Ultrasound Localization Research Articles

A Forked Microvascular Phantom for Ultrasound Localization Microscopy Investigations.

In the development of ultrasound localization microscopy (ULM) methods, appropriate test beds are needed to facilitate algorithmic performance calibration. Here, we present the design of a new ULM-compatible microvascular phantom with a forked, V-shaped wall-less flow channel pair ( 250 μ m channel width) that is bifurcated at a separation rate of 50 μ m/mm. The lumen core was fabricated using additive manufacturing, and it was molded within a polyvinyl alcohol (PVA) tissue-mimicking slab using the lost-core casting method. Measured using optical microscopy, the lumen core's flow channel width was found to be 252 ± 15 μ m with a regression-derived flow channel separation gradient of 50.89 μ m/mm. The new phantom's applicability in ULM performance analysis was demonstrated by feeding microbubble (MB) contrast flow (1.67 to 167 μ L/s flow rates) through the phantom's inlet and generating ULM images with a previously reported method. Results showed that, with longer acquisition times (10 s or longer), ULM image quality was expectedly improved, and the variance of ULM-derived flow channel measurements was reduced. Also, at axial depths near the lumen's bifurcation point, the current ULM algorithm showed difficulty in properly discerning between the two flow channels because of the narrow channel-to-channel separation distance. Overall, the new phantom serves well as a calibration tool to test the performance of ULM methods in resolving small vasculature.

VoxelMorph-Based Deep Learning Motion Correction for Ultrasound Localization Microscopy of Spinal Cord.

Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization microscopy (ULM) offers super-resolution imaging of microvasculature by localizing and tracking individual microbubbles across multiple frames. However, a long data acquisition often involves significant motion artifacts caused by breathing and heartbeat, which further impairs the resolution of ULM. This effect is particularly pronounced in spinal cord imaging due to respiratory movement. We propose a VoxelMorph-based deep learning motion correction method to enhance ULM performance in spinal cord imaging. Simulations were conducted to demonstrate the motion estimation accuracy of the proposed method, achieving a mean absolute error of 8 μm. Results from in vivo experiments show that the proposed method efficiently compensates for rigid and nonrigid motion, providing improved resolution with smaller vascular diameters and enhanced microvessel reconstruction after motion correction. Nonrigid deformation fields with varying displacement magnitudes were applied to in vivo data for assessing the robustness of the algorithm.

Deep Learning in Ultrasound Localization Microscopy

Deep Learning in Ultrasound Localization Microscopy

Deep Power-aware Tunable Weighting for Ultrasound Microvascular Imaging.

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood flow information through plane wave transmissions at high frame rates. However, low signal-to-noise ratio of plane waves causes low image quality. Adaptive beamformers have been proposed to suppress noise energy to achieve higher image quality accompanied by increasing computational complexity. Deep learning (DL) leverages powerful hardware capabilities to enable rapid implementation of noise suppression at the cost of flexibility. To enhance the applicability of DL-based methods, in this work, we propose a deep power-aware tunable (DPT) weighting (i.e., postfilter) for delay-and-sum (DAS) beamforming to improve UMI by enhancing plane wave images. The model, called Yformer is a hybrid structure combining convolution and Transformer. With the DAS beamformed and compounded envelope image as input, Yformer can estimate both noise power and signal power. Furthermore, we utilize the obtained powers to compute pixel-wise weights by introducing a tunable noise control factor, which is tailored for improving the quality of different UMI applications. In vivo experiments on the rat brain demonstrate that Yformer can accurately estimate the powers of noise and signal with the structural similarity index (SSIM) higher than 0.95. The performance of the DPT weighting is comparable to that of superior adaptive beamformer in uPDI with low computational cost. The DPT weighting was then applied to four different datasets of ULM, including public simulation, public rat brain, private rat brain, and private rat liver datasets, showing excellent generalizability using the model trained by the private rat brain dataset only. In particular, our method indirectly improves the resolution of liver ULM from 25.24 μm to 18.77 μm by highlighting small vessels. In addition, the DPT weighting exhibits more details of blood vessels with faster processing, which has the potential to facilitate the clinical applications of high-quality UMI.

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.

Non-invasive Deep-Brain Imaging with 3D Integrated Photoacoustic Tomography and Ultrasound Localization Microscopy (3D-PAULM).

