Pulse Wave Velocity (PWV) is a surrogate marker of arterial stiffness linked to cardiovascular morbidity. Pulse Wave Imaging (PWI) is a technique developed by our group for imaging the pulse wave propagation in vivo. PWI requires high temporal and spatial resolution which conventional ultrasonic imaging is unable to simultaneously provide. Coherent compounding is known to address this tradeoff and provides full aperture images at high frame rates. This study aims to implement PWI using coherent compounding within a GPU-accelerated framework. The results of the implemented method were validated using a silicone phantom against static testing. Reproducibility of the measured PWVs was tested in the right common carotid of six healthy subjects (n = 6) approximately 10–15 mm before the bifurcation during two cardiac cycles over the course of 1–3 days. Good agreement of the measured PWVs (3.97±1.21m/s, 4.08±1.15m/s, p = 0.74) was found. The effects of frame rate, transmission angle and number of compounded plane waves on PWI performance were investigated in the six healthy volunteers. Performance metrics such as the reproducibility of the PWVs, the coefficient of determination (r2), the SNR of the PWI axial wall velocities (SNRvPWI) and the percentage of lateral positions where the pulse wave appears to arrive at the same time-point indicating inadequacy of the temporal resolution (i.e., Temporal Resolution Misses) were used to evaluate the effect of each parameter. Compounding plane waves transmitted at 1° increments yielded optimal performance, generating significantly higher r2 and SNRvPWI values (p≤0.05). Higher frame rates (≥1667 Hz) produced improvements with significant gains in the r2 coefficient (p≤ 0.05) and significant increase in both r2 and SNRvPWI from single plane wave imaging to 3-plane wave compounding (p≤0.05). Optimal performance was established at 2778 Hz with 3 plane waves and at 1667 Hz with 5 plane waves.
Ultrasonic strain imaging has been applied to echocardiography and carries great potential to be used as a tool in the clinical setting. Two-dimensional (2-D) strain estimation may be useful when studying the heart due to the complex, three-dimensional deformation of the cardiac tissue. Increasing the framerate used for motion estimation, i.e. motion estimation rate (MER), has been shown to improve the precision of the strain estimation, although maintaining the spatial resolution necessary to view the entire heart structure in a single heartbeat remains challenging at high MERs. Two previously developed methods, the temporally unequispaced acquisition sequence (TUAS) and the diverging beam sequence (DBS), have been used in the past to successfully estimate in vivo axial strain at high MERs without compromising spatial resolution. In this study, a stochastic assessment of 2-D strain estimation precision is performed in vivo for both sequences at varying MERs (65, 272, 544, 815 Hz for TUAS; 250, 500, 1000, 2000 Hz for DBS). 2-D incremental strains were estimated in five healthy volunteers using a normalized cross-correlation function and a least-squares strain estimator. Both sequences were shown capable of estimating 2-D incremental strains in vivo. The conditional expected value of the elastographic signal-to-noise ratio (E(SNRe|ε)) was used to compare strain estimation precision of both sequences at multiple MERs over a wide range of clinical strain values. The results here indicate that axial strain estimation precision is much more dependent on MER than lateral strain estimation, while lateral estimation is more affected by strain magnitude. MER should be increased at least above 544 Hz to avoid suboptimal axial strain estimation. Radial and circumferential strain estimations were influenced by the axial and lateral strain in different ways. Furthermore, the TUAS and DBS were found to be of comparable precision at similar MERs.
Harmonic Motion Imaging for Focused Ultrasound (HMIFU) is a recently developed High-Intensity Focused Ultrasound (HIFU) treatment monitoring method. HMIFU utilizes an Amplitude-Modulated (fAM = 25 Hz) HIFU beam to induce a localized focal oscillatory motion, which is simultaneously estimated and imaged by confocally-aligned imaging transducer. HMIFU feasibilities have been previously shown in silico, in vitro, and in vivo in 1-D or 2-D monitoring of HIFU treatment. 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 composed of a 93-element HIFU transducer (fcenter = 4.5MHz) and coaxially-aligned 64-element phased array (fcenter = 2.5MHz) for displacement excitation and motion estimation, respectively. A single transmit beam with divergent beam transmit 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. The present work developed and implemented a sparse matrix beamforming onto a fully-integrated, clinically relevant system, which can stream displacement images up to 15 Hz using a GPU-based processing, an increase of 100 fold in rate of streaming displacement images compared to conventional CPU-based conventional beamforming and reconstruction processing. The achieved feedback rate is also currently the fastest and only approach that does not require interrupting the HIFU treatment amongst the acoustic radiation force based HIFU imaging techniques. Results in phantom experiments showed reproducible displacement imaging, and monitoring of twenty two in vitro HIFU treatments using the new 2D 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.
