Real time SVD-based clutter filtering using randomized singular value decomposition and spatial downsampling for micro-vessel imaging on a Verasonics ultrasound system.

Singular value decomposition (SVD)-based clutter filters can robustly reject the tissue clutter as compared with the conventional high pass filter-based clutter filters. However, the computational burden of SVD makes real time SVD-based clutter filtering challenging (e.g. frame rate at least 10-15 Hz with region of interest of about 4 × 4 cm2). Recently, we proposed an acceleration method based on randomized SVD (rSVD) clutter filtering and randomized spatial downsampling, which can significantly reduce the computational complexity without compromising the clutter rejection capability. However, this method has not been implemented on an ultrasound scanner and tested for its performance. In this study, we implement this acceleration method on a Verasonics scanner using a multi-core CPU architecture, and evaluate the selections of the imaging and processing parameters to enable real time micro-vessel imaging. The Blood-to-Clutter Ratio (BCR) performance was evaluated on a Verasonics machine with different settings of parameters such as block size and ensemble size. The demonstration of real time process was implemented on a 12-core CPU (downsampling factor of 12, 12-threads in this study) host computer. The processing time of the rSVD-based clutter filter was less than 30 ms and BCRs were higher than 20 dB as the block size, ensemble size and the rank of tissue clutter subspace were set as 30 × 30, 45 and 26 respectively. We also demonstrate that the micro-vessel imaging frame rate of the proposed architecture can reach approximately 22 Hz when the block size, ensemble size and the rank of tissue clutter subspace were set as 20 × 20 pixels, 45 and 26 respectively (using both images and supplementary videos). The proposed method may be important for real time 2D scanning of tumor microvessels in 3D to select and store the most representative 2D view with most abnormal micro-vessels for better diagnosis.

High-frame-rate volume imaging using sparse-random-aperture compounding.

High-frame-rate volume imaging (HFR-VI) aims to provide high-quality images with high-temporal information. Despite its potential, HFR-VI translation into clinical applications has been challenging due to the high cost of the equipment required to drive matrix probes with a large number of elements. The goal of this study is to introduce and test sparse-random-aperture compounding (SRAC), a technique that allows use of matrix probes with an ultrasound system that has fewer channels while maintaining high frame rates. Four scanning methods were implemented with a 256-channel system using a 4-to-1 multiplexer and a 3 MHz matrix probe with 1024 elements. These methods used three types of waves, either single-diverging waves (SDW), multiplane-diverging waves (MDW) or wide beams (WB); and were driven using one to four SRAC. All methods were also implemented in a 1024-channel multisystem. The main-lobe-to-side-lobe ratio (MLSLR) and the contrast ratio (CR) were studied using a string phantom and a CIRS phantom, respectively. The results showed an increase in the MLSLR and CR as a function of the number of SRAC. The multisystem provided the best results for the MLSLR. However, four SRAC outperformed the multisystem with respect to CR. The method using SDW provided the highest frame rates (i.e. 1875 and 7500 Hz for four and one SRAC, respectively), however it provided the lowest image quality. The two methods using MDWs showed a good compromise between image quality and frame rate (i.e. 187 to 750 Hz for four and one SRAC). WB provided the best image quality at the expense of frame rate (i.e. 18 to 75 Hz for four and one SRAC). Our results suggest that SRAC in combination with the tested scanning methods can provide a low-channel count alternative for HFR-VI systems and allows a tunable tradeoff between image quality and frame rate guided by the desired application.