Two-Stage Motion Correction for Super-Resolution Ultrasound Imaging in Human Lower Limb

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy

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 in the diagnosis of breast tumors and prediction of relevant histologic biomarkers associated with prognosis in humans: the protocol for a prospective, multicenter study

BACKGROUND

Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature. Ultrasound localization microscopy (ULM) can provide detailed microvascular architecture at super-resolution. The objective of this trial is to explore the role of ULM in distinguishing benign from malignant breast tumors and to explore the correlations between ULM qualitative and quantitative parameters and histologic biomarkers in malignant breast tumors.

METHODS/DESIGN

This prospective and multicenter study will include 83 patients with breast tumors that will undergo ULM. 55 patients will be assigned to the malignant group, and 28 patients will be assigned to the benign group. The primary outcome is the differences in the qualitative parameters (microvasculature morphology, distribution, and flow direction) between benign and malignant breast tumors on ULM. Secondary outcomes include (1) differences in the quantitative parameters (microvasculature density, tortuosity, diameter, and flow velocity) between benign and malignant breast tumors based on ULM; (2) diagnostic performance of the qualitative parameters in distinguishing benign and malignant breast tumors; (3) diagnostic performance of the quantitative parameters in distinguishing benign and malignant breast tumors; (4) relationships between the qualitative parameters and histologic biomarkers in malignant breast tumors; (5) relationships between the quantitative parameters and histologic biomarkers in malignant breast tumors; and (6) the evaluation of inter-reader and intra-reader reproducibility.

DISCUSSION

Detecting vascularity in breast tumors is of great significance to differentiate benign from malignant tumors and to predict histologic biomarkers. These histologic biomarkers, such as ER, PR, HER2 and Ki67, are closely related to prognosis evaluation. This trial will provide maximum information about the microvasculature of breast tumors and thereby will help with the formulation of subsequent differential diagnosis and the prediction of histologic biomarkers.

TRIAL REGISTRATION NUMBER/DATE

Chinese Clinical Trial Registry ChiCTR2100048361/6th/July/2021. This study is a part of that clinical trial.

Ultrasound super-resolution imaging for non-invasive assessment of microvessel in prostate lesion

BACKGROUND

Prostate cancer (PCa) is the leading cause of cancer-related morbidity and mortality in men worldwide. An early and accurate diagnosis is crucial for effective treatment and prognosis. Traditional invasive procedures such as image-guided prostate biopsy often cause discomfort and complications, deterring some patients from undergoing these necessary tests. This study aimed to explore the feasibility and clinical value of using ultrasound super-resolution imaging (US SRI) for non-invasively assessing the microvessel characteristics of prostate lesion.

METHODS

This study included 127 patients with prostate lesion who presented at Renmin Hospital of Wuhan University between November 2023 and June 2024 were included in this study. All the patients underwent transrectal US (TRUS), contrast-enhanced US (CEUS), and US SRI. CEUS parameters of time-intensity curve (TIC): arrival time (AT), rising time (RT), time to peak (TTP), peak intensity (PKI), falling time (FT), mean transit time (MTT), ascending slope (AS), descending slope (DS), D/A slope ratio (SR), and area under the TIC (AUC). US SRI parameters: microvessel density (MVD), microvessel diameter (D), microvessel velocity (V), microvessel tortuosity (T), and fractal number (FN), were analyzed and compared between prostate benign and malignant lesion.

RESULTS

The tumor markers of prostate in the malignant group were all higher than those in the benign group, and the differences were statistically significant (P < 0.001). The TIC parameters of CEUS revealed that the PKI, AS, DS, and AUC were significantly higher in the malignant group than in the benign group (P < 0.001), whereas the RT, TTP and FT in the malignant group were significantly lower (P < 0.001). Malignant lesion exhibited significantly higher MVD, larger D, faster V, greater T, and more complex FN than benign lesion (P < 0.001).

CONCLUSIONS

US SRI is a promising non-invasive imaging modality that can provide detailed microvessel characteristics of prostate lesion, offering an advancement in the differential diagnosis for prostate lesion. And, US SRI may be a valuable tool in clinical practice with its ability to display and quantify microvessel with high precision.

Focal liver lesions: multiparametric microvasculature characterization via super-resolution ultrasound imaging

BACKGROUND

Noninvasive and functional imaging of the focal liver lesion (FLL) vasculature at microscopic scales is clinically challenging. We investigated the feasibility of using super-resolution ultrasound (SR-US) imaging for visualizing and quantifying the microvasculature of intraparenchymal FLLs.

METHODS

Patients with FLLs between June 2022 and February 2023 were prospectively screened. Following bolus injection of microbubbles at clinical concentration, SR-US was performed using a high frame rate (350-500 Hz) modified ultrasound scanner and a convex array transducer with a central frequency of 3.1 MHz.

RESULTS

In total, 47 pathologically proven FLLs at a depth of 5.7 ± 1.7 cm (mean ± standard deviation) were included: 30 hepatocellular carcinomas (HCC), 11 liver metastases (LM), and 6 focal nodular hyperplasias (FNH). The smallest detectable vessel size of the hepatic microvasculature was 128.4 ± 18.6 μm (mean ± standard deviation) at a depth of 8 cm. Significant differences were observed among the three types of lesions in terms of pattern categories, vessel density, minimum flow velocity, and perfusion index. We observed higher vessel density for FNH versus liver parenchyma (p < 0.001) as well as fractal dimension and local flow direction entropy value for FNH versus HCC (p = 0.002 and p < 0.001, respectively) and for FNH versus LM (p = 0.006 and p = 0.002, respectively).

CONCLUSION

Multiparametric SR-US showed that these three pathological types of FLLs have specific microvascular phenotypes. Vessel density, fractal dimension and local flow direction entropy served as valuable parameters in distinguishing between benign and malignant FLLs.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT06018142).

RELEVANCE STATEMENT

Multiparametric SR-US imaging offers precise morphological and functional assessment of the microvasculature of intraparenchymal focal liver lesions, providing insights into tumor heterogeneity and angiogenesis.

KEY POINTS

Super-resolution (SR)-US imaging allowed morphological and functional evaluation of intraparenchymal hepatic lesion microvasculature. Hepatocellular carcinoma, liver metastasis, and focal nodular hyperplasia exhibit distinct microvascular architectures and hemodynamic profiles. Multiparametric microvasculature characterization via SR-US imaging facilitates the differentiation between benign and malignant microvascular phenotypes.

Non-invasive 4D transcranial functional ultrasound and ultrasound localization microscopy for multimodal imaging of neurovascular response

A long-standing goal of neuroimaging is the non-invasive volumetric assessment of whole brain function and structure at high spatial and temporal resolutions. Functional ultrasound (fUS) and ultrasound localization microscopy (ULM) are rapidly emerging techniques that promise to bring advanced brain imaging and therapy to the clinic with the safety and low-cost advantages associated with ultrasound. fUS has been used to study cerebral hemodynamics at high temporal resolutions while ULM has been used to study cerebral microvascular structure at high spatial resolutions. These two methods have complementary spatio-temporal characteristics, making them ideally suited for multimodal imaging, but both suffer from limitations associated with transcranial ultrasound imaging. Here, these two methods are combined on the same data acquisition, completely non-invasively, using contrast-enhancements, which solves the dual challenges of sensitivity during transcranial imaging and the ability to implement super-resolution. From this combined approach, the cerebral blood flow, activated brain region, brain connectivity, vessel diameter, and vessel velocity were all calculated from the same data acquisition. During stimulation periods, there was a statistically significant (p<0.0001) increase in cerebral blood flow, diameter, and global velocity, but a decrease in velocity in the activated region. Additionally, the global flow increased (p=0.11) and connectivity decreased (24.7%) when compared to baseline. This multimodal approach allows for the study of the relationship between cerebral hemodynamics (30 ms resolution) and the microvasculature (14.6 μm resolution) using one ultrasound scan.

