functional ultrasound imaging (fUSI)
Alan Urban, PhD
Assistant professor, Department of Neurosciences, KU Leuven
Group leader, NERF and VIB
Member of the Leuven Brain Institute
The Urban lab is focused in Research and Development of functional ultrasound imaging (fUSI) technology. fUSI allows imaging of brain activity based on the small blood flow changes that occur when neurons are active. fUSI is designed for large-scale imaging, and it combines many advantages, including compatibility with awake and freely moving animals, a high spatial resolution (up to 100x100x100um), a large field of view (up to 3x5cm), deep imaging (up to 5cm), a high temporal resolution (up to 100ms) and a high sensitivity.
Don't hesitate to contact us if you are interested in evaluating the fUSI technology for your research.
Figure 1: Brain-wide volumetric functional ultrasound imaging (vfUSI) adapted from Brunner et al., Neuron, 2020.
We believe that fUSI technology holds great potential for neuroscience research and clinics. Yet, bringing emerging technology impacting the high-end medical imaging market remains challenging, especially for an academic laboratory. At the NERF, we are surrounded by specialists from academia and industry playing a crucial role in lowering this barrier. Our vision is that developing appropriate brain imaging technologies is only possible if one also pioneers neuroscience research. The Urban Lab is a multidisciplinary team combining neuroscientists and engineers in various fields, including physics, mathematics, computing science, and biology. We also welcome team members with a medical background (M.D./Ph.D.) in multiple specialties (e.g., neuroradiology, neurosurgery, intensive care, neonatology).
LET'S BUILD THE FUTURE OF fUSI TOGETHER!
We aim to expand fUSI technology by tailoring it to the needs of the scientific community across several fields, research topics, and models. For this purpose, we actively collaborate with a growing network of scientists and key opinion leaders (KOL) in academics, clinics, and industries across Belgium and abroad (EU, UK, USA, Asia, and South America).
The current active “NERF fUSI community” consists of over 25 principal investigators and their teams. We also establish direct relationships with stakeholders, including patient associations, research institutes, hospitals, the medical industry, and regulatory authorities in different countries, to better understand market needs.
We are committed to developing premium quality tools for research and biomedical applications. Our activities include:
1) Extending fUSI hardware and software capabilities toward higher spatiotemporal resolution, sensitivity, specificity, larger field of view, and ease of use.
2) Unifying fUSI data acquisition and analytics toward the real-time understanding of brain functions.
3) Broadening and refining the applicability of fUSI for neuroscience research and clinics.
Don't hesitate to contact us if you are interested in evaluating the fUSI technology for your research.
DEVELOPMENT OF ULTRASOUND HARDWARE
fUSI requires high-quality ultrasound transducers which combine a large number of channels (up to 2048), a high frequency (15-20 MHz), and low pitch (the distance between piezoelectric elements) that are necessary for high-resolution imaging (up to 100um3) and high framerate (up to 10 Hz). Such transducers are often challenging to produce based on standard methods.
With our academia and industry partners, we are exploring new ultrasound transducer technologies, including thin film, single crystal, silicon technologies c/pMUT, active and passive.
Figure 2: Example of configuration with four 64-channels linear transducers stacked into a dedicated 3D printed holder. This strategy allows imaging of four cross-sections simultaneously and it has been developed as a low-cost alternative to volumetric (4D) fUSI technology.
Ultrasound transducers with lots of elements, such as those used for volumetric 4D fUSI require high channel count electronics (1024 ch or more) that can be controlled individually both in transmission (Tx) and reception (Rx). Each element needs to be excited simultaneously for plane wave imaging to compensate for the small active element size and provide a sufficient overall dynamic footprint to generate good acoustic power for imaging with adequate SNR. The NERF is developing DEUS-I, a Dedicated Ultrasound Electronic for ultrasound imaging with unprecedented capabilities (1024 Rx/Tx), portable and low-cost. The DEUS-I electronics has been designed to meet all regulatory requirements for the clinical market, and the certificate of Conformity - CE Marking is in progress.
