Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

Super-resolution ultrasound imaging of ischaemia flow: An in silico study

Super-resolution ultrasound (SRU) is a new ultrasound imaging mode that promises to facilitate the detection of microvascular disease by providing new vascular bio-markers that are directly linked to microvascular pathophysiology, thereby augmenting current knowledge and potentially enabling new treatment. Such a capability can be developed through thorough understanding as articulated by means of mathematical models. In this study, a 2D numerical flow model is adopted for generating flow adaptation in response to ischaemia, in order to determine the ability of SRU to register the resulting flow perturbations. The flow model results demonstrate that variations in flow behaviour in response to locally induced ischaemia can be significant throughout the entire vascular bed. Measured velocities have variations that are dependent on the location of ischaemia, with median values ranging between 2-7 mms-1. Moreover, the distinction between healthy and ischaemic networks are recorded accurately in the SRU results showing excellent agreement between SRU maps and the model. Up to 7-fold spatial resolution improvement to conventional contrast ultrasound was achieved in microbubble localisation while the detection precision and recall was consistently above 98%. The microbubble tracking precision was of a similar accuracy, whereas the recall was reduced (77%) under varying ischaemic impacted flow. Further, regions with velocities up to 30 mms-1 are in excellent agreement with SRU maps, while at regions that include a proportion of higher velocities, the median velocity values are within 1.28%-3.32% of the ground-truth. In conclusion, SRU is a highly promising methodology for the direct measurement of microvascular flow dynamics and may provide a valuable tool for the understanding and subsequent modelling of behaviour in the vascular bed.

Microbubble tracking based on partial smoothing-based adaptive generalized labelled Multi-Bernoulli filter for super-resolution imaging

Super-resolution ultrasound (SRUS) can image the vasculature at microscopic resolution according to microbubble (MB) localization, with velocity vector maps obtained based on MB tracking information. High MB concentrations can reduce the acquisition time of SRUS imaging, however adjacent and intersecting vessels are difficult to distinguish, thus decreasing resolution. Low acquisition frame rates affect the precision of flow velocity estimation. This study proposes a partial smoothing-based adaptive generalized labeled multi-Bernoulli filter (SAGLMB) to precisely track the MB motion at different flow velocities. SAGLMB employs a generalized labelled multi-Bernoulli filter (GLMB) for MB trajectory allocation to separate adjacent and intersecting vessels. Furthermore, the nonlinear motion of MB was predicted by an unscented Kalman filter, and a cardinalized probability hypothesis density filter was applied to suppress clutter interference. Finally, the trajectories were smoothed by unscented Rauch-Tung-Striebel to improve the resolution of the SRUS image. The simulation results demonstrate that SAGLMB outperforms the conventional bipartite graph-based tracking at high MB concentrations, achieving at least an 8.55 % improvement in the correctly paired precision, with 3 times increase in the structural similarity index measure. Moreover, SAGLMB can obtain more precise flow velocity estimations with a 4 times improvement than the conventional method. The SRUS results of rabbit kidney show that the proposed method significantly improves resolution of adjacent and intersecting vessels at higher MB concentrations and maintains this performance as the acquisition frame rate decreases. Furthermore, the rat brain microvascular network was reconstructed with 9.21 μm (λ/11.1) resolution. Therefore, SAGLMB can achieve robust SRUS imaging at high concentrations and low acquisition frame rates.

Vessel recovery using ultrasound localisation microscopy: An in silico comparative study between minimum variance and delay-and-sum beamformers

The use of particle localisation and tracking algorithms on Contrast Enhanced Ultrasound (CEUS) or other ultrasound mode image data containing sparse microbubble (MB) populations, can produce super-resolved vascularization maps. Typically such data stem from conventional delay and sum (DAS) beamforming that is used widely in ultrasound imaging modes. Recently, adaptive beamforming has shown significant improvement in spatial resolution, but its value to super-resolution image analysis approaches is not fully understood. The in silico study here evaluates the performance of combining minimum variance beamformers (MV BF), established to provide improved lateral resolution, compared to DAS BFs with single particle detection. The isolated effect of a range of simplified image-affecting factors such as flow profile, pulse length, noise, vessel separations and data availability is considered. The study aims to assess the vessel recovery performance using the different beamformers and investigate the link with MB detection and localisation. The MV BF was shown to provide improved microvessel position accuracy compared to conventional DAS BFs. In particular, vessel separations between 0.3-4 λ provided superior localisation uncertainty with the MV. In addition, for a separation of 0.36λ, vessel recovery was achieved with both methods but the use of MV eliminated artifacts that appear as additional vessels. These results were found to be linked to improved MB detection and localisation for the MV BF, which is proposed as suitable for testing in Ultrasound Localisation Microscopy (ULM) imaging using patient data.