Background Accurate determination of regional areas of arrhythmic triggers is of key interest to diagnose arrhythmias and optimize their treatment. Electromechanical wave imaging (EWI) is an ultrasound technique that can image the transient deformation in the myocardium following electrical activation and therefore has the potential to detect and characterize location of triggers of arrhythmias. Objectives The objectives of this study are to investigate the relationship between electromechanical and electrical activation of the left-ventricular (LV) endocardial surface during epicardial and endocardial pacing as well as during sinus rhythm and also to investigate the distribution of electromechanical delays. Methods In this study, six canines were investigated. Two external electrodes were sutured onto the epicardial surface of the left ventricle (LV). A 64-electrode basket catheter was inserted through the apex of the LV. Ultrasound channel data were acquired at 2000 frames/s during epicardial and endocardial pacing as well as during sinus rhythm. Electromechanical and electrical activation maps were synchronously obtained from the ultrasound data and the basket catheter respectively. Results The mean correlation coefficient between electromechanical and electrical activation was R=0.81 for epicardial anterior pacing, R=0.79 for epicardial lateral pacing, R=0.69 for endocardial pacing and R=0.56 for sinus rhythm. Conclusions The electromechanical activation sequence determined by EWI follows the electrical activation sequence and more specifically in the case of pacing. This finding is of key interest in the role that EWI can play in the detection of the anatomical source of arrhythmias and the planning of pacing therapies such as cardiovascular resynchronization therapy.
Ultrasound elastography, a technique used to assess mechanical properties of soft tissue is of major interest in the detection of breast cancer as it is stiffer than the surroundings. Techniques such as ultrasound quasi-static elastography have been developed to assess the strain distribution in soft tissues in two dimensions using a quasi-static compression. However, tumors can exhibit very heterogeneous shape, a three dimensions approach would be then necessary to measure accurately the tumor volume and remove operator dependency. To ensure this issue, several 3-D quasi-static elastographic approaches have been proposed. However, all these approaches suffered from a long acquisition time to acquire 3-D volumes resulting in the impossibility to perform real-time and the creation of artifacts. The long acquisition time comes from both the use of focused ultrasound emissions and the fact that the volume was made from a stack of two dimensions images acquired by mechanically translating an ultrasonic array. Being able to acquire volume at high volume rates is thus crucial to perform real-time with a simple freehand compression and to avoid signal decorrelation coming from hand motions or natural motions such as the respiratory. In this study we developed for the first time, the 3-D ultrafast ultrasound quasi-static elastography method to estimate 3-D axial strain distribution in vivo in real-time. Acquisitions were performed with a 2-D matrix array probe of 256 elements (16-by-16 elements). 100 plane waves were emitted at a volume rate of 100 volumes/sec during a continuous motorized compression. 3-D B-mode volumes and 3-D B-mode cumulative axial strain volumes were estimated on a two-layers gelatin phantom with different stiffness, in a stiff inclusion embedded in a soft gelatin phantoms, in a soft inclusion embedded in a stiff gelatin phantom and in an ex vivo canine liver before and after a high focused ultrasound (HIFU) ablation. In each case, we were able to image in real-time and in entire volumes the axial strain distribution and were able to detect the differences between stiff and soft structures with a good sensitivity. In addition, we were able to detect the stiff lesion in the ex vivo canine liver after HIFU ablation. Finally, we demonstrated the in vivo feasibility of the method using freehand compression on the calf of a human volunteer and were able to retrieve 3-D axial strain volume in real-time depicting the differences in stiffness of the two muscles which compose the calf. The 3-D ultrafast ultrasound quasi-static elastography method could have a major clinical impact for the real-time detection in three dimensions of breast cancer in patients using a simple freehand scanning.
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