Ultrasound Localization Microscopy Research Articles

Transthoracic ultrasound localization microscopy of myocardial vasculature in patients.

Myocardial microvasculature and haemodynamics are indicative of potential microvascular diseases for patients with symptoms of coronary heart disease in the absence of obstructive coronary arteries. However, imaging microvascular structure and flow within the myocardium is challenging owing to the small size of the vessels and the constant movement of the patient's heart. Here we show the feasibility of transthoracic ultrasound localization microscopy for imaging myocardial microvasculature and haemodynamics in explanted pig hearts and in patients in vivo. Through a customized data-acquisition and processing pipeline with a cardiac phased-array probe, we leveraged motion correction and tracking to reconstruct the dynamics of microcirculation. For four patients, two of whom had impaired myocardial function, we obtained super-resolution images of myocardial vascular structure and flow using data acquired within a breath hold. Myocardial ultrasound localization microscopy may facilitate the understanding of myocardial microcirculation and the management of patients with cardiac microvascular diseases.

Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

Context-aware deep learning enables high-efficacy localization of high concentration microbubbles for super-resolution ultrasound localization microscopy

Ultrasound localization microscopy (ULM) enables deep tissue microvascular imaging by localizing and tracking intravenously injected microbubbles circulating in the bloodstream. However, conventional localization techniques require spatially isolated microbubbles, resulting in prolonged imaging time to obtain detailed microvascular maps. Here, we introduce LOcalization with Context Awareness (LOCA)-ULM, a deep learning-based microbubble simulation and localization pipeline designed to enhance localization performance in high microbubble concentrations. In silico, LOCA-ULM enhanced microbubble detection accuracy to 97.8% and reduced the missing rate to 23.8%, outperforming conventional and deep learning-based localization methods up to 17.4% in accuracy and 37.6% in missing rate reduction. In in vivo rat brain imaging, LOCA-ULM revealed dense cerebrovascular networks and spatially adjacent microvessels undetected by conventional ULM. We further demonstrate the superior localization performance of LOCA-ULM in functional ULM (fULM) where LOCA-ULM significantly increased the functional imaging sensitivity of fULM to hemodynamic responses invoked by whisker stimulations in the rat brain.

Inside Front Cover: Super‐resolution ultrasound localization microscopy for the non‐invasive imaging of human testicular microcirculation and its differential diagnosis role in male infertility (View 2/2024)

Inside Front Cover: Super‐resolution ultrasound localization microscopy for the non‐invasive imaging of human testicular microcirculation and its differential diagnosis role in male infertility (View 2/2024)

Abstract 4164: Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment

Abstract Photoacoustic (PA) imaging can map the physiological conditions of tissues and track the biodistribution of contrast agents. Ultrasound localization microscopy (ULM) with microbubbles provides deep-tissue super-resolution blood vessel images and blood velocity maps. The integration of these techniques offers a potential tool for oncological applications, such as visualizing tumor hypoxia and vasculature. Additionally, because PA imaging and ULM can share the same ultrasound detector and data-acquisition system, PA images can seamlessly fuse with ULM images without requiring post co-registration processing. However, the acquisition time of ULM is considerably longer (several minutes per frame) compared to PA imaging (10 milliseconds to 100 milliseconds per frame), limiting the simultaneous acquisition of the two imaging modalities. To address this issue, we developed a deep learning model based on U-Net to accelerate the frame rate of ULM using information from sparse microbubble locations and power Doppler images. By enabling fast ULM acquisition, we developed an interleaved PA/fast ULM imaging technique to simultaneously capture photoacoustic and ULM images. Our experiments demonstrated up to a 16 times improvement in acquisition speed with U-Net, utilizing in vivo mouse brain and skin tumor data. The hybrid imaging sequence achieved super-resolution vascular and tissue oxygenation imaging with less than 2 seconds of acquisition time per frame in vivo. The implementation of this rapid dual imaging scheme enables the longitudinal monitoring of 3D tumor microvasculature and hypoxia conditions using a common linear ultrasound transducer. We monitored tumor growth of epidermoid carcinoma (A431) in a mouse model from Day 7 to Day 17 of tumor implantation, and the results reveal a significant decrease in tumor vasculature density and oxygen saturation as the tumor grew larger over time. The results indicate a positive relationship between oxygen saturation and vessel density. Our findings showcase the potential of this innovative technique for advancing deep tissue, high-resolution, multi-biological parameters imaging in oncological research. Citation Format: Shensheng Zhao, Sayantani Basu, Roy H. Campbell, Yang Zhao, Yun-Sheng Chen. Hybrid photoacoustic imaging and fast ultrasound localization microscopy to probe the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4164.

