Assessing the performance of ultrafast vector flow imaging in the neonatal heart via multiphysics modeling and In vitro experiments

Ultrafast vector flow imaging would benefit newborn patients with congenital heart disorders, but still requires thorough validation before translation to clinical practice. This paper investigates 2-D speckle tracking (ST) of intraventricular blood flow in neonates when transmitting diverging waves at ultrafast frame rate. Computational and in vitro studies enabled us to quantify the performance and identify artifacts related to the flow and the imaging sequence. First, synthetic ultrasound images of a neonate's left ventricular flow pattern were obtained with the ultrasound simulator Field II by propagating point scatterers according to 3-D intraventricular flow fields obtained with computational fluid dynamics (CFD). Noncompounded diverging waves (opening angle of 60 degrees) were transmitted at a pulse repetition frequency of 9 kHz. ST of the B-mode data provided 2-D flow estimates at 180 Hz, which were compared with the CFD flow field. We demonstrated that the diastolic inflow jet showed a strong bias in the lateral velocity estimates at the edges of the jet, as confirmed by additional in vitro tests on a jet flow phantom. Furthermore, ST performance was highly dependent on the cardiac phase with low flows (< 5 cm/s), high spatial flow gradients, and out-of-plane flow as deteriorating factors. Despite the observed artifacts, a good overall performance of 2-D ST was obtained with a median magnitude underestimation and angular deviation of, respectively, 28% and 13.5 degrees during systole and 16% and 10.5 degrees during diastole

Eigen-based clutter filter design for ultrasound color flow imaging: A review

Proper suppression of tissue clutter is a prerequisite for visualizing flow accurately in ultrasound color flow imaging. Among various clutter suppression methods, the eigen- based filter has shown potential because it can theoretically adapt its stopband to the actual clutter characteristics even when tissue motion is present. This paper presents a formative review on how eigen-based filters should be designed to improve their practical efficacy in adaptively suppressing clutter without affecting the blood flow echoes. Our review is centered around a comparative assessment of two eigen-filter design considerations: 1) eigen-component estimation approach (single-ensemble vs. multi-ensemble formulations), and 2) filter order selection mechanism (eigenvalue-based vs. frequencybased algorithms). To evaluate the practical efficacy of existing eigen-filter designs, we analyzed their clutter suppression level in two in vivo scenarios with substantial tissue motion (intra-operative coronary imaging and thyroid imaging). Our analysis shows that, as compared with polynomial regression filters (with or without instantaneous clutter downmixing), eigen-filters that use a frequency-based algorithm for filter order selection generally give Doppler power images with better contrast between blood and tissue regions. Results also suggest that both multi-ensemble and single-ensemble eigen-estimation approaches have their own advantages and weaknesses in different imaging scenarios. It may be beneficial to develop an algorithmic way of defining the eigen-filter formulation so that its performance advantages can be better realized. © 2010 IEEE.published_or_final_versio

In vivo investigation of filter order influence in eigen-based clutter filtering for color flow imaging

Eigen-based adaptive filters have shown potential for providing a superior attenuation of clutter in color flow imaging. Critical for the success of this technique is the correct selection of filter order. In this work we review and compare filter order selection schemes for eigen-based filters in an in vivo context. Data was acquired from a thyroid tumor (PRF = 250 Hz, ensemble size = 12), where substantial tissue movement was present due to carotid artery pulsations, respiratory movements, and probe navigation. Eigen-filtering performance was evaluated for 1) an eigenvalue spectrum threshold, 2) a threshold on the ratio of successive eigenvalues, and 3) a threshold on eigenvector mean frequency estimated by the autocorrelation approach. Based on the observed eigenvalue and eigen-frequency distributions in analytical and in vivo examples, all filter order algorithms investigated suffered from potential pitfalls in specific Doppler scenarios. In the in vivo examples, the fixed order eigenfilter gave a sufficient suppression of clutter, but also removed substantial blood signal. Thresholding the ratio of eigenvalues better retained signal from blood, but also spurious artifacts was observed. The most consistent results were achieved by thresholding the mean frequency of the eigenvectors. The results demonstrate that given a suitable filter order algorithm, robust filtering can be achieved with the eigen-based approach. © 2007 IEEE.published_or_final_versio