A Novel 2x2D Radial Basis Functions-based Interpolation for Short Acquisition Time and Relaxed Frame Rate Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) has become a potent technique for microvascular imaging using ultrasound waves. However, one major challenge is the high frame rate and lengthy acquisition time needed to produce super-resolved (SR) images. To overcome this, our goal is to relax the frame rate and shorten this acquisition time while preserving SR image quality, thereby enhancing ULM's clinical applicability. To this end, we propose two distinct strategies: first, we suggest acquiring the data at lower frame rate followed by applying the reconstruction technique to compensate the lost information due to the low frame rate imaging. Secondly, to tackle the prolonged acquisition time, we propose compressing acquisition time by a compression ratio (CR), which can degrade SR image quality due to reduced temporal information. To mitigate this, we temporally upsample the in-phase-quadrature (IQ) data by a factor equal to the CR after the compressed acquisition. Additionally, we introduce a novel bi-directional (2x2D) interpolation using radial basis function (RBF)-based reconstruction to estimate unknown values in the 3D IQ data (x-z-t), thereby enhancing temporal resolution. The rationale behind using 2x2D interpolation is its ability to integrate spatiotemporal information from two orthogonal x-t and z-t planes, effectively addressing anisotropies and non-uniformities in microbubble motion. This 2x2D approach improves the reconstruction of microbubbles' dynamics by interpolating along both x and z directions. The method was tested on rat brain and ratkidney datasets recorded at 1kHz, demonstrating relaxing the frame rate to 100 Hz (using the first strategy) and a reduction in acquisition time by a factor of 3 to 4 (using the second strategy) while maintaining SR image quality comparable to the original uncompressed data, including density and velocity maps.

Optically Validated Microvascular Phantom for Super-Resolution Ultrasound Imaging

Super-resolution ultrasound (SRUS) visualizes microvasculature beyond the ultrasound (US) diffraction limit (wavelength( )/2) by localizing and tracking spatially isolated microbubble (MB) contrast agents. SRUS phantoms typically consist of simple tube structures, where diameter channels below m are not available. Furthermore, these phantoms are generally fragile and unstable, have limited ground truth validation, and their simple structure limits the evaluation of SRUS algorithms. To aid SRUS development, robust and durable phantoms with known and physiologically relevant microvasculature are needed for repeatable SRUS testing. This work proposes a method to fabricate durable microvascular phantoms that allow optical gauging for SRUS validation. The methodology used a microvasculature negative print embedded in a Polydimethylsiloxane (PDMS) to fabricate a microvascular phantom. Branching microvascular phantoms with variable microvascular density were demonstrated with optically validated vessel diameters down to m ( ; m). SRUS imaging was performed and validated with optical measurements. The average SRUS error was m ( ) with a standard deviation error of m. The average error decreased to m ( ) once the number of localized MBs surpassed 1000 per estimated diameter. In addition, less than 10% variance of acoustic and optical properties and the mechanical toughness of the phantoms measured a year after fabrication demonstrated their long-term durability. This work presents a method to fabricate durable and optically validated complex microvascular phantoms which can be used to quantify SRUS performance and facilitate its further development.

Influence of Image Discretization and Patch Size on Microbubble Localization Precision

For ultrasound localization microscopy, the localization of microbubbles (MBs) is an essential part to obtain super-resolved maps of the vasculature. This article analyzes the impact of image discretization and patch size on the precision of different MB localization methods to reconcile different observations from previous studies, provide an estimate of feasible localization precision, and derive guidelines for an optimal parameter selection. For this purpose, the images of MBs were simulated with Gaussian point-spread functions (PSFs) of varying width parameter at randomly generated subpixel positions, and Rician distributed noise was added. Four localization methods were tested on the patches of different sizes (number of pixels ): Gaussian fit (GF), radial symmetry (RS) method, calculation of center of mass (CoM), and peak detection (PD). Additionally, the Cramér-Rao lower bound (CRLB) for the given estimation problem was calculated. Our results show that the localization precision is strongly influenced by the ratio of the PSF width parameter to the pixel size , as well as the patch size N. The best parameter combination depends on the localization method. Generally, very small ratios as well as large ratios in combination with small N lead to performance degradation. The GF as a representative of a model-based fit comes close to the CRLB and always performs best for the ratios given by image discretization if N is adapted to the PSF. To achieve good results with the GF and the RS method, a good rule of thumb is to set the pixel sizes and the patch sizes .

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.

