The human cerebral microvasculature is both essential for brain function and highly vulnerable, yet its in-vivo structure and local hemodynamics remain largely unexplored due to the lack of imaging techniques capable of resolving deep microvascular flow in humans. This limitation not only constrains fundamental neurovascular research, but poses life-altering risks during neurosurgery, where damage to small perforating arteries can have devastating neurological consequences. Specifically, the deep cerebral perforators, branching from the major trunks of the Circle of Willis, supply essential regions of the cerebral central core but remain beyond the resolution of current intraoperative imaging modalities. Here, we report a first in-human cohort study (10 patients) demonstrating the use of 4-dimensional ultrasound localization microscopy (4D-ULM) for the intraoperative visualization of cerebral microvascular anatomy and hemodynamics. In eight patients, 4D-ULM enabled volumetric mapping of deep perforators with sub-millimeter spatial resolution (≈140 μm) at depths reaching 7 cm. This approach revealed detailed flow patterns within the previously inaccessible deep vascular networks of the human brain. Our results open new opportunities for studying microvascular physiology and could enhance intraoperative decision-making by providing high-resolution hemodynamic data, paving the way for improved microsurgical precision in neurosurgical procedures. ### Competing Interest Statement A.J.J. and F.D.C. work for Oldelft Ultrasound. The other authors have no conflicts of interest to disclose. ### Funding Statement The work described in this article was funded by the Medical Delta program Ultrafast Ultrasound for the Heart and Brain; and the Dutch Heart Foundation (Hartstichting) as part of project number 03-004-2022- 0044. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Medical Ethics Review Committee of Erasmus Medical Center Rotterdam gave ethical approval for this work (MEC-2018-037) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

Three-dimensional ultrasound provides enhanced visualization of the carotid artery (CA) anatomy and volumetric flow, offering improved accuracy for cardiovascular diagnosis and monitoring. However, fully populated matrix transducers with large apertures are complex and costly to implement. Computational ultrasound imaging (cUSi) offers a promising alternative by enabling simplified hardware design through model-based reconstruction and spatial field encoding. In this work, we present a 3-D cUSi system tailored for CA imaging, consisting of a 240-element matrix probe with a 40 & times; 24 mm(2) large aperture and a spatial encoding mask. We describe the system's design, characterization, and image reconstruction. Phantom experiments show that computational reconstruction using matched filtering (MF) significantly improves volumetric image quality over delay-and-sum (DAS), with spatial encoding enhancing lateral resolution at the cost of reduced contrast ratio (CR). Least-squares with QR (LSQR)-based reconstruction was demonstrated to further improve resolution and suppress artifacts. Using both Hadamard and 16-angle plane wave transmission schemes, the system achieved high-resolution images with reasonable contrast, supporting the feasibility of 4-D CA imaging applications.

Volumetric ultrafast ultrasound imaging demands reconstruction of images with millions of voxels thousands of times per second, creating computational challenges that limit both real-time feedback and easy offline analysis. Graphics processing units (GPUs) are well suited to this workload, yet we show that standard delay-and-sum implementations underutilize GPU resources through fragmented memory access patterns, even when sufficient computational capacity is available. Three optimization strategies address this: aligning memory access with GPU transfer granularity, halving memory traffic through mixed-precision storage, and exploiting spatial locality to utilize tensor core arithmetic. Together, these achieve kilohertz frame rates for 128^3-voxel grids with 1024-element arrays, substantially outperforming existing implementations while maintaining image quality. This enables real-time volumetric imaging at scales previously restricted to offline processing, supporting applications such as intraoperative brain imaging and brain-computer interfaces where immediate feedback is essential. We release our implementation as part of the open-source library.