Single-ensemble-based eigen-processing methods for color flow imaging-Part I. the Hankel-SVD filter

Because of their adaptability to the slow-time signal contents, eigen-based filters have shown potential in improving the flow detection performance of color flow images. This paper proposes a new eigen-based filter called the Hankel-SVD filter that is intended to process each slow- time ensemble individually. The new filter is derived using the notion of principal Hankel component analysis, and it achieves clutter suppression by retaining only the principal components whose order is greater than the clutter eigen- space dimension estimated from a frequency-based analysis algorithm. To assess its efficacy, the Hankel-SVD filter was first applied to synthetic slow-time data (ensemble size: 10) simulated from two different sets of flow parameters that model: (1) arterial imaging (blood velocity: 0 to 38.5 cm/s, tissue motion: up to 2 mm/s, transmit frequency: 5 MHz, pulse repetition period: 0.4 ms) and 2) deep vessel imaging (blood velocity: 0 to 19.2 cm/s, tissue motion: up to 2 cm/s, transmit frequency: 2 MHz, pulse repetition period: 2.0 ms). In the simulation analysis, the post-filter clutter- to-blood signal ratio (CBR) was computed as a function of blood velocity. Results show that for the same effective stopband size (50 Hz), the Hankel-SVD filter has a narrower transition region in the post-filter CBR curve than that of another type of adaptive filter called the clutter- downmixing filter. The practical efficacy of the proposed filter was tested by application to in vivo color flow data obtained from the human carotid arteries (transmit frequency: 4 MHz, pulse repetition period: 0.333 ms, ensemble size: 10). The resulting power images show that the Hankel-SVD filter can better distinguish between blood and moving- tissue regions (about 9 dB separation in power) than the clutter-downmixing filter and a fixed-rank multi-ensemble- based eigen-filter (which showed a 2 to 3 dB separation). © 2006 IEEE.published_or_final_versio

Blind source separation for clutter and noise suppression in ultrasound imaging:review for different applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several 'source' signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection

Signal Processing Methods for Quantitative Power Doppler Microvascular Angiography

Operator-dependent instrument settings and the likelihood of image artifacts are two challenges for reliably using three-dimensional (3-D) power Doppler angiography in flow depiction and quantification applications. To address the operator-dependent settings challenge, an automated method for wall filter cut-off selection, the wall filter selection curve (WFSC) method, was developed using flow-phantom images. The flow-phantom WFSCs guided the development of a theoretical signal model relating color pixel density (CPD) and wall filter cut-off frequency. Simulations using the theoretical model were used to define criteria for the WFSC method to be applied to unprocessed power Doppler signals from 3-D vasculature. The adapted WFSC method was combined with a 3-D skeletonization and vessel network reconstruction method to present a two-stage processing method aimed at improving vascular detection, visualization and quantification. The two-stage method was evaluated using two in vivo models; a murine tumor model was used to test the performance of the method in a flow quantification application and a chick embryo chorioallantoic membrane (CAM) model was used to evaluate the method’s value for flow depiction applications. Applying the WFSC method to flow-phantom images improved vessel delineation and vascular quantification to within 3% of the vascular volume fraction of the phantom. Criteria for the WFSC method from the simulations were to assess at least 100 cut-off frequencies and that the CPD variability should be less than 5% to ensure quantification accuracy. Large variations in the cut-off frequency selected using the WFSC among images acquired at different time points and across different animals in the murine tumor model signified the relevance of spatially and temporally adjusting the cut-off frequency. The two-stage method improved visualization of the vascular network and significantly reduced artifacts in both the tumor and CAM models in comparison to images using conventional Doppler processing. In the CAM model, vessel diameters measured in two-stage processed images were more accurate than measurements in images exported from a commercial scanner. The proposed signal processing methods increase accuracy and robustness of qualitative and quantitative studies using 3-D power Doppler angiography to assess vascular networks for flow depiction and quantification