Coronary Flow Assessment Using 3-Dimensional Ultrafast Ultrasound Localization Microscopy

Demeulenaere, Oscar; Sandoval, Zulma; Matéo, Philippe; Dizeux, Alexandre; Villemain, Olivier; Gallet, Romain; Ghaleh, Bijan; Deffieux, Thomas; Demené, Charlie; Tanter, Mickaël; Papadacci, Clément; Pernot, Mathieu

doi:10.1016/j.jcmg.2022.02.008

Cited by 43 publications

(38 citation statements)

References 24 publications

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“…. CC-BY 4.0 International license made available under a Of course, 3D ULM imaging can be achieved using either fully populated 2D matrix array technologies 17 or Raw Column Arrays (RCA) technologies [22][23][24][25] . However, to date, conventional linear arrays are mainly used for ULM imaging because they are cheaper, simpler and have a much better sensitivity compared to matrix arrays.…”

Section: Discussionmentioning

confidence: 99%

“…Very recently, the technique was taken to another level with the achievement of functional ultrasound localisation microscopy, shining light on the brain neurovascular activity at a microscopic resolution 15 . To date, the vast majority of these studies have been done using 2D imaging, as both the linear transducer arrays and driving electronics are far more common and less expensive than matrix arrays, raw-column arrays, and thousand-channels or multiplexed electronics required to perform 3D ultrasound localisation microscopy 16,17 . Of course, the relative simpler implementation of 2D ultrasound localisation is at a cost, as the lack of information in the elevation direction makes velocimetry inaccurate.…”

Section: Introductionmentioning

confidence: 99%

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Backscattering amplitude in Ultrasound Localization Microscopy

Renaudin

Pezet

Ialy-Radio

et al. 2023

Preprint

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In the last decade, Ultrafast Ultrasound Localisation Microscopy has taken non-invasive deep vascular imaging down to the microscopic level. By imaging diluted suspensions of circulating microbubbles in the blood stream at kHz framerate and localising the center of their individual point spread function with a sub-resolution precision, it enabled to break the unvanquished trade-off between depth of imaging and resolution by microscopically mapping the microbubbles flux and velocities deep into tissue. However, ULM also suffers limitations. Many small vessels are not visible in the ULM images due to the noise level in areas dimly explored by the microbubbles. Moreover, as the vast majority of studies are performed using 2D imaging, quantification is limited to in-plane velocity or flux measurements which hinders the accurate velocity determination and quantification. Here we show that the backscattering amplitude of each individual microbubble can also be exploited to produce backscattering images of the vascularization with a higher sensitivity compared to conventional ULM images. By providing valuable information about the relative distance of the microbubble to the 2D imaging plane in the out-of-plane direction, backscattering ULM images introduces a physically relevant 3D rendering perception in the vascular maps. It also retrieves the missing information about the out-of-plane motion of microbubbles and provides a way to improve 3D flow and velocity quantification using 2D ULM. These results pave the way to improved visualization and quantification for 2D and 3D ULM.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Backscattering amplitude in Ultrasound Localization Microscopy

Renaudin

Pezet

Ialy-Radio

et al. 2023

Preprint

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“…Early studies conducted by Viessmann et al [1], Desailly et al [2][3], and O'Reilly et al [4] showed that one can break the resolution limit of acoustic waves by localizing microbubbles in the blood stream. The seminal papers by Errico et al [5] and Christensen-Jeffries et al [6] catalyzed the growth of the field, leading to many subsequent reports with successful in vivo applications [7][8][9][10][11][12][13][14][15][16] and technical advancements of super-resolution ultrasound imaging [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…[5] and Christensen-Jeffries et al . [6] catalyzed the growth of the field, leading to many subsequent reports with successful in vivo applications [7-16] and technical advancements of super-resolution ultrasound imaging [17-32].…”

Section: Introductionmentioning

confidence: 99%

Contrast-free Super-resolution Doppler (CS Doppler) based on Deep Generative Neural Networks

You

Lowerison

Shin

et al. 2022

Preprint

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Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micron scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive post-processing times. In this study, we present a contrast-free super-resolution Doppler (CS Doppler) technique that uses deep generative networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS Doppler could achieve an approximate 2-fold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS Doppler showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio of 25.52. Moreover, CS Doppler was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS Doppler was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep generative network also allows CS Doppler to be implemented for real-time imaging. These features of CS Doppler offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

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“…Alternatively, machine learning-based approaches for MB localization were shown to be able to handle higher MB concentrations by disentangling the interference pattern of spatially overlapping MBs [20]- [22]. ULM can also be triggered with electrocardiograms to observe phenomenons independently from the cardiac cycle such as pulsatility [23], or avoid big motion [24].…”

Section: Introductionmentioning

confidence: 99%