Fast 2-D ultrasound strain imaging: the benefits of using a GPU

Accelerating 3-D GPU-based Motion Tracking for Ultrasound Strain Elastography Using Sum-Tables: Analysis and Initial Results

Now, with the availability of 3-D ultrasound data, a lot of research efforts are being devoted to developing 3-D ultrasound strain elastography (USE) systems. Because 3-D motion tracking, a core component in any 3-D USE system, is computationally intensive, a lot of efforts are under way to accelerate 3-D motion tracking. In the literature, the concept of Sum-Table has been used in a serial computing environment to reduce the burden of computing signal correlation, which is the single most computationally intensive component in 3-D motion tracking. In this study, parallel programming using graphics processing units (GPU) is used in conjunction with the concept of Sum-Table to improve the computational efficiency of 3-D motion tracking. To our knowledge, sum-tables have not been used in a GPU environment for 3-D motion tracking. Our main objective here is to investigate the feasibility of using sum-table-based normalized correlation coefficient (ST-NCC) method for the above-mentioned GPU-accelerated 3-D USE. More specifically, two different implementations of ST-NCC methods proposed by Lewis et al. and Luo-Konofagou are compared against each other. During the performance comparison, the conventional method for calculating the normalized correlation coefficient (NCC) was used as the baseline. All three methods were implemented using compute unified device architecture (CUDA; Version 9.0, Nvidia Inc., CA, USA) and tested on a professional GeForce GTX TITAN X card (Nvidia Inc., CA, USA). Using 3-D ultrasound data acquired during a tissue-mimicking phantom experiment, both displacement tracking accuracy and computational efficiency were evaluated for the above-mentioned three different methods. Based on data investigated, we found that under the GPU platform, Lou-Konofaguo method can still improve the computational efficiency (17–46%), as compared to the classic NCC method implemented into the same GPU platform. However, the Lewis method does not improve the computational efficiency in some configuration or improves the computational efficiency at a lower rate (7–23%) under the GPU parallel computing environment. Comparable displacement tracking accuracy was obtained by both methods.

Live Ultrasound Color-Encoded Speckle Imaging Platform for Real-Time Complex Flow Visualization In Vivo

Complex flow patterns are prevalent in the vasculature, but they are difficult to image noninvasively in real time. This paper presents the first real-time scanning platform for a high-frame-rate ultrasound technique called color-encoded speckle imaging (CESI) and its use in visualizing arterial flow dynamics in vivo. CESI works by simultaneously rendering flow speckles and color-coded flow velocity estimates on a time-resolved basis. Its live implementation was achieved by integrating a 192-channel programmable ultrasound front-end module, a 4.8-GB/s capacity data streaming link, and a series of computing kernels implemented on the graphical processing unit (GPU) for beamforming and Doppler processing. A slow-motion replay mode was also included to offer coherent visualization of CESI frames acquired at high frame rate [3000 frames per second (fps) in our experiments]. The live CESI scanning platform was found to be effective in facilitating real-time image guidance (at least 20 fps for live video display with 55-fps GPU processing throughout). In vivo pilot trials also showed that live CESI, when running in replay mode, can temporally resolve triphasic flow at the brachial bifurcation and can reveal flow dynamics in the brachial vein during a fist-clenching maneuver. Overall, live CESI has potential for use in routine investigations in vivo that seek to identify complex flow dynamics in real time and relate these dynamics to vascular physiology.

Real-Time Crawling Wave Sonoelastography for Human Muscle Characterization: Initial Results

Imaging of musculoskeletal tissue dynamics is currently an exploratory field with the goal of aiding rehabilitation and performance evaluation of pathological or asymptomatic patients. In this pilot study, initial elasticity assessments of the biceps brachii were conducted in a novel crawling wave sonoelastography (CWS) system implemented on a research ultrasound instrument with graphical processing unit capabilities, displaying quantitative elasticity values at 4 frames per second. The CWS system computes the tissue stiffness with the generation of an interference pattern from external vibrators, which can overcome depth limitations of imaging systems with internal excitation sources. Validation on gelatin-based phantoms reported low bias of elasticity values (4.7%) at low excitation frequencies. Preliminary results on in vivo muscle characterization are in accordance with average elasticity values for relaxed and contracted tissues found in the literature, as well as for a range of weight loads.

GPU Accelerated Multilevel Lagrangian Carotid Strain Imaging

A multilevel Lagrangian carotid strain imaging algorithm is analyzed to identify computational bottlenecks for implementation on a graphics processing unit (GPU). Displacement tracking including regularization was found to be the most computationally expensive aspect of this strain imaging algorithm taking about 2.2 h for an entire cardiac cycle. This intensive displacement tracking was essential to obtain Lagrangian strain tensors. However, most of the computational techniques used for displacement tracking are parallelizable, and hence GPU implementation is expected to be beneficial. A new scheme for subsample displacement estimation referred to as a multilevel global peak finder was also developed since the Nelder-Mead simplex optimization technique used in the CPU implementation was not suitable for GPU implementation. GPU optimizations to minimize thread divergence and utilization of shared and texture memories were also implemented. This enables efficient use of the GPU computational hardware and memory bandwidth. Overall, an application speedup of was obtained enabling the algorithm to finish in about 50 s for a cardiac cycle. Last, comparison of GPU and CPU implementations demonstrated no significant difference in the quality of displacement vector and strain tensor estimation with the two implementations up to a 5% interframe deformation. Hence, a GPU implementation is feasible for clinical adoption and opens opportunity for other computationally intensive techniques.