Vasa vasorum interna in the carotid wall of active forms of Takayasu arteritis evidenced by ultrasound localization microscopy.

Introduction: Takayasu arteritis (TA) is associated with microvascularization of the wall of large arteries and is related to inflammation. Ultrasound localization microscopy (ULM), combining ultrafast ultrasound imaging with microbubble (MB) injection, can track the path of MBs within the arterial wall and thus provide imaging of the vasa vasorum. From the analysis of MB tracks in the common carotid arteries of patients with active TA, we report the presence of microvessels in connection with the carotid lumen (i.e., vasa vasorum interna [VVI]). Methods: ULM maps were obtained on five patients with active disease in the observational single-center series of the TAK-UF study. MB tracks connected to the carotid lumen were automatically identified, allowing the reconstruction of VVI. Results: MB tracking allows us to observe a microvascular network on the inner part of the wall, with some vessels in communication with the carotid lumen. This type of vessel was identified in all patients with active TA (n = 5) with a median of 2.2 [1.1-3.0] vessels per acquisition (2D longitudinal view of 3 cm of the common carotid artery). The blood flow within these vessels is mainly centrifugal; that is, toward the adventitia (88% [54-100] of MB tracks with flow directed to the outer part of the wall). Conclusion: VVI are present in humans in the case of active TA and emphasize the involvement of the intima in the pathological process. ClinicalTrials.gov Identifier: NCT03956394.

Equivalent-time-active-cavitation-imaging enables vascular-resolution blood-brain-barrier-opening-therapy planning

Objective. Linking cavitation and anatomy was found to be important for predictable outcomes in focused-ultrasound blood-brain-barrier-opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either (1) do not leverage the full resolution capabilities of ultrasound imaging or (2) place constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map. Approach. Herein, we develop BandPass-sampled-equivalent-time-active-cavitation-imaging (BP-ETACI), derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood–brain barrier opening with long therapeutic bursts using a 1.5 MHz focused transducer in the brain of C57BL/6 mice. Main results. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for (1) monitoring focused-ultrasound blood-brain-barrier-opening (FUS-BBBO), but also for (2) therapy planning and target verification. Significance. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.

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.

Phase Aberration Correction for In Vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network.

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.

Evaluation of tumor microvasculature with 3D ultrasound localization microscopy based on 2D matrix array.

Evaluation of tumor microvascular morphology is of great significance in tumor diagnosis, therapeutic effect prediction, and surgical planning. Recently, two-dimensional ultrasound localization microscopy (2DULM) has demonstrated its superiority in the field of microvascular imaging. However, it suffers from planar dependence and is unintuitive. We propose a novel three-dimensional ultrasound localization microscopy (3DULM) to avoid these limitations. We investigated 3DULM based on a 2D array for tumor microvascular imaging. After intravenous injection of contrast agents, all elements of the 2D array transmit and receive signals to ensure a high and stable frame rate. Microbubble signal extraction, filtering, positioning, tracking, and other processing were used to obtain a 3D vascular map, flow velocity, and flow direction. To verify the effectiveness of 3DULM, it was validated on double helix tubes and rabbit VX2 tumors. Cisplatin was used to verify the ability of 3DULM to detect microvascular changes during tumor treatment. In vitro, the sizes measured by 3DULM at 3mm and 13mm were 178 [Formula: see text] and 182 [Formula: see text], respectively. In the rabbit tumors, we acquired 9000 volumes to reveal vessels about 30 [Formula: see text] in diameter, which surpasses the diffraction limit of ultrasound in traditional ultrasound imaging, and the results matched with micro-angiography. In addition, there were significant changes in vascular density and curvature between the treatment and control groups. The effectiveness of 3DULM was verified in vitro and in vivo. Hence, 3DULM may have potential applications in tumor diagnosis, tumor treatment evaluation, surgical protocol guidance, and cardiovascular disease. 3D ultrasound localization microscopy is highly sensitive to microvascular changes; thus, it has clinical potential for tumor diagnosis and treatment evaluation. • 3D ultrasound localization microscopy is demonstrated on double helix tubes and rabbit VX2 tumors. • 3D ultrasound localization microscopy can reveal vessels about 30 [Formula: see text] in diameter-far smaller than traditional ultrasound. • This form of imaging has potential applications in tumor diagnosis, tumor treatment evaluation, surgical protocol guidance, and cardiovascular disease.

Super-Resolution Ultrasound Reveals Cerebrovascular Impairment in a Mouse Model of Alzheimer's Disease.

