Cerebral microcirculation mapped by echo particle tracking velocimetry quantifies the intracranial pressure and detects ischemia

Dual-Channel in Spatial-Frequency Domain CycleGAN for perceptual enhancement of transcranial cortical vascular structure and function

Observing cortical vascular structures and functions using laser speckle contrast imaging (LSCI) at high resolution plays a crucial role in understanding cerebral pathologies. Usually, open-skull window techniques have been applied to reduce scattering of skull and enhance image quality. However, craniotomy surgeries inevitably induce inflammation, which may obstruct observations in certain scenarios. In contrast, image enhancement algorithms provide popular tools for improving the signal-to-noise ratio (SNR) of LSCI. The current methods were less than satisfactory through intact skulls because the transcranial cortical images were of poor quality. Moreover, existing algorithms do not guarantee the accuracy of dynamic blood flow mappings. In this study, we develop an unsupervised deep learning method, named Dual-Channel in Spatial-Frequency Domain CycleGAN (SF-CycleGAN), to enhance the perceptual quality of cortical blood flow imaging by LSCI. SF-CycleGAN enabled convenient, non-invasive, and effective cortical vascular structure observation and accurate dynamic blood flow mappings without craniotomy surgeries to visualize biodynamics in an undisturbed biological environment. Our experimental results showed that SF-CycleGAN achieved a SNR at least 4.13 dB higher than that of other unsupervised methods, imaged the complete vascular morphology, and enabled the functional observation of small cortical vessels. Additionally, the proposed method showed remarkable robustness and could be generalized to various imaging configurations and image modalities, including fluorescence images, without retraining.

Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning

With a spatial resolution of tens of microns, ultrasound localization microscopy (ULM) reconstructs microvascular structures and measures intravascular flows by tracking microbubbles (1-5 μm) in contrast enhanced ultrasound (CEUS) images. Since the size of CEUS bubble traces, e.g. 0.5-1 mm for ultrasound with a wavelength λ = 280 μm, is typically two orders of magnitude larger than the bubble diameter, accurately localizing microbubbles in noisy CEUS data is vital to the fidelity of the ULM results. In this paper, we introduce a residual learning based supervised super-resolution blind deconvolution network (SupBD-net), and a new loss function for a self-supervised blind deconvolution network (SelfBD-net), for detecting bubble centers at a spatial resolution finer than λ/10. Our ultimate purpose is to improve the ability to distinguish closely located microvessels and the accuracy of the velocity profile measurements in macrovessels. Using realistic synthetic data, the performance of these methods is calibrated and compared against several recently introduced deep learning and blind deconvolution techniques. For bubble detection, errors in bubble center location increase with the trace size, noise level, and bubble concentration. For all cases, SupBD-net yields the least error, keeping it below 0.1 λ. For unknown bubble trace morphology, where all the supervised learning methods fail, SelfBD-net can still maintain an error of less than 0.15 λ. SupBD-net also outperforms the other methods in separating closely located bubbles and parallel microvessels. In macrovessels, SupBD-net maintains the least errors in the vessel radius and velocity profile after introducing a procedure that corrects for terminated tracks caused by overlapping traces. Application of these methods is demonstrated by mapping the cerebral microvasculature of a neonatal pig, where neighboring microvessels separated by 0.15 λ can be readily distinguished by SupBD-net and SelfBD-net, but not by the other techniques. Hence, the newly proposed residual learning based methods improve the spatial resolution and accuracy of ULM in micro- and macro-vessels.

High spatiotemporal mapping of cortical blood flow velocity with an enhanced accuracy

Cerebral blood flow velocity is one of the most essential parameters related to brain functions and diseases. However, most existing mapping methods suffer from either inaccuracy or lengthy sampling time. In this study, we propose a particle-size-related calibration method to improve the measurement accuracy and a random-access strategy to suppress the sampling time. Based on the proposed methods, we study the long-term progress of cortical vasculopathy and abnormal blood flow caused by glioma, short-term variations of blood flow velocity under different anesthetic depths, and cortex-wide connectivity of the rapid fluctuation of blood flow velocities during seizure onset. The experimental results demonstrate that the proposed calibration method and the random-access strategy can improve both the qualitative and quantitative performance of velocimetry techniques and are also beneficial for understanding brain functions and diseases from the perspective of cerebral blood flow.