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. Several 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 microbubble distribution. We first provide an overview of how existing studies vary in the constitution of their datasets or in the tasks targeted by the 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.

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

OBJECTIVE

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.

METHOD

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.

RESULTS

Our results show that 3-D AWSALM images of the phantom covering a mm 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.

CONCLUSION

Our study demonstrates large FOV 3-D AWSALM using an RCA probe.

SIGNIFICANCE

3-D AWSALM offers an unique in-vivo imaging tool for fast, selective and large FOV vascular flow mapping.

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 (MBs) 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 (DL) motion correction method to enhance the 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 m. 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.

Ultrasound Localization Microscopy Precision of Clinical 3-D Ultrasound Systems

Ultrasound localization microscopy (ULM) is becoming well established in preclinical applications. For its translation into clinical practice, the localization precision achievable with commercial ultrasound (US) scanners is crucial-especially with volume imaging, which is essential for dealing with out-of-plane motion. Here, we propose an easy-to-perform method to estimate the localization precision of 3-D US scanners. With this method, we evaluated imaging sequences of the Philips Epiq 7 US device using the X5-1 and the XL14-3 matrix transducers and also tested different localization methods. For the X5-1 transducer, the best lateral, elevational, and axial precision was 109, 95, and m for one contrast mode, and 29, 22, and m for the other. The higher frequency XL14-3 transducer yielded precisions of 17, 38, and m using the harmonic imaging mode. Although the center of mass was the most robust localization method also often providing the best precision, the localization method has only a minor influence on the localization precision compared to the impact by the imaging sequence and transducer. The results show that with one of the imaging modes of the X5-1 transducer, precisions comparable to the XL14-3 transducer can be achieved. However, due to localization precisions worse than m, reconstruction of the microvasculature at the capillary level will not be possible. These results show the importance of evaluating the localization precision of imaging sequences from different US transducers or scanners in all directions before using them for in vivo measurements.

Efficient Microbubble Trajectory Tracking in Ultrasound Localization Microscopy Using a Gated Recurrent Unit-Based Multitasking Temporal Neural Network

Ultrasound localization microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical tradeoff between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps such as pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the gated recurrent unit-based multitasking temporal neural network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. In addition, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer replacements to systematically evaluate the performance of various temporal neural networks, including recurrent neural network (RNN), long short-term memory network (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. In addition, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. The model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.

Whole Organ Volumetric Sensing Ultrasound Localization Microscopy for Characterization of Kidney Structure

Glomeruli are the filtration units of the kidney and their function relies heavily on their microcirculation. Despite its obvious diagnostic importance, an accurate estimation of blood flow in the capillary bundle within glomeruli defies the resolution of conventional imaging modalities. Ultrasound Localization Microscopy (ULM) has demonstrated its ability to image in-vivo deep organs in the body. Recently, the concept of sensing ULM or sULM was introduced to classify individual microbubble behavior based on the expected physiological conditions at the micrometric scale. In the kidney of both rats and humans, it revealed glomerular structures in 2D but was severely limited by planar projection. In this work, we aim to extend sULM in 3D to image the whole organ and in order to perform an accurate characterization of the entire kidney structure. The extension of sULM into the 3D domain allows better localization and more robust tracking. The 3D metrics of velocity and pathway angular shift made glomerular mask possible. This approach facilitated the quantification of glomerular physiological parameter such as an interior traveled distance of approximately 7.5 ±0.6 microns within the glomerulus. This study introduces a technique that characterize the kidney physiology which can serve as a method to facilite pathology assessment. Furthermore, its potential for clinical relevance could serve as a bridge between research and practical application, leading to innovative diagnostics and improved patient care.

Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning

Contrast-free ultrasound quantitative microvasculature imaging shows promise in several applications, including the assessment of benign and malignant lesions. However, motion represents one of the major challenges in imaging tumor microvessels in organs that are prone to physiological motions. This study aims at addressing potential microvessel image degradation in in vivo human thyroid due to its proximity to carotid artery. The pulsation of the carotid artery induces inter-frame motion that significantly degrades microvasculature images, resulting in diagnostic errors. The main objective of this study is to reduce inter-frame motion artifacts in high-frame-rate ultrasound imaging to achieve a more accurate visualization of tumor microvessel features. We propose a low-complex deep learning network comprising depth-wise separable convolutional layers and hybrid adaptive and squeeze-and-excite attention mechanisms to correct inter-frame motion in high-frame-rate images. Rigorous validation using phantom and in-vivo data with simulated inter-frame motion indicates average improvements of 35% in Pearson correlation coefficients (PCCs) between motion corrected and reference data with respect to that of motion corrupted data. Further, reconstruction of microvasculature images using motion-corrected frames demonstrates PCC improvement from 31 to 35%. Another thorough validation using in-vivo thyroid data with physiological inter-frame motion demonstrates average improvement of 20% in PCC and 40% in mean inter-frame correlation. Finally, comparison with the conventional image registration method indicates the suitability of proposed network for real-time inter-frame motion correction with 5000 times reduction in motion corrected frame prediction latency.

Super-Resolution Ultrasound Imaging Using the Erythrocytes-Part I: Density Images

A new approach for vascular super-resolution (SR) imaging using the erythrocytes as targets (SUper-Resolution ultrasound imaging of Erythrocytes (SURE) imaging) is described and investigated. SURE imaging does not require fragile contrast agent bubbles, making it possible to use the maximum allowable mechanical index (MI) for ultrasound scanning for an increased penetration depth. A synthetic aperture (SA) ultrasound sequence was employed with 12 virtual sources (VSs) using a 10-MHz GE L8-18i-D linear array hockey stick probe. The axial resolution was [Formula: see text]m) and the lateral resolution was [Formula: see text]m). Field IIpro simulations were conducted on 12.5- μ m radius vessel pairs with varying separations. A vessel pair with a separation of 70 μ m could be resolved, indicating a SURE image resolution below half a wavelength. A Verasonics research scanner was used for the in vivo experiments to scan the kidneys of Sprague-Dawley rats for up to 46 s to visualize their microvasculature by processing from 0.1 up to 45 s of data for SURE imaging and for 46.8 s for SR imaging with a SonoVue contrast agent. Afterward, the renal vasculature was filled with the ex vivo micro-computed tomography (CT) contrast agent Microfil, excised, and scanned in a micro-CT scanner at both a 22.6- μ m voxel size for 11 h and for 20 h in a 5- μ m voxel size for validating the SURE images. Comparing the SURE and micro-CT images revealed that vessels with a diameter of 28 μ m, five times smaller than the ultrasound wavelength, could be detected, and the dense grid of microvessels in the full kidney was shown for scan times between 1 and 10 s. The vessel structure in the cortex was also similar to the SURE and SR images. Fourier ring correlation (FRC) indicated a resolution capability of 29 μ m. SURE images are acquired in seconds rather than minutes without any patient preparation or contrast injection, making the method translatable to clinical use.

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.

Motion correction of 3D dynamic contrast-enhanced ultrasound imaging without anatomical B-Mode images: Pilot evaluation in eight patients

BACKGROUND

Dynamic contrast-enhanced ultrasound (DCE-US) is highly susceptible to motion artifacts arising from patient movement, respiration, and operator handling and experience. Motion artifacts can be especially problematic in the context of perfusion quantification. In conventional 2D DCE-US, motion correction (MC) algorithms take advantage of accompanying side-by-side anatomical B-Mode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side B-Mode images, which makes MC challenging.

PURPOSE

This work introduces a novel MC algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets.

