SIMUS: an open-source simulator for ultrasound imaging. Part II: comparison with three popular simulators

Computational ultrasound imaging has become a well-established methodology in the ultrasound community. In the accompanying paper (part I), we described a new ultrasound simulator (SIMUS) for Matlab, which belongs to the Matlab UltraSound Toolbox (MUST). SIMUS can generate pressure fields and radiofrequency RF signals for simulations in medical ultrasound imaging. It works in a harmonic domain and uses linear equations derived from far-field and paraxial approximations. In this article (part II), we illustrate how SIMUS compares with three popular ultrasound simulators (Field II, k-Wave, and Verasonics) for a homogeneous medium. We designed different transmit sequences (focused, planar, and diverging wavefronts) and calculated the corresponding 2-D and 3-D (with elevation focusing) RMS pressure fields. SIMUS produced pressure fields similar to those of Field II and k-Wave. The acoustic fields provided by the Verasonics simulator were significantly different from those of SIMUS and k-Wave, although the overall appearance remained consistent. Our simulations tend to demonstrate that SIMUS is reliable and can be used on a par with Field II and k-Wave for realistic ultrasound simulations.Comment: to be submitte

Experimental validation of a novel technique for ultrasound imaging of cardiac fiber orientation

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Ultrafast Cardiac Imaging Using Deep Learning For Speckle-Tracking Echocardiography

High-quality ultrafast ultrasound imaging is based on coherent compounding from multiple transmissions of plane waves (PW) or diverging waves (DW). However, compounding results in reduced frame rate, as well as destructive interferences from high-velocity tissue motion if motion compensation (MoCo) is not considered. While many studies have recently shown the interest of deep learning for the reconstruction of high-quality static images from PW or DW, its ability to achieve such performance while maintaining the capability of tracking cardiac motion has yet to be assessed. In this paper, we addressed such issue by deploying a complex-weighted convolutional neural network (CNN) for image reconstruction and a state-of-the-art speckle tracking method. The evaluation of this approach was first performed by designing an adapted simulation framework, which provides specific reference data, i.e. high quality, motion artifact-free cardiac images. The obtained results showed that, while using only three DWs as input, the CNN-based approach yielded an image quality and a motion accuracy equivalent to those obtained by compounding 31 DWs free of motion artifacts. The performance was then further evaluated on non-simulated, experimental in vitro data, using a spinning disk phantom. This experiment demonstrated that our approach yielded high-quality image reconstruction and motion estimation, under a large range of velocities and outperforms a state-of-the-art MoCo-based approach at high velocities. Our method was finally assessed on in vivo datasets and showed consistent improvement in image quality and motion estimation compared to standard compounding. This demonstrates the feasibility and effectiveness of deep learning reconstruction for ultrafast speckle-tracking echocardiography

Experimental 3-D Ultrasound Imaging with 2-D Sparse Arrays using Focused and Diverging Waves

International audienceThree dimensional ultrasound (3-D US) imaging methods based on 2-D array probes are increasingly investigated. However, the experimental test of new 3-D US approaches is contrasted by the need of controlling very large numbers of probe elements. Although this problem may be overcome by the use of 2-D sparse arrays, just a few experimental results have so far corroborated the validity of this approach. In this paper, we experimentally compare the performance of a fully wired 1024-element (32 × 32) array, assumed as reference, to that of a 256-element random and of an " optimized " 2-D sparse array, in both focused and compounded diverging wave (DW) transmission modes. The experimental results in 3-D focused mode show that the resolution and contrast produced by the optimized sparse array are close to those of the full array while using 25% of elements. Furthermore, the experimental results in 3-D DW mode and 3-D focused mode are also compared for the first time and they show that both the contrast and the resolution performance are higher when using the 3-D DW at volume rates up to 90/second which represent a 36x speed up factor compared to the focused mode

