Dynamic Ultrasound Localization Microscopy Without ECG-Gating.

OBJECTIVE: Dynamic Ultrasound Localization Microscopy (DULM) has first been developed for non-invasive Pulsatility measurements in the rodent brain. DULM relies on the localization and tracking of microbubbles (MBs) injected into the bloodstream, to obtain highly resolved velocity and density cine-loops. Previous DULM techniques required ECG-gating, limiting its application to specific datasets, and increasing acquisition time. The objective of this study is to eliminate the need for ECG-gating in DULM experiments by introducing a motion-matching method for time registration. METHODS: We developed a motion-matching algorithm based on tissue Doppler that leverages the cyclic tissue motion within the brain. Tissue Doppler was estimated for each group of frames in the acquisitions, at multiple locations identified as local maxima in the skin above the skull. Subsequently, each group of frames was time-registered to a reference group by delaying it based on the maximum correlation value between their respective tissue Doppler signals. This synchronization ensured that each group of frames aligned with the brain tissue motion of the reference group, and consequently, with its cardiac cycle. As a result, velocities of MBs could be averaged to retrieve flow velocity variations over time. RESULTS: Initially validated in ECG-gated acquisitions in a rat model (n = 1), the proposed method was successfully applied in a mice model in 2D (n = 3) and in a feline model in 3D (n = 1). Performing time-registration with the proposed motion-matching method or by using ECG-gating leads to similar results. For the first time, dynamic velocity and density cine-loops were extracted without the need for any information on the animal ECG, and complex dynamic markers such as the Pulsatility index were estimated. CONCLUSION: Results suggest that DULM can be performed without external gating, enabling the use of DULM on any ULM dataset where enough MBs are detectable. Time registration by motion-matching represents a significant advancement in DULM techniques, making DULM more accessible by simplifying its experimental complexity.

Quantitative pulsatility measurements using 3D dynamic ultrasound localization microscopy.

A rise in blood flow velocity variations (i.e. pulsatility) in the brain, caused by the stiffening of upstream arteries, is associated with cognitive impairment and neurodegenerative diseases. The study of this phenomenon requires brain-wide pulsatility measurements, with large penetration depth and high spatiotemporal resolution. The development of dynamic ultrasound localization microscopy (DULM), based on ULM, has enabled pulsatility measurements in the rodent brain in 2D. However, 2D imaging accesses only one slice of the brain and measures only 2D-projected and hence biased velocities . Herein, we present 3D DULM: using a single ultrasound scanner at high frame rate (1000-2000 Hz), this method can produce dynamic maps of microbubbles flowing in the bloodstream and extract quantitative pulsatility measurements in the cat brain with craniotomy and in the mouse brain through the skull, showing a wide range of flow hemodynamics in both large and small vessels. We highlighted a decrease in pulsatility along the vascular tree in the cat brain, which could be mapped with ultrasound down to a few tens of micrometers for the first time. We also performed an intra-animal validation of the method by showing consistent measurements between the two sides of the Willis circle in the mouse brain. Our study provides the first step towards a new biomarker that would allow the detection of dynamic abnormalities in microvessels in the brain, which could be linked to early signs of neurodegenerative diseases.

In Vivo Pulsatility Measurement of Cerebral Microcirculation in Rodents Using Dynamic Ultrasound Localization Microscopy.

An increased pulse pressure, due to arteries stiffening with age and cardiovascular disease, may lead to downstream brain damage in microvessels and cognitive decline. Brain-wide imaging of the pulsatility propagation from main feeding arteries to capillaries in small animals could improve our understanding of the link between pulsatility and cognitive decline. However, it requires higher spatiotemporal resolution and penetration depth than currently available with existing brain imaging techniques. Herein, we show the feasibility of performing Dynamic Ultrasound Localization Microscopy (DULM), a novel imaging approach to capture hemodynamics with a subwavelength resolution. By producing cine-loops of flowing microbubbles in 2D in the whole rodent brain lasting several cardiac cycles, DULM performed pulsatility measurements in microvessels in-depth, in vivo, with and without craniotomy. Cortical veins and arteries were shown to have a significatively different pulsatility index and the method was compared against Contrast Enhanced Ultrafast Ultrasound Doppler (CEUFD) pulsatility measurements.

