4D Functional Imaging of the Rat Brain Using a Large Aperture Row-Column Array

Sauvage, Jack; Porée, Jonathan; Rabut, Claire; Férin, Guillaume; Flesch, Martin; Rosinski, Bogdan; An, Ning; Tanter, Mickaël; Pernot, Mathieu; Thomas, Daniel

doi:10.1109/tmi.2019.2959833

Cited by 66 publications

(49 citation statements)

References 46 publications

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“…Although recent promising advances in the development of 2D Matrix arrays or Row Columns arrays have been proposed for full 3D functional ultrasound imaging [34,35,36], their sensitivity remains limited which still precludes transcranial imaging in mice. The proposed approach still remains valid for volumetric imaging even if the need for perfectly positioning the probe prior to the acquisition is reduced as there remain more possibilities to register volumes in post-processing.…”

Section: Discussionmentioning

confidence: 99%

Fully-automatic ultrasound-based neuro-navigation : The functional ultrasound brain GPS

Nouhoum

Ferrier²,

Osmanski³

et al. 2021

Preprint

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Recent advances in ultrasound imaging triggered by ultrafast plane waves transmission have rendered functional ultrasound (fUS) imaging a valuable neuroimaging modality capable of mapping cerebral vascular networks, but also to indirectly capture neuronal activity with high sensitivity thanks to the neurovascular coupling. However, the expansion of fUS imaging is still limited by the difficulty to identify cerebral structures during experiments based solely on the Doppler images and the shape of the vessels. In order to tackle this challenge, this study introduces the vascular brain positioning system (BPS), a GPS of the brain. The BPS is a whole-brain neuro-navigation system based on the on-the-fly automatic alignment of ultrafast ultrasensitive transcranial Power Doppler volumic images to common templates such as the Allen mouse brain Common Coordinates Framework. This method relies on the online registration of the complex cerebral vascular fingerprint of the studied animal to a pre-aligned reference vascular atlas, thus allowing rapid matching and identification of brain structures. We quantified the accuracy of the automatic registration using super-resolution vascular images obtained at the microscopic scale using Ultrasound Localization Microscopy and found a positioning error of 44 µm and 96 µm for intra-animal and inter-animals vascular registration, respectively. The proposed BPS approach outperforms the manual vascular landmarks recognition performed by expert neuroscientists (inter-annotator errors of 215 µm and 259 µm). Using the online BPS approach coupled with the Allen Atlas, we demonstrated the capability of the system to position itself automatically over chosen anatomical structures and to obtain corresponding functional activation maps even in complex oblique planes. Finally, we show that the system can be used to acquire and estimate functional connectivity matrices automatically. The proposed functional ultrasound on the fly neuro-navigation approach allows automatic brain navigation and could become a key asset to ensure standardized experiments and protocols for non-expert and expert researchers.

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Section: Discussionmentioning

confidence: 99%

Fully-automatic ultrasound-based neuro-navigation : The functional ultrasound brain GPS

Nouhoum

Ferrier²,

Osmanski³

et al. 2021

Preprint

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“…2: Dual peak apodization used in receive to introduce a transverse oscillation receive field. [35], [44]. However, only the power Doppler energy was estimated, which is inherently a one dimensional view of the flow.…”

Section: Velocity Estimation Using Rcasmentioning

confidence: 99%

“…Power Doppler estimation was also implemented on a large 128+128 elements RCA with plane wave emissions [27], but grating lobes were here at a level of -5 dB with 4 transmit events. The method was refined in [35] where it was used in functional brain imaging. Power Doppler is a qualitative measure of blood flow Power, and no directional information is readily obtained through the measure.…”