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

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

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 andin vivoexperiments.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 vivoexperiment results show that the AM-Net can reconstruct ∼24.3μm diameter micro-vessels and separate two ∼28.3μm diameter micro-vessels. Furthermore, when processing a 128 × 128 pixels image in simulation experiments and an 896 × 1280 pixels imagein vivoexperiment, 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.

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.

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.

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.

Ultrasound Localization Microscopy Using Deep Neural Network

Noninvasive imaging of microvascular structures in deep tissues provides morphological and functional information for clinical diagnosis and monitoring. Ultrasound localization microscopy (ULM) is an emerging imaging technique that can generate microvascular structures with subwavelength diffraction resolution. However, the clinical utility of ULM is hindered by technical limitations, such as long data acquisition time, high microbubble (MB) concentration, and inaccurate localization. In this article, we propose a Swin transformer-based neural network to perform end-to-end mapping to implement MB localization. The performance of the proposed method was validated using synthetic and in vivo data using different quantitative metrics. The results indicate that our proposed network can achieve higher precision and better imaging capability than previously used methods. Furthermore, the computational cost of processing per frame is 3-4 times faster than traditional methods, which makes the real-time application of this technique feasible in the future.

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.

Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.

Performance benchmarking of microbubble-localization algorithms for ultrasound localization microscopy

Ultrafast ultrasound localization microscopy can be used to detect the subwavelength acoustic scattering of intravenously injected microbubbles to obtain haemodynamic maps of the vasculature of animals and humans. The quality of the haemodynamic maps depends on signal-to-noise ratios and on the algorithms used for the localization of the microbubbles and the rendering of their trajectories. Here we report the results of benchmarking of the performance of seven microbubble-localization algorithms. We used metrics for localization errors, localization success rates, processing times and a measure of the reprojection of the localization of the microbubbles on the original beamformed grid. We combined eleven metrics into an overall score and tested the algorithms in three simulated microcirculation datasets, and in angiography datasets of the brain of a live rat after craniotomy, an excised rat kidney and a mammary tumour in a live mouse. The algorithms, metrics and datasets, which we have made openly available at https://github.com/AChavignon/PALA and https://doi.org/10.5281/zenodo.4343435 , will facilitate the identification or generation of optimal microbubble-localization algorithms for specific applications.

Super-Resolution Ultrasound Imaging Can Quantify Alterations in Microbubble Velocities in the Renal Vasculature of Rats

Super-resolution ultrasound imaging, based on the localization and tracking of single intravascular microbubbles, makes it possible to map vessels below 100 µm. Microbubble velocities can be estimated as a surrogate for blood velocity, but their clinical potential is unclear. We investigated if a decrease in microbubble velocity in the arterial and venous beds of the renal cortex, outer medulla, and inner medulla was detectable after intravenous administration of the α1-adrenoceptor antagonist prazosin. The left kidneys of seven rats were scanned with super-resolution ultrasound for 10 min before, during, and after prazosin administration using a bk5000 ultrasound scanner and hockey-stick probe. The super-resolution images were manually segmented, separating cortex, outer medulla, and inner medulla. Microbubble tracks from arteries/arterioles were separated from vein/venule tracks using the arterial blood flow direction. The mean microbubble velocities from each scan were compared. This showed a significant prazosin-induced velocity decrease only in the cortical arteries/arterioles (from 1.59 ± 0.38 to 1.14 ± 0.31 to 1.18 ± 0.33 mm/s, p = 0.013) and outer medulla descending vasa recta (from 0.70 ± 0.05 to 0.66 ± 0.04 to 0.69 ± 0.06 mm/s, p = 0.026). Conclusively, super-resolution ultrasound imaging makes it possible to detect and differentiate microbubble velocity responses to prazosin simultaneously in the renal cortical and medullary vascular beds.

Deep Learning-Based Microbubble Localization for Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)-based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radio frequency (RF) and in-phase and quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% compared to conventional localization. In addition, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of this article support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data.

In Vivo assessment of hypertensive nephrosclerosis using ultrasound localization microscopy

PURPOSE

As a typical chronic kidney disease (CKD), hypertensive nephrosclerosis (HN) is a common syndrome of hypertension, characterized by chronic kidney microvascular damage. Early diagnosis of microvascular damage using conventional ultrasound imaging encounters challenges in sensitivity and specificity owing to the inherent diffraction limit. Ultrasound localization microscopy (ULM) has been developed to obtain microvasculature and microvascular hemodynamics within the kidney, and would be a promising tool for early diagnosis of CKD.