METHODS

In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of three consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We applied the algorithm to imaging studies from eight patients with metastatic lesions in the liver and assessed improvements in original versus motion corrected 3D DCE-US cine using: (i) frame-to-frame volumetric overlap of segmented lesions, (ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and (iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion.

RESULTS

We noted improvements in frame-to-frame lesion overlap across all patients, from 68% ± 13% without correction to 83% ± 3% with MC (p = 0.023). Frame-to-frame similarity as assessed by NCC also improved on two different sets of time points from 0.694 ± 0.057 (original cine) to 0.862 ± 0.049 (corresponding MC cine) and 0.723 ± 0.066 to 0.886 ± 0.036 (p ≤ 0.001 for both). TIC analysis displayed a significant decrease in RMSE (p = 0.018) and a significant increase in R2 goodness-of-fit (p = 0.029) for the patient cohort.

CONCLUSIONS

Overall, results suggest decreases in 3D DCE-US motion after applying the proposed algorithm.

Microbubble detection on ultrasound imaging by utilizing phase patterned waves

Objective.Super-resolution ultrasonography offers the advantage of visualization of intricate microvasculature, which is crucial for disease diagnosis. Mapping of microvessels is possible by localizing microbubbles (MBs) that act as contrast agents and tracking their location. However, there are limitations such as the low detectability of MBs and the utilization of a diluted concentration of MBs, leading to the extension of the acquisition time. We aim to enhance the detectability of MBs to reduce the acquisition time of acoustic data necessary for mapping the microvessels.Approach.We propose utilizing phase patterned waves (PPWs) characterized by spatially patterned phase distributions in the incident beam to achieve this. In contrast to conventional ultrasound irradiation methods, this irradiation method alters bubble interactions, enhancing the oscillation response of MBs and generating more significant scattered waves from specific MBs. This enhances the detectability of MBs, thereby enabling the detection of MBs that were undetectable by the conventional method. The objective is to maximize the overall detection of bubbles by utilizing ultrasound imaging with additional PPWs, including the conventional method. In this paper, we apply PPWs to ultrasound imaging simulations considering bubble-bubble interactions to elucidate the characteristics of PPWs and demonstrate their efficacy by employing PPWs on MBs fixed in a phantom by the experiment.Main results.By utilizing two types of PPWs in addition to the conventional ultrasound irradiation method, we confirmed the detection of up to 93.3% more MBs compared to those detected using the conventional method alone.Significance.Ultrasound imaging using additional PPWs made it possible to increase the number of detected MBs, which is expected to improve the efficiency of bubble detection.

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.

Understanding the effects of microbubble concentration on localization accuracy in super-resolution ultrasound imaging

Super-resolution ultrasound (SRUS) through localising and tracking of microbubbles (MBs) can achieve sub-wavelength resolution for imaging microvascular structure and flow dynamics in deep tissuein vivo. The technique assumes that signals from individual MBs can be isolated and localised accurately, but this assumption starts to break down when the MB concentration increases and the signals from neighbouring MBs start to interfere. The aim of this study is to gain understanding of the effect of MB-MB distance on ultrasound images and their localisation. Ultrasound images of two MBs approaching each other were synthesised by simulating both ultrasound field propagation and nonlinear MB dynamics. Besides the distance between MBs, a range of other influencing factors including MB size, ultrasound frequency, transmit pulse sequence, pulse amplitude and localisation methods were studied. The results show that as two MBs approach each other, the interference fringes can lead to significant and oscillating localisation errors, which are affected by both the MB and imaging parameters. When modelling a clinical linear array probe operating at 6 MHz, localisation errors between 20 and 30μm (∼1/10 wavelength) can be generated when MBs are ∼500μm (2 wavelengths or ∼1.7 times the point spread function (PSF)) away from each other. When modelling a cardiac probe operating at 1.5 MHz, the localisation errors were as high as 200μm (∼1/5 wavelength) even when the MBs were more than 10 wavelengths apart (2.9 times the PSF). For both frequencies, at smaller separation distances, the two MBs were misinterpreted as one MB located in between the two true positions. Cross-correlation or Gaussian fitting methods were found to generate slightly smaller localisation errors than centroiding. In conclusion, caution should be taken when generating and interpreting SRUS images obtained using high agent concentration with MBs separated by less than 1.7 to 3 times the PSF, as significant localisation errors can be generated due to interference between neighbouring MBs.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Three-dimensional super resolution ultrasound imaging with a multi-frequency hemispherical phased array

BACKGROUND

High resolution imaging of the microvasculature plays an important role in both diagnostic and therapeutic applications in the brain. However, ultrasound pulse-echo sonography imaging the brain vasculatures has been limited to narrow acoustic windows and low frequencies due to the distortion of the skull bone, which sacrifices axial resolution since it is pulse length dependent.

PURPOSE

To overcome the detect limit, a large aperture 256-module sparse hemispherical transmit/receive array was used to visualize the acoustic emissions of ultrasound-vaporized lipid-coated decafluorobutane nanodroplets flowing through tube phantoms and within rabbit cerebral vasculature in vivo via passive acoustic mapping and super resolution techniques.

METHODS

Nanodroplets were vaporized with 55 kHz burst-mode ultrasound (burst length = 145 μs, burst repetition frequency = 9-45 Hz, peak negative acoustic pressure = 0.10-0.22 MPa), which propagates through overlying tissues well without suffering from severe distortions. The resulting emissions were received at a higher frequency (612 or 1224 kHz subarray) to improve the resulting spatial resolution during passive beamforming. Normal resolution three-dimensional images were formed using a delay, sum, and integrate beamforming algorithm, and super-resolved images were extracted via Gaussian fitting of the estimated point-spread-function to the normal resolution data.

RESULTS

With super resolution techniques, the mean lateral (axial) full-width-at-half-maximum image intensity was 16 ± 3 (32 ± 6) μm, and 7 ± 1 (15 ± 2) μm corresponding to ∼1/67 of the normal resolution at 612 and 1224 kHz, respectively. The mean positional uncertainties were ∼1/350 (lateral) and ∼1/180 (axial) of the receive wavelength in water. In addition, a temporal correlation between nanodroplet vaporization and the transmit waveform shape was observed, which may provide the opportunity to enhance the signal-to-noise ratio in future studies.

CONCLUSIONS

Here, we demonstrate the feasibility of vaporizing nanodroplets via low frequency ultrasound and simultaneously performing spatial mapping via passive beamforming at higher frequencies to improve the resulting spatial resolution of super resolution imaging techniques. This method may enable complete four-dimensional vascular mapping in organs where a hemispherical array could be positioned to surround the target, such as the brain, breast, or testicles.

Deep learning for fast denoising filtering in ultrasound localization microscopy

Objective.Addition of a denoising filter step in ultrasound localization microscopy (ULM) has been shown to effectively reduce the error localizations of microbubbles (MBs) and achieve resolution improvement for super-resolution ultrasound (SR-US) imaging. However, previous image-denoising methods (e.g. block-matching 3D, BM3D) requires long data processing times, making ULM only able to be processed offline. This work introduces a new way to reduce data processing time through deep learning.Approach.In this study, we propose deep learning (DL) denoising based on contrastive semi-supervised network (CS-Net). The neural network is mainly trained with simulated MBs data to extract MB signals from noise. And the performances of CS-Net denoising are evaluated in bothin vitroflow phantom experiment andin vivoexperiment of New Zealand rabbit tumor.Main results.Forin vitroflow phantom experiment, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of single microbubble image are 26.91 dB and 4.01 dB, repectively. Forin vivoanimal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24μm and two microvessels separated by 46μm could be clearly displayed. Most importantly,, the CS-Net denoising speeds forin vitroandin vivoexperiments were 0.041 s frame-1and 0.062 s frame-1, respectively.Significance.DL denoising based on CS-Net can improve the resolution of SR-US as well as reducing denoising time, thereby making further contributions to the clinical real-time imaging of ULM.