Section: Introductionmentioning

confidence: 99%

Fast 3-D Velocity Estimation in 4-D Using a 62 + 62 Row–Column Addressed Array

Schou

Jorgensen

Beers³

et al. 2021

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

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This paper presents an imaging scheme capable of estimating the full 3-D velocity vector field in a volume using rowcolumn addressed (RCA) arrays at a high volume rate. A 62+62 RCA array is employed with an interleaved synthetic aperture sequence. It contains repeated emissions with rows and columns interleaved with B-mode emissions. The sequence contains 80 emissions in total and can provide continuous volumetric data at a volume rate above 125 Hz. A transverse oscillation crosscorrelation estimator determines all three velocity components. The approach is investigated using Field II simulations and measurements using a specially built 3 MHz 62+62 RCA array connected to the SARUS experimental scanner. Both the B-mode and flow sequences have a penetration depth of 14 cm when measured on a tissue mimicking phantom (0.5 dB/[MHz•cm] attenuation). Simulations of a parabolic flow in a 12 mm diameter vessel at a depth of 30 mm, beam-to-flow angle of 90 • , and xyrotation of 45 • gave a standard deviation (SD) of (3.3, 3.4, 0.4)% and bias of (-3.3, -3.9, -0.1)%, for (v v v x x x , v v v y y y , v v v z z z ). Decreasing the beam-to-flow angle to 60 • gave a SD of (8.9, 9.1, 0.8)% and bias of (-7.6, -9.5, -7.2)%, showing a slight increase. Measurements were carried out using a similar setup, and pulsing at 2 kHz yielded comparable results at 90 • with a SD of (5.8, 5.5, 1.1)% and bias of (1.4, -6.4, 2.4)%. At 60 • the SD was (5.2, 4.7 1.2)% and bias (-4.6, 6.9, -7.4)%. Results from measurements across all tested settings showed a maximum SD of 6.8% and a maximum bias of 15.8%, for a peak velocity of 10 cm/s. A tissue mimicking phantom with a straight vessel was used to introduce clutter, tissue motion, and a pulsating flow. The pulsating velocity magnitude was estimated across 10 pulse periods and yielded an SD of 10.9%. The method was capable of estimating transverse flow components precisely, but underestimated the flow with small beam-to-flow angles. The sequence provided continuous data in both time and space throughout the volume, allowing for retrospective analysis of the flow. Moreover, B-mode planes can be selected retrospectively anywhere in the volume. This shows that tensor velocity imaging (full 3-D volumetric vector flow imaging) can be estimated in 4-D (x x x

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“…For this reason, real-time fUS imaging is challenging even when processing is performed in a graphics processing unit (GPU) and often requires dedicated hardware implementations, effectively limiting the usability and clinical translation of this imaging technology. This problem is even more relevant for recently developed four-dimensional fUS imaging sequences that aim to image volumetric cerebral blood volume changes 15, 16 . In addition, long ultrasound exposure times raise concerns for the induced bioeffects, even at diagnostic levels of intensity 3, 17 .…”

Section: Introductionmentioning

confidence: 99%

“…The need to acquire and process large ultrasound datasets poses high demands on the hardware platform in terms of storage capacity and computational power, with data throughputs on the order of 240 MSa/image (see calculations in the Supplementary Note). These requirements make real-time fUS imaging challenging even in graphics processing unit (GPU) implementations, and such considerations are becoming increasingly relevant for volumetric fUS sequences 17,18 . Importantly, long ultrasound exposure time raises concerns about potential adverse bioeffects, even at diagnostic intensity levels 3,19 .…”

Section: Introductionmentioning

confidence: 99%

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

Ianni

Airan

2020

Preprint

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Functional ultrasound imaging is rapidly establishing itself as a state-of-the-art neuroimaging modality owing to its ability to image neural activation in awake and mobile small animals, its relatively low cost, and its unequaled portability. To achieve high blood flow sensitivity in the brain microvasculature, functional ultrasound relies on long sequences of ultrasound data acquired at high frame rates, which pose high demands on the hardware architecture and effectively limit the practical utility and clinical translation of this imaging modality. Here we propose Deep-fUS, a deep learning approach that aims to significantly reduce the amount of ultrasound data necessary, while retaining the imaging performance. We trained a neural network to learn the power Doppler reconstruction function from sparse sequences of compound ultrasound data, using ground truth images from high-quality in vivo acquisitions in rats, and with a custom loss function. The network produces highly accurate images with restored sensitivity in the smaller blood vessels even when using heavily under-sampled data. We tested the network performance in a task-evoked functional neuroimaging application, demonstrating that time series of power Doppler images can be reconstructed with adequate accuracy to compute functional activity maps. Notably, the network reduces the occurrence of motion artifacts in awake functional ultrasound imaging experiments. The proposed platform can facilitate the development of this neuroimaging modality in any setting where dedicated hardware is not available or even in clinical scanners, opening the way to new potential applications based on this technology. To our knowledge, this is the first attempt to implement a convolutional neural network approach for power Doppler reconstruction from sparse ultrasonic datasets.

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4D Functional Imaging of the Rat Brain Using a Large Aperture Row-Column Array

Cited by 66 publications

References 46 publications

Fully-automatic ultrasound-based neuro-navigation : The functional ultrasound brain GPS

Fully-automatic ultrasound-based neuro-navigation : The functional ultrasound brain GPS

Fast 3-D Velocity Estimation in 4-D Using a 62 + 62 Row–Column Addressed Array

Deep-fUS: Functional ultrasound imaging of the brain using deep learning and sparse data

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