Super-resolution Ultrasound Imaging Research Articles

Ultrasound super resolution imaging for accurate uterus tumor detection and malignancy prediction

The term ‘tumor’ describes an atypical development of cells that forms a mass inside an organ; depending on the organ's invasive nature and propensity for metastasis, the growth may be benign or malignant. Improving patient outcomes requires the early detection of malignant tumors. Despite its lower resolution and noise problems, ultrasound, which is frequently used to diagnose uterine tumors, is safer and more economical than magnetic resonance imaging (MRI) scans and biopsies. Ultrasound pictures can be processed using methods including denoising, enhancement, segmentation, and feature extraction to get around these restrictions and boost their quality. Enhanced ultrasound images can reach even higher accuracy, cementing them as plausible alternatives to MRI. A comparative analysis of copious relevant image de-speckling, image enhancement, segmentation, and feature extraction methods are carried out. Higher resolution and superior quality, strong segmented real-time ultrasound uterus tumour images are produced by using diffusion-based hybrid filters, Super Resolution Convolutional Neural Networks (SRCNN), and U-net segmentation technique. The Grey Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) are used to extract textural features. With the help of several machine learning approaches, such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Random Forest classifiers (RFC), the extracted characteristics are immediately sent to classifiers to classify uterus tumours from ultrasound images between benign and malignant. For the categorization of uterine tumors, the RFC classifier outperformed the other classifiers. The viability of cancer detection using ultrasound pictures is significantly strengthened by the suggested machine learning methodology. Multiple hospitals provided data on ultrasound pictures of uterine tumors, which were used to develop the model and obtain the prediction findings. A radiologist with 17 years of expertise in diagnostic radiology further assessed this dataset. We could produce high-quality ultrasonic real-time images of uterine tumor datasets with the suggested machine learning model at a 97.8 % accuracy rate utilizing RFC.

Ultrafast 3-D Transcutaneous Super Resolution Ultrasound Using Row-Column Array Specific Coherence-Based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, in Rabbit and in Human Study

ObjectiveThis study aimed to realise 3-D super-resolution ultrasound imaging transcutaneously with a row-column array which has far fewer independent electronic channels and a wider field of view than typical fully addressed 2-D matrix arrays. The in vivo image quality of the row-column array is generally poor, particularly when imaging non-invasively. This study aimed to develop a suite of image formation and post-processing methods to improve image quality and demonstrate the feasibility of ultrasound localisation microscopy using a row-column array, transcutaneously on a rabbit model and in a human. MethodsTo achieve this, a processing pipeline was developed which included a new type of rolling window image reconstruction, which integrated a row-column array specific coherence-based beamforming technique with acoustic sub-aperture processing. This and other processing steps reduced the ‘secondary’ lobe artefacts, and noise and increased the effective frame rate, thereby enabling ultrasound localisation images to be produced. ResultsUsing an in vitro cross tube, it was found that the procedure reduced the percentage of ‘false’ locations from ∼26% to ∼15% compared to orthogonal plane wave compounding. Additionally, it was found that the noise could be reduced by ∼7 dB and the effective frame rate was increased to over 4000 fps. In vivo, ultrasound localisation microscopy was used to produce images non-invasively of a rabbit kidney and a human thyroid. ConclusionIt has been demonstrated that the proposed methods using a row-column array can produce large field of view super-resolution microvascular images in vivo and in a human non-invasively.

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 tissue in-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 6MHz, localisation errors between 20 to 30 µm (∼1/10th 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 200 µm (∼1/5th 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.

[Experimental Environment in Radiology: Sustainable Research].