Contrast-Free Super-Resolution Power Doppler (CS-PD) Based on Deep Neural Networks

Super-resolution ultrasound microvessel imaging based on ultrasound localization microscopy (ULM) is an emerging imaging modality that is capable of resolving micrometer-scaled vessels deep into tissue. In practice, ULM is limited by the need for contrast injection, long data acquisition, and computationally expensive postprocessing times. In this study, we present a contrast-free super-resolution power Doppler (CS-PD) technique that uses deep networks to achieve super-resolution with short data acquisition. The training dataset is comprised of spatiotemporal ultrafast ultrasound signals acquired from in vivo mouse brains, while the testing dataset includes in vivo mouse brain, chicken embryo chorioallantoic membrane (CAM), and healthy human subjects. The in vivo mouse imaging studies demonstrate that CS-PD could achieve an approximate twofold improvement in spatial resolution when compared with conventional power Doppler. In addition, the microvascular images generated by CS-PD showed good agreement with the corresponding ULM images as indicated by a structural similarity index of 0.7837 and a peak signal-to-noise ratio (PSNR) of 25.52. Moreover, CS-PD was able to preserve the temporal profile of the blood flow (e.g., pulsatility) that is similar to conventional power Doppler. Finally, the generalizability of CS-PD was demonstrated on testing data of different tissues using different imaging settings. The fast inference time of the proposed deep neural network also allows CS-PD to be implemented for real-time imaging. These features of CS-PD offer a practical, fast, and robust microvascular imaging solution for many preclinical and clinical applications of Doppler ultrasound.

Evaluation of Early Diabetic Kidney Disease Using Ultrasound Localization Microscopy: A Feasibility Study

OBJECTIVE

The purpose of this study is to detect the hemodynamic changes of microvessels in the early stage of diabetic kidney disease (DKD) and to test the feasibility of ultrasound localization microscopy (ULM) in early diagnosis of DKD.

METHODS

In this study, streptozotocin (STZ) induced DKD rat model was used. Normal rats served as the control group. Conventional ultrasound, contrast-enhanced ultrasound (CEUS), and ULM data were collected and analyzed. The kidney cortex was divided into four segments, which are 0.25-0.5 mm (Segment 1), 0.5-0.75 mm (Segment 2), 0.75-1 mm (Segment 3), and 1-1.25 mm (Segment 4) away from the renal capsule, respectively. The mean blood flow velocities of arteries and veins in each segment were separately calculated, and also the velocity gradients and overall mean velocities of arteries and veins. Mann-Whitney U test was used for comparison of the data.

RESULTS

Quantitative results of microvessel velocity obtained by ULM show that the arterial velocity of Segments 2, 3, and 4, and the overall mean arterial velocity of the four segments in the DKD group are significantly lower than those in the normal group. The venous velocity of Segment 3 and the overall mean venous velocity of the four segments in the DKD group are higher than those in the normal group. The arterial velocity gradient in the DKD group is lower than that in the normal group.

CONCLUSION

ULM can visualize and quantify the blood flow and may be used for early diagnosis of DKD.

Nanodroplet-Based Super-Resolution Ultrasound Localization Microscopy

Over the past decade, super-resolution ultrasound localization microscopy (SR-ULM) has revolutionized ultrasound imaging with its capability to resolve the microvascular structures below the ultrasound diffraction limit. The introduction of this imaging technique enables the visualization, quantification, and characterization of tissue microvasculature. The early implementations of SR-ULM utilize microbubbles (MBs) that require a long image acquisition time due to the requirement of capturing sparsely isolated microbubble signals. The next-generation SR-ULM employs nanodroplets that have the potential to significantly reduce the image acquisition time without sacrificing the resolution. This review discusses various nanodroplet-based ultrasound localization microscopy techniques and their corresponding imaging mechanisms. A summary is given on the preclinical applications of SR-ULM with nanodroplets, and the challenges in the clinical translation of nanodroplet-based SR-ULM are presented while discussing the future perspectives. In conclusion, ultrasound localization microscopy is a promising microvasculature imaging technology that can provide new diagnostic and prognostic information for a wide range of pathologies, such as cancer, heart conditions, and autoimmune diseases, and enable personalized treatment monitoring at a microlevel.

Fast 3D Super-Resolution Ultrasound With Adaptive Weight-Based Beamforming

OBJECTIVE

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes.

METHOD

This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p-th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo.

RESULTS

Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe, suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS.

CONCLUSION

The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments.

SIGNIFICANCE

Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface

A noninvasive brain-computer interface is a central task in the comprehensive analysis and understanding of the brain and is an important challenge in international brain-science research. Current implanted brain-computer interfaces are cranial and invasive, which considerably limits their applications. The development of new noninvasive reading and writing technologies will advance substantial innovations and breakthroughs in the field of brain-computer interfaces. Here, we review the theory and development of the ultrasound brain functional imaging and its applications. Furthermore, we introduce latest advancements in ultrasound brain modulation and its applications in rodents, primates, and human; its mechanism and closed-loop ultrasound neuromodulation based on electroencephalograph are also presented. Finally, high-frequency acoustic noninvasive brain-computer interface is prospected based on ultrasound super-resolution imaging and acoustic tweezers.

Doppler and Pair-Wise Optical Flow Constrained 3D Motion Compensation for 3D Ultrasound Imaging

Volumetric (3D) ultrasound imaging using a 2D matrix array probe is increasingly developed for various clinical procedures. However, 3D ultrasound imaging suffers from motion artifacts due to tissue motions and a relatively low frame rate. Current Doppler-based motion compensation (MoCo) methods only allow 1D compensation in the in-range dimension. In this work, we propose a new 3D-MoCo framework that combines 3D velocity field estimation and a two-step compensation strategy for 3D diverging wave compounding imaging. Specifically, our framework explores two constraints of a round-trip scan sequence of 3D diverging waves, i.e., Doppler and pair-wise optical flow, to formulate the estimation of the 3D velocity fields as a global optimization problem, which is further regularized by the divergence-free and first-order smoothness. The two-step compensation strategy is to first compensate for the 1D displacements in the in-range dimension and then the 2D displacements in the two mutually orthogonal cross-range dimensions. Systematical in-silico experiments were conducted to validate the effectiveness of our proposed 3D-MoCo method. The results demonstrate that our 3D-MoCo method achieves higher image contrast, higher structural similarity, and better speckle patterns than the corresponding 1D-MoCo method. Particularly, the 2D cross-range compensation is effective for fully recovering image quality.

Ultrasound localization microscopy

Ultrasound Localization Microscopy (ULM) is an emerging technique that provides impressive super-resolved images of microvasculature, i.e., images with much better resolution than the conventional diffraction-limited ultrasound techniques and is already taking its first steps from preclinical to clinical applications. In comparison to the established perfusion or flow measurement methods, namely contrast-enhanced ultrasound (CEUS) and Doppler techniques, ULM allows imaging and flow measurements even down to the capillary level. As ULM can be realized as a post-processing method, conventional ultrasound systems can be used for. ULM relies on the localization of single microbubbles (MB) of commercial, clinically approved contrast agents. In general, these very small and strong scatterers with typical radii of 1-3 µm are imaged much larger in ultrasound images than they actually are due to the point spread function of the imaging system. However, by applying appropriate methods, these MBs can be localized with sub-pixel precision. Then, by tracking MBs over successive frames of image sequences, not only the morphology of vascular trees but also functional information such as flow velocities or directions can be obtained and visualized. In addition, quantitative parameters can be derived to describe pathological and physiological changes in the microvasculature. In this review, the general concept of ULM and conditions for its applicability to microvessel imaging are explained. Based on this, various aspects of the different processing steps for a concrete implementation are discussed. The trade-off between complete reconstruction of the microvasculature and the necessary measurement time as well as the implementation in 3D are reviewed in more detail, as they are the focus of current research. Through an overview of potential or already realized preclinical and clinical applications - pathologic angiogenesis or degeneration of vessels, physiological angiogenesis, or the general understanding of organ or tissue function - the great potential of ULM is demonstrated.