Functional ultrasound imaging (fUSI) enables brain-wide mapping of hemodynamic activity in awake rodents, offering a powerful bridge between systems neuroscience in animals and human neuroimaging. However, extending fUSI beyond tightly controlled sensory paradigms to more naturalistic behaviors is limited by motion-related confounds arising from locomotion, physiological arousal, and movement-linked neural activity. Here, we introduce a behavior-informed modeling framework that explicitly incorporates continuous measurements of running speed and head motion into a general linear model to account for motion-related variance while preserving interpretable task-related signals. We validate this approach in two head-fixed paradigms with distinct motion profiles: a visual stimulation task with minimal stimulus-driven movement, and a noxious stimulation task in which locomotion and arousal are intrinsic to the behavioral response. In both cases, explicit behavioral modeling recovers neural response patterns that more closely resemble low-motion reference conditions than blind, model-free denoising approaches. Critically, during noxious stimulation, behavior-informed modeling preserves shock-intensity-dependent activity in the primary somatosensory cortex that is attenuated by global component removal. These findings demonstrate that explicit modeling of behavior enables reliable interpretation of brain-wide fUSI signals during naturalistic, high-motion conditions, opening the door to studying affective and cognitive processes that were previously difficult to access with functional ultrasound imaging. ### Competing Interest Statement The authors have declared no competing interest. European Research Council, 758703 the The Dutch Organization for Scientific Research (NWO), 108845 the The Dutch Organization for Scientific Research (NWO), OCENW.XL21.XL21.069 4TU Program, Precision Medicine the Marie-Sklodowska Curie Fellowship, MIC-101032769 the Medical Delta Ultrafast Heart & Brain Program

Computational ultrasound imaging (cUSi) with few elements and spatial field encoding can provide high-resolution volumetric B-mode imaging. In this work, we extend its application to 4D carotid artery (CA) flow imaging using a custom large-aperture 240-element matrix probe. We implemented a frequency band-based matched filtering strategy that balances resolution and contrast. The system's inherent imaging capabilities were evaluated and validated in flow phantom and human CA experiments. In the phantom study, 3D/4D power Doppler image and speckle-tracking analyses confirmed the system's ability to resolve flow structures and hemodynamics. In the human study, the CA bifurcation flow structure and its local pulsatile flow dynamics were successfully reconstructed. These results demonstrate the feasibility of using a large-footprint, few-element cUSi system for 4D CA flow assessment.

The microvasculature and hemodynamics of human brain tumors and other lesions have remained largely unexplored in vivo due to the limited resolution of conventional imaging techniques, and may present new opportunities in biomarker identification and neurosurgeries. Ultrasound Localization Microscopy (ULM), which tracks freely circulating intravascular microbubbles used as contrast agents, overcomes these limitations and has been used to visualize vascular structures and flow. In this study, we performed intraoperative ULM during brain surgeries involving resection of brain tumors (N = 3 meningiomas, N = 3 brain metastasis, N = 3 high grade gliomas) and an arteriovenous malformation (AVM) (N = 1). ULM provided microvascular images of the human brain, resolving vessels down to 35 μm, revealing clear differences in structure and dynamics between tumor and surrounding healthy tissue. By following individual microbubbles, vessel connectivity was probed and used to identify feeding, draining, and non-tumoral vessels. In one case, a 3D ULM map of an AVM was generated with 226 μm resolution, allowing us to resolve its complex internal structure, including feeding and draining vessels. Intraoperative ULM enables visualization of human brain vasculature and hemodynamics at unprecedented resolutions, and may directly aid tumor and AVM resections by providing flow paths and speeds through compact niduses. Ultrasound localization microscopy during human brain surgery revealed vascular structure and dynamics of brain lesions at sub-millimeter resolution.

Ultrafast Doppler ultrasound imaging allows for detailed images of blood flow inside the brain during neurosurgical interventions. In this work, we extend this new imaging technique to geometrically accurate volumetric reconstructions using freehand 2D ultrafast ultrasound acquisitions in conjunction with optical position tracking. We show how the Doppler signal can be derived from a moving freehand ultrasound scan. These filtered 2D images are subsequently mapped onto a shared 3D reference space using a normalized convolution function. The proposed methodology allows for highly detailed volumetric reconstructions of cerebral and tumor blood flow. The dense vascular networks show intriguing blood vessel morphology with vessels down to several hundred micrometers in diameter. By adding patient-co-registered volumetric reconstruction to ultrafast Doppler ultrasound, we have created a 3D intra-operative imaging technique that is unmatched in terms of resolution, ease of use, and visualization capabilities.