Research requires large sums of money that are indirectly provided by taxpayers. It is therefore important that research is sustainable and does not just serve the career development of individuals. The aim of this article is to discuss what sustainable research in radiology is, how it can be organised and, above all, to show that it is possible.There are various approaches to achieving sustainability, ranging from purely gaining knowledge to translated devices and contrast agents, and to new clinical applications. The first step is to clarify exactly what is intended to be achieved with the research and critically weigh up the novelty value and the expected impact. This should be followed by careful, long-term planning of the project over a period of 5-15 years with the definition of clear sub-steps. Securing funding is just as important here as regular communication of the results. It often makes sense to involve the regulatory authorities and commercialisation partners in the project at an early stage.Academic radiology should not limit itself to serving as a test platform for imaging devices and contrast agents from industry, but should try to realise its own ideas and developments. Many academic centres around the world have shown that this is possible. Examples from my own research, particularly in relation to the development and translation of super-resolution ultrasound imaging and the development of diagnostics and nanopharmaceuticals, are explained in this article and challenges at various stages of development are discussed. Young radiologists are encouraged to set bigger and more long-term goals in order to influence and develop our field in a sustainable way. · Sustainable research requires creativity and careful planning. · Sustainable research can start at several stages of the technical readiness level. · Long-term planning of the overall concept (5-15 years) with clear intermediate steps is essential. · Cooperation with industry is often useful. · Acquisition of third-party funding must be ensured at the same time.

Super-Resolution Ultrasound Imaging for Monitoring the Therapeutic Efficacy of a Vascular Disrupting Agent in an Animal Model of Breast Cancer.

Evaluate the use of super-resolution ultrasound (SRUS) imaging for the early detection of tumor response to treatment using a vascular-disrupting agent (VDA). A population of 28 female nude athymic mice (Charles River Laboratories) were implanted with human breast cancer cells (MDA-MB-231, ATCC) in the mammary fat pad and allowed to grow. Ultrasound imaging was performed using a Vevo 3100 scanner (FUJIFILM VisualSonics Inc) equipped with the MX250 linear array transducer immediately before and after receiving bolus injections of a microbubble (MB) contrast agent (Definity, Lantheus Medical Imaging) via the tail vein. Following baseline ultrasound imaging, VDA drug (combretastatin A4 phosphate, CA4P, Sigma Aldrich) or control saline was injected via the placed catheter. After 4 or 24 hours, repeat ultrasound imaging along the same tumor cross-section occurred. Direct intratumoral pressure measurements were obtained using a calibrated sensor. All raw ultrasound data were saved for offline processing and SRUS image reconstruction using custom MATLAB software (MathWorks Inc). From a region encompassing the tumor space and the entire postprocessed ultrasound image sequence, time MB count (TMC) curves were generated in addition to traditional SRUS maps reflecting MB enumeration at each pixel location. Peak enhancement (PE) and wash-in rate (WIR) were extracted from these TMC curves. At termination, intratumoral microvessel density (MVD) was quantified using tomato lectin labeling of patent blood vessels. SRUS images exhibited a clear difference between control and treated tumors. While there was no difference in any group parameters at baseline (0 hour, P > .09), both SRUS-derived PE and WIR measurements in tumors treated with VDA exhibited significant decreases by 4 (P = .03 and P = .05, respectively) and 24 hours (P = .02 and P = .01, respectively), but not in control group tumors (P > .22). Similarly, SRUS derived microvascular maps were not different at baseline (P = .81), but measures of vessel density were lower in treated tumors at both 4 and 24 hours (P < .04). An inverse relationship between intratumoral pressure and both PE and WIR parameters were found in control tumors (R2  > .09, P < .03). SRUS imaging is a new modality for assessing tumor response to treatment using a VDA.

Broad Elevation Projection Super-Resolution Ultrasound (BEP-SRUS) Imaging With a 1-D Unfocused Linear Array.

