High frame rate velocity-coded speckle imaging platform for coherent blood flow visualization

Session 2aBA - Biomedical Acoustics: Biomedical Ultrasound Imaging Instrumentation: no. 2aBA1 (Invited Paper)Non-invasive imaging of blood flow at over 100 fps (i.e. beyond video display range) is known to be of clinical interest given that such a high frame rate is essential for coherent visualization of complex hemodynamic events like flow turbulence. From a technical standpoint, getting into this frame rate range has became possible with the advent of broad-view ultrasound imaging paradigms that can track motion over an entire field-of-view using few pulse-echo firings. Leveraging on an imaging paradigm known as plane wave excitation, a novel high-frame-rate flow visualization technique has been developed to depict both blood speckle motion (using B-flow imaging principles) and flow velocities (using conventional color flow imaging principles). Experimental demonstration of this method has been carried out using a channel-domain research platform that supports real-time pre-beamformed data acquisition (SonixDAQ) and a high-throughput processing engine that is based upon graphical processing unit technology (developed in-house by the authors). In a case with a 417 fps frame rate (based on 5000 Hz pulse repetition frequency and slow-time ensemble size of 12), results show that high-frame-rate velocity-coded speckle imaging can more coherently trace fast-moving blood flow than conventional color flow imaging. Acknowledgement: Research Grants Council of Hong Kong (GRF 785811M)published_or_final_versio

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

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

Eigen-based clutter filters for color flow imaging: Single-ensemble vs. multi-ensemble approaches

In designing eigen-based clutter filters for color flow imaging, one of the challenges is to develop an accurate way of estimating the eigen-components that represent clutter in slow-time ensembles. To provide new insights on the problem, this paper presents a comparative analysis on how eigen-filters perform when using eigen-estimation methods that involve multiple ensembles or a single ensemble. The analysis consists of two parts: 1) a comparative review on the principles behind different eigen-estimation methods; 2) an eigen-filtering experiment done with coronary flow imaging data acquired from a porcine during bypass graft operation. For an imaging case containing tissue motion due to myocardial contraction, our analysis showed that the single-ensemble eigen-filter shared similar performance with a multi-ensemble eigen-filter that uses small (5×5) ensemble windows (with about 1 dB difference in clutter suppression level). Results also showed that a multi-ensemble eigen-filter with large (20x20) ensemble windows yielded poorer performance (clutter suppression level was 3 to 6 dB lower). © 2007 IEEE.published_or_final_versionThe 2007 IEEE Ultrasonics Symposium, New York, N.Y., 28-31 October 2007. In Conference Proceedings of IEEE Ultrasonics Symposium, 2007, p. 1101-110

Single-ensemble-based eigen-processing methods for color flow imaging-Part II. the matrix pencil estimator

Parametric spectral estimators can potentially be used to obtain flow estimates directly from raw slow-time ensembles whose clutter has not been suppressed. We present a new eigen-based parametric flow estimation method called the matrix pencil, whose principles are based on a matrix form under the same name. The presented method models the slow-time signal as a sum of dominant complex sinusoids in the slow-time ensemble, and it computes the principal Doppler frequencies by using a generalized eigenvalue problem formulation and matrix rank reduction principles. Both fixed-rank (rank-one, rank-two) and adaptive-rank matrix pencil flow estimators are proposed, and their potential applicability to color flow signal processing is discussed. For the adaptive-rank estimator, the nominal rank was defined as the minimum eigen-structure rank that yields principal frequency estimates with a spread greater than a prescribed bandwidth. In our initial performance evaluation, the fixed-rank matrix pencil estimators were applied to raw color flow data (transmit frequency: 5 MHz; pulse repetition period: 0.175 ms; ensemble size: 14) acquired from a steady flow phantom (70 cm/s at centerline) that was surrounded by rigid-tissue-mimicking material. These fixed-rank estimators produced velocity maps that are well correlated with the theoretical flow profile (correlation coefficient: 0.964 to 0.975). To facilitate further evaluation, the matrix pencil estimators were applied to synthetic slow-time data (transmit frequency: 5 MHz; pulse repetition period: 1.0 ms; ensemble size: 10) modeling flow scenarios without and with tissue motion (up to 1 cm/s). The bias and root-mean-squared error of the estimators were computed as a function of blood-signal-to-noise ratio and blood velocity. The matrix pencil flow estimators showed that they are comparatively less biased than most of the existing frequency-based flow estimators like the lag-one autocorrelator. © 2006 IEEE.published_or_final_versio

Real-time imaging of plasma membrane dynamics in sonoporation: from perforation to recovery

The Conference program & abstracts' website is located at http://www.ewh.ieee.org/conf/uffc/2013/Session IUS1-K3: Metrology and sonoporation: abstract no. IUS1-K3-4BACKGROUND, MOTIVATION AND OBJECTIVE: To properly harness sonoporation for therapeutic applications, it is unarguably vital to characterize the fundamental biophysical processes involved. Of particular relevance are two membrane-level processes that epitomize the notion of sonoporation: 1) how membrane perforation is induced by ultrasound-microbubble interactions, and 2) how the membrane remodels itself following an episode of sonoporation. Acquiring direct observations of these processes is however not a straightforward task (ironically, these membrane-level events have yet to be convincingly demonstrated in-situ). In this investigation, our aim is to acquire the first series of direct evidence on the time course of membrane perforation and recovery in sonoporation. In particular, we seek to unravel the time-varying surface topography of sonoporated cell membrane in-situ. STATEMENT OF CONTRIBUTION/METHODS: A real-time imaging platform for monitoring of cell-microbubble interactions was first developed, and it was a composite system that coupled a 1 MHz ultrasound module to a laser scanning confocal microscope. A nose-cone shaped waveguide (1” diameter, 7.5 cm height) was devised to align the ultrasound beam focus to the microscope’s imaging plane. This waveguide was angled at 45 deg. with respect …postprin