Beamforming is a well-known technique to combine signals from multiple sensors. It has a wide range of application domains. This paper introduces the Tensor-Core Beamformer: a generic, optimized beamformer library that harnesses the computational power of GPU tensor cores to accelerate beamforming computations. The library hides the complexity of tensor cores from the user, and supports 16-bit and 1-bit precision. An extensive performance evaluation on NVIDIA and AMD GPUs shows that the library outperforms traditional beamforming on regular GPU cores by a wide margin, at much higher energy efficiency. In the 16-bit mode, it achieves over 600 TeraOps/s on an AMD MI300X GPU, while approaching 1 TeraOp/J. In the 1-bit mode, it breaks the 3 PetaOps/s barrier and achieves over 10 TeraOps/J on an NVIDIA A100 GPU. The beamforming library can be easily integrated into existing pipelines. We demonstrate its use for medical ultrasound and radio-astronomical instruments.

Ultrafast imaging, which uses unfocussed transmissions to form images, provides very high frame rates at the cost of low signal-to-noise ratio (SNR). This loss of SNR becomes especially apparent when imaging deeper structures. Ultrafast imaging is mostly used in combination with Doppler processing. Even if we apply tissue-separation filters, they lead to significant energy loss and decrease the SNR. Previous work showed that this loss in SNR and, hence, penetration depth can be partially regained using coded transmissions. However, these codes are mostly either standard or randomly generated and can be improved with a design rooted in an optimization scheme. To address this limitation, we design an optimized code tailored to ultrasound imaging with unfocused transmissions represented by a generalized encoding matrix in a linear signal model. We employ the minimization of the Cramer-Rao lower bound (CRB) over the unknown coding matrix as a way to optimize the code. Due to the high computational cost of the resulting optimization problems, we also introduce a trace-constraint optimization problem based on the Fisher information matrix (FIM). Simulation results show that the optimized code provides higher SNR in deep image regions than previously tested coding schemes such as the Barker code, albeit with a trade-off for decreased resolution. On the other hand, the application of least-squares QR (LSQR) mitigates this resolution degradation. Lastly, the optimized code was tested in simulations using a numerical model of a clinical transducer setting, demonstrating its potential for higher SNR in ultrafast Doppler imaging.

Imagine being able to study the human brain in real-world scenarios while the subject displays natural behaviors such as locomotion, social interaction, or spatial navigation. The advent of ultrafast ultrasound imaging brings us closer to this goal with functional ultrasound imaging (fUSi), a mobile neuroimaging technique. Here, we present real-time fUSi monitoring of brain activity during walking in a subject with a clinically approved sonolucent skull implant. Our approach uses personalized 3D-printed fUSi helmets for stability, optical tracking for cross-modal validation with functional magnetic resonance imaging, advanced signal processing to estimate hemodynamic responses, and facial tracking of a lick licking paradigm. These combined efforts allowed us to show consistent fUSi signals over 20 months, even during high motion activities such as walking. These results demonstrate the feasibility of fUSi for monitoring brain activity in real-world contexts, marking an important milestone for fUSi-based insights in clinical and neuroscientific research.

Dynamic susceptibility contrast (DSC) MRI is commonly part of brain tumor imaging. For quantitative analysis, measurement of the arterial input function and tissue concentration time curve is required. Usually, a linear relationship between the MR signal changes and contrast agent concentration ([Gd]) is assumed, even though this is a known simplification. The aim of this study was to develop a realistic 3D simulation model as an efficient method to assess the relationship between ΔR2 (*) and [Gd] both in whole blood and brain tissue. We modified an open-source 3D simulation model to study different red blood cell configurations for assessing whole-blood ΔR2 (*) versus [Gd]. The results were validated against previously obtained 2D data and in vitro data. Furthermore, hematocrit levels (30%-50%) and field strengths (1.5-3.0-7.0 T) were varied. Subsequently, realistic tumor vascular networks were derived from intraoperative high framerate Doppler ultrasound data to study the influence of vascular structure and orientation with respect to the main magnetic field (1.5-3.0-7.0 T) for the calculation of ΔR2 (*) versus [Gd] in brain tissue. For whole blood, good agreement of the 3D model was found with in vitro and 2D simulation data when red blood cells were aligned with the blood flow. For brain tissue, minor differences were found between the vascular networks. The effect of vessel direction with respect to B0 was apparent in case of clear directionality of the main vessels. The dependency on field strength agreed with previous reports. In conclusion, we have shown that the relationship between ΔR2 (*) and [Gd] is affected by the organization of red blood cells and orientation of blood vessels with respect to the main magnetic field, as well as the field strength. These findings are important for further optimization of the realistic 3D model that could eventually be used to improve the estimation of hemodynamic parameters from DSC-MRI.

