Paul Xing

Objective: To assess the role of brain hemodynamics in neurodegenerative diseases, current imaging technologies remain insufficient due to limitations in spatial or temporal resolution for quantitative mapping of pulsatile flow in the whole brain. This study aims to demonstrate the feasibility of 3D transcranial Dynamic Ultrasound Localization Microscopy (DULM) for spatiotemporal blood flow measurements in the brain, addressing limitations of 2D imaging for velocity estimation within the 3D complex vascularized structures. Methods: We used a (128+128)-element, 12 MHz Row-Column Array (RCA) to perform transcranial DULM imaging in anesthetized mice (n = 7 in total). The RCA setup allows for reduced element count while maintaining a large field of view and high frame rate compared to matrix arrays. Transcranial images were acquired at a 750-Hz volume rate using an optimized microbubble concentration and a sequence of 42 tilted plane waves. Microbubbles were localized and tracked, enabling super-resolved dynamic density and velocity maps of the 3D brain vascular network. Results: Pulsatile flows were observed with 3D DULM in 7 mice. The segmentation of cortical vessels indicated that pulsatility in arteries was significantly higher than in veins, consistent across all mice and aligning with findings in existing literature. Conclusion: This study demonstrates for the first time the feasibility and reproducibility of obtaining high spatiotemporal resolution images of mouse brain vasculature using transcranial DULM with a RCA. Significance: This work highlights the potential of RCA 3D DULM for non-invasive cerebral hemodynamics studies, it might enable comprehensive vascular imaging suitable for research in early-stage neurodegenerative diseases.

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. One Sentence Summary: Ultrasound localization microscopy during human brain surgery revealed vascular structure and dynamics of brain lesions at sub-millimeter resolution.

Objective: Transcranial ultrasound imaging is currently limited by attenuation and aberration induced by the skull. First used in contrast-enhanced ultrasound (CEUS), highly echoic microbubbles allowed for the development of novel imaging modalities such as ultrasound localization microscopy (ULM). Herein, we develop an inverse problem approach to aberration correction (IPAC) that leverages the sparsity of microbubble signals. Methods: We propose to use the a priori knowledge of the medium based upon microbubble localization and wave propagation to build a forward model to link the measured signals directly to the aberration function. A standard least-squares inversion is then used to retrieve the aberration function. We first validated IPAC on simulated data of a vascular network using plane wave as well as divergent wave emissions. We then evaluated the reproducibility of IPAC in vivo in 5 mouse brains. Results: We showed that aberration correction improved the contrast of CEUS images by 4.6 dB. For ULM images, IPAC yielded sharper vessels, reduced vessel duplications, and improved the resolution from 21.1 $\mu$m to 18.3 $\mu$m. Aberration correction also improved hemodynamic quantification for velocity magnitude and flow direction. Conclusion: We showed that IPAC can perform skull-induced aberration correction and improved power Doppler as well as ULM images acquired on the mouse brain. Significance: This technique is promising for more reliable transcranial imaging of the brain vasculature with potential non-invasive clinical applications.

Ultrasound Localization Microscopy (ULM) is a non-invasive technique that allows for the imaging of micro-vessels in vivo, at depth and with a resolution on the order of ten microns. ULM is based on the sub-resolution localization of individual microbubbles injected in the bloodstream. Mapping the whole angioarchitecture requires the accumulation of microbubbles trajectories from thousands of frames, typically acquired over a few minutes. ULM acquisition times can be reduced by increasing the microbubble concentration, but requires more advanced algorithms to detect them individually. Several deep learning approaches have been proposed for this task, but they remain limited to 2D imaging, in part due to the associated large memory requirements. Herein, we propose the use of sparse tensor neural networks to enable deep learning-based 3D ULM by improving memory scalability with increased dimensionality. We study several approaches to efficiently convert ultrasound data into a sparse format and study the impact of the associated loss of information. When applied in 2D, the sparse formulation reduces the memory requirements by a factor 2 at the cost of a small reduction of performance when compared against dense networks. In 3D, the proposed approach reduces memory requirements by two order of magnitude while largely outperforming conventional ULM in high concentration settings. We show that Sparse Tensor Neural Networks in 3D ULM allow for the same benefits as dense deep learning based method in 2D ULM i.e. the use of higher concentration in silico and reduced acquisition time.