Increasing evidence has suggested a link between cerebrovascular disease and the cognitive impairment associated with Alzheimer's disease. However, detailed descriptions of microvascular changes across brain regions and how they relate to other more traditional pathology have been lacking. Additionally, the efforts to elucidate the interplay between cerebral microvascular function and Alzheimer's disease progression are complicated by the necessity of probing deep-brain structures since early-stage Alzheimer's disease typically involves hippocampal pathology. The purpose of this study was to examine changes in microvascular dynamics in a mouse model of Alzheimer's disease using cohorts that were age-matched to wild-type controls. Data from both sexes were included in this study. Super-resolution ultrasound localization microscopy revealed microvascular functional and structural features throughout the whole brain depth to visualize and quantify. We found that functional decreases in hippocampal and entorhinal flow velocity preceded structural derangements in regional vascular density. Co-registered histological sectioning confirmed the regionalized perfusion deficits seen on ultrasound imaging, which were co-localized with amyloid beta plaque deposition. In addition to providing global vascular quantifications of deep brain structures with a high local resolution, this technology also permitted velocity-profile analysis of individual vessels and, in some cases, allowed for decoupling of arterial and venous flow contributions. These data suggest that microvascular pathology is an early and pervasive feature of Alzheimer's disease and may represent a novel therapeutic target for this disease.

Visualization of Renal Glomeruli in Human Native Kidneys With Sensing Ultrasound Localization Microscopy.

Kidney diseases significantly impact individuals' quality of life and strongly reduce life expectancy. Glomeruli play a crucial role in kidney function. Current imaging techniques cannot visualize them due to their small size. Sensing ultrasound localization microscopy (sULM) has shown promising results for visualizing in vivo the glomeruli of human kidney grafts. This study aimed to evaluate the ability of sULM to visualize glomeruli in vivo in native human kidneys despite their depth and a shorter duration of ultrasound acquisition limited by the period of the patient's apnea. Sensing ultrasound localization microscopy parameters in native kidneys and kidney grafts and their consequence regarding glomeruli detection were also compared. Exploration by sULM was conducted in 15 patients with native kidneys and 5 with kidney allografts. Glomeruli were counted using a normalized distance metric projected onto sULM density maps. The difference in the acquisition time, the kidney depth, and the frame rate between native kidneys and kidney grafts and their consequence regarding glomeruli detection were assessed. Glomerular visualization was achieved in 12 of 15 patients with native kidneys. It failed due to impossible breath-holding for 2 patients and a too-deep kidney for 1 patient. Sensing ultrasound localization microscopy found 16 glomeruli per square centimeter in the native kidneys (6-31) and 33 glomeruli per square centimeter in kidney transplant patients (18-55). This study demonstrated that sULM can visualize glomeruli in native human kidneys in vivo. The proposed method may have many hypothetical applications, including biomarker development, assisting biopsy, or potentially avoiding it. It establishes a framework for improving the detection of local microstructural pathology, influencing the evaluation of allografts, and facilitating disease monitoring in the native kidney.

Super‐resolution ultrasound localization microscopy for the non‐invasive imaging of human testicular microcirculation and its differential diagnosis role in male infertility

AbstractTesticular microcirculation is closely related to spermatogenic function and seminiferous tubular function. The diagnosis and monitoring of testicular diseases can be associated with testicular microcirculation; however, there are currently no effective non‐invasive methods for super‐resolution imaging of testicular microcirculation. In this study, we introduced state‐of‐the‐art graph‐based tracking with the Kalman motion model algorithm to non‐invasively image human testicular microcirculation for the first time with a regular frame‐rate clinical ultrasound imaging system (37 Hz). Two distinct testicular vessels with an 81 µm separation were resolved in the testicular vasculature, surpassing all other imaging modalities. In a retrospective study, we performed contrast‐enhanced ultrasound examinations(CEUS) and ultrasound localization microscopy (ULM) processing on the included 76 infertile patients and 15 healthy controls from August 2021 to May 2023 and obtained super‐resolution images of testicular microcirculation with sub‐diffraction resolution. Through the results of one‐way analysis of variance tests and receiver operating characteristic analyses, we found that the ULM‐based parameters hold promise as clinical guidance for differentiating between non‐obstructive and obstructive male infertility. The mean vessel diameter achieved an area under the curve (AUC) of 0.920 (95% confidence interval [CI]: 0.847–0.994, p < .001) with a cut‐off value of 170.9 µm in oligoasthenospermia, and an AUC of 0.952 (95% CI: 0.875–1.000, p < .001) with a cut‐off value of 169.9 µm in azoospermia patients, respectively, addressing a significant clinical challenge.