Dynamic Myocardial Ultrasound Localization Angiography.

Dynamic Myocardial Ultrasound Localization Angiography (MULA) is an ultrasound-based imaging modality destined to enhance the diagnosis and treatment monitoring of coronary pathologies. Current diagnosis methods of coronary artery disease focus on the observation of vessel narrowing in the coronary vasculature to assess the organ's condition. However, we would strongly benefit from mapping and measuring flow from intramyocardial arterioles and capillaries as they are the direct vehicle of the myocardium blood income. With the advent of ultrafast ultrasound scanners, imaging modalities based on the localization and tracking of injected microbubbles allow for the subwavelength resolution imaging of an organ's vasculature. Yet, the application of these vascular imaging modalities relies on an accumulation of cine loops of a region of interest undergoing no or minimal tissue motion. This work introduces the MULA framework that combines 1) the mapping of the dynamics of the microvascular flow using an ultrasound sequence triggered by the electrocardiogram with a 2) novel Lagrangian beamformer based on non-rigid motion registration algorithm to form images directly in the myocardium's material coordinates and thus correcting for the large myocardial motion and deformation. Specifically, we show that this framework enables the non-invasive imaging of the angioarchitecture and dynamics of intramyocardial flow in vessels as small as a few tens of microns in the rat's beating heart in vivo.

Sparse channel sampling for ultrasound localization microscopy (SPARSE-ULM).

Ultrasound localization microscopy (ULM) has recently enabled the mapping of the cerebral vasculaturein vivowith a resolution ten times smaller than the wavelength used, down to ten microns. However, with frame rates up to 20000 frames per second, this method requires large amount of data to be acquired, transmitted, stored, and processed. The transfer rate is, as of today, one of the main limiting factors of this technology. Herein, we introduce a novel reconstruction framework to decrease this quantity of data to be acquired and the complexity of the required hardware by randomly subsampling the channels of a linear probe. Method performance evaluation as well as parameters optimization were conductedin silicousing the SIMUS simulation software in an anatomically realistic phantom and then compared toin vivoacquisitions in a rat brain after craniotomy. Results show that reducing the number of active elements deteriorates the signal-to-noise ratio and could lead to false microbubbles detections but has limited effect on localization accuracy. In simulation, the false positive rate on microbubble detection deteriorates from 3.7% for 128 channels in receive and 7 steered angles to 11% for 16 channels and 7 angles. The average localization accuracy ranges from 10.6µm and 9.93µm for 16 channels/3 angles and 128 channels/13 angles respectively. These results suggest that a compromise can be found between the number of channels and the quality of the reconstructed vascular network and demonstrate feasibility of performing ULM with a reduced number of channels in receive, paving the way for low-cost devices enabling high-resolution vascular mapping.

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

Ultrasound Localization Microscopy (ULM) can resolve the microvascular bed down to a few micrometers. To achieve such performance, microbubble contrast agents must perfuse the entire microvascular network. Microbubbles are then located individually and tracked over time to sample individual vessels, typically over hundreds of thousands of images. To overcome the fundamental limit of diffraction and achieve a dense reconstruction of the network, low microbubble concentrations must be used, which leads to acquisitions lasting several minutes. Conventional processing pipelines are currently unable to deal with interference from multiple nearby microbubbles, further reducing achievable concentrations. This work overcomes this problem by proposing a Deep Learning approach to recover dense vascular networks from ultrasound acquisitions with high microbubble concentrations. A realistic mouse brain microvascular network, segmented from 2-photon microscopy, was used to train a three-dimensional convolutional neural network (CNN) based on a V-net architecture. Ultrasound data sets from multiple microbubbles flowing through the microvascular network were simulated and used as ground truth to train the 3D CNN to track microbubbles. The 3D-CNN approach was validated in silico using a subset of the data and in vivo in a rat brain. In silico, the CNN reconstructed vascular networks with higher precision (81%) than a conventional ULM framework (70%). In vivo, the CNN could resolve micro vessels as small as 10 µ m with an improvement in resolution when compared against a conventional approach.