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.

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.