Improving sub-pixel accuracy in ultrasound localization microscopy using supervised and self-supervised deep learning

With a spatial resolution of tens of microns, ultrasound localization microscopy (ULM) reconstructs microvascular structures and measures intravascular flows by tracking microbubbles (1–5 μm) in contrast enhanced ultrasound (CEUS) images. Since the size of CEUS bubble traces, e.g. 0.5–1 mm for ultrasound with a wavelength λ = 280 μm, is typically two orders of magnitude larger than the bubble diameter, accurately localizing microbubbles in noisy CEUS data is vital to the fidelity of the ULM results. In this paper, we introduce a residual learning based supervised super-resolution blind deconvolution network (SupBD-net), and a new loss function for a self-supervised blind deconvolution network (SelfBD-net), for detecting bubble centers at a spatial resolution finer than λ/10. Our ultimate purpose is to improve the ability to distinguish closely located microvessels and the accuracy of the velocity profile measurements in macrovessels. Using realistic synthetic data, the performance of these methods is calibrated and compared against several recently introduced deep learning and blind deconvolution techniques. For bubble detection, errors in bubble center location increase with the trace size, noise level, and bubble concentration. For all cases, SupBD-net yields the least error, keeping it below 0.1 λ. For unknown bubble trace morphology, where all the supervised learning methods fail, SelfBD-net can still maintain an error of less than 0.15 λ. SupBD-net also outperforms the other methods in separating closely located bubbles and parallel microvessels. In macrovessels, SupBD-net maintains the least errors in the vessel radius and velocity profile after introducing a procedure that corrects for terminated tracks caused by overlapping traces. Application of these methods is demonstrated by mapping the cerebral microvasculature of a neonatal pig, where neighboring microvessels separated by 0.15 λ can be readily distinguished by SupBD-net and SelfBD-net, but not by the other techniques. Hence, the newly proposed residual learning based methods improve the spatial resolution and accuracy of ULM in micro- and macro-vessels.

Volumetric ultrasound localization microscopy with diverging cylindrical waves.

Transcranial ultrasound plays a limited role in neuroradiology due to its lack of resolution, planar imaging, and user-dependency. By breaching the diffraction limit using injected microbubbles, volumetric ultrasound localization microscopy (ULM) could help alleviate those issues. However, performing 3D ultrasound imaging at a high frame rate with sufficient signal-to-noise ratio to track individual microbubbles through the skull remains a challenge, especially with a portable scanner. In this study, we describe a ULM sequence suitable for volumetric transcranial imaging exploiting cylindrical emissions on multiplexed matrix probes, through simulations, hydrophone measurements, and flow phantoms. This geometry leads to a doubling of the peak acoustic pressure, up to 400 kPa, with respect to spherical emission and improved volume rate, up to 180 Hz. Cylindrical emissions also improve ULM saturation rate by 60% through a skull phantom. The assessment of microbubble velocity was also improved from 33% error in the average flow measured with spherical waves to a 5% error with cylindrical waves. Conversely, we demonstrate the detrimental impacts of cylindrical waves toward the field of view and isotropic sensitivity. Nevertheless, due to its enhanced signal-to-noise ratio and 3D nature, such a cylindrical volumetric sequence could be beneficial for ULM as a diagnostic tool in humans, especially when portability is a necessity.

RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts.

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.

ULTRA-SR Challenge: Assessment of Ultrasound Localization and TRacking Algorithms for Super-Resolution Imaging.

With the widespread interest and uptake of super-resolution ultrasound (SRUS) through localization and tracking of microbubbles, also known as ultrasound localization microscopy (ULM), many localization and tracking algorithms have been developed. ULM can image many centimeters into tissue in-vivo and track microvascular flow non-invasively with sub-diffraction resolution. In a significant community effort, we organized a challenge, Ultrasound Localization and TRacking Algorithms for Super-Resolution (ULTRA-SR). The aims of this paper are threefold: to describe the challenge organization, data generation, and winning algorithms; to present the metrics and methods for evaluating challenge entrants; and to report results and findings of the evaluation. Realistic ultrasound datasets containing microvascular flow for different clinical ultrasound frequencies were simulated, using vascular flow physics, acoustic field simulation and nonlinear bubble dynamics simulation. Based on these datasets, 38 submissions from 24 research groups were evaluated against ground truth using an evaluation framework with six metrics, three for localization and three for tracking. In-vivo mouse brain and human lymph node data were also provided, and performance assessed by an expert panel. Winning algorithms are described and discussed. The publicly available data with ground truth and the defined metrics for both localization and tracking present a valuable resource for researchers to benchmark algorithms and software, identify optimized methods/software for their data, and provide insight into the current limits of the field. In conclusion, Ultra-SR challenge has provided benchmarking data and tools as well as direct comparison and insights for a number of the state-of-the art localization and tracking algorithms.