METHODS

In this study, the advantage of quantitative indexes obtained by using ULM (mean arterial blood flow speeds of different segments of interlobular arteries) over indexes obtained using conventional clinical serum (β2-microglobulin, serum urea nitrogen and creatinine) and urine (24-hour urine volume and urine protein) tests and ultrasound Doppler imaging [peak systolic velocity (PSV), end-diastolic velocity (EDV) and resistance index (RI)] and contrast-enhanced ultrasound imaging [CEUS; rise time (RT), peak intensity (IMAX), mean transit time (mTT) and area under the time-intensity curve (AUC)] for early diagnosis of HN was investigated. Examinations were carried out on 6 spontaneously hypertensive rats (SHR) and 5 normal Wistar-Kyoto (WKY) rats at the age of 10 weeks.

RESULTS

The experimental results showed that the indicators derived from conventional clinical inspections (serum and urine tests) and ultrasound imaging (PSV, EDV, RI, RT, IMAX, mTT and AUC) did not show significant difference between hypertensive and healthy rats (p > 0.05), while the TTP of the SHR group (28.52 ± 5.52 s) derived from CEUS is significantly higher than that of the WKY group (18.68 ± 7.32 s; p < 0.05). The mean blood flow speed in interlobular artery of SHR (12.47 ± 1.06 mm/s) derived from ULM is significantly higher than that of WKY rats (10.13 ± 1.17 mm/s; p < 0.01).

CONCLUSION

The advantages of ULM over conventional clinical inspections and ultrasound imaging methods for early diagnosis of HN were validated. The quantitative results showed that ULM can effectively diagnose HN at the early stage by detecting the blood flow speed changes of interlobular arteries. ULM may promise a reliable technique for early diagnosis of HN in the future. This article is protected by copyright. All rights reserved.

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.

Characterization of the Ultrasound Localization Microscopy Resolution Limit in the Presence of Image Degradation

Ultrasound localization microscopy (ULM) has been able to overcome the diffraction limit of ultrasound imaging. The resolution limit of ULM has been previously modeled using the Cramér-Rao lower bound (CRLB). While this model has been validated in a homogeneous medium, it estimates a resolution limit, which has not yet been achieved in vivo. In this work, we investigated the effects of three sources of image degradation on the resolution limit of ULM. The Fullwave simulation tool was used to simulate acquisitions of transabdominal contrast-enhanced data at depth. The effects of reverberation clutter, trailing clutter, and phase aberration were studied. The resolution limit, in the presence of reverberation clutter alone, was empirically measured to be up to 39 times worse in the axial dimension and up to 2.1 times worse in the lateral dimension than the limit predicted by the CRLB. While reverberation clutter had an isotropic impact on the resolution, trailing clutter had a constant impact on both dimensions across all signal-to-trailing-clutter ratios (STCR). Phase aberration had a significant impact on the resolution limit over the studied analysis ranges. Phase aberration alone degraded the resolution limit up to 70 and 160 [Formula: see text] in the lateral and axial dimensions, respectively. These results illustrate the importance of phase aberration correction and clutter filtering in ULM postprocessing. The analysis results were demonstrated through the simulation of the ULM process applied to a cross-tube model that was degraded by each of the three aforementioned sources of degradation.

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.

Ultrasound microvasculature imaging with entropy-based radiality super-resolution (ERSR)

Objective:Microvasculature is highly relevant to the occurrence and development of pathologies such as cancer and diabetes. Ultrasound localization microscopy (ULM) has bypassed the diffraction limit and demonstrated its great potential to provide new imaging modality and establish new diagnostic criteria in clinical application. However, sparse microbubble distribution can be a significant bottleneck for improving temporal resolution, even for further clinical translation. Other important challenges for ULM to tackle in clinic also include high microbubble concentration and low frame rate.Approach:As part of the efforts to facilitate clinical translation, this paper focused on the low frame rate and the high microbubble distribution issue and proposed a new super-resolution imaging strategy called entropy-based radiality super-resolution (ERSR). The feasibility of ERSR is validated with simulations, phantom experiment and contrast-enhanced ultrasound scan of rabbit sciatic nerve with clinical accessible ultrasound system.Main results:ERSR can achieve 10 times improvement in spatial resolution compared to conventional ultrasound imaging, higher temporal resolution (∼10 times higher) and contrast-to-noise ratio under high-density microbubbles, compared with ULM under low-density microbubbles.Significance:We conclude ERSR could be a valuable imaging tool with high spatio-temporal resolution for clinical diagnosis and assessment of diseases potentially.