Photoacoustic computed tomography (PACT) is a proven technology for imaging hemodynamics in deep brain of small animal models. PACT is inherently compatible with ultrasound (US) imaging, providing complementary contrast mechanisms. While PACT can quantify the brain's oxygen saturation of hemoglobin (sO2), US imaging can probe the blood flow based on the Doppler effect. Further, by tracking gas-filled microbubbles, ultrasound localization microscopy (ULM) can map the blood flow velocity with sub-diffraction spatial resolution. In this work, we present a 3D deep-brain imaging system that seamlessly integrates PACT and ULM into a single device, 3D-PAULM. Using a low ultrasound frequency of 4 MHz, 3D-PAULM is capable of imaging the brain hemodynamic functions with intact scalp and skull in a totally non-invasive manner. Using 3D-PAULM, we studied the mouse brain functions with ischemic stroke. Multi-spectral PACT, US B-mode imaging, microbubble-enhanced power Doppler (PD), and ULM were performed on the same mouse brain with intrinsic image co-registration. From the multi-modality measurements, we further quantified blood perfusion, sO2, vessel density, and flow velocity of the mouse brain, showing stroke-induced ischemia, hypoxia, and reduced blood flow. We expect that 3D-PAULM can find broad applications in studying deep brain functions on small animal models.

Deep Learning in Ultrasound Localization Microscopy: Applications and Perspectives.

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.

A Tracking prior to Localization workflow for Ultrasound Localization Microscopy.

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.

Transcutaneous Imaging of Rabbit Kidney Using 3-D Acoustic Wave Sparsely Activated Localization Microscopy with a Row-Column-Addressed Array.

Super-resolution ultrasound (SRUS) imaging through localizing and tracking microbubbles, also known as ultrasound localization microscopy (ULM), can produce sub-diffraction resolution images of micro-vessels. We have recently demonstrated 3-D selective SRUS with a matrix array and phase change contrast agents (PCCAs). However, this method is limited to a small field of view (FOV) and by the complex hardware required. This study proposed 3-D acoustic wave sparsely activated localization microscopy (AWSALM) using PCCAs and a 128+128 row-column-addressed (RCA) array, which offers ultrafast acquisition with over 6 times larger FOV and 4 times reduction in hardware complexity than a 1024-element matrix array. We first validated this method on an in-vitro microflow phantom and subsequently demonstrated non-invasively on a rabbit kidney in-vivo. Our results show that 3-D AWSALM images of the phantom covering a 25×25×40 mm 3 volume can be generated under 5 seconds with an 8 times resolution improvement over the system point spread function. The full volume of the rabbit kidney can be covered to generate 3-D microvascular structure, flow speed and direction super-resolution maps under 15 seconds, combining the large FOV of RCA with the high resolution of SRUS. Additionally, 3-D AWSALM is selective and can visualize the microvasculature within the activation volume and downstream vessels in isolation. Sub-sets of the kidney microvasculature can be imaged through selective activation of PCCAs. Our study demonstrates large FOV 3-D AWSALM using an RCA probe. 3-D AWSALM offers an unique in-vivo imaging tool for fast, selective and large FOV vascular flow mapping.

Non-invasive ultrasound localization microscopy (ULM) in azoospermia: connecting testicular microcirculation to spermatogenic functions.

Rationale: Azoospermia is a significant reproductive challenge. Differentiating between non-obstructive azoospermia (NOA) and obstructive azoospermia (OA) is crucial as each type requires distinct management strategies. Testicular microcirculation plays a profound role in spermatogenic functions. However, current diagnostic methods are limited in their ability to effectively elucidate this crucial connection. Methods: We employed ultrasound localization microscopy (ULM) to visualize testicular microcirculation in NOA and OA patients and quantified the testicular hemodynamic parameters. Pearson correlation analysis was conducted to investigate the inner connection between parameters of testicular microcirculation and clinical spermatogenic functions. We conducted multiple logistic regression analysis to establish a new diagnostic model that integrates follicle-stimulating hormone (FSH) and mean vascular diameter to distinguish NOA from OA. Results: Our findings demonstrated significant differences in vascular parameters between NOA and OA, with NOA characterized by lower mean vascular diameter (p < 0.001), vessel density (p < 0.001), and fractal number (p < 0.001). Testicular volume showed a moderate positive correlation with mean vascular diameter (r = 0.419, p < 0.01) and vessel density (r = 0.415, p < 0.01); Mean vascular diameter exhibited negative correlations with both FSH (r = -0.214, p < 0.05) and age (r = -0.240, p < 0.05); FSH (r = -0.202, p < 0.05) and luteinizing hormone (LH) (r = -0.235, p < 0.05) were negatively correlated with mean blood flow velocity. The diagnostic model demonstrated an area under the curve (AUC) of 0.968. We also reported a method to map the vascular pressure distribution derived from the blood flow velocity generated by ULM. Conclusions: ULM provides a non-invasive and detailed assessment of testicular microvascular dynamics. The ULM-derived vascular parameters are able to connect testicular microcirculation to spermatogenic functions. The combination of FSH and mean vascular diameter enhances diagnostic precision and holds potential for distinguishing NOA from OA.