"No-reflow" phenomenon in acute ischemic stroke

Acute ischemic stroke (AIS) afflicts millions of individuals worldwide. Despite the advancements in thrombolysis and thrombectomy facilitating proximal large artery recanalization, the resultant distal hypoperfusion, referred to "no-reflow" phenomenon, often impedes the neurological function restoration in patients. Over half a century of scientific inquiry has validated the existence of cerebral "no-reflow" in both animal models and human subjects. Furthermore, the correlation between "no-reflow" and adverse clinical outcomes underscores the necessity to address this phenomenon as a pivotal strategy for enhancing AIS prognoses. The underlying mechanisms of "no-reflow" are multifaceted, encompassing the formation of microemboli, microvascular compression and contraction. Moreover, a myriad of complex mechanisms warrant further investigation. Insights gleaned from mechanistic exploration have prompted advancements in "no-reflow" treatment, including microthrombosis therapy, which has demonstrated clinical efficacy in improving patient prognoses. The stagnation in current "no-reflow" diagnostic methods imposes limitations on the timely application of combined therapy on "no-reflow" post-recanalization. This narrative review will traverse the historical journey of the "no-reflow" phenomenon, delve into its underpinnings in AIS, and elucidate potential therapeutic and diagnostic strategies. Our aim is to equip readers with a swift comprehension of the "no-reflow" phenomenon and highlight critical points for future research endeavors.

Cerebral Microvascular Imaging in Infants: Scan Technique and Potential Clinical Applications

ABSTRACT

Brain ultrasound in infants, although widely utilized, provides limited functional insights into the brain. Although color and power Doppler ultrasounds have allowed quantitative assessment of cerebral macrovascular flow dynamics, there is no standardized tool integrated into the current neurosonography protocol that allows cerebral microvascular flow assessment. The evaluation of anatomic and functional changes in cerebral microvessels is important, as microvascular alterations have been shown to precede macrovascular and tissue injury in a variety of neurologic diseases of infancy. In this regard, the cerebral microvascular imaging technique is a commercially available, advanced Doppler technique in which slow flow of cerebral microvessels can be detected via a static noise suppression algorithm. This article therefore shares the basic scan technique and clinical examples of the integrated use of microvascular imaging in neurosonography for infants, setting the stage for future clinical integration of the technique.

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface

A noninvasive brain-computer interface is a central task in the comprehensive analysis and understanding of the brain and is an important challenge in international brain-science research. Current implanted brain-computer interfaces are cranial and invasive, which considerably limits their applications. The development of new noninvasive reading and writing technologies will advance substantial innovations and breakthroughs in the field of brain-computer interfaces. Here, we review the theory and development of the ultrasound brain functional imaging and its applications. Furthermore, we introduce latest advancements in ultrasound brain modulation and its applications in rodents, primates, and human; its mechanism and closed-loop ultrasound neuromodulation based on electroencephalograph are also presented. Finally, high-frequency acoustic noninvasive brain-computer interface is prospected based on ultrasound super-resolution imaging and acoustic tweezers.

Super-resolution ultrasound microvascular imaging: Is it ready for clinical use?

The field of super-resolution ultrasound microvascular imaging has been rapidly growing over the past decade. By leveraging contrast microbubbles as point targets for localization and tracking, super-resolution ultrasound pinpoints the location of microvessels and measures their blood flow velocity. Super-resolution ultrasound is the first in vivo imaging modality that can image micron-scale vessels at a clinically relevant imaging depth without tissue destruction. These unique capabilities of super-resolution ultrasound provide structural (vessel morphology) and functional (vessel blood flow) assessments of tissue microvasculature on a global and local scale, which opens new doors for many enticing preclinical and clinical applications that benefit from microvascular biomarkers. The goal of this short review is to provide an update on recent advancements in super-resolution ultrasound imaging, with a focus on summarizing existing applications and discussing the prospects of translating super-resolution imaging to clinical practice and research. In this review, we also provide brief introductions of how super-resolution ultrasound works, how does it compare with other imaging modalities, and what are the tradeoffs and limitations for an audience who is not familiar with the technology.

A Template for Translational Bioinformatics: Facilitating Multimodal Data Analyses in Preclinical Models of Neurological Injury

Background

Pediatric neurological injury and disease is a critical public health issue due to increasing rates of survival from primary injuries (e.g., cardiac arrest, traumatic brain injury) and a lack of monitoring technologies and therapeutics for the treatment of secondary neurological injury. Translational, preclinical research facilitates the development of solutions to address this growing issue but is hindered by a lack of available data frameworks and standards for the management, processing, and analysis of multimodal data sets.

