A hybrid CPU-GPGPU approach for real-time elastography

Parallelization of the Honeybee Search Algorithm for Object Tracking

Object tracking refers to the relocation of specific objects in consecutive frames of a video sequence. Presently, this visual task is still considered an open research issue, and the computer science community attempted solutions from the standpoint of methodologies, algorithms, criteria, benchmarks, and so on. This article introduces a GPU-parallelized swarm algorithm, called the Honeybee Search Algorithm (HSA), which is a hybrid algorithm combining swarm intelligence and evolutionary algorithm principles, and was previously designed for three-dimensional reconstruction. This heuristic inspired by the search for food of honeybees, and here adapted to the problem of object tracking using GPU parallel computing, is extended from the original proposal of HSA towards video processing. In this work, the normalized cross-correlation (NCC) criteria is used as the fitness function. Experiments using 314 video sequences of the ALOV benchmark provides evidence about the quality regarding tracking accuracy and processing time. Also, according to these experiments, the proposed methodology is robust to high levels of Gaussian noise added to the image frames, and this confirms that the accuracy of the original NCC is preserved with the advantage of acceleration, offering the possibility of accelerating latest trackers using this methodology.

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.

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.

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.

Ultrasound elastography assessment of bone/soft tissue interface

We report on the use of elastographic imaging techniques to assess the bone/soft tissue interface, a region that has not been previously investigated but may provide important information about fracture and bone healing. The performance of axial strain elastograms and axial shear strain elastograms at the bone/soft tissue interface was studied ex vivo on intact and fractured canine and ovine tibias. Selected ex vivo results were corroborated on intact sheep tibias in vivo. The elastography results were statistically analyzed using elastographic image quality tools. The results of this study demonstrate distinct patterns in the distribution of the normalized local axial strains and axial shear strains at the bone/soft tissue interface with respect to the background soft tissue. They also show that the relative strength and distribution of the elastographic parameters change in the presence of a fracture and depend on the degree of misalignment between the fracture fragments. Thus, elastographic imaging modalities might be used in the future to obtain information regarding the integrity of bones and to assess the severity of fractures, alignment of bone fragments as well as to follow bone healing.

Elastography using multi-stream GPU: an application to online tracked ultrasound elastography, in-vivo and the da Vinci Surgical System

A system for real-time ultrasound (US) elastography will advance interventions for the diagnosis and treatment of cancer by advancing methods such as thermal monitoring of tissue ablation. A multi-stream graphics processing unit (GPU) based accelerated normalized cross-correlation (NCC) elastography, with a maximum frame rate of 78 frames per second, is presented in this paper. A study of NCC window size is undertaken to determine the effect on frame rate and the quality of output elastography images. This paper also presents a novel system for Online Tracked Ultrasound Elastography (O-TRuE), which extends prior work on an offline method. By tracking the US probe with an electromagnetic (EM) tracker, the system selects in-plane radio frequency (RF) data frames for generating high quality elastograms. A novel method for evaluating the quality of an elastography output stream is presented, suggesting that O-TRuE generates more stable elastograms than generated by untracked, free-hand palpation. Since EM tracking cannot be used in all systems, an integration of real-time elastography and the da Vinci Surgical System is presented and evaluated for elastography stream quality based on our metric. The da Vinci surgical robot is outfitted with a laparoscopic US probe, and palpation motions are autonomously generated by customized software. It is found that a stable output stream can be achieved, which is affected by both the frequency and amplitude of palpation. The GPU framework is validated using data from in-vivo pig liver ablation; the generated elastography images identify the ablated region, outlined more clearly than in the corresponding B-mode US images.

Sparse matrix beamforming and image reconstruction for 2-D HIFU monitoring using harmonic motion imaging for focused ultrasound (HMIFU) with in vitro validation

Harmonic motion imaging for focused ultrasound (HMIFU) utilizes an amplitude-modulated HIFU beam to induce a localized focal oscillatory motion simultaneously estimated. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system. A single divergent transmit beam was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface with frame rates up to 15 Hz, a 100-fold increase compared to conventional CPU-based processing. The real-time feedback rate does not require interrupting the HIFU treatment. Results in phantom experiments showed reproducible HMI images and monitoring of 22 in vitro HIFU treatments using the new 2-D system demonstrated reproducible displacement imaging, and monitoring of 22 in vitro HIFU treatments using the new 2-D system showed a consistent average focal displacement decrease of 46.7 ±14.6% during lesion formation. Complementary focal temperature monitoring also indicated an average rate of displacement increase and decrease with focal temperature at 0.84±1.15%/(°)C, and 2.03±0.93%/(°)C , respectively. These results reinforce the HMIFU capability of estimating and monitoring stiffness related changes in real time. Current ongoing studies include clinical translation of the presented system for monitoring of HIFU treatment for breast and pancreatic tumor applications.

Scanning-mode 2D acoustic radiation force impulse (s2D-ARFI) imaging based on GPU acceleration

Acoustic radiation force impulse (ARFI) technique is a quantitative method for tissue stiffness assessment. It has been proved to be less operator dependent than the quasi-static elastography, and has more simple hardware architecture than the supersonic shearwave imaging (SSI) technique, which make it easier to be miniaturized for some special clinical applications. However, unlike the SSI, ARFI cannot provide real-time 2D images of tissue stiffness distribution mainly due to its data-intensive and time-consuming algorithms. In this study, the algorithms of ARFI were modified and improved to fit for the parallel computation on graphics processing unit (GPU), and the quasi-real-time scanning-mode 2D ARFI images (s2D-ARFI) were implemented on a self-developed compact system. High ratio of the time consumptions between the algorithms using CPU and using GPU has been verified, and it was also proved that there was no distinct difference between the stiffness images obtained by these two methods. The s2D-ARFI provides us an additional choice for quantitatively imaging the tissue stiffness, and has a potential to be miniaturized and used in the emergency treatments in field first-aid and the donor evaluation for organ transplantation.

Effect of permeability on the performance of elastographic imaging techniques

Elastography is a well-established imaging modality. While a number of studies aimed at evaluating the performance of elastographic techniques are retrievable in the literature, very little information is available on the effects that the presence of an underlying permeability contrast in the tissue may have on the resulting elastograms. Permeability is a fundamental tissue parameter, which characterizes the ease with which fluid can move within a tissue. This parameter plays a central role both biomechanically in the description of the temporal behavior of fluid-filled tissues and clinically in the development of a number of diagnostic and therapeutic modalities. In this paper, we present a simulation study that investigates selected elastographic image quality factors in nonhomogeneous materials, modeled as poroelastic media with different geometries and permeability contrasts. The results of this study indicate that the presence of an underlying permeability contrast may create a new contrast mechanism in the spatial and temporal distributions of the axial strains and the effective Poisson's ratios experienced by the tissue and as imaged by the corresponding elastograms. The effect of permeability on the elastographic image quality factors analyzed in this study was found to be a nonsymmetric function of the underlying mechanical contrast between background and target, the geometry of the material and the boundary conditions.