Volumetric Ultrasound Localization Microscopy.

Super-resolution ultrasound (SRUS) has evolved significantly with the advent of Ultrasound Localization Microscopy (ULM). This technique enables sub-wavelength resolution imaging using microbubble contrast agents. Initially confined to 2D imaging, ULM has progressed towards volumetric approaches, allowing for comprehensive three-dimensional visualization of microvascular networks. This review explores the technological advancements and challenges associated with volumetric ULM, focusing on key aspects such as transducer design, acquisition speed, data processing algorithms, or integration into clinical practice. We discuss the limitations of traditional 2D ULM, including dependency on precise imaging plane selection and compromised resolution in microvasculature quantification. In contrast, volumetric ULM offers enhanced spatial resolution and allowed motion correction in all direction, promising transformative insights into microvascular pathophysiology. By examining current research and future directions, this review highlights the potential of volumetric ULM to contribute significantly to diagnostic across various medical conditions, including cancers, arteriosclerosis, strokes, diabetes, and neurodegenerative diseases.

Patient with Fibroadenoma on Biopsy, Found to have Phyllodes on Final Pathology: A Case Report

Distinguishing between fibroadenomas and phyllodes tumors is a challenge in breast surgery, despite advances in both radiology and pathology. In this case report, we analyze a patient presenting with a breast mass with multiple core needle biopsy results consistent with fibroadenoma, who underwent enucleation and was found to have phyllodes tumor on final pathology, thereby requiring surgical re-excision. This case report highlights the importance of patient clinical presentation in differentiating fibroadenomas and phyllodes tumors and explores how to achieve appropriate margins upon surgical re-excision after prior enucleation of phyllodes tumor via ultrasound localization of a seroma.

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.

Versatile Ultrasound-Compatible Microfluidic Platform for In Vitro Microvasculature Flow Research and Imaging Optimization.

Ultrasound localization microscopy (ULM) enables the creation of super-resolved images and velocity maps by localizing and tracking microbubble contrast agents through a vascular network over thousands of frames of ultrafast plane wave images. However, a significant challenge lies in developing ultrasound-compatible microvasculature phantoms to investigate microbubble flow and distribution in controlled environments. In this study, we introduce a new class of gelatin-based microfluidic-inspired phantoms uniquely tailored for ULM studies. These devices allow for the creation of complex and reproducible microvascular networks featuring channel diameters as small as 100 μm. Our experiments focused on microbubble behavior under ULM conditions within bifurcating and converging vessel phantoms. We evaluated the impact of bifurcation angles (25, 45, and 55°) and flow rates (0.01, 0.02, and 0.03 mL/min) on the acquisition time of branching channels. Additionally, we explored the saturation time effect of narrow channels branching off larger ones. Significantly longer acquisition times were observed for the narrow vessels, with an average increase of 72% when a 100 μm channel branched off from a 300 μm channel and an average increase of 90% for a 200 μm channel branching off from a 500 μm channel. The robustness of our fabrication method is demonstrated through the creation of two trifurcating microfluidic phantoms, including one that converges back into a single channel, a configuration that cannot be achieved through traditional methods. This new class of ULM phantoms serves as a versatile platform for noninvasively studying complex flow patterns using ultrasound imaging, unlocking new possibilities for in vitro microvasculature research and imaging optimization.