Various methods have been proposed to enhance image quality in ultrafast ultrasound. Coherent compounding can improve image quality using multiple steered diverging transmits when motion occurring between transmits is corrected for. Harmonic imaging, a standard technique in conventional focused echocardiography, has been adapted for ultrafast imaging, reducing clutter. Coherence-based approaches have also been shown to increase contrast in clinical settings by enhancing signals from coherent echoes and reducing clutter. Herein, we introduce a simple, unified framework that combines motion-correction, harmonic imaging, and angular-coherence, showing for the first time that their benefits can be combined in real-time. Validation was conducted through in vitro testing on a spinning disk model and in vivo on 4 volunteers. In vitro results confirmed the unified framework capability to achieve high contrast in large-motion contexts up to 17 cm/s. In vivo testing highlighted proficiency in generating images of high quality during low and high tissue velocity phases of the cardiac cycle. Specifically, during ventricular filling, the unified framework increased the gCNR from 0.47 to 0.87 when compared against coherent compounding.

Ultrasound Localization Microscopy (ULM) is a novel super-resolution imaging technique that can image the vasculature in vivo at depth with resolution far beyond the conventional limit of diffraction. By relying on the localization and tracking of clinically approved microbubbles injected in the blood stream, ULM can provide not only anatomical visualization but also hemodynamic quantification of the microvasculature of different tissues. Various deep-learning approaches have been proposed to address challenges in ULM including denoising, improving microbubble localization, estimating blood flow velocity or performing aberration correction. Proposed deep learning methods often outperform their conventional counterparts by improving image quality and reducing processing time. In addition, their robustness to high concentrations of microbubbles can lead to reduced acquisition times in ULM, addressing a major hindrance to ULM clinical application. Herein, we propose a comprehensive review of the diversity of deep learning applications in ULM focusing on approaches assuming a sparse microbubbles distribution. We first provide an overview of how existing studies vary in the constitution of their datasets or in the tasks targeted by deep learning model. We also take a deeper look into the numerous approaches that have been proposed to improve the localization of microbubbles since they differ highly in their formulation of the optimization problem, their evaluation, or their network architectures. We finally discuss the current limitations and challenges of these methods, as well as the promises and potential of deep learning for ULM in the future.

Ultrasound Localization Microscopy (ULM) has proven effective in resolving microvascular structures and local mean velocities at sub-diffraction-limited scales, offering high-resolution imaging capabilities. Dynamic ULM (DULM) enables the creation of angiography or velocity movies throughout cardiac cycles. Currently, these techniques rely on a Localization-and-Tracking (LAT) workflow consisting in detecting microbubbles (MB) in the frames before pairing them to generate tracks. While conventional LAT methods perform well at low concentrations, they suffer from longer acquisition times and degraded localization and tracking accuracy at higher concentrations, leading to biased angiogram reconstruction and velocity estimation. In this study, we propose a novel approach to address these challenges by reversing the current workflow. The proposed method, Tracking-and-Localization (TAL), relies on first tracking the MB and then performing localization. Through comprehensive benchmarking using both in silico and in vivo experiments and employing various metrics to quantify ULM angiography and velocity maps, we demonstrate that the TAL method consistently outperforms the reference LAT workflow. Moreover, when applied to DULM, TAL successfully extracts velocity variations along the cardiac cycle with improved repeatability. The findings of this work highlight the effectiveness of the TAL approach in overcoming the limitations of conventional LAT methods, providing enhanced ULM angiography and velocity imaging.

Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers (μm). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration function rather than corrected images also confers enhanced explainability to the network. In addition, 3D spatiotemporal convolutions were used for the network to leverage entire microbubble tracks. For training and validation, we used an anatomically and hemodynamically realistic mouse brain microvascular network model to simulate the flow of microbubbles in presence of aberration. The proposed CV-CNN performance was compared the coherence-based method by using microbubble tracks. We then confirmed the capability of the proposed network to generalize to transcranial in vivo data in the mouse brain (n=3). Vascular reconstructions using a locally predicted aberration function included additional and sharper vessels. The CV-CNN was more robust than the coherence-based method and could perform aberration correction in a 6-month-old mouse. After correction, we measured a resolution of 15.6 μm for younger mice, representing an improvement of 25.8 %, while the resolution was improved by 13.9 % for the 6-month-old mouse. This work leads to different applications for complex-valued convolutions in biomedical imaging and strategies to perform transcranial ULM.