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.

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 ( [Formula: see text]). 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 recently introduced 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 to 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 [Formula: see text] 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.

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 (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.

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.

Ultrafast acoustoelectric imaging (UAI) is a novel method for the mapping of biological current densities, which may improve the diagnosis and monitoring of cardiac activation diseases such as arrhythmias. This paper evaluates the feasibility of performing UAI in beating rat hearts. A previously described system based on a 256-channel ultrasound research platform fitted with a 5-MHz linear array was used for simultaneous UAI, ultrafast B-mode, and electrocardiogram (ECG) recordings. In this paper, rat hearts (n = 4) were retroperfused within a Langendorff isolated heart system. A pair of Ag/Cl electrodes were positioned on the epicardium to simultaneously record ECG and UAI signals for imaging frame rates of up to 1000 Hz and a mechanical index of 1.3. To account for the potential effect of motion on the UAI maps, acquisitions for n = 3 hearts were performed with and without suppression of the mechanical contraction using 2,3-butanedione monoxime. Current densities were detected for all four rats in the region of the atrio-ventricular node, with an average contrast-to-noise ratios of 12. The UAI signals' frequency matched the sinus rhythm, even without mechanical contraction, suggesting that the signals measured correspond to physiological electrical activation. UAI signals appeared at the apex and within the ventricular walls with a delay estimated at 29 ms. Finally, the signals from different electrode positions along the myocardium wall showed the possibility of mapping the electrical activation throughout the heart. These results show the potential of UAI for cardiac activation mapping in vivo and in real time.

Ultrasound localization microscopy can map blood vessels with a resolution much smaller than the wavelength by localizing microbubbles. The current implementations of the technique are limited to 2-D planes or small fields of view in 3-D. These suffer from minute-long acquisitions, out-of-plane microbubbles, and tissue motion. In this paper, we exploit the recent development of 4D ultrafast ultrasound imaging to insonify an isotropic volume up to 20 000 times per second and perform localization microscopy in the three dimensions. Specifically, a 32 ×32 elements, 9-MHz matrix-array probe connected to a 1024-channel programmable ultrasound scanner was used to achieve sub-wavelength volumetric imaging of both the structure and vector flow of a complex 3D structure (a main canal branching out into two side canals). To cope with the large volumes and the need to localize the bubbles in the three dimensions, novel algorithms were developed based on deconvolution of the beamformed microbubble signal. For tracking, individual particles were paired following a Munkres assignment method, and velocimetry was done following a Lagrangian approach. ULM was able to clearly represent the 3-D shape of the structure with a sharp delineation of canal edges (as small as [Formula: see text]) and separate them with a spacing as low as [Formula: see text]. The compounded volume rate of 500 Hz was sufficient to describe velocities in 2.5-150-mm/s range and to reduce the maximum acquisition time to 12 s. This paper demonstrates the feasibility of in vitro 3-D ultrafast ultrasound localization microscopy and opens up the way toward in vivo volumetric ULM.

Harmonic motion imaging for focused ultrasound (HMIFU) utilizes an amplitude-modulated HIFU beam to induce a localized focal oscillatory motion simultaneously estimated. The objective of this study is to develop and show the feasibility of a novel fast beamforming algorithm for image reconstruction using GPU-based sparse-matrix operation with real-time feedback. In this study, the algorithm was implemented onto a fully integrated, clinically relevant HMIFU system. A single divergent transmit beam was used while fast beamforming was implemented using a GPU-based delay-and-sum method and a sparse-matrix operation. Axial HMI displacements were then estimated from the RF signals using a 1-D normalized cross-correlation method and streamed to a graphic user interface with frame rates up to 15 Hz, a 100-fold increase compared to conventional CPU-based processing. The real-time feedback rate does not require interrupting the HIFU treatment. Results in phantom experiments showed reproducible HMI images and monitoring of 22 in vitro HIFU treatments using the new 2-D system demonstrated reproducible displacement imaging, and monitoring of 22 in vitro HIFU treatments using the new 2-D system showed a consistent average focal displacement decrease of 46.7 ±14.6% during lesion formation. Complementary focal temperature monitoring also indicated an average rate of displacement increase and decrease with focal temperature at 0.84±1.15%/(°)C, and 2.03±0.93%/(°)C , respectively. These results reinforce the HMIFU capability of estimating and monitoring stiffness related changes in real time. Current ongoing studies include clinical translation of the presented system for monitoring of HIFU treatment for breast and pancreatic tumor applications.

Electromechanical wave imaging (EWI) has recently been introduced as a noninvasive, ultrasound-based imaging modality, which could map the electrical activation of the heart in various echocardiographic planes in mice, dogs, and humans in vivo. By acquiring radio-frequency (RF) frames at very high frame rates (390-520 Hz), the onset of small, localized, transient deformations resulting from the electrical activation of the heart, i.e., generating the electromechanical wave (EMW), can be mapped. The correlation between the EMW and the electrical activation speed and pacing scheme has previously been reported. In this study, we pursue the development of EWI using both displacements and strains and analysis of the EMW properties in dogs in vivo for early detection of ischemia. EWI was performed in normal and ischemic open-chest dogs during sinus rhythm. Ischemia of increasing severity was obtained by gradually obstructing the left-anterior descending (LAD) coronary artery flow. We also introduce the novel method of motion-matching that achieves the reconstruction of the full EWI ciné-loop at very high frame rates even when the ECG may be irregular or unavailable. Incremental displacements were previously used by our group to map the EMW. This paper focuses on the associated incremental strains, which facilitate the interpretation of the EMW by relating it directly to contraction. Moreover, we define the onset of the EMW as the time, at which the incremental strains change sign after the onset of the QRS complex of the ECG. Based on this definition, isochronal representations of the EMW were generated using a semi-automated method. The isochronal representation of the EMW during sinus rhythm was reproducible and shown similar to electrical activation maps previously reported in the literature. After segmentation using a contour-tracking method, the two- and four-chamber views were imaged and displayed in bi-plane views, allowing a 3-D interpretation of the EMW. EWI was shown to be sensitive to the presence of intermediate ischemia. EWI localized the ischemic region when the LAD flow was obstructed at 60% and beyond and was capable of mapping the increase of the ischemic region size as the LAD occlusion level increased. In conclusion, the activation maps and wave patterns obtained with EWI were similar to the electrical equivalents previously reported in the literature. Moreover, EWI was found to be sensitive enough to detect and map intermediate ischemia. Those results indicate that EWI could be used to assess the conduction properties of the myocardium, and detect its ischemic onset and disease progression entirely noninvasively.

Photo-acoustic (PA) imaging has been developed for different purposes, but recently, the modality has gained interest with applications to small animal imaging. As a technique it is sensitive to endogenous optical contrast present in tissues and, contrary to diffuse optical imaging, it promises to bring high resolution imaging for in vivo studies at midrange depths (3-10 mm). Because of the limited amount of radiation tissues can be exposed to, existing reconstruction algorithms for circular tomography require a great number of measurements and averaging, implying long acquisition times. Time-resolved PA imaging is therefore possible only at the cost of complex and expensive electronics. This paper suggests a new reconstruction strategy using the compressed sensing formalism which states that a small number of linear projections of a compressible image contain enough information for reconstruction. By directly sampling the image to recover in a sparse representation, it is possible to dramatically reduce the number of measurements needed for a given quality of reconstruction.

Unsecured electronic circuits leak physical syndromes correlated to the data they handle. Side-channels attacks, like SPA or DPA, exploit this information leakage. We provide balanced and memoryless CMOS structures for a 2-input secured NAND gate.