Super-resolution ultrasound microvascular imaging: Is it ready for clinical use?

The field of super-resolution ultrasound microvascular imaging has been rapidly growing over the past decade. By leveraging contrast microbubbles as point targets for localization and tracking, super-resolution ultrasound pinpoints the location of microvessels and measures their blood flow velocity. Super-resolution ultrasound is the first in vivo imaging modality that can image micron-scale vessels at a clinically relevant imaging depth without tissue destruction. These unique capabilities of super-resolution ultrasound provide structural (vessel morphology) and functional (vessel blood flow) assessments of tissue microvasculature on a global and local scale, which opens new doors for many enticing preclinical and clinical applications that benefit from microvascular biomarkers. The goal of this short review is to provide an update on recent advancements in super-resolution ultrasound imaging, with a focus on summarizing existing applications and discussing the prospects of translating super-resolution imaging to clinical practice and research. In this review, we also provide brief introductions of how super-resolution ultrasound works, how does it compare with other imaging modalities, and what are the tradeoffs and limitations for an audience who is not familiar with the technology.

Super-resolution Ultrasound Microvascular Angiography for Spinal Cord Penumbra Imaging

OBJECTIVE

After spinal cord injury (SCI) or ischemia, timely intervention in the penumbra, such as recanalization and tissue reperfusion, is essential for preservation of its function. However, limited by imaging resolution and micro-blood flow sensitivity, golden standard angiography modalities, including magnetic resonance angiography (MRA) and digital subtraction angiography (DSA), are still not applicable for spinal cord microvascular imaging. Regarding spinal cord penumbra, to the best of authors' knowledge, currently, there is no efficient in vivo imaging modality for its evaluation. With tens-of-micrometer resolution and deep penetration, advanced ultrasound localization microscopy (ULM) could potentially meet the needs of emergent diagnosis and long-term monitoring of spinal cord penumbra.

METHODS

ULM microvasculature imaging was performed on rats with all laminae removed to obtain the blood supply in major spinal cord segments (C5-L5). For adult rats with spinal cord penumbra induced by compression injury (1 s, 10 s and 15 s), ULM imaging was conducted. The corresponding angiography results are investigated in terms of microvessel saturation, morphology, and flow velocity. The Basso/Beattie/Bresnahan (BBB) locomotor rating scale and hematoxylin and eosin staining were utilized for model validation and comparison.

RESULTS

The feasibility of ULM enabling spinal cord penumbra imaging and development monitoring was demonstrated. The focal injury core and penumbra can be clearly identified using the proposed method. Significant difference of perfusion can be observed after 1 s, 10 s and 15 s compression. Quantitative results show a high correlation between in vivo ultrasonic angiography, BBB functional evaluation and ex vivo histology assessment under different compression duration.

CONCLUSION

It is demonstrated that the super-resolution ULM micro-vasculature imaging can be used to evaluate the penumbra in spinal cord at acute and early stage of chronic phase, providing an efficient modality for micro-hemodynamics monitoring of the spinal cord.

Frame rate effects and their compensation on super-resolution microvessel imaging using ultrasound localization microscopy

Ultrasound localization microscopy (ULM) breaks the diffraction limit and allows imaging microvasculature at micrometric resolution while preserving the penetration depth. Frame rate plays an important role for high-quality ULM imaging, but there is still a lack of review and investigation of the frame rate effects on ULM. This work aims to clarify how frame rate influences the performance of ULM, including the effects of microbubble detection, localization and tracking. The performance of ULM was evaluated using an in vivo rat brain dataset (15.6 MHz, 3 tilted plane waves (-5°, 0°, +5°), at a compounded frame rate of 1000 Hz) with different frame rates. Quantification methods, including Fourier ring correlation and saturation parameter, were applied to analyze the spatial resolution and reconstruction efficiency, respectively. In addition, effects on each crucial step in ULM processing were further analyzed. Results showed that when frame rates dropped from 1000 Hz to 250 Hz, the spatial resolution deteriorated from 9.9 μm to 15.0 μm. Applying a velocity constraint was able to improve the ULM performance, but inappropriate constraint may artificially result in high apparent resolution. For the dataset, compared with the results of 1000 Hz frame rate, the velocity was underestimated at 100 Hz with 47.18% difference and the saturation was reduced from 55.00% at 1000 Hz to 43.34% at 100 Hz. Analysis showed that inadequate frame rate generated unreliable microbubble detection, localization and tracking as well as incomplete track reconstruction, resulting in the deterioration in spatial resolution, the underestimation in velocity measurement and the decrease in saturation. Finally, a guidance of determining the frame rate requirement was discussed by considering the required spatial sampling points based on vessel morphology, clutter filtering method, tracking algorithm and acquisition time, which provides indications for future clinical application of ULM method.

In vivo ocular microvasculature imaging in rabbits with 3D ultrasound localization microscopy

Morphological and hemodynamic changes in the ocular vasculature are important signs of various ocular diseases. The evaluation of the ocular microvasculature with high resolution is valuable in comprehensive diagnoses. However, it is difficult for current optical imaging techniques to visualize the posterior segment and retrobulbar microvasculature due to the limited penetration depth of light, particularly when the refractive medium is opaque. Thus, we have developed a 3D ultrasound localization microscopy (ULM) imaging method to visualize the ocular microvasculature in rabbits with micron-scale resolution. We used a 32 × 32 matrix array transducer (center frequency: 8 MHz) with a compounding plane wave sequence and microbubbles. Block-wise singular value decomposition spatiotemporal clutter filtering and block-matching 3D denoising were implemented to extract the flowing microbubble signals at different imaging depths with high signal-to-noise ratios. The center points of microbubbles were localized and tracked in 3D space to achieve the micro-angiography. The in vivo results demonstrate the ability of 3D ULM to visualize the microvasculature of the eye in rabbits, where vessels down to 54 μm were successfully revealed. Moreover, the microvascular maps indicated the morphological abnormalities in the eye with retinal detachment. This efficient modality shows potential for use in the diagnosis of ocular diseases.

Assessment of Transarterial Chemoembolization Using Super-resolution Ultrasound Imaging and a Rat Model of Hepatocellular Carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer diagnosed annually in 600,000 people worldwide. A common treatment is transarterial chemoembolization (TACE), which interrupts the blood supply of oxygen and nutrients to the tumor mass. The need for repeat TACE treatments may be assessed in the weeks after therapy with contrast-enhanced ultrasound (CEUS) imaging. Although the spatial resolution of traditional CEUS has been restricted by the diffraction limit of ultrasound (US), this physical barrier has been overcome by a recent innovation known as super-resolution US (SRUS) imaging. In short, SRUS enhances the visible details of smaller microvascular structures on the 10 to 100 µm scale, which unlocks a host of new clinical opportunities for US.

METHODS

In this study, a rat model of orthotopic HCC is introduced and TACE treatment response (to a doxorubicin-lipiodol emulsion) is assessed using longitudinal SRUS and magnetic resonance imaging (MRI) performed at 0, 7 and 14 d. Animals were euthanized at 14 d for histological analysis of excised tumor tissue and determination of TACE response, that is, control, partial response or complete response. CEUS imaging was performed using a pre-clinical US system (Vevo 3100, FUJIFILM VisualSonics Inc.) equipped with an MX201 linear array transducer. After administration of a microbubble contrast agent (Definity, Lantheus Medical Imaging), a series of CEUS images were collected at each tissue cross-section as the transducer was mechanically stepped at 100 μm increments. SRUS images were formed at each spatial position, and a microvascular density metric was calculated. Microscale computed tomography (microCT, OI/CT, MILabs) was used to confirm TACE procedure success, and tumor size was monitored using a small animal MRI system (BioSpec 3T, Bruker Corp.).