Super-resolution ultrasound (SRUS) through localizing spatially isolated microbubbles (MBs) has been demonstrated to overcome the wave diffraction limit and reveal the microvascular structure and flow information at the microscopic scale. However, 3-D SRUS imaging remains a challenge due to the fabrication and computational complexity of 2-D matrix array probes. Inspired by X-ray radiography which can present information within a volume in a single projection image with much simpler hardware than X-ray computerized tomography (CT), this study investigates the feasibility of broad elevation projection super-resolution (BEP-SR) ultrasound using a 1-D unfocused linear array. Both simulation and in vitro experiments were conducted on 3-D microvessel phantoms. In vivo demonstration was done on the Rabbit kidney. Data from a 1-D linear array with and without an elevational focus were synthesized by summing up row signals acquired from a 2-D matrix array with and without delays. A full 3-D reconstruction was also generated as the reference, using the same data of the 2-D matrix array but without summing row signals. Results show that using an unfocused 1-D array probe, BEP-SR can capture significantly more information within a volume in both vascular structure and flow velocity than the conventional 1-D elevational-focused probe. Compared with the 2-D projection image of the full 3-D SRUS results using the 2-D array probe with the same aperture size, the 2-D projection SRUS image of BEP-SR has similar volume coverage, using 32 folds fewer independent elements. This study demonstrates BEP-SR's ability of high-resolution imaging of microvascular structures and flow velocity within a 3-D volume at significantly reduced costs. The proposed BEP method could significantly benefit the clinical translation of the SRUS imaging technique by making it more affordable and repeatable.

Pattern recognition of microcirculation with super-resolution ultrasound imaging provides markers for early tumor response to anti-angiogenic therapy.

Rationale: Cancer treatment outcome is traditionally evaluated by tumor volume change in clinics, while tumor microvascular heterogeneity reflecting tumor response has not been fully explored due to technical limitations. Methods: We introduce a new paradigm in super-resolution ultrasound imaging, termed pattern recognition of microcirculation (PARM), which identifies both hemodynamic and morphological patterns of tumor microcirculation hidden in spatio-temporal space trajectories of microbubbles. Results: PARM demonstrates the ability to distinguish different local blood flow velocities separated by a distance of 24 μm. Compared with traditional vascular parameters, PARM-derived heterogeneity parameters prove to be more sensitive to microvascular changes following anti-angiogenic therapy. Particularly, PARM-identified "sentinel" microvasculature, exhibiting evident structural changes as early as 24 hours after treatment initiation, correlates significantly with subsequent tumor volume changes (|r| > 0.9, P < 0.05). This provides prognostic insight into tumor response much earlier than clinical criteria. Conclusions: The ability of PARM to noninvasively quantify tumor vascular heterogeneity at the microvascular level may shed new light on early-stage assessment of cancer therapy.

The development of a 1.25 MHz 1024-channel sparse array for human transcranial imaging: in vitro characterization

Ultrasound imaging is overwhelmingly used as 2D modality even though 3D imaging capabilities have existed for decades. Recent generational shifts toward super-resolution ultrasound imaging and functional ultrasound imaging, especially in the brain, have generated renewed and sustained interest in acquiring truly volumetric, 4D data. However, volumetric imaging approaches are currently limited to small animals, due in part to the difficulty of imaging transcranially in humans and due to a lack of imaging arrays designed for this purpose. Clinical translation of these recent techniques as well as conventional diagnostic B-mode imaging may thus benefit from array designs that capitalize on large channel count imaging systems. We have designed and developed a 1024-channel sparse array with a 65 mm circular aperture and a 1–2 MHz bandwidth. This unique transducer achieves an aperture that is far larger than conventional matrix probes using a sparse arrangement of elements ordered in a density-tapered spiral design. This design has significantly decreased grating lobes compared to a matrix array probe. The large aperture of this probe also enables acquisition over a large field of view with a significant depth of more than 100 mm. Simulations, acoustic characterization, and in vitro tests demonstrate that this transducer achieves a high focal gain that enables ultrasonic visualization beneath the human skull and at large depths due to its low F-number capabilities. Furthermore, we show that this transducer is capable of high point target contrast and high soft tissue contrast, with contrast-to-noise ratios up to 1.9 when imaging transcranially through a 3 mm thick section of human skull. Because of the large surface area of this probe, it can capture over 3 coherence lengths in each dimension and is, therefore, able to able to ‘average out’ the aberration over a large surface area. This transducer is poised to have a significant clinical impact in transcranial human imaging.