Morphological Reconstruction Improves Microvessel Mapping in Super-Resolution Ultrasound

Generation of super-resolution (SR) ultrasound (US) images, created from the successive localization of individual microbubbles in the circulation, has enabled the visualization of microvascular structure and flow at a level of detail that was not possible previously. Despite rapid progress, tradeoffs between spatial and temporal resolution may challenge the translation of this promising technology to the clinic. To temper these tradeoffs, we propose a method based on morphological image reconstruction. This method can extract from ultrafast contrast-enhanced US (CEUS) images hundreds of microbubble peaks per image (312-by-180 pixels) with intensity values varying by an order of magnitude. Specifically, it offers a fourfold increase in the number of peaks detected per frame, requires on the order of 100 ms for processing, and is robust to additive electronic noise (down to 3.6-dB CNR in CEUS images). By integrating this method to an SR framework, we demonstrate a sixfold improvement in spatial resolution, when compared with CEUS, in imaging chicken embryo microvessels. This method that is computationally efficient and, thus, scalable to large data sets may augment the abilities of SR-US in imaging microvascular structure and function.

Ultrasound Microvascular Imaging Based on Super-Resolution Radial Fluctuations

OBJECTIVES

Super-resolution ultrasound (SRUS) has become a tool for in vivo microvascular imaging. Most of the SRUS methods are based on microbubble localization: namely, ultrasound localization microscopy (ULM). The aim of this study was to develop a nonlocalization SRUS method and verify its feasibility in microvascular imaging.

METHODS

We introduce a new super-resolution strategy based on the postprocessing of contrast-enhanced ultrasound. The proposed method, which is termed ultrasound diffraction attenuation microscopy (UDAM), uses super-resolution radial fluctuations instead of microbubble localization to overcome acoustic diffraction limits. Biceps of Japanese long-ear white rabbits were adopted to validate its feasibility on muscle vascular imaging, using a clinical accessible ultrasound system at a frame rate of 30 Hz under a single bolus injection of SonoVue (Bracco SpA, Milan, Italy). The super-resolution image was compared with the maximum-intensity projection and ULM.

RESULTS

The animal study illustrates that the proposed UDAM can obtain super-resolution microvascular images of rabbits' muscles under a single bolus injection of SonoVue with a 150-second contrast-enhanced ultrasound video. Both ULM and UDAM can achieve a very similar vascular structure with the maximum-intensity projection but much higher spatial resolution. The measurement of 1-dimensional signals shows that UDAM can distinguish the subwavelength structures and substantial reduce the full width at half-maximum of microvessels.

CONCLUSIONS

We conclude UDAM provides a noninvasive tool for in vivo super-resolution microvascular imaging.

Superharmonic Ultrasound for Motion-Independent Localization Microscopy: Applications to Microvascular Imaging From Low to High Flow Rates

Recent advances in high frame rate biomedical ultrasound have led to the development of ultrasound localization microscopy (ULM), a method of imaging microbubble (MB) contrast agents beyond the diffraction limit of conventional coherent imaging techniques. By localizing and tracking the positions of thousands of individual MBs, ultrahigh resolution vascular maps are generated which can be further analyzed to study disease. Isolating bubble echoes from tissue signal is a key requirement for super-resolution imaging which relies on the spatiotemporal separability and localization of the bubble signals. To date, this has been accomplished either during acquisition using contrast imaging sequences or post-beamforming by applying a spatiotemporal filter to the B-mode images. Superharmonic imaging (SHI) is another contrast imaging method that separates bubbles from tissue based on their strongly nonlinear acoustic properties. This approach is highly sensitive, and, unlike spatiotemporal filters, it does not require decorrelation of contrast agent signals. Since this superharmonic method does not rely on bubble velocity, it can detect completely stationary and moving bubbles alike. In this work, we apply SHI to ULM and demonstrate an average improvement in SNR of 10.3-dB in vitro when compared with the standard singular value decomposition filter approach and an increase in SNR at low flow ( [Formula: see text]/frame) from 5 to 16.5 dB. Additionally, we apply this method to imaging a rodent kidney in vivo and measure vessels as small as [Formula: see text] in diameter after motion correction.

Super-resolution Ultrasound Imaging

The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.