Figure 3: Validation of DEUS-I electronics in awake mice for vascular imaging and brain activity measurement using Compound Plane Wave Doppler imaging at high PRF.
DEVELOPMENT OF ULTRASOUND SOFTWARE
OPEN-SOURCE ANALYSIS SOFTWARE
fUSI research is booming, and there is an increasing demand for a standardized analysis workflow. The Urban Lab at NERF is developing the fUSI Analyzer (FSA), an open-access analytics pipeline for fUSI data analysis, in collaboration with many neurosciences and computer science teams.
We are developing a dedicated software suite that provides a user-friendly graphical interface that allows users to analyze and display the fUSI data set. This software includes pre-processing functions like filtering, smoothing, and registration of the brain data to the mouse Allen Brain Atlas. It also provides a set of data analysis strategies such as correlation analysis, T-test, general linear model, spatiotemporal clustering, and resting-state analysis. Finally, the software improves data visualization, including several displaying functions to generate the region-time trace (barcode-like figure), the time course plot, the activation brain map, and the 3D rendering of the brain activation.
Several predefined analysis pipelines simplify data analysis for beginners and non-programmers based on standardized workflow. This software suite is a centerpiece of the fUSI ecosystem and may support a significant adoption of the fUSI technology among the scientific imaging community. The software is currently in beta testing and has already been distributed to several laboratories with a Creative Common open-source license. Ultimately, it will be made available through the standard channels (i.e., Github).
AUTOMATED BRAIN REGISTRATION AND SEGMENTATION
Image registration is a crucial step in various biomedical imaging applications. It provides the ability to geometrically align one dataset with another and is a prerequisite for all imaging applications that compare datasets across subjects, imaging modalities, or time. Image segmentation is also essential in medical image analysis and is often the first and most critical step in many clinical applications. In fUSI analysis, image segmentation is commonly used for measuring and visualizing the brain's anatomical structures, analyzing brain changes, delineating pathological regions, and for surgical planning and image-guided interventions.
Registration and segmentation are challenging for a non-expert. It is both time-consuming and error-prone, which impairs data analytics and reproducibility. We are developing advanced solutions for geometrically transforming any acquired data (plane or volume) to match a reference coordinates framework on an atlas with high precision and in real time.
DIGITALIZATION OF VASCULAR DATA
One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of a reduction of blood supply and correlate these responses with observations of brain damage. It is, therefore, crucial to establish a detailed 3D organization of the brain vasculature. The Urban lab is currently developing an automated image segmentation and registration tool in collaboration with the IPI department, Dr. Danilo Babin, Dr. Bart Goosens and Dr. Wielfried Philips in Ghent, Belgium. This new automated tool based on deep learning is designed for real-time decision support.
Figure 4: Automated digitalization enables quantitative measurement of the brain vasculature (vessel length, density, tortuosity...).
HIGH RESOLUTION IMAGING WITHOUT CONTRAST AGENT
We are developing new methods and algorithms to improve image quality, resolution and sensitivty.
Figure 5: Mouse brain vasculature. Left side original image. Right size: Improved image using the same amount of acquired data.
SYSTEMS NEUROSCIENCE RESEARCH
Systems neuroscience is a subdiscipline of neuroscience and systems biology that studies the structure and function of neural circuits and systems. Systems neuroscience includes how nerve cells behave when connected to form neural pathways, neural circuits, and more extensive brain networks. We study how different neural circuits analyze sensory information, form perceptions of the external world, make decisions, and execute movements. We try to bridge molecular and cellular approaches to understanding brain structure and function and typically combine various techniques for understanding networks of neurons, including electrophysiology and imaging technologies.