Ultrasound Open Platforms for Next-Generation Imaging Technique Development

Open platform (OP) ultrasound systems are aimed primarily at the research community. They have been at the forefront of the development of synthetic aperture, plane wave, shear wave elastography, and vector flow imaging. Such platforms are driven by a need for broad flexibility of parameters that are normally preset or fixed within clinical scanners. OP ultrasound scanners are defined to have three key features including customization of the transmit waveform, access to the prebeamformed receive data, and the ability to implement real-time imaging. In this paper, a formative discussion is given on the development of OPs from both the research community and the commercial sector. Both software- and hardware-based architectures are considered, and their specifications are compared in terms of resources and programmability. Software-based platforms capable of real-time beamforming generally make use of scalable graphics processing unit architectures, whereas a common feature of hardware-based platforms is the use of field-programmable gate array and digital signal processor devices to provide additional on-board processing capacity. OPs with extended number of channels (>256) are also discussed in relation to their role in supporting 3-D imaging technique development. With the increasing maturity of OP ultrasound scanners, the pace of advancement in ultrasound imaging algorithms is poised to be accelerated.

Ultrasound-based liver tracking utilizing a hybrid template/optical flow approach

PURPOSE

With the ongoing shift toward reduced invasiveness in many surgical procedures, methods for tracking moving targets within the body become vital. Non-invasive treatment methods such as stereotactic radiation therapy and high intensity focused ultrasound, in particular, rely on the accurate localization of targets throughout treatment to ensure optimal treatment provision. This work aims at developing a robust, accurate and fast method for target tracking based on ultrasound images.

METHODS

A method for tracking of targets in real-time ultrasound image data was developed, based on the combination of template matching, dense optical flow and image intensity information. A weighting map is generated from each of these approaches which are then normalized, weighted and combined, with the weighted mean position then calculated to predict the current position. The approach was evaluated on the Challenge for Liver Ultrasound Tracking 2015 dataset, consisting of a total of 24 training and 39 test datasets with a total of 53 and 85 annotated targets throughout the liver, respectively.

RESULTS

The proposed method was implemented in MATLAB and achieved an accuracy of [Formula: see text] (95%: 1.91) mm and [Formula: see text] (95%: 1.85) mm on the training and test data, respectively. Tracking frequencies of between 8 and 36 fps (mean of 22 fps) were observed, largely dependent on the size of the region of interest. The achieved results represent an improvement in mean accuracy of approximately 0.3 mm over the reported methods in existing literature.

CONCLUSIONS

This work describes an accurate and robust method for the tracking of points of interest within 2D ultrasound data, based on a combination of multi-template matching, dense optical flow and relative image intensity information.

A GPU-Accelerated 3-D Coupled Subsample Estimation Algorithm for Volumetric Breast Strain Elastography

Our primary objective of this paper was to extend a previously published 2-D coupled subsample tracking algorithm for 3-D speckle tracking in the framework of ultrasound breast strain elastography. In order to overcome heavy computational cost, we investigated the use of a graphic processing unit (GPU) to accelerate the 3-D coupled subsample speckle tracking method. The performance of the proposed GPU implementation was tested using a tissue-mimicking phantom and in vivo breast ultrasound data. The performance of this 3-D subsample tracking algorithm was compared with the conventional 3-D quadratic subsample estimation algorithm. On the basis of these evaluations, we concluded that the GPU implementation of this 3-D subsample estimation algorithm can provide high-quality strain data (i.e., high correlation between the predeformation and the motion-compensated postdeformation radio frequency echo data and high contrast-to-noise ratio strain images), as compared with the conventional 3-D quadratic subsample algorithm. Using the GPU implementation of the 3-D speckle tracking algorithm, volumetric strain data can be achieved relatively fast (approximately 20 s per volume [2.5 cm ×2.5 cm ×2.5 cm]).

Advances in ultrasound elasticity imaging

The most troublesome of ultrasonic B-mode imaging is the difficulty of accurately diagnosing cancers, benign tumors, and cysts because they appear similar to each other in B-mode images. The human soft tissue has different physical characteristics of ultrasound depending on whether it is normal or not. In particular, cancers in soft tissue tend to be harder than the surrounding tissue. Thus, ultrasound elasticity imaging can be advantageously used to detect cancers. To measure elasticity, a mechanical force is applied to a region of interest, and the degree of deformation measured is rendered as an image. Depending on the method of applying stress and measuring strain, different elasticity imaging modalities have been reported, including strain imaging, sonoelastography, vibro-acoustography, transient elastography, acoustic radiation force impulse imaging, supersonic imaging, and strain-rate imaging. In this paper, we introduce various elasticity imaging methods and explore their technical principles and characteristics.