RESULTS

Although there were no differences at baseline (p > 0.15), both microvascular density levels and tumor size measures from the complete responder cases at 14 d were considerably lower and smaller, respectively, than those in the partial responder or control group animals. Histological analysis revealed tumor-to-necrosis levels of 8.4%, 51.1% and 100%, for the control, partial responder and complete responder groups, respectively (p < 0.005).

CONCLUSION

SRUS imaging is a promising modality for assessing early changes in microvascular networks in response to tissue perfusion-altering interventions such as TACE treatment of HCC.

Fast and Selective Super-Resolution Ultrasound In Vivo With Acoustically Activated Nanodroplets

Perfusion by the microcirculation is key to the development, maintenance and pathology of tissue. Its measurement with high spatiotemporal resolution is consequently valuable but remains a challenge in deep tissue. Ultrasound Localization Microscopy (ULM) provides very high spatiotemporal resolution but the use of microbubbles requires low contrast agent concentrations, a long acquisition time, and gives little control over the spatial and temporal distribution of the microbubbles. The present study is the first to demonstrate Acoustic Wave Sparsely-Activated Localization Microscopy (AWSALM) and fast-AWSALM for in vivo super-resolution ultrasound imaging, offering contrast on demand and vascular selectivity. Three different formulations of acoustically activatable contrast agents were used. We demonstrate their use with ultrasound mechanical indices well within recommended safety limits to enable fast on-demand sparse activation and destruction at very high agent concentrations. We produce super-localization maps of the rabbit renal vasculature with acquisition times between 5.5 s and 0.25 s, and a 4-fold improvement in spatial resolution. We present the unique selectivity of AWSALM in visualizing specific vascular branches and downstream microvasculature, and we show super-localized kidney structures in systole (0.25 s) and diastole (0.25 s) with fast-AWSALM outperforming microbubble based ULM. In conclusion, we demonstrate the feasibility of fast and selective imaging of microvascular dynamics in vivo with subwavelength resolution using ultrasound and acoustically activatable nanodroplet contrast agents.

Detecting Early Ocular Choroidal Melanoma Using Ultrasound Localization Microscopy

Ocular choroidal melanoma (OCM) is the most common ocular primary malignant tumor in adults, and there is an increasing emphasis on its early detection and treatment worldwide. The main obstacle in early detection of OCM is its overlapping clinical features with benign choroidal nevus. Thus, we propose ultrasound localization microscopy (ULM) based on the image deconvolution algorithm to assist the diagnosis of small OCM in early stages. Furthermore, we develop ultrasound (US) plane wave imaging based on three-frame difference algorithm to guide the placement of the probe on the field of view. A high-frequency Verasonics Vantage system and an L22-14v linear array transducer were used to perform experiments on both custom-made modules in vitro and a SD rat with ocular choroidal melanoma in vivo. The results demonstrate that our proposed deconvolution method implement more robust microbubble (MB) localization, reconstruction of microvasculature network in a finer grid and more precise flow velocity estimation. The excellent performance of US plane wave imaging was successfully validated on the flow phantom and in an in vivo OCM model. In the future, the super-resolution ULM, a critical complementary imaging modality, can provide doctors with conclusive suggestions for early diagnosis of OCM, which is significant for the treatment and prognosis of patients.

Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL)-based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data ( L frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging l -frame raw US images with l being significantly smaller than L . To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows  ∼ 40 -fold reduction in data acquisition time and  ∼ 61 -fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time (  ∼ 0.33 s) and ultrafast data processing speed (  ∼ 0.60 s) that makes it promising to observe rapid biological activities in vivo.

Super-Resolution Ultrasound Localization Microscopy of Microvascular Structure and Flow for Distinguishing Metastatic Lymph Nodes - An Initial Human Study

PURPOSE

Detecting and distinguishing metastatic lymph nodes (LNs) from those with benign lymphadenopathy are crucial for cancer diagnosis and prognosis but remain a clinical challenge. A recent advance in super-resolution ultrasound (SRUS) through localizing individual microbubbles has broken the diffraction limit and tracking enabled in vivo noninvasive imaging of vascular morphology and flow dynamics at a microscopic level. In this study we hypothesize that SRUS enables quantitative markers to distinguish metastatic LNs from benign ones in patients with lymphadenopathy.

MATERIALS AND METHODS

Clinical contrast-enhanced ultrasound image sequences of LNs from 6 patients with lymph node metastasis and 4 with benign lymphadenopathy were acquired and motion-corrected. These were then used to generate super-resolution microvascular images and super-resolved velocity maps. From these SRUS images, morphological and functional measures were obtained including micro-vessel density, fractal dimension, mean flow speed, and Local Flow Direction Irregularity (LFDI) measuring the variance in local flow direction. These measures were compared between pathologically proven reactive and metastasis LNs.

RESULTS

Our initial results indicate that the difference in the indicator of flow irregularity (LFDI) derived from the SRUS images is statistically significant between the two groups. The LFDI is 60% higher in metastatic LNs compared with reactive nodes.

CONCLUSION

This pilot study demonstrates the feasibility of super-resolution ultrasound for clinical imaging of lymph nodes and the potential of using the irregularity of local blood flow directions afforded by SRUS for the characterization of LNs.

Ultrasound super-resolution imaging for differential diagnosis of breast masses

OBJECTIVE

Ultrasound imaging has been widely used in breast cancer screening. Recently, ultrasound super-resolution imaging (SRI) has shown the capability to break the diffraction limit to display microvasculature. However, the application of SRI on differential diagnosis of breast masses remains unknown. Therefore, this study aims to evaluate the feasibility and clinical value of SRI for visualizing microvasculature and differential diagnosis of breast masses.

METHODS

B mode, color-Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS) images of 46 patients were collected respectively. SRI were generated by localizations of each possible contrast signals. Micro-vessel density (MVD) and microvascular flow rate (MFR) were calculated from SRI and time to peak (TTP), peak intensity (PI) and area under the curve (AUC) were obtained by quantitative analysis of CEUS images respectively. Pathological results were considered as the gold standard. Independent chi-square test and multivariate logistic regression analysis were performed using these parameters to examine the correlation.

RESULTS

The results showed that SRI technique could be successfully applied on breast masses and display microvasculature at a significantly higher resolution than the conventional CDFI and CEUS images. The results showed that the PI, AUC, MVD and MFR of malignant breast masses were significantly higher than those of benign breast masses, while TTP was significantly lower than that of benign breast masses. Among all five parameters, MVD showed the highest positive correlation with the malignancy of breast masses.

CONCLUSIONS

SRI is able to successfully display the microvasculature of breast masses. Compared with CDFI and CEUS, SRI can provide additional morphological and functional information for breast masses. MVD has a great potential in assisting the differential diagnosis of breast masses as an important imaging marker.

Ultrasound super-resolution imaging for the differential diagnosis of thyroid nodules: A pilot study

Objective

Ultrasound imaging provides a fast and safe examination of thyroid nodules. Recently, the introduction of super-resolution imaging technique shows the capability of breaking the Ultrasound diffraction limit in imaging the micro-vessels. The aim of this study was to evaluate its feasibility and value for the differentiation of thyroid nodules.