High frame rate vector flow imaging of stenotic carotid bifurcation: computational modeling and analysis

Poster Session Session P1Aa. Beam Formation: Computational Aspects And Artifact Reduction: no. P1Ab-1Analysis of the complex blood flow pattern in the carotid bifurcation is clinically important to the diagnosis of carotid stenoses. We hypothesize that the use of high frame rate imaging methods such as plane wave excitation, together with vector flow estimators like block matching, may potentially be a suitable imaging problem to this problem. This paper presents our team’s initial efforts in developing a high frame rate vector flow imaging framework that is based on plane wave excitation principles and a high dynamic range block matching algorithm that incorporates least squares fitting principles. We have conducted a series of Field II simulations on straight tubes and carotid bifurcation to evaluate the estimation accuracy and imaging performance of our framework. Results indicate that high-frame-rate vector flow imaging is capable of visualizing complex blood flow. It has potential to be further developed into a new clinical technique for vascular diagnoses.published_or_final_versionThe 2011 IEEE International Ultrasonics Symposium (IUS), Orlando, FL., 18-21 October 2011. In IEEE International Ultrasonics Symposium Proceedings, 2011, p. 409-41

Flow velocity mapping using contrast enhanced high-frame-rate plane wave ultrasound and image tracking: methods and initial in vitro and in vivo evaluation

Ultrasound imaging is the most widely used method for visualising and quantifying blood flow in medical practice, but existing techniques have various limitations in terms of imaging sensitivity, field of view, flow angle dependence, and imaging depth. In this study, we developed an ultrasound imaging velocimetry approach capable of visualising and quantifying dynamic flow, by combining high-frame-rate plane wave ultrasound imaging, microbubble contrast agents, pulse inversion contrast imaging and speckle image tracking algorithms. The system was initially evaluated in vitro on both straight and carotid-mimicking vessels with steady and pulsatile flows and in vivo in the rabbit aorta. Colour and spectral Doppler measurements were also made. Initial flow mapping results were compared with theoretical prediction and reference Doppler measurements and indicate the potential of the new system as a highly sensitive, accurate, angle-independent and full field-of-view velocity mapping tool capable of tracking and quantifying fast and dynamic flows

Real-time GPU-based software beamformer designed for advanced imagingmethods research

High computational demand is known to be a technical hurdle for real-timeimplementation of advanced methods like synthetic aperture imaging (SAI) andplane wave imaging (PWI) that work with the pre-beamform data of each arrayelement. In this paper, we present the development of a software beamformer forSAI and PWI with real-time parallel processing capacity. Our beamformer designcomprises a pipelined group of graphics processing units (GPU) that are hostedwithin the same computer workstation. During operation, each available GPU isassigned to perform demodulation and beamforming for one frame of pre-beamformdata acquired from one transmit firing (e.g. point firing for SAI). Tofacilitate parallel computation, the GPUs have been programmed to treat thecalculation of depth pixels from the same image scanline as a block ofprocessing threads that can be executed concurrently, and it would repeat thisprocess for all scanlines to obtain the entire frame of image data i.e.low-resolution image (LRI). To reduce processing latency due to repeated accessof each GPU's global memory, we have made use of each thread block's fast-sharedmemory (to store an entire line of pre-beamform data during demodulation),created texture memory pointers, and utilized global memory caches (to streamrepeatedly used data samples during beamforming). Based on this beamformerarchitecture, a prototype platform has been implemented for SAI and PWI, and itsLRI processing throughput has been measured for test datasets with 40 MHzsampling rate, 32 receive channels, and imaging depths between 5-15 cm. Whenusing two Fermi-class GPUs (GTX-470), our beamformer can compute LRIs of512-by-255 pixels at over 3200 fps and 1300 fps respectively for imaging depthsof 5 cm and 15 cm. This processing throughput is roughly 3.2 times higher than aTesla-class GPU (GTX-275). © 2010 IEEE.published_or_final_versionThe 2010 IEEE International Ultrasonics Symposium, San Diego, CA., 11-14 October 2010. In Proceedings of IEEE IUS, 2010, p. 1920-192

GPU-based beamformer: Fast realization of plane wave compounding and synthetic aperture imaging

Although they show potential to improve ultrasound image quality, plane wave (PW) compounding and synthetic aperture (SA) imaging are computationally demanding and are known to be challenging to implement in real-time. In this work, we have developed a novel beamformer architecture with the real-time parallel processing capacity needed to enable fast realization of PW compounding and SA imaging. The beamformer hardware comprises an array of graphics processing units (GPUs) that are hosted within the same computer workstation. Their parallel computational resources are controlled by a pixel-based software processor that includes the operations of analytic signal conversion, delay-and-sum beamforming, and recursive compounding as required to generate images from the channel-domain data samples acquired using PW compounding and SA imaging principles. When using two GTX-480 GPUs for beamforming and one GTX-470 GPU for recursive compounding, the beamformer can compute compounded 512 × 255 pixel PW and SA images at throughputs of over 4700 fps and 3000 fps, respectively, for imaging depths of 5 cm and 15 cm (32 receive channels, 40 MHz sampling rate). Its processing capacity can be further increased if additional GPUs or more advanced models of GPU are used. © 2011 IEEE.published_or_final_versio