Three-Dimensional Super-Resolution Imaging Using a Row-Column Array

A 3-D super-resolution (SR) pipeline based on data from a row-column (RC) array is presented. The 3-MHz RC array contains 62 rows and 62 columns with a half wavelength pitch. A synthetic aperture (SA) pulse inversion sequence with 32 positive and 32 negative row emissions is used for acquiring volumetric data using the SARUS research ultrasound scanner. Data received on the 62 columns are beamformed on a GPU for a maximum volume rate of 156 Hz when the pulse repetition frequency is 10 kHz. Simulated and 3-D printed point and flow microphantoms are used for investigating the approach. The flow microphantom contains a 100- [Formula: see text] radius tube injected with the contrast agent SonoVue. The 3-D processing pipeline uses the volumetric envelope data to find the bubble's positions from their interpolated maximum signal and yields a high resolution in all three coordinates. For the point microphantom, the standard deviation on the position is (20.7, 19.8, 9.1) [Formula: see text]. The precision estimated for the flow phantom is below [Formula: see text] in all three coordinates, making it possible to locate structures on the order of a capillary in all three dimensions. The RC imaging sequence's point spread function has a size of 0.58 × 1.05 × 0.31 mm³ ( 1.17λ×2.12λ×0.63λ ), so the possible volume resolution is 28900 times smaller than for SA RC B-mode imaging.

3-D Super-Resolution Ultrasound Imaging With a 2-D Sparse Array

High-frame-rate 3-D ultrasound imaging technology combined with super-resolution processing method can visualize 3-D microvascular structures by overcoming the diffraction-limited resolution in every spatial direction. However, 3-D super-resolution ultrasound imaging using a full 2-D array requires a system with a large number of independent channels, the design of which might be impractical due to the high cost, complexity, and volume of data produced. In this study, a 2-D sparse array was designed and fabricated with 512 elements chosen from a density-tapered 2-D spiral layout. High-frame-rate volumetric imaging was performed using two synchronized ULA-OP 256 research scanners. Volumetric images were constructed by coherently compounding nine-angle plane waves acquired at a pulse repetition frequency of 4500 Hz. Localization-based 3-D super-resolution images of two touching subwavelength tubes were generated from 6000 volumes acquired in 12 s. Finally, this work demonstrates the feasibility of 3-D super-resolution imaging and super-resolved velocity mapping using a customized 2-D sparse array transducer.

Super-Resolution Imaging Through the Human Skull

High-resolution transcranial ultrasound imaging in humans has been a persistent challenge for ultrasound due to the imaging degradation effects from aberration and reverberation. These mechanisms depend strongly on skull morphology and have high variability across individuals. Here, we demonstrate the feasibility of human transcranial super-resolution imaging using a geometrical focusing approach to efficiently concentrate energy at the region of interest, and a phase correction focusing approach that takes the skull morphology into account. It is shown that using the proposed focused super-resolution method, we can image a 208- [Formula: see text] microtube behind a human skull phantom in both an out-of-plane and an in-plane configuration. Individual phase correction profiles for the temporal region of the human skull were calculated and subsequently applied to transmit-receive a custom focused super-resolution imaging sequence through a human skull phantom, targeting the 208- [Formula: see text] diameter microtube at 68.5 mm in depth and at 2.5 MHz. Microbubble contrast agents were diluted to a concentration of 1.6×10⁶ bubbles/mL and perfused through the microtube. It is shown that by correcting for the skull aberration, the RF signal amplitude from the tube improved by a factor of 1.6 in the out-of-plane focused emission case. The lateral registration error of the tube's position, which in the uncorrected case was 990 [Formula: see text], was reduced to as low as 50 [Formula: see text] in the corrected case as measured in the B-mode images. Sensitivity in microbubble detection for the phase-corrected case increased by a factor of 1.48 in the out-of-plane imaging case, while, in the in-plane target case, it improved by a factor of 1.31 while achieving an axial registration correction from an initial 1885- [Formula: see text] error for the uncorrected emission, to a 284- [Formula: see text] error for the corrected counterpart. These findings suggest that super-resolution imaging may be used far more generally as a clinical imaging modality in the brain.

Coherent Multi-Transducer Ultrasound Imaging

This work extends the effective aperture size by coherently compounding the received radio frequency data from multiple transducers. As a result, it is possible to obtain an improved image, with enhanced resolution, an extended field of view (FoV), and high-acquisition frame rates. A framework is developed in which an ultrasound imaging system consisting of N synchronized matrix arrays, each with partly shared FoV, take turns to transmit plane waves (PWs). Only one individual transducer transmits at each time while all N transducers simultaneously receive. The subwavelength localization accuracy required to combine information from multiple transducers is achieved without the use of any external tracking device. The method developed in this study is based on the study of the backscattered echoes received by the same transducer and resulting from a targeted scatterer point in the medium insonated by the multiple ultrasound probes of the system. The current transducer locations along with the speed of sound in the medium are deduced by optimizing the cross correlation between these echoes. The method is demonstrated experimentally in 2-D for two linear arrays using point targets and anechoic lesion phantoms. The first demonstration of a free-hand experiment is also shown. Results demonstrate that the coherent multi-transducer ultrasound imaging method has the potential to improve ultrasound image quality, improving resolution, and target detectability. Compared with coherent PW compounding using a single probe, lateral resolution improved from 1.56 to 0.71 mm in the coherent multi-transducer imaging method without acquisition frame rate sacrifice (acquisition frame rate 5350 Hz).