Methods

In this study, B-mode, contrast-enhanced ultrasound, and color Doppler flow imaging examinations were performed on thyroid nodules in 24 patients. Super-resolution imaging was performed to visualize the microvasculature with finer details. Microvascular flow rate (MFR) and micro-vessel density (MVD) within thyroid nodules were computed. The MFR and MVD were used to differentiate the benign and malignant thyroid nodules with pathological results as a gold standard.

Results

Super-resolution imaging (SRI) technique can be successfully applied on human thyroid nodules to visualize the microvasculature with finer details and obtain the useful clinical information MVD and MFR to help differential diagnosis. The results suggested that the mean value of the MFR within benign thyroid nodule was 16.76 ± 6.82 mm/s whereas that within malignant thyroid was 9.86 ± 4.54 mm/s. The mean value of the MVD within benign thyroid was 0.78 while the value for malignant thyroid region was 0.59. MFR and MVD within the benign thyroid nodules were significantly higher than those within the malignant thyroid nodules respectively (p < 0.01).

Conclusions

This study demonstrates the feasibility of ultrasound super-resolution imaging to show micro-vessels of human thyroid nodules via a clinical ultrasound platform. The important imaging markers, such as MVD and MFR, can be derived from SRI to provide more useful clinical information. It has the potential to be a new tool for aiding differential diagnosis of thyroid nodules.

Anatomic and Functional Imaging Using Row-Column Arrays

Row-column (RC) arrays have the potential to yield full 3-D ultrasound imaging with a greatly reduced number of elements compared to fully populated arrays. They, however, have several challenges due to their special geometry. This review article summarizes the current literature for RC imaging and demonstrates that full anatomic and functional imaging can attain a high quality using synthetic aperture (SA) sequences and modified delay-and-sum beamforming. Resolution can approach the diffraction limit with an isotropic resolution of half a wavelength with low sidelobe levels, and the field of view can be expanded by using convex or lensed RC probes. GPU beamforming allows for three orthogonal planes to be beamformed at 30 Hz, providing near real-time imaging ideal for positioning the probe and improving the operator's workflow. Functional imaging is also attainable using transverse oscillation and dedicated SA sequence for tensor velocity imaging for revealing the full 3-D velocity vector as a function of spatial position and time for both blood velocity and tissue motion estimation. Using RC arrays with commercial contrast agents can reveal super-resolution imaging (SRI) with isotropic resolution below [Formula: see text]. RC arrays can, thus, yield full 3-D imaging at high resolution, contrast, and volumetric rates for both anatomic and functional imaging with the same number of receive channels as current commercial 1-D arrays.

Super-Resolution Ultrasound Through Sparsity-Based Deconvolution and Multi-Feature Tracking

Ultrasound super-resolution imaging through localisation and tracking of microbubbles can achieve sub-wave-diffraction resolution in mapping both micro-vascular structure and flow dynamics in deep tissue in vivo. Currently, it is still challenging to achieve high accuracy in localisation and tracking particularly with limited imaging frame rates and in the presence of high bubble concentrations. This study introduces microbubble image features into a Kalman tracking framework, and makes the framework compatible with sparsity-based deconvolution to address these key challenges. The performance of the method is evaluated on both simulations using individual bubble signals segmented from in vivo data and experiments on a mouse brain and a human lymph node. The simulation results show that the deconvolution not only significantly improves the accuracy of isolating overlapping bubbles, but also preserves some image features of the bubbles. The combination of such features with Kalman motion model can achieve a significant improvement in tracking precision at a low frame rate over that using the distance measure, while the improvement is not significant at the highest frame rate. The in vivo results show that the proposed framework generates SR images that are significantly different from the current methods with visual improvement, and is more robust to high bubble concentrations and low frame rates.

Independent Component Analysis Filter for Small Vessel Contrast Imaging During Fast Tissue Motion

Suppressing tissue clutter is an essential step in blood flow estimation and visualization, even when using ultrasound contrast agents. Blind source separation (BSS)-based clutter filter for high-framerate ultrasound imaging has been reported to perform better in tissue clutter suppression than the conventional frequency-based wall filter and nonlinear contrast pulsing schemes. The most notable BSS technique, singular value decomposition (SVD) has shown compelling results in cases of slow tissue motion. However, its performance degrades when the tissue motion is faster than the blood flow speed, conditions that are likely to occur when imaging the small vessels, such as in the myocardium. Independent component analysis (ICA) is another BSS technique that has been implemented as a clutter filter in the spatiotemporal domain. Instead, we propose to implement ICA in the spatial domain where motion should have less impact. In this work, we propose a clutter filter with the combination of SVD and ICA to improve the contrast-to-background ratio (CBR) in cases where tissue velocity is significantly faster than the flow speed. In an in vitro study, the range of fast tissue motion velocity was 5-25 mm/s and the range of flow speed was 1-12 mm/s. Our results show that the combination of ICA and SVD yields 7-10 dB higher CBR than SVD alone, especially in the tissue high-velocity range. The improvement is crucial for cardiac imaging where relatively fast myocardial motions are expected.

A Review of Clinical Applications for Super-resolution Ultrasound Localization Microscopy

Microvascular structure and hemodynamics are important indicators for the diagnosis and assessment of many diseases and pathologies. The structural and functional imaging of tissue microvasculature in vivo is a clinically significant objective for the development of many imaging modalities. Contrast-enhanced ultrasound (CEUS) is a popular clinical tool for characterizing tissue microvasculature, due to the moderate cost, wide accessibility, and absence of ionizing radiation of ultrasound. However, in practice, it remains challenging to demonstrate microvasculature using CEUS, due to the resolution limit of conventional ultrasound imaging. In addition, the quantification of tissue perfusion by CEUS remains hindered by high operator-dependency and poor reproducibility. Inspired by super-resolution optical microscopy, super-resolution ultrasound localization microscopy (ULM) was recently developed. ULM uses the same ultrasound contrast agent (i.e. microbubbles) in CEUS. However, different from CEUS, ULM uses the location of the microbubbles to construct images, instead of using the backscattering intensity of microbubbles. Hence, ULM overcomes the classic compromise between imaging resolution and penetration, allowing for the visualization of capillary-scale microvasculature deep within tissues. To date, many in vivo ULM results have been reported, including both animal (kidney, brain, spinal cord, xenografted tumor, and ear) and human studies (prostate, tibialis anterior muscle, and breast cancer tumors). Furthermore, a variety of useful biomarkers have been derived from using ULM for different preclinical and clinical applications. Due to the high spatial resolution and accurate blood flow speed estimation (approximately 1 mm/s to several cm/s), ULM presents as an enticing alternative to CEUS for characterizing tissue microvasculature in vivo. This review summarizes the principles and present applications of CEUS and ULM, and discusses areas where ULM can potentially provide a better alternative to CEUS in clinical practice and areas where ULM may not be a better alternative. The objective of the study is to provide clinicians with an up-to-date review of ULM technology, and a practical guide for implementing ULM in clinical research and practice.

In Vivo Ultrasound Localization Microscopy Imaging of the Kidney's Microvasculature With Block-Matching 3-D Denoising

Structural abnormalities and functional changes of renal microvascular networks play a significant pathophysiologic role in the occurrence of kidney diseases. Super-resolution ultrasound imaging has been successfully utilized to visualize the microvascular network and provide valuable diagnostic information. To prevent the burst of microbubbles, a lower mechanical index (MI) is generally used in ultrasound localization microscopy (ULM) imaging. However, high noise levels lead to incorrect signal localizations in relatively low-MI settings and deep tissue. In this study, we implemented a block-matching 3-D (BM3D) image-denoising method, after the application of singular value decomposition filtering, to further suppress the noise at various depths. The in vitro flow-phantom results show that the BM3D method helps the significant reduction of the error localizations, thus improving the localization accuracy. In vivo rhesus macaque experiments help conclude that the BM3D method improves the resolution more than other image-based denoising techniques, such as the nonlocal means method. The obtained clutter-filtered images with fewer incorrect localizations can enable robust ULM imaging, thus helping in establishing an effective diagnostic tool.

