High-frame-rate echocardiography using diverging transmit beams and parallel receive beamforming

Hasegawa, Hideyuki; Kanai, Hiroshi

doi:10.1007/s10396-011-0304-0

Cited by 197 publications

(104 citation statements)

References 27 publications

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“…Of the two, it is often the decorrelation induced by very large strains that is more detrimental to strain estimation in cardiac applications. One particular focus of researchers for many years has been framerate, since the smaller inter-frame (incremental) strains imaged at higher framerates exhibit less RF signal decorrelation, and hence better motion estimation (Alam and Ophir, 1997; Chen et al, 2009; D’hooge et al, 2000; Hasegawa and Kanai, 2011; Wang et al, 2008). …”

Section: Introductionmentioning

confidence: 99%

Stochastic precision analysis of 2D cardiac strain estimationin vivo

2014

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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.

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Section: Introductionmentioning

confidence: 99%

Stochastic precision analysis of 2D cardiac strain estimationin vivo

2014

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“…In order to increase frame rate while preserving the number of scan lines, ultrafast imaging using unfocused transmit beam has been proposed. These approaches use plane waves (PW) [6] or diverging waves (DW) [7], [8] to image a wide field of view. However, in PW or DW imaging, the acoustic energy of unfocused beams is spread onto a wider area, resulting in a deterioration of the quality of reconstructed images if no additional processing is performed.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction for Diverging-Wave Imaging Using Deep Convolutional Neural Networks

Millioz

Garcia

et al. 2020

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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In recent years, diverging-wave (DW) ultrasound imaging has become a very promising methodology for cardiovascular imaging due to its high temporal resolution. However, if they are limited in number, DW transmits provide lower image quality compared with classical focused schemes. A conventional reconstruction approach consists in summing series of ultrasound signals coherently, at the expense of the frame rate. To deal with this limitation, we propose a convolutional neural networks (CNN) architecture for high-quality reconstruction of DW ultrasound images using a small number of transmissions. Given the spatially varying properties of DW images along depth, we adopted the inception model composed of the concatenation of multi-scale convolutional kernels. Incorporating inception modules aims at capturing different image features with multi-scale receptive fields. A mapping between low-quality images and corresponding high-quality compounded reconstruction was learned by training the network using in vitro and in vivo samples. The performance of the proposed approach was evaluated in terms of contrast-to-noise ratio and lateral resolution, and compared with standard compounding method and conventional CNN methods. The results demonstrate that our method could produce highquality images using only three DWs, yielding an image quality equivalent to the one obtained with standard compounding of 31 DWs and outperforming more conventional CNN architectures in terms of complexity, inference time and image quality.

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“…High frame rate ultrasound has consistently been shown to be of particular use in cardiac applications to capture the rapid deformations experienced by the heart. 19,20 Second, EWI uses a strain estimation technique based on cross-correlation of radiofrequency (RF) signals instead of conventional B-mode block matching, allowing for the calculation of subpixel displacements (on the order of 10 μm) that occur when imaging at such a high frame rate. Several ultrasound-based research groups have reported the superiority of crosscorrelation techniques compared to B-mode block matching, especially for small deformations.…”

Section: Introductionmentioning

confidence: 99%

Imaging the Propagation of the Electromechanical Wave in Heart Failure Patients with Cardiac Resynchronization Therapy

Bunting

Lambrakos

Kemper

et al. 2016

Pacing Clinical Electrophis

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Background Current electrocardiographic and echocardiographic measurements in heart failure (HF) do not take into account the complex interplay between electrical activation and local wall motion. The utilization of novel technologies to better characterize cardiac electromechanical behavior may lead to improved response rates with cardiac resynchronization therapy (CRT). Electromechanical Wave Imaging (EWI) is a non-invasive ultrasound-based technique that uses the transient deformations of the myocardium to track the intrinsic electromechanical wave that precedes myocardial contraction. In this paper, we investigate the performance and reproducibility of EWI in the assessment of HF patients and CRT. Methods EWI acquisitions were obtained in 5 healthy controls and 16 HF patients with and without CRT pacing. Responders (n=8) and non-responders (n=8) to CRT were identified retrospectively on the basis of left ventricular (LV) reverse remodeling. Electromechanical activation maps were obtained in all patients and used to compute a quantitative parameter describing the mean activation time of the LV lateral wall (LWAT). Results Mean LWAT was increased by 52.1 ms in HF patients in native rhythm compared to controls (p<0.01). For all HF patients, CRT pacing initiated a different electromechanical activation sequence. Responders exhibited a 56.4±28.9 ms reduction in LWAT with CRT pacing (p<0.01), while non-responders showed no significant change. Conclusion In this initial feasibility study, EWI was capable of characterizing local cardiac electromechanical behavior as it pertains to HF and CRT response. Activation sequences obtained with EWI allow for quantification of LV lateral wall electromechanical activation, thus providing a novel method for CRT assessment.

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High-frame-rate echocardiography using diverging transmit beams and parallel receive beamforming

Cited by 197 publications

References 27 publications

Stochastic precision analysis of 2D cardiac strain estimationin vivo

Stochastic precision analysis of 2D cardiac strain estimationin vivo

Reconstruction for Diverging-Wave Imaging Using Deep Convolutional Neural Networks

Imaging the Propagation of the Electromechanical Wave in Heart Failure Patients with Cardiac Resynchronization Therapy

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