An Ultrasound Array of Emitter-Receiver Stacks for Microbubble-Based Therapy.

Most therapeutic ultrasound devices place emitters and receivers in separate locations, so that the long therapeutic pulses (>1 ms) can be emitted while receivers monitor the procedure. However, with such placement, emitters and receivers are competing for the same space, producing a trade-off between emission efficiency and reception sensitivity. Taking advantage of recent studies demonstrating that short-pulse ultrasound can be used therapeutically, we aimed to develop a device that overcomes such trade-offs. The array was composed of emitter-receiver stacks, which enabled both emission and reception from the same location. Each element was made of a lead zirconate titanate (PZT)-polyvinylidene fluoride (PVDF) stack. The PZT (frequency: 500 kHz, diameter: 16 mm) was used for emission and the PVDF (thickness: 28 µm, diameter: 16 mm) for broadband reception. 32 elements were assembled in a 3D-printed dome-shaped frame (focal length: 150 mm; [Formula: see text]-number: 1) and was tested in free-field and through an ex-vivo human skull. In free-field, the array had a 4.5 × 4.5 × 32 mm focus and produced a peak-negative pressure (PNP) of 2.12 MPa at its geometric center. The electronic steering range was ±15 mm laterally and larger than ±15 mm axially. Through the skull, the array produced a PNP of 0.63 MPa. The PVDF elements were able to localize broadband microbubble emissions across the skull. We built the first multi-element array for short-pulse and microbubble-based therapeutic applications. Stacked arrays overcome traditional trade-offs between the transmission and reception quality and have the potential to create a step change in treatment safety and efficacy.

Dual-Probe Transcranial Full-Waveform Inversion: A Brain Phantom Feasibility Study.

OBJECTIVE: Despite being a low-cost, portable and safe medical imaging technique, transcranial ultrasound imaging is not used widely in adults because of the severe degradation and distortion of signals caused by the skull. Full-waveform inversion (FWI) has recently been found to have potential as an effective method for transcranial ultrasound tomography to obtain high-quality, subwavelength-resolution acoustic models of the brain using low-frequency ultrasound data. In this study is the first demonstration of this method in recovering a high-resolution 2-D reconstruction of a brain and skull ultrasound imaging phantom using experimentally acquired data. METHODS: A 2:5 scale brain phantom encased within a 3-D-printed skull-mimicking layer was created to simulate a clinical transcranial imaging target. To obtain tomographic ultrasound data on the brain and skull phantom, a tomographic ultrasound acquisition system was designed and implemented using commercially available low-frequency cardiac probes. FWI reconstructions of the brain and skull phantom were performed using the acquired tomographic data and were compared with corresponding synthetic reconstructions. This comparison was used to evaluate the feasibility of the proposed imaging system when employing different transducer array configurations. RESULTS: We demonstrate the successful FWI reconstruction of the brain phantom within the skull mimic from experimentally acquired tomographic ultrasound data. To mitigate the effects of the skull-mimicking material, a reflection-matching algorithm was applied to model the morphology of the skull layer prior to performing the inversion. CONCLUSION: The findings of this study provide a promising step toward the clinical use of FWI for transcranial ultrasound imaging in adults.

Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models.

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston operating at 500 kHz), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross-comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons.

Design and Construction of a Low-Frequency Ultrasound Acquisition Device for 2-D Brain Imaging Using Full-Waveform Inversion.

The main techniques used to image the brain and obtain structural data are magnetic resonance imaging and X-ray computed tomography. These techniques produce images with high spatial resolution, but with the disadvantage of requiring very large equipment with special installation needs. In addition, X-ray tomography uses ionizing radiation, which limits their use. Ultrasound imaging is a safe technology that is delivered using compact and mobile devices. However, conventional ultrasound reconstruction techniques have failed to obtain images of the brain because of, fundamentally, the presence of the skull and the distortion that it produces on ultrasound. Recent studies have indicated that full-waveform inversion, a computational technique originally from Earth science, has the potential to generate accurate 3-D images of the brain. This technology can overcome the limitations of conventional ultrasound imaging, but a prototype for transcranial applications does not yet exist. Here, we investigate different designs of an annular array of ultrasound transducers to optimize the number of elements and rotations needed to conduct transcranial imaging with full-waveform inversion. This device uses small-diameter, low-frequency transducers that readily propagate ultrasound through the skull with good signal-to-noise ratios. It also incorporates the use of rotations to produce a high-density coverage of the target and acquire redundant traces that are beneficial for full-waveform inversion. We have built a ring of 40 transducers to illustrate that this design is capable of reconstructing images of the brain, retrieving its anatomy and acoustic properties with millimeter resolution. Laboratory results reveal the ability of this device to successfully image a 2.5-D brain- and skull-mimicking phantom using full-waveform inversion. To our knowledge, this is the first prototype ever used for transcranial-like imaging. The importance of these findings and their implications for the design of a 3-D reconstruction system with possible clinical applications are discussed.

Stride: A flexible software platform for high-performance ultrasound computed tomography.

BACKGROUND AND OBJECTIVE: Advanced ultrasound computed tomography techniques like full-waveform inversion are mathematically complex and orders of magnitude more computationally expensive than conventional ultrasound imaging methods. This computational and algorithmic complexity, and a lack of open-source libraries in this field, represent a barrier preventing the generalised adoption of these techniques, slowing the pace of research, and hindering reproducibility. Consequently, we have developed Stride, an open-source Python library for the solution of large-scale ultrasound tomography problems. METHODS: On one hand, Stride provides high-level interfaces and tools for expressing the types of optimisation problems encountered in medical ultrasound tomography. On the other, these high-level abstractions seamlessly integrate with high-performance wave-equation solvers and with scalable parallelisation routines. The wave-equation solvers are generated automatically using Devito, a domain-specific language, and the parallelisation routines are provided through the custom actor-based library Mosaic. RESULTS: We demonstrate the modelling accuracy achieved by our wave-equation solvers through a comparison (1) with analytical solutions for a homogeneous medium, and (2) with state-of-the-art modelling software applied to a high-contrast, complex skull section. Additionally, we show through a series of examples how Stride can handle realistic numerical and experimental tomographic problems, in 2D and 3D, and how it can scale robustly from a local multi-processing environment to a multi-node high-performance cluster. CONCLUSIONS: Stride enables researchers to rapidly and intuitively develop new imaging algorithms and to explore novel physics without sacrificing performance and scalability. This will lead to faster scientific progress in this field and will significantly ease clinical translation.