ULM-MbCNRT: In vivo Ultrafast Ultrasound Localization Microscopy by Combining Multi-branch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascular to be rendered at sub-wavelength resolution. Nevertheless, to obtain such superior spatial resolution, it is necessary to spend tens of seconds gathering numerous ultrasound (US) frames to accumulate MB events required, resulting in ULM imaging still suffering from trade-offs between imaging quality, data acquisition time and data processing speed. In this paper, we present a new deep learning (DL) framework combining multi-branch CNN and recursive Transformer, termed as ULM-MbCNRT, that is capable of reconstructing a super-resolution image directly from a temporal mean low-resolution image generated by averaging much fewer raw US frames, i.e., implement an ultrafast ULM imaging. To evaluate the performance of ULM-MbCNRT, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-MbCNRT achieves high-quality ULM imaging with ~10-fold reduction in data acquisition time and ~130-fold reduction in computation time compared to the previous DL method (e.g., the modified sub-pixel convolutional neural network, ULM-mSPCN). For the in vivo experiments, when comparing to the ULM-mSPCN, ULM-MbCNRT allows ~37-fold reduction in data acquisition time (~0.8 s) and ~2134-fold reduction in computation time (~0.87 s) without sacrificing spatial resolution. It implies that ultrafast ULM imaging holds promise for observing rapid biological activity in vivo, potentially improving the diagnosis and monitoring of clinical conditions.

Curved toroidal row column addressed transducer for 3D ultrafast ultrasound imaging.

3D Imaging of the human heart at high frame rate is of major interest for various clinical applications. Electronic complexity and cost has prevented the dissemination of 3D ultrafast imaging into the clinic. Row column addressed (RCA) transducers provide volumetric imaging at ultrafast frame rate by using a low electronic channel count, but current models are ill-suited for transthoracic cardiac imaging due to field-of-view limitations. In this study, we proposed a mechanically curved RCA with an aperture adapted for transthoracic cardiac imaging (24 × 16 mm²). The RCA has a toroidal curved surface of 96 elements along columns (curvature radius rC = 4.47 cm) and 64 elements along rows (curvature radius rR = 3 cm). We implemented delay and sum beamforming with an analytical calculation of the propagation of a toroidal wave which was validated using simulations (Field II). The imaging performance was evaluated on a calibrated phantom. Experimental 3D imaging was achieved up to 12 cm deep with a total angular aperture of 30° for both lateral dimensions. The Contrast-to-Noise ratio increased by 12 dB from 2 to 128 virtual sources. Then, 3D Ultrasound Localization Microscopy (ULM) was characterized in a sub-wavelength tube diameter. Finally, 3D ULM was demonstrated on a perfused ex-vivo swine heart to image the coronary microcirculation.

Deep learning for fast super-resolution ultrasound microvessel imaging

Objective. Ultrasound localization microscopy (ULM) enables microvascular reconstruction by localizing microbubbles (MBs). Although ULM can obtain microvascular images that are beyond the ultimate resolution of the ultrasound (US) diffraction limit, it requires long data processing time, and the imaging accuracy is susceptible to the density of MBs. Deep learning (DL)-based ULM is proposed to alleviate these limitations, which simulated MBs at low-resolution and mapped them to coordinates at high-resolution by centroid localization. However, traditional DL-based ULMs are imprecise and computationally complex. Also, the performance of DL is highly dependent on the training datasets, which are difficult to realistically simulate. Approach. A novel architecture called adaptive matching network (AM-Net) and a dataset generation method named multi-mapping (MMP) was proposed to overcome the above challenges. The imaging performance and processing time of the AM-Net have been assessed by simulation and in vivo experiments. Main results. Simulation results show that at high density (20 MBs/frame), when compared to other DL-based ULM, AM-Net achieves higher localization accuracy in the lateral/axial direction. In vivo experiment results show that the AM-Net can reconstruct ∼24.3 μm diameter micro-vessels and separate two ∼28.3 μm diameter micro-vessels. Furthermore, when processing a 128 × 128 pixels image in simulation experiments and an 896 × 1280 pixels image in vivo experiment, the processing time of AM-Net is ∼13 s and ∼33 s, respectively, which are 0.3–0.4 orders of magnitude faster than other DL-based ULM. Significance. We proposes a promising solution for ULM with low computing costs and high imaging performance.