Investigation of Microbubble Detection Methods for Super-Resolution Imaging of Microvasculature

Ultrasound super-resolution techniques use the response of microbubble (MB) contrast agents to visualize the microvasculature. Techniques that localize isolated bubble signals first require detection algorithms to separate the MB and tissue responses. This work explores the three main MB detection techniques for super-resolution of microvasculature. Pulse inversion (PI), differential imaging (DI), and singular value decomposition (SVD) filtering were compared in terms of the localization accuracy, precision, and contrast-to-tissue ratio. MB responses were simulated based on the properties of Sonovue and using the Marmottant model. Nonlinear propagation through tissue was modeled using the k-Wave software package. For the parameters studied, the results show that PI is most appropriate for low frequency applications, but also most dependent on transducer bandwidth. SVD is preferable for high frequency acquisition where localization precision on the order of a few microns is possible. PI is largely independent of flow direction and speed compared to SVD and DI, so is appropriate for visualizing the slowest flows and tortuous vasculature. SVD is unsuitable for stationary MBs and can introduce a localization error on the order of hundreds of microns over the speed range 0-2 mm/s and flow directions from lateral (parallel to probe) to axial (perpendicular to probe). DI is only suitable for flow rates >0.5 mm/s or as flow becomes more axial. Overall, this study develops an MB and tissue nonlinear simulation platform to improve understanding of how different MB detection techniques can impact the super-resolution process and explores some of the factors influencing the suitability of each.

Fast Acoustic Wave Sparsely Activated Localization Microscopy (fast-AWSALM): Ultrasound Super-Resolution using Plane-Wave Activation of Nanodroplets

Localization-based ultrasound super-resolution imaging using microbubble contrast agents and phase-change nano-droplets has been developed to visualize microvascular structures beyond the diffraction limit. However, the long data acquisition time makes the clinical translation more challenging. In this study, fast acoustic wave sparsely activated localization microscopy (fast-AWSALM) was developed to achieve super-resolved frames with sub-second temporal resolution, by using low-boiling-point octafluoropropane nanodroplets and high frame rate plane waves for activation, destruction, as well as imaging. Fast-AWSALM was demonstrated on an in vitro microvascular phantom to super-resolve structures that could not be resolved by conventional B-mode imaging. The effects of the temperature and mechanical index on fast-AWSALM was investigated. Experimental results show that sub-wavelength micro-structures as small as 190 lm were resolvable in 200 ms with plane-wave transmission at a center frequency of 3.5 MHz and a pulse repetition frequency of 5000 Hz. This is about a 3.5 fold reduction in point spread function full-width-half-maximum compared to that measured in conventional B-mode, and two orders of magnitude faster than the recently reported AWSALM under a non-flow/very slow flow situations and other localization based methods. Just as in AWSALM, fast-AWSALM does not require flow, as is required by current microbubble based ultrasound super resolution techniques. In conclusion, this study shows the promise of fast-AWSALM, a super-resolution ultrasound technique using nanodroplets, which can generate super-resolution images in milli-seconds and does not require flow.

3-D Motion Correction for Volumetric Super-Resolution Ultrasound Imaging

Motion during image acquisition can cause image degradation in all medical imaging modalities. This is particularly relevant in 2-D ultrasound imaging, since out-of-plane motion can only be compensated for movements smaller than elevational beamwidth of the transducer. Localization based super-resolution imaging creates even a more challenging motion correction task due to the requirement of a high number of acquisitions to form a single super-resolved frame. In this study, an extension of two-stage motion correction method is proposed for 3-D motion correction. Motion estimation was performed on high volumetric rate ultrasound acquisitions with a handheld probe. The capability of the proposed method was demonstrated with a 3-D microvascular flow simulation to compensate for handheld probe motion. Results showed that two-stage motion correction method reduced the average localization error from 136 to 18 μm.