Williams syndrome is a developmental disorder caused by a microdeletion entailing the loss of a single copy of 25-27 genes on chromosome 7q11.23. Patients suffer from cardiovascular and neuropsychological symptoms. Structural abnormalities of the cardiovascular system in Williams syndrome have been attributed to the hemizygous loss of the elastin (ELN) gene. In contrast, the neuropsychological consequences of Williams syndrome, including sensorimotor deficits, hypersociability, and cognitive impairments, have been mainly attributed to altered expression of transcription factors, like LIMK1, GTF2I, and GTF2IRD1, while the potential impact of altered cerebrovascular function has been largely overlooked. To study the relationship between Williams syndrome mutations and vascularization of both the heart and brain, we generated a mouse model carrying a relatively long microdeletion Del(5Ncf1-Fkbp6). Heterozygous Del(5Ncf1-Fkbp6) mice had elongated and tortuous aortas but, unlike Eln haploinsufficient mice, showed no signs of structural cardiac hypertrophy. Remarkably, we also observed structural abnormalities in coronary and brain vessels, including disorganized extracellular matrices. Importantly, the mutant mice faithfully replicated both cardiovascular and neuropsychological symptoms observed in patients. The phenotype was even more comprehensive than in former models, with structure-function correlations evident in aberrant auditory and motor behaviors resembling those in patients with Williams syndrome. Together, our findings suggest that not only cardiovascular but also neuropsychological symptoms in Williams syndrome may be driven in part by vascular abnormalities affecting both heart and brain.

Ultrasonography could allow operator-independent examination and continuous monitoring of the carotid artery (CA) but normally requires complex and expensive transducers, especially for 3-D. By employing computational ultrasound imaging (cUSi), using an aberration mask and model-based reconstruction, a monitoring device could be constructed with a more affordable simple transducer design comprising only a few elements. We aim to apply the cUSi concept to create a CA monitoring system. The system's possible configurations for the 2-D imaging case were explored using a linear array setup emulating a cUSi device in silico, followed by in vitro testing and in vivo CA imaging. Our study shows enhanced reconstruction performance with the use of an aberrating mask, improved lateral resolution through proper choice of the mask delay variation, and more accurate reconstructions using least-squares with QR (LSQR) decomposition compared to matched filtering (MF). Together, these advancements enable B-mode reconstruction and power Doppler imaging (PDI) of the CA with sufficient quality for monitoring using a configuration of 12 transceivers coupled with a random aberration mask with a maximum delay variation of four wave periods (WPs).

Four-dimensional ultrasound imaging of complex biological systems such as the brain is technically challenging because of the spatiotemporal sampling requirements. We present computational ultrasound imaging (cUSi), an imaging method that uses complex ultrasound fields that can be generated with simple hardware and a physical wave prediction model to alleviate the sampling constraints. cUSi allows for high-resolution four-dimensional imaging of brain hemodynamics in awake and anesthetized mice.

Functional ultrasound (fUS) is an emerging neuroimaging modality that records changes in local blood dynamics. While it is known that the brain can respond variably to the same stimuli presented at different time instants, the extent to which fUS detects this variability based on the measured hemodynamics remains an open question. In this work, we characterize trial variability using fUS by estimating activation coefficients per trial using region-specific hemodynamic response functions. Our visual fUS experiments conducted on a mouse consistently reveal an increase of trial variability from the lateral geniculate nucleus to the visual cortex across different brain slices. These results are in parallel with prior findings in neuronal studies, suggesting a link between fluctuations of the evoked fUS response and true neural variability.