Deep learning for fast super-resolution ultrasound microvessel imaging

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

Nomogram Based on Super-Resolution Ultrasound Images Outperforms in Predicting Benign and Malignant Breast Lesions

Nomogram Based on Super-Resolution Ultrasound Images Outperforms in Predicting Benign and Malignant Breast Lesions

Acceleration-Based Kalman Tracking for Super-Resolution Ultrasound Imaging In Vivo.

Super-resolution ultrasound (SRUS) can image microvascular structure and flow at subwave-diffraction resolution based on localizing and tracking microbubbles (MBs). Currently, tracking MBs accurately under limited imaging frame rates and high MB concentrations remains a challenge, especially under the effect of cardiac pulsatility and in highly curved vessels. In this study, an acceleration-incorporated MB motion model is introduced into a Kalman tracking framework. The tracking performance was evaluated using simulated microvasculature with different MB motion parameters, concentrations, and acquisition frame rates, and in vivo human breast tumor US datasets. The simulation results show that the acceleration-based method outperformed the nonacceleration-based method at different levels of acceleration and acquisition frame rates and achieved significant improvement in true positive rate (TPR; up to 11.3%) and false negative rate (FNR; up to 13.2%). The proposed method can also reduce errors in vasculature reconstruction via the acceleration-based nonlinear interpolation, compared with linear interpolation (up to [Formula: see text]). The tracking results from temporally downsampled low frame rate in vivo datasets from human breast tumors show that the proposed method has better MB tracking performance than the baseline method, if using results from the initial high frame data as a reference. Finally, the acceleration estimated from tracking results also provides a spatial speed gradient map that may contain extra valuable diagnostic information.

3D Acoustic Wave Sparsely Activated Localization Microscopy With Phase Change Contrast Agents.

The aim of this study is to demonstrate 3-dimensional (3D) acoustic wave sparsely activated localization microscopy (AWSALM) of microvascular flow in vivo using phase change contrast agents (PCCAs). Three-dimensional AWSALM using acoustically activable PCCAs was evaluated on a crossed tube microflow phantom, the kidney of New Zealand White rabbits, and the brain of C57BL/6J mice through intact skull. A mixture of C 3 F 8 and C 4 F 10 low-boiling-point fluorocarbon gas was used to generate PCCAs with an appropriate activation pressure. A multiplexed 8-MHz matrix array connected to a 256-channel ultrasound research platform was used for transmitting activation and imaging ultrasound pulses and recording echoes. The in vitro and in vivo echo data were subsequently beamformed and processed using a set of customized algorithms for generating 3D super-resolution ultrasound images through localizing and tracking activated contrast agents. With 3D AWSALM, the acoustic activation of PCCAs can be controlled both spatially and temporally, enabling contrast on demand and capable of revealing 3D microvascular connectivity. The spatial resolution of the 3D AWSALM images measured using Fourier shell correlation is 64 μm, presenting a 9-time improvement compared with the point spread function and 1.5 times compared with half the wavelength. Compared with the microbubble-based approach, more signals were localized in the microvasculature at similar concentrations while retaining sparsity and longer tracks in larger vessels. Transcranial imaging was demonstrated as a proof of principle of PCCA activation in the mouse brain with 3D AWSALM. Three-dimensional AWSALM generates volumetric ultrasound super-resolution microvascular images in vivo with spatiotemporal selectivity and enhanced microvascular penetration.

Super-resolution ultrasound imaging approaches to visualise changes in gut structure

Super-resolution ultrasound imaging approaches to visualise changes in gut structure

Super-Resolution Ultrasound Imaging of Renal Vascular Alterations in Zucker Diabetic Fatty Rats during the Development of Diabetic Kidney Disease.

