal spreading using signals echoed from a single scatterer at the transmit focus as a kernel for the deconvolution. The Wiener filter is applied to the received waveforms before performing the hybrid strategy. The Wiener filter is shown to reduce interference due to the interaction between the excitation pulse and the transfer functions of the transducer elements, thus benefiting the axial resolution of the generated images. We validate the proposed method and compare it with other beamforming strategies through a series of experiments, including simulation, phantom, and in vivo studies. The results show that our approach can substantially improve both spatial resolution and contrast over the unified PB algorithm, while still maintaining the good features of this beamformer. The simplicity and good performance of our method show its potential for use in clinical applications.

Combining autocorrelation signals with delay multiply and sum beamforming algorithm for ultrasound imaging

Beamformer is one of the most important components in ultrasound imaging system. The delay and sum (DAS) beamforming algorithm has been widely used in recent decades due to its simplicity and robustness. However, it has poor impact on resolution and contrast. A new beamformer named filtered delay multiply and sum (F-DMAS) which was an alternative of delay multiply and sum (DMAS) was proposed to overcome these shortcomings of DAS. Although F-DMAS partially enhances the image quality, its performance still has room for improvement. Therefore, a novel beamformer named lag-based delay multiply and sum (L-DMAS) which combines autocorrelation signals with DMAS algorithm is proposed by us to improve its efficiency. Field II was employed to synthesize a point target phantom and a cyst phantom to compare the performance between DAS, F-DMAS, double stage delay multiply and sum (DS-DMAS), and L-DMAS. We also estimate the performance of four algorithms on experimental data and in vivo data. These results show that both DS-DMAS and L-DMAS are better than DAS and F-DMAS in each case. In some cases, DS-DMAS and L-DMAS have little difference in performance, but in other cases, L-DMAS outperforms DS-DMAS. Graphical Abstract.

Double-Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging

In ultrasound (US) imaging, delay and sum (DAS) is the most common beamformer, but it leads to low-quality images. Delay multiply and sum (DMAS) was introduced to address this problem. However, the reconstructed images using DMAS still suffer from the level of side lobes and low noise suppression. Here, a novel beamforming algorithm is introduced based on expansion of the DMAS formula. We found that there is a DAS algebra inside the expansion, and we proposed use of the DMAS instead of the DAS algebra. The introduced method, namely double-stage DMAS (DS-DMAS), is evaluated numerically and experimentally. The quantitative results indicate that DS-DMAS results in an approximately 25% lower level of side lobes compared with DMAS. Moreover, the introduced method leads to 23%, 22% and 43% improvement in signal-to-noise ratio, full width at half-maximum and contrast ratio, respectively, compared with the DMAS beamformer.