Methods

Here, we present a generalizable data framework that was implemented for large animal research at the Children's Hospital of Philadelphia to address this technological gap. The presented framework culminates in an interactive dashboard for exploratory analysis and filtered data set download.

Results

Compared with existing clinical and preclinical data management solutions, the presented framework accommodates heterogeneous data types (single measure, repeated measures, time series, and imaging), integrates data sets across various experimental models, and facilitates dynamic visualization of integrated data sets. We present a use case of this framework for predictive model development for intra-arrest prediction of cardiopulmonary resuscitation outcome.

Conclusions

The described preclinical data framework may serve as a template to aid in data management efforts in other translational research labs that generate heterogeneous data sets and require a dynamic platform that can easily evolve alongside their research.

In vivo ocular microvasculature imaging in rabbits with 3D ultrasound localization microscopy

Morphological and hemodynamic changes in the ocular vasculature are important signs of various ocular diseases. The evaluation of the ocular microvasculature with high resolution is valuable in comprehensive diagnoses. However, it is difficult for current optical imaging techniques to visualize the posterior segment and retrobulbar microvasculature due to the limited penetration depth of light, particularly when the refractive medium is opaque. Thus, we have developed a 3D ultrasound localization microscopy (ULM) imaging method to visualize the ocular microvasculature in rabbits with micron-scale resolution. We used a 32 × 32 matrix array transducer (center frequency: 8 MHz) with a compounding plane wave sequence and microbubbles. Block-wise singular value decomposition spatiotemporal clutter filtering and block-matching 3D denoising were implemented to extract the flowing microbubble signals at different imaging depths with high signal-to-noise ratios. The center points of microbubbles were localized and tracked in 3D space to achieve the micro-angiography. The in vivo results demonstrate the ability of 3D ULM to visualize the microvasculature of the eye in rabbits, where vessels down to 54 μm were successfully revealed. Moreover, the microvascular maps indicated the morphological abnormalities in the eye with retinal detachment. This efficient modality shows potential for use in the diagnosis of ocular diseases.

A concise guide to transtemporal contrast-enhanced ultrasound in children

Brain contrast-enhanced ultrasound offers insights into the brain beyond the anatomic information offered by conventional grayscale ultrasound. In infants, the open fontanelles serve as acoustic windows. In children, whose fontanelles are closed, the temporal bone serves as the ideal acoustic window due to its relatively smaller thickness than the other skull bones. Diagnosis of common neurologic diseases such as stroke, hemorrhage, and hydrocephalus has been performed using the technique. Transtemporal ultrasound and contrast-enhanced ultrasound, however, are rarely used in children due to the prevalent notion that the limited acoustic penetrance degrades diagnostic quality. This review seeks to provide guidelines for the use of transtemporal brain contrast-enhanced ultrasound in children.

Bedside Cerebral Blood Flow Quantification in Neonates

Measurement of blood flow to the brain in neonates would be a very valuable addition to the medical diagnostic armamentarium. Such conditions such as assessment of closure of a patent ductus arteriosus (PDA) would greatly benefit from such an evaluation. However, measurement of cerebral blood flow in a clinical setting has proven very difficult and, as such, is rarely employed. Present techniques are often cumbersome, difficult to perform and potentially dangerous for very low birth weight (VLBW) infants. We have been developing an ultrasound blood volume flow technique that could be routinely used to assess blood flow to the brain in neonates. By scanning through the anterior fontanelles of 10 normal, full-term newborn infants, we were able to estimate total brain blood flows that closely match those published in the literature using much more invasive and technically demanding methods. Our method is safe, easy to do, does not require contrast agents and can be performed in the baby's incubator. The method has the potential for monitoring and assessing blood flows to the brain and could be used to routinely assess cerebral blood flow in many different clinical conditions.

Brain contrast-enhanced ultrasound and elastography in infants

Advanced ultrasound techniques, including brain contrast-enhanced ultrasonography and elastography, are increasingly being explored to better understand infant brain health. While conventional brain ultrasonography provides a convenient, noninvasive means of assessing major intracranial pathologies, its value in revealing functional and physiologic insights into the brain lags behind advanced imaging techniques such as magnetic resonance imaging. In this regard, contrast-enhanced ultrasonography provides highly precise functional information on macrovascular and microvascular perfusion, while brain elastography offers information on brain stiffness that may be associated with relevant physiological factors of diagnostic, therapeutic, and/or prognostic utility. This review details the technical background, current understanding and utility, and future directions of these two emerging advanced ultrasound techniques for neonatal brain applications.