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.

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

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

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

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

OBJECTIVE: 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). MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: Three-dimensional AWSALM generates volumetric ultrasound super-resolution microvascular images in vivo with spatiotemporal selectivity and enhanced microvascular penetration.

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

OBJECTIVE: Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes. METHOD: This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p-th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo. RESULTS: Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe, suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS. CONCLUSION: The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments. SIGNIFICANCE: Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications.

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

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

Ultrafast 3-D Ultrasound Imaging Using Row-Column Array-Specific Frame-Multiply-and-Sum Beamforming.

Row-column arrays have been shown to be able to generate 3-D ultrafast ultrasound images with an order of magnitude less independent electronic channels than traditional 2-D matrix arrays. Unfortunately, row-column array images suffer from major imaging artifacts due to high sidelobes, particularly when operating at high frame rates. This article proposes a row-column-specific beamforming technique, for orthogonal plane-wave transmissions, row-column-specific frame multiply and sum (RC-FMAS), that exploits the incoherent nature of certain row-column array artifacts. A series of volumetric images is produced using row or column transmissions of 3-D plane waves. The voxelwise geometric mean of the beamformed volumetric images from each row and column pair is taken prior to compounding, which drastically reduces the incoherent imaging artifacts in the resulting image compared to traditional coherent compounding. The effectiveness of this technique was demonstrated in silico and in vitro, and the results show a significant reduction in sidelobe level with over 16-dB improvement in sidelobe to main-lobe energy ratio. Significantly improved contrast was demonstrated with contrast ratio increased by ~10 dB and generalized contrast-to-noise ratio increased by 158% when using the proposed new method compared to the existing delay and sum during in vitro studies. The new technique allowed for higher quality 3-D imaging while maintaining high frame rate potential.

MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints.

Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced (DTCE™) post-processed images found on Siemens clinical-grade scanners. Training MimickNet only requires post-processed image samples from a scanner of interest without the need for explicit pairing to DAS data. This flexibility allows MimickNet to hypothetically approximate any manufacturer's post-processing without access to the pre-processed data. MimickNet post-processing achieves a 0.940 ± 0.018 structural similarity index measurement (SSIM) compared to clinical-grade post-processing on a 400 cine-loop test set, 0.937 ± 0.025 SSIM on a prospectively acquired dataset, and 0.928 ± 0.003 SSIM on an out-of-distribution cardiac cine-loop after gain adjustment. To our knowledge, this is the first work to establish deep learning models that closely approximate ultrasound post-processing found in current medical practice. MimickNet serves as a clinical post-processing baseline for future works in ultrasound image formation to compare against. Additionally, it can be used as a pretrained model for fine-tuning towards different post-processing techniques. To this end, we have made the MimickNet software, phantom data, and permitted in vivo data open-source at https://github.com/ouwen/MimickNet.