Individuals with diabetes at risk of developing diabetic kidney disease (DKD) are challenging to identify using currently available clinical methods. Prognostic accuracy and initiation of treatment could be improved by a quantification of the renal microvascular rarefaction and the increased vascular tortuosity during the development of DKD. Super-resolution ultrasound (SRUS) imaging is an in vivo technique capable of visualizing blood vessels at sizes below 75 µm. This preclinical study aimed to investigate the alterations in renal blood vessels' density and tortuosity in a type 2 diabetes rat model, Zucker diabetic fatty (ZDF) rats, as a prediction of DKD. Lean age-matched Zucker rats were used as controls. A total of 36 rats were studied, subdivided into ages of 12, 22, and 40 weeks. Measured albuminuria indicated the early stage of DKD, and the SRUS was compared with the ex vivo micro-computed tomography (µCT) of the same kidneys. Assessed using the SRUS imaging, a significantly decreased cortical vascular density was detected in the ZDF rats from 22 weeks of age compared to the healthy controls, concomitant with a significantly increased albuminuria. Already by week 12, a trend towards a decreased cortical vascular density was found prior to the increased albuminuria. The quantified vascular density in µCT corresponded with the in vivo SRUS imaging, presenting a consistently lower vascular density in the ZDF rats. Regarding vessel tortuosity, an overall trend towards an increased tortuosity was present in the ZDF rats. SRUS shows promise for becoming an additional tool for monitoring and prognosing DKD. In the future, large-scale animal studies and human trials are needed for confirmation.

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 both in vitro flow phantom experiment and in vivo experiment of New Zealand rabbit tumor. Main results. For in vitro flow 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. For in vivo animal experiment , the SNR and CNR were 12.29 dB and 6.06 dB. In addition, single microvessel of 24 μm and two microvessels separated by 46 μm could be clearly displayed. Most importantly,, the CS-Net denoising speeds for in vitro and in vivo experiments were 0.041 s frame−1 and 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.

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

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. 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. Nanodroplets were vaporized with 55kHz burst-mode ultrasound (burst length=145μs, burst repetition frequency=9-45Hz, peak negative acoustic pressure=0.10-0.22MPa), which propagates through overlying tissues well without suffering from severe distortions. The resulting emissions were received at a higher frequency (612 or 1224kHz 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. 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 1224kHz, 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. 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.

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

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. 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. 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. The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments. Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

Single Image based Super Resolution Ultrasound Imaging Using Residual Learning of Wavelet Features.

The generation of super resolution ultrasound images from the low-resolution (LR) brightness mode (B-mode) images acquired by the portable point of care ultrasound systems has been of sufficient interest in the recent past. With the advancements in deep learning, there have been numerous attempts in this direction. However, all the approaches have been concentrated on employing the direct image as the input to the neural network. In this work, a stationary wavelet (SWT) decomposition is employed to extract the features from the input LR image which is passed through a modified residual network and the learned features are combined using the inverse SWT to reconstruct the high resolution (HR) image at a 4× scale factor. The proposed approach when compared to the state-of-the art approaches, results in an improved high resolution reconstruction.Clinical relevance- The proposed approach will enable the generation of high-resolution images from portable ultrasound systems, allowing for easier interpretation and faster diagnostics in primary care settings.

A super-resolution ultrasound imaging method based on active-modulated super-resolution optical fluctuation imaging.

Super resolution ultrasound imaging (SR-US) methods including super-resolution optical fluctuation imaging (SOFI) have been successfully demonstrated to improve imaging performance of ultrasound (US). However, the imaging quality of US improved by conventional SOFI depends on the probability of microbubbles (MB) appearing in imaging regions. Current SOFI-based ultrasound imaging methods usually fix the probability of MBs, ignoring the effect of probability characteristics, leading to artifacts in high-order SOFI images. Inspired by active-modulated SOFI (AR-SOFI), in this paper, we propose a new method, termed as AR-SOFI-US, for further improving the performance of SR-US, which is achieved by effectively controlling the probabilities of MBs on an appropriate range. Through a series of numerical simulations, we compare the imaging resolution at differing MB probabilities and demonstrate that by controlling the probabilities of MBs when they appear in the imaging regions, incorporating the proposed AR-SOFI-US method, we can improve the spatial resolution of SR-US to a higher degree, especially for the high-order SOFI imaging results.