CIRCUIT DYSFUNCTION IN AMBLYOPIA (ERA-NET NEURON)
The project Understanding brain circuit dysfunction in amblyopia using large-scale multimodal recordings in a new visuomotor task applied to animal models and patients (UnscrAMBLY) is led by Dr. Daniel Hillier (TTK, Hungary). We aim to provide a network view of the neuronal origins of amblyopia by leveraging the discriminative power of a visuomotor prediction test in a joint clinical and basic science proof-of-concept study covering mice, cats, and humans.
INNATE DEFENSIVE BEHAVIOUR CIRCUITS (FWO)
Rapidly approaching objects, known as visual looming, is an intrinsic and unconditional warning cue to elicit an automatic defensive response in dealing with emergencies. It can be observed in virtually all animal species, including humans. In this collaborative project with Dr. Karl Farrow (NERF, Belgium), we combine behavioral recordings in a virtual reality environment with volumetric (4D) fUSI to study brain-wide activity in response to looming stimulations that mimic an approaching aerial predator to initiate a rapid escape response. The objective is to understand the role of the superior colliculus (SC)-lateral posterior nucleus (LP)-basolateral amygdala (BLA) pathway for detecting visual threats in mice and look for the contribution of other circuits.
RELIEVE PAIN DIRECTLY IN THE BRAIN (FWO)
Pain is an essential feature of our bodies that alerts us to danger or injury. Several therapies exist, but no universal pain reliever has been identified. In collaboration with Dr. Thomas Voets (CBD-VIB, Belgium) Our goal is to modulate precise brain circuits to relieve pain instantaneously without the need for drugs. We study pain in head-fixed mice and simultaneously record brain-wide activity using volumetric functional ultrasound imaging (vfUSI) and animal behavior using fast video cameras. The facial expression of pain will also be decoded using artificial intelligence.
fUSI FOR PATIENT CARE
The Urban lab is involved in proof of concept clinical trials in collaboration with several teams at UZ Leuven. The objective is to bring the transforming fUSI technology to patients. Such a goal implies i) identification of the medical need and opportunities, ii) creating the necessary links with the clinical teams to build the research project, iii) developing the fUSI hardware, software, and protocols dedicated to the clinics, and, finally, iv) validating the technology in patients and specific diseases/conditions.
The current research is focused on neonates with hypoxic ischemic encephalopathy (HIE), young adults, and adults (Dysplasia, Epilepsy, Brain tumor, Traumatic Brain Injury).
Our team is actively involved with the following research centers and networks.
VIB-KU Leuven Center for Brain & Disease research
Leuven Brain Institute (LBI)
KU Leuven Institute for Micro- and Nanoscale Integration (LIMNI)
Leuven Cancer Institute (LKI)
Image Processing and Interpretation Research (IPI), Ghent University
Department of Neurosurgery, University Hospital Leuven
Department of Biomedical Data Sciences, Leiden University Medical Center (LUMC), Netherlands
Neurosurgery Department, LUMC, Netherlands
Neurosurgery Department, University Medical Center (UMC) Utrecht, Netherlands
Medical University of Innsbruck, Austria
Cambridge Neuroscience, University of Cambridge, UK
Institute of Ophthalmology, University College London, UK
Brain and Mind Research Institute, Cornell University, USA
School of Optometry & Vision Science, University of California, Berkeley, USA
Princeton Neuroscience Institute, Princeton University, USA.
Department of Neuroscience, Washington University in St. Louis, USA.
Cold Spring Harbor Laboratory, USA
Department of Neuroscience, San Raffaelle Hospital, Italy
Max Planck Institute for Biological Intelligence, Munich, Germany
Department of Experimental Neurology, Berlin, Germany
Center for the Neurobiology of Learning and Memory, University of California Irvine, USA
Biomedical Imaging and Artificial Intelligence, University of British Columbia, Canada
Department of Electronics, University of Alcalá, Spain
Institute of Cognitive Neuroscience and Psychology, Budapest, Hungary
Brain Institute, University of Rio Grande do Norte, Brasil
Institute of Molecular and Clinical Ophthalmology Basel, Switzerland