Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping

Soloukey, Sadaf; Vincent, Arnaud; Satoer, Djaina; Mastik, Frits; Smits, Marion; Dirven, Clemens M.F.; Strydis, Christos; Bosch, Johannes G.; Steen, Antonius F.W. van der; Zeeuw, Chris I. De; Koekkoek, S.K.E.; Kruizinga, Pieter

doi:10.3389/fnins.2019.01384

Cited by 82 publications

(58 citation statements)

References 42 publications

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“…In creating sparse sequences, we chose to select only the initial portion of the original sequence over zeroing interleaved frames (e.g., discarding 3 frames every 4 in the case of 25% density) to take advantage of the shorter observation window. This approach has the benefit of reducing the occurrence of motion artifacts and signal degradation due to data scrubbing, which are inevitable factors in mobile rodent fUS imaging experiments 7,44 and handheld applications 5 and are more likely to appear with longer observation times.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Similar to blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI), the detected fUS hemodynamic response provides an indirect measurement of local spiking activity via neurovascular coupling 2 . However, fUS has higher sensitivity and spatiotemporal resolution than fMRI and uses more affordable and portable equipment, opening the possibility for functional neuroimaging performed directly at the bedside 3–5 . Preclinically, fUS enables imaging of neural activity in awake and freely behaving small animals, eliminating the confounds introduced by anesthesia/sedation or physical restraint in behavioral experiments 6,7 .…”

Section: Introductionmentioning

confidence: 99%

“…In addition, fUS imaging has proven useful for imaging resting state and task-evoked functional connectivity in the rat and mouse brain 2,8,9 , for visualizing vascular dynamics during spontaneous rapid-eye-movement sleep 10 , and for mapping neural activation in primates during cognition tasks and visual stimulation 11, 12 . In humans, fUS has been successfully used intraoperatively during brain tumor removal surgeries 4,5 and to visualize epileptic activity in neonates through the anterior fontanel window 3 .…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ianni

Airan

2020

Preprint

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“…Such flow imaging techniques have yielded valuable information in selected applications, for instance in diagnoses of retinal disorders using OCT angiography (OCTA) [3] , [4] , [5] and of cardiac valve insufficiency using Color Doppler echocardiography [6] , [7] . Recent years have seen an intensive research effort directed at microvascular brain imaging using Power Doppler ultrasound, which allows non- or mildly invasive assessment of functional neurological response from rodents to humans [8] , [9] , [10] , [11] .…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic flow velocity imaging based on complex field decorrelation

et al. 2021

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Photoacoustic (PA) imaging can be used to monitor flowing blood inside the microvascular and capillary bed. Ultrasound speckle decorrelation based velocimetry imaging was previously shown to accurately estimate blood flow velocity in mouse brain (micro-)vasculature. Translating this method to photoacoustic imaging will allow simultaneous imaging of flow velocity and extracting functional parameters like blood oxygenation. In this study, we use a pulsed laser diode and a quantitative method based on normalized first order field autocorrelation function of PA field fluctuations to estimate flow velocities in an ink tube phantom and in the microvasculature of the chorioallantoic membrane of a chicken embryo. We demonstrate how the decorrelation time of signals acquired over frames are related to the flow speed and show that the PA flow analysis based on this approach is an angle independent flow velocity imaging method.

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Multiparametric Brain Hemodynamics Imaging Using a Combined Ultrafast Ultrasound and Photoacoustic System

Chen,

Mirg,

Gaddale

et al. 2024

Advanced Science

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Studying brain‐wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help gain new insights into the mechanisms of neuro‐ diseases and ‐disorders. Nonetheless, this task is challenging, primarily due to the complexity of neurovascular coupling, which encompasses interdependent hemodynamic parameters including cerebral blood volume (CBV), cerebral blood flow (CBF), and cerebral oxygen saturation (SO2). The current brain imaging technologies exhibit inherent limitations in resolution, sensitivity, and imaging depth, restricting their capacity to comprehensively capture the intricacies of cerebral functions. To address this, a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform is reported, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head‐mountable device, to quantitatively map individual dynamics of CBV, CBF, and SO2 as well as contrast agent enhanced brain imaging at high spatiotemporal resolutions. Following systematic characterization, the fUSPA system is applied to study brain‐wide cerebrovascular reactivity (CVR) at single‐vessel resolution via relative changes in CBV, CBF, and SO2 in response to hypercapnia stimulation. These results show that cortical veins and arteries exhibit differences in CVR in the stimulated state and consistent anti‐correlation in CBV oscillations during the resting state, demonstrating the multiparametric fUSPA system's unique capabilities in investigating complex mechanisms of brain functions.

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Functional Ultrasound (fUS) During Awake Brain Surgery: The Clinical Potential of Intra-Operative Functional and Vascular Brain Mapping

Cited by 82 publications

References 42 publications

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

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

Photoacoustic flow velocity imaging based on complex field decorrelation

Multiparametric Brain Hemodynamics Imaging Using a Combined Ultrafast Ultrasound and Photoacoustic System

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