Study on the Application of Super-Resolution Ultrasound for Cerebral Vessel Imaging in Rhesus Monkeys

Background: Ultrasound is ideal for displaying intracranial great vessels but not intracranial microvessels and terminal vessels. Even with contrast agents, the imaging effect is still unsatisfactory. In recent years, significant theoretical advances have been achieved in super-resolution imaging. The latest commonly used ultrafast plane-wave ultrasound Doppler imaging of the brain and microbubble-based super-resolution ultrasound imaging have been applied to the imaging of cerebral microvessels and blood flow in small animals such as mice but have not been applied to in vivo imaging of the cerebral microvessels in monkeys and larger animals. In China, preliminary research results have been obtained using super-resolution imaging in certain fields but rarely in fundamental and clinical experiments on large animals. In recent years, we have conducted a joint study with the Xi'an Jiaotong University to explore the application and performance of this new technique in the diagnosis of cerebrovascular diseases in large animals.

Objective: To explore the characteristics and advantages of microbubble-based super-resolution ultrasound imaging of intracranial vessels in rhesus monkeys compared with conventional transcranial ultrasound.

Methods: First, the effectiveness and feasibility of the super-resolution imaging technique were verified by modular simulation experiments. Then, the imaging parameters were adjusted based on in vitro experiments. Finally, two rhesus monkeys were used for in vivo experiments of intracranial microvessel imaging.

Results: Compared with conventional plane-wave imaging, super-resolution imaging could measure the inner diameters of cerebral microvessels at a resolution of 1 mm or even 0.7 mm and extract blood flow information. In addition, it has a better signal-to-noise ratio (5.625 dB higher) and higher resolution (~30-fold higher). The results of the experiments with rhesus monkeys showed that microbubble-based super-resolution ultrasound imaging can achieve an optimal resolution at the micron level and an imaging depth >35 mm.

Conclusion: Super-resolution imaging can realize the monitoring imaging of high-resolution and fast calculation of microbubbles in the process of tissue damage, providing an important experimental basis for the clinical application of non-invasive transcranial ultrasound.

Measuring Image Resolution in Ultrasound Localization Microscopy

The resolution of an imaging system is usually determined by the width of its point spread function and is measured using the Rayleigh criterion. For most system, it is in the order of the imaging wavelength. However, super resolution techniques such as localization microscopy in optical and ultrasound imaging can resolve features an order of magnitude finer than the wavelength. The classical description of spatial resolution no longer applies and new methods need to be developed. In optical localization microscopy, the Fourier Ring Correlation has showed to be an effective and practical way to estimate spatial resolution for Single Molecule Localization Microscopy data. In this work, we wish to investigate how this tool can provide a direct and universal estimation of spatial resolution in Ultrasound Localization Microscopy. Moreover, we discuss the concept of spatial sampling in Ultrasound Localization Microscopy and demonstrate how the Nyquist criterion for sampling drives the spatial/temporal resolution tradeoff. We measured spatial resolution on five different datasets over rodent's brain, kidney and tumor finding values between [Formula: see text] and [Formula: see text] for precision of localization between [Formula: see text] and [Formula: see text]. Eventually, we discuss from those in vivo datasets how spatial resolution in Ultrasound Localization Microscopy depends on both the localization precision and the total number of detected microbubbles. This study aims to offer a practical and theoretical framework for image resolution in Ultrasound Localization Microscopy.

Faster super-resolution ultrasound imaging with a deep learning model for tissue decluttering and contrast agent localization

Super-resolution ultrasound (SR-US) imaging allows visualization of microvascular structures as small as tens of micrometers in diameter. However, use in the clinical setting has been impeded in part by ultrasound (US) acquisition times exceeding a breath-hold and by the need for extensive offline computation. Deep learning techniques have been shown to be effective in modeling the two more computationally intensive steps of microbubble (MB) contrast agent detection and localization. Performance gains by deep networks over conventional methods are more than two orders of magnitude and in addition the networks can localize overlapping MBs. The ability to separate overlapping MBs allows use of higher contrast agent concentrations and reduces US image acquisition time. Herein we propose a fully convolutional neural network (CNN) architecture to perform the operations of MB detection as well as localization in a single model. Termed SRUSnet, the network is based on the MobileNetV3 architecture modified for 3-D input data, minimal convergence time, and high-resolution data output using a flexible regression head. Also, we propose to combine linear B-mode US imaging and nonlinear contrast pulse sequencing (CPS) which has been shown to increase MB detection and further reduce the US image acquisition time. The network was trained within silicodata and tested onin vitrodata from a tissue-mimicking flow phantom, and onin vivodata from the rat hind limb (N = 3). Images were collected with a programmable US system (Vantage 256, Verasonics Inc., Kirkland, WA) using an L11-4v linear array transducer. The network exceeded 99.9% detection accuracy onin silicodata. The average localization accuracy was smaller than the resolution of a pixel (i.e.λ/8). The average processing time on a Nvidia GeForce 2080Ti GPU was 64.5 ms for a 128 × 128-pixel image.

In Vivo Motion Correction in Super-Resolution Imaging of Rat Kidneys

Super-resolution (SR) imaging has the potential of visualizing the microvasculature down to the 10- [Formula: see text] level, but motion induced by breathing, heartbeats, and muscle contractions are often significantly above this level. This article, therefore, introduces a method for estimating tissue motion and compensating for this. The processing pipeline is described and validated using Field II simulations of an artificial kidney. In vivo measurements were conducted using a modified bk5000 research scanner (BK Medical, Herlev, Denmark) with a BK 9009 linear array probe employing a pulse amplitude modulation scheme. The left kidney of ten Sprague-Dawley rats was scanned during open laparotomy. A 1:10 diluted SonoVue contrast agent (Bracco, Milan, Italy) was injected through a jugular vein catheter at 100 [Formula: see text]/min. Motion was estimated using speckle tracking and decomposed into contributions from the heartbeats, breathing, and residual motion. The estimated peak motions and their precisions were: heart: axial- [Formula: see text] and lateral- [Formula: see text], breathing: axial- [Formula: see text] and lateral- [Formula: see text], and residual: axial-30 [Formula: see text] and lateral-90 [Formula: see text]. The motion corrected microbubble tracks yielded SR images of both bubble density and blood vector velocity. The estimation was, thus, sufficiently precise to correct shifts down to the 10- [Formula: see text] capillary level. Similar results were found in the other kidney measurements with a restoration of resolution for the small vessels demonstrating that motion correction in 2-D can enhance SR imaging quality.

Super-Resolution Ultrasound Localization Microscopy on a Rabbit Liver VX2 Tumor Model: An Initial Feasibility Study

Ultrasound localization microscopy can image microvasculature in vivo without sacrificing imaging penetration depth. However, the reliance on super-resolution inference limits the applicability of the technique because subpixel tissue motion can corrupt microvascular reconstruction. Consequently, the majority of previous pre-clinical research on this super-resolution procedure has been restricted to low-motion experimental models with ample motion correction or data rejection, which precludes the imaging of organ sites that exhibit a high degree of respiratory and other motion. In this article, we present a novel anesthesia protocol in rabbits that induces safe, controllable periods of apnea to enable the long image-acquisition times required for ultrasound localization microscopy. We apply this protocol to a VX2 liver tumor model undergoing sorafenib therapy and compare the results to super-resolution images from conventional high-dose isoflurane anesthesia. We find that the apneic protocol was necessary to correctly identify the poorly vascularized tumor cores, as verified by immunohistochemistry, and to reveal the tumoral microvascular architecture.