In recent decades, increasing ultrasound frame rates has been the main motivation behind many novel ultrasound imaging applications [1]–[3]. With this work, we propose an efficient ultrafast FPGA beamformer that applies coherent compounding, through a delay-reuse optimization.

Computational ultrasound imaging (cUSi) offers high-resolution 3D imaging with simpler hardware by relying on computational power. Central to cUSi is a large model matrix that stores all pulse-echo signals. For 3D imaging this matrix easily surpasses 1 terabyte, hindering in-memory storage and real-time processing. This paper presents a solution for cUSi through an aberrating layer by introducing a virtual array concept, which uses transfer functions to map data from the real to a virtual array, enabling the use of conventional reconstruction techniques like delay-and-sum (DAS). We demonstrate the mathematical similarity of this approach to using a full model matrix and validate it with promising imaging results.

Analysis of functional neuroimaging data aims to unveil spatial and temporal patterns of interest. Existing analysis methods fall into two categories: fully data-driven approaches and those reliant on prior information, e.g. the stimulus time course. While using the stimulus signal directly can help identify the activated brain areas, it is known that the relationship between stimuli and the brain's response exhibits nonlinear and time-varying characteristics. As such, relying completely on the stimulus signal to describe the brain's temporal response leads to a restricted interpretation of the brain function. In this paper, we present a new technique called Evoked Component Analysis (ECA), which leverages prior information up to a defined extent. This is achieved by including the general linear model (GLM) design matrix as a regulatory term and estimating the factor matrices in both space and time through an alternating minimization approach. We apply ECA to 2D and swept-3D functional ultrasound (fUS) experiments conducted with mice. When decomposing 2D fUS data, we employ GLM regularization at various intensities to emphasize the role of prior information. Furthermore, we show that incorporating multiple hemodynamic response functions within the design matrix can provide valuable insights into region-specific characteristics of evoked activity. Finally, we use ECA to analyze swept-3D fUS data recorded from five mice engaged in two distinct visual tasks. Swept-3D fUS images the 3D brain sequentially using a moving probe, resulting in different slice acquisition time instants. We show that ECA can estimate factor matrices with a fine resolution at each slice acquisition time instant and yield higher t-statistics compared to GLM and correlation analysis for all subjects.

ObjectiveIntraoperative Doppler ultrasound imaging of human brain vasculature is an emerging neuro-imaging modality that offers vascular brain mapping with unprecedented spatiotemporal resolution. At present, however, access to the human brain using Doppler Ultrasound is only possible in this intraoperative context, posing a significant challenge for validation of imaging techniques. This challenge necessitates the development of realistic flow phantoms outside of the neurosurgical operating room as external platforms for testing hardware and software. An ideal ultrasound flow phantom should provide reference-like values in standardized topologies such as a slanted pipe, and allow for measurements in structures closely resembling vascular morphology of actual patients. Additionally, the phantom should be compatible with other clinical cerebrovascular imaging modalities. To meet these criteria, we developed and validated a versatile, multimodal MRI- and ultrasound Doppler phantom.MethodsOur approach incorporates the latest advancements in phantom research using tissue-mimicking material and 3D-printing with water-soluble resin to create wall-less patient-specific lumens, compatible for ultrasound and MRI.ResultsWe successfully produced three distinct phantoms: a slanted pipe, a y-shape phantom representing a bifurcating vessel and an arteriovenous malformation (AVM) derived from clinical Digital Subtraction Angiography (DSA)-data of the brain. We present 3D ultrafast power Doppler imaging results from these phantoms, demonstrating their ability to mimic complex flow patterns as observed in the human brain. Furthermore, we showcase the compatibility of our phantom with Magnetic Resonance Imaging (MRI).ConclusionWe developed an MRI- and Doppler Ultrasound-compatible flow-phantom using customizable, water-soluble resin prints ranging from geometrical forms to patient-specific vasculature.