A sparse reconstruction framework for Fourier-based plane wave imaging

International audienceUltrafast imaging based on plane-wave (PW) insonification is an active area of research due to its capability of reaching high frame rates. Among PW imaging methods, Fourier-based approaches have demonstrated to be competitive compared with traditional delay and sum methods. Motivated by the success of compressed sensing techniques in other Fourier imaging modalities, like magnetic resonance imaging, we propose a new sparse regularization framework to reconstruct high-quality ultrasound (US) images. The framework takes advantage of both the ability to formulate the imaging inverse problem in the Fourier domain and the sparsity of US images in a sparsifying domain. We show, by means of simulations, in vitro and in vivo data, that the proposed framework significantly reduces image artifacts, i.e., measurement noise and sidelobes, compared with classical methods, leading to an increase of the image quality

Passive cavitation imaging using an open ultrasonic system and time reversal reconstruction

Les maladies cardiovasculaires sont la première cause de mortalité dans le monde. Elles sont le plus souvent provoquées par l'obstruction des vaisseaux par des caillots sanguins entrainant un manque d'oxygène dans les cellules. La thrombolyse ultrasonore extracorporelle constituerait un traitement innovant utilisant des ultrasons focalisés pour détruire les caillots sanguins en tirant parti de l'aspect mécanique de la cavitation acoustique. Un prototype a été conçu et amélioré afin de contrôler l'activité de cavitation. Pour suivre le processus de cavitation en temps réel, un système d'imagerie ultrasonore ouvert est utilisé. Les données brutes sont acquises en utilisant une sonde linéaire de 64 éléments actifs dans un mode d'imagerie passive. Lors du traitement de ces données, sur le principe du retournement temporel, le signal acoustique enregistré par la sonde est retro-propagées afin de localiser l'activité de cavitation

Contrôle temporel et spatial de la cavitation acoustique pour des tests de thrombolyse ultrasonore extracorporelle

Les ultrasons focalisés permettent de détruire des caillots sanguins notamment en exploitant les effets mécaniques associés à la cavitation acoustique, dont la dynamique complexe reste un obstacle à l'élaboration d'un dispositif thérapeutique. Un meilleur contrôle de cette dynamique est donc nécessaire pour le développement d'une telle application. Un système permettant le contrôle temporel et spatial de la cavitation en régime pulsé a donc été développé dans le but de réaliser des tests de thrombolyse ultrasonore extracorporelle. Ce système utilise, d'une part, un transducteur focalisé, un hydrophone et une boucle de rétroaction, réalisée à l'aide d'un dispositif FPGA, pour réguler l'activité de cavitation et, d'autre part, un système d'échographie, et un bras robotisé permettant le placement et le balayage par la sonde de thérapie du caillot sanguin à traiter. Le contrôle de la cavitation a été testé et caractérisé en eau dégazée. Les essais ont montré, d'une part, que le système de régulation permet d'atteindre un niveau de cavitation souhaité en régime pulsé de manière très reproductible et avec une bonne stabilité temporelle et, d'autre part, qu'il permet de repérer où se situe le nuage de cavitation le long de l'axe acoustique

The impact of signal-to-noise ratio, diffusion-weighted directions and image resolution in cardiac diffusion tensor imaging - insights from the ex-vivo rat heart

Background: Cardiac diffusion tensor imaging (DTI) is limited by scan time and signal-to-noise (SNR) restrictions. This invariably leads to a trade-off between the number of averages, diffusion-weighted directions (ND), and image resolution. Systematic evaluation of these parameters is therefore important for adoption of cardiac DTI in clinical routine where time is a key constraint. Methods: High quality reference DTI data were acquired in five ex-vivo rat hearts. We then retrospectively set 2 ≤ SNR ≤ 97, 7 ≤ ND ≤ 61, varied the voxel volume by up to 192-fold and investigated the impact on the accuracy and precision of commonly derived parameters. Results: For maximal scan efficiency, the accuracy and precision of the mean diffusivity is optimised when SNR is maximised at the expense of ND. With typical parameter settings used clinically, we estimate that fractional anisotropy may be overestimated by up to 13% with an uncertainty of ±30%, while the precision of the sheetlet angles may be as poor as ±31°. Although the helix angle has better precision of ±14°, the transmural range of helix angles may be under-estimated by up to 30° in apical and basal slices, due to partial volume and tapering myocardial geometry. Conclusions: These findings inform a baseline of understanding upon which further issues inherent to in-vivo cardiac DTI, such as motion, strain and perfusion, can be considered. Furthermore, the reported bias and reproducibility provides a context in which to assess cardiac DTI biomarkers