A CASE OF BILATERAL CRYPTORCHIDISM, ULTRASOUND DIAGNOSIS AND SURGICAL THERAPY IN TOMCAT

"Bilateral cryptorchidism is seldom in tomcats. Clinical diagnosis is challenging because the testicles can be located in the abdominal cavity, a situation in which they cannot be palpated, or in the inguinal region, a situation in which they are difficult to identify due to the subcutaneous fat layer. Ultrasound is the most available imaging method for diagnosing this pathology. The testicles have a characteristic structure that is easy to recognize. Ultrasound localization of the testicles allows the approach through a smaller incision, and the use of a castration hook to extract the testicle from the abdominal cavity. The blood vessels and ductus deferens can be ligated with absorbable suture material, but can also be sealed with bipolar forceps. The reason why vascular sealing with bipolar forceps was chosen was to reduce surgical time and to reduce the amount of embedded foreign material. From a histopathological point of view, coagulation necrosis was observed on both blood vessels and ductus deferens. The suture of the abdominal wall was made in a continuous pattern. Skin staples were used to save time."

Longitudinal 3-D Visualization of Microvascular Disruption and Perfusion Changes in Mice During the Evolution of Glioblastoma Using Super-Resolution Ultrasound.

Glioblastoma is an aggressive brain cancer with a very poor prognosis in which less than 6% of patients survive more than 5 years post-diagnosis. The outcome of this disease for many patients may be improved by early detection. This could provide clinicians with the information needed to take early action for treatment. In this work, we present the utilization of a noninvasive, fully volumetric ultrasonic imaging method to assess microvascular change during the evolution of glioblastoma in mice. Volumetric ultrasound localization microscopy (ULM) was used to observe statistically significant (p<0.05) reduction in the appearance of functional vasculature over the course of three weeks. We also demonstrate evidence suggesting the reduction of vascular flow for vessels peripheral to the tumor. With an 82.5% consistency rate in acquiring high-quality vascular images, we demonstrate the possibility of volumetric ULM as a longitudinal method for microvascular characterization of neurological disease.

Addendum: Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy.

Addendum: Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy.

Chorionic bump: ultrasound predictor of adverse pregnancy outcome

For the first time R. Harris et al. in 2006 a chorionic bump was described, detected by ultrasound examination in the first trimester of pregnancy, having characteristic ultrasound signs and localization. Modern knowledge about the etiology and clinical significance of chorionic bump for predicting the outcome of pregnancy is insufficient and contradictory. Inconsistency of the data may be due to the rarity of occurrence, a small sample of the studied groups, the lack of histopathological studies. Among doctors who make ultrasound investigations there is a low awareness of chorionic bump and as a result an erroneous interpretation of sonography data. Nevertheless, there is more and more evidence that this sign is a predictor of an adverse outcome of pregnancy. Doctors who make ultrasound investigations in the first trimester of pregnancy should be aware of the chorionic bump and be able to differentiate it with other pathological conditions. If a chorionic bump is detected, ultrasound examination in dynamics should be recommended. Based on the study of domestic and foreign literature, this scientific review presents modern ideas about the pathomorphology of chorionic bump, informations about significance in terms of predicting the outcome of pregnancy, and the main aspects of ultrasound diagnostics and differential diagnosis of this pathology.

Ultrasound Localization Microscopy for Breast Cancer Imaging in Patients: Protocol Optimization and Comparison with Shear Wave Elastography

Ultrasound localization microscopy (ULM) has gained increasing attention in recent years because of its ability to visualize blood vessels at super-resolution. The field of oncology, in particular, could benefit from detailed vascular characterization, for example, for diagnosis and therapy monitoring. This study was aimed at refining ULM for breast cancer patients by optimizing the measurement protocol, identifying translational challenges and combining ULM and shear wave elastography. We computed ULM images of 11 patients with breast cancer by recording contrast-enhanced ultrasound (CEUS) sequences and post-processing them in an offline pipeline. For CEUS, two different doses and injection speeds of SonoVue were applied. The best injection protocol was determined based on quantitative parameters derived from so-called occurrence maps. In addition, a suitable measurement time window was determined, also considering the occurrence of motion. ULM results were compared with shear wave elastography and histological vessel density. At the higher dose and injection speed, the highest number of microbubbles, number of tracks and vessel coverage were achieved, leading to the most detailed representation of tumor vasculature. Even at the highest concentration, no significant overlay of microbubble signals occurred. Motion significantly reduced the number of usable frames, thus limiting the measurement window to 3.5 min. ULM vessel coverage was comparable to the histological vessel fraction and correlated significantly with mean tumor elasticity. The settings for microbubble injection strongly influence ULM images, thus requiring optimized protocols for different indications. Patient and examiner motion was identified as the main translational challenge for ULM.