On the Effects of Spatial Sampling Quantization in Super-Resolution Ultrasound Microvessel Imaging

Ultrasound super-resolution (SR) microvessel imaging technologies are rapidly emerging and evolving. The unprecedented combination of imaging resolution and penetration promises a wide range of preclinical and clinical applications. This paper concerns spatial quantization error in SR imaging, a common issue that involves a majority of current SR imaging methods. While quantization error can be alleviated by the microbubble localization process (e.g., via upsampling or parametric fitting), it is unclear to what extent the localization process can suppress the spatial quantization error induced by discrete sampling. It is also unclear when low spatial sampling frequency will result in irreversible quantization errors that cannot be suppressed by the localization process. This paper had two goals: 1) to systematically investigate the effect of quantization in SR imaging and establish principles of adequate SR imaging spatial sampling that yield minimal quantization error with proper localization methods and 2) to compare the performance of various localization methods and study the level of tolerance of each method to quantization. We conducted experiments on a small wire target and on a microbubble flow phantom. We found that the Fourier analysis of an oversampled spatial profile of the microbubble signal could provide reliable guidance for selecting beamforming spatial sampling frequency. Among various localization methods, parametric Gaussian fitting and centroid-based localization on upsampled data had better microbubble localization performance and were less susceptible to quantization error than peak intensity-based localization methods. When spatial sampling resolution was low, parametric Gaussian fitting-based localization had the best performance in suppressing quantization error, and could produce acceptable SR microvessel imaging with no significant quantization artifacts. The findings from this paper can be used in practice to help intelligently determine the minimum requirement of spatial sampling for robust microbubble localization to avoid adding or even reduce the burden of computational cost and data storage that are commonly associated with SR imaging.

Super-Resolution Imaging With Ultrafast Ultrasound Imaging of Optically Triggered Perfluorohexane Nanodroplets

Super-resolution imaging with moving microbubbles has shown potential in identifying fine details of deep-lying vascular compartments. To image the extravascular targets, this paper has employed nanometer-sized, optically triggered perfluorohexane nanodroplets (PFHnDs). In response to pulsed laser irradiation, the PFHnDs repeatedly vaporize and stochastically recondense, resulting in random changes of ultrasound signals. Our previous study has shown that the stochastic recondensation of the PFHnDs can be used to isolate individual PFHnDs for super-resolution imaging. This paper introduces an improved method for super-resolution imaging with ultrafast ultrasound imaging of PFHnDs. The previous method was based on subtraction of two consecutive ultrasound images to detect signals from recondensed, isolated droplets, whereas our current method compounds respective multiple pre- and post-recondensation ultrafast ultrasound images prior to subtraction to improve the spatial resolution further. To evaluate the axial and lateral resolutions of our method, we repeatedly imaged a phantom containing PFHnDs using a programmable ultrasound system synchronized with a pulsed laser system. As a result, our method improved the lateral and axial resolutions by 54% and 68%, respectively, over the previous super-resolution imaging approach, indicating that it can be used for localizing extravascular molecular targets with superior accuracy.

Ultrasound Localization Microscopy and Super-Resolution: A State of the Art

Because it drives the compromise between resolution and penetration, the diffraction limit has long represented an unreachable summit to conquer in ultrasound imaging. Within a few years after the introduction of optical localization microscopy, we proposed its acoustic alter ego that exploits the micrometric localization of microbubble contrast agents to reconstruct the finest vessels in the body in-depth. Various groups now working on the subject are optimizing the localization precision, microbubble separation, acquisition time, tracking, and velocimetry to improve the capacity of ultrasound localization microscopy (ULM) to detect and distinguish vessels much smaller than the wavelength. It has since been used in vivo in the brain, the kidney, and tumors. In the clinic, ULM is bound to improve drastically our vision of the microvasculature, which could revolutionize the diagnosis of cancer, arteriosclerosis, stroke, and diabetes.

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

The structure of microvasculature cannot be resolved using conventional ultrasound (US) imaging due to the fundamental diffraction limit at clinical US frequencies. It is possible to overcome this resolution limitation by localizing individual microbubbles through multiple frames and forming a superresolved image, which usually requires seconds to minutes of acquisition. Over this time interval, motion is inevitable and tissue movement is typically a combination of large- and small-scale tissue translation and deformation. Therefore, super-resolution (SR) imaging is prone to motion artifacts as other imaging modalities based on multiple acquisitions are. This paper investigates the feasibility of a two-stage motion estimation method, which is a combination of affine and nonrigid estimation, for SR US imaging. First, the motion correction accuracy of the proposed method is evaluated using simulations with increasing complexity of motion. A mean absolute error of 12.2 was achieved in simulations for the worst-case scenario. The motion correction algorithm was then applied to a clinical data set to demonstrate its potential to enable in vivo SR US imaging in the presence of patient motion. The size of the identified microvessels from the clinical SR images was measured to assess the feasibility of the two-stage motion correction method, which reduced the width of the motion-blurred microvessels to approximately 1.5-fold.