Spatial Response Identification Enables Robust Experimental Ultrasound Computed Tomography.

Ultrasound computed tomography techniques have the potential to provide clinicians with 3-D, quantitative and high-resolution information of both soft and hard tissues such as the breast or the adult human brain. Their practical application requires accurate modeling of the acquisition setup: the spatial location, orientation, and impulse response (IR) of each ultrasound transducer. However, the existing calibration methods fail to accurately characterize these transducers unless their size can be considered negligible when compared with the dominant wavelength, which reduces signal-to-noise ratios below usable levels in the presence of high-contrast tissues such as the skull. In this article, we introduce a methodology that can simultaneously estimate the location, orientation, and IR of the ultrasound transducers in a single calibration. We do this by extending spatial response identification (SRI), an algorithm that we have recently proposed to estimate transducer IRs. Our proposed methodology replaces the transducers in the acquisition device with a surrogate model whose effective response matches the experimental data by fitting a numerical model of wave propagation. This results in a flexible and robust calibration procedure that can accurately predict the behavior of the ultrasound acquisition device without ever having to know where the real transducers are or their individual IR. Experimental results using a ring acquisition system show that SRI produces calibrations of significantly higher quality than standard methodologies across all transducers, both in transmission and in reception. Experimental full-waveform inversion (FWI) reconstructions of a tissue-mimicking phantom demonstrate that SRI generates more accurate reconstructions than those produced with standard calibration techniques.

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging.

Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides morphological information with limited diagnostic value. Ultrasound computed tomography (USCT) uses energy in both transmission and reflection when imaging the breast to provide more diagnostically relevant quantitative tissue properties, but it is often based on time-of-flight tomography or similar ray approximations of the wave equation, resulting in reconstructed images with low resolution. Full-waveform inversion (FWI) is based on a more accurate approximation of wave-propagation phenomena and can consequently produce very high resolution images using frequencies below 1 megahertz. These low frequencies, however, are not available in most USCT acquisition systems, as they use transducers with central frequencies well above those required in FWI. To circumvent this problem, we designed, trained, and implemented a two-dimensional convolutional neural network to artificially generate missing low frequencies in USCT data. Our results show that FWI reconstructions using experiment data after the application of the proposed method successfully converged, showing good agreement with X-ray CT and reflection ultrasound-tomography images.

Spatial Response Identification for Flexible and Accurate Ultrasound Transducer Calibration and its Application to Brain Imaging.

Accurate wave-equation modeling is becoming increasingly important in modern imaging and therapeutic ultrasound methodologies, such as ultrasound computed tomography, optoacoustic tomography, or high-intensity-focused ultrasound. All of them rely on the ability to accurately model the physics of wave propagation, including accurate characterization of the ultrasound transducers, the physical devices that are responsible for generating and recording ultrasound energy. However, existing methods fail to characterize the transducer response with the accuracy required to fully exploit the capabilities of these emerging imaging and therapeutic techniques. Consequently, we have designed a new algorithm for ultrasound transducer calibration and modeling: spatial response identification (SRI). This method introduces a parameterization of the ultrasound transducer and provides a method to calibrate the transducer model using experimental data, based on a formulation of the problem that is completely independent of the discretization chosen for the transducer or the number of parameters used. The proposed technique models the transducer as a linear time-invariant system that is spatially heterogeneous, and identifies the model parameters that are best at explaining the experimental data while honoring the full wave equation. SRI generates a model that can accommodate the complex, heterogeneous spatial response seen experimentally for ultrasound transducers. Experimental results show that SRI outperforms standard methods both in transmission and reception modes. Finally, numerical experiments using full-waveform inversion demonstrate that existing transducer-modeling approaches are insufficient to produce successful reconstructions of the human brain, whereas errors in our SRI algorithm are sufficiently small to allow accurate image reconstructions.

Full-waveform inversion imaging of the human brain.

Magnetic resonance imaging and X-ray computed tomography provide the two principal methods available for imaging the brain at high spatial resolution, but these methods are not easily portable and cannot be applied safely to all patients. Ultrasound imaging is portable and universally safe, but existing modalities cannot image usefully inside the adult human skull. We use in silico simulations to demonstrate that full-waveform inversion, a computational technique originally developed in geophysics, is able to generate accurate three-dimensional images of the brain with sub-millimetre resolution. This approach overcomes the familiar problems of conventional ultrasound neuroimaging by using the following: transcranial ultrasound that is not obscured by strong reflections from the skull, low frequencies that are readily transmitted with good signal-to-noise ratio, an accurate wave equation that properly accounts for the physics of wave propagation, and adaptive waveform inversion that is able to create an accurate model of the skull that then compensates properly for wavefront distortion. Laboratory ultrasound data, using ex vivo human skulls and in vivo transcranial signals, demonstrate that our computational experiments mimic the penetration and signal-to-noise ratios expected in clinical applications. This form of non-invasive neuroimaging has the potential for the rapid diagnosis of stroke and head trauma, and for the provision of routine monitoring of a wide range of neurological conditions.