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

open-UST: An Open-Source Ultrasound Tomography Transducer Array System

Fast imaging methods are needed to promote clinical adoption of ultrasound tomography (UST), and more widely available UST hardware could support the experimental validation of new measurement configurations. In this work, an open-source 256-element transducer ring array was developed (morganjroberts.github. io/open-UST) and manufactured using rapid prototyping, for only £2k. Novel manufacturing techniques were used, resulting in a 1.17° mean beam axis skew angle, a [Formula: see text] mean element position error, and a [Formula: see text] deviation in matching layer thickness. The nominal acoustic performance was measured using hydrophone scans and watershot data, and the 61.2 dB signal-to-noise ratio (SNR), 55.4° opening angle, 10.2 mm beamwidth, and 54% transmit-receive bandwidth (-12 dB) were found to be similar to existing systems and compatible with state-of-the-art full-waveform-inversion image reconstruction methods. The interelement variation in acoustic performance was typically < 10% without using normalization, meaning that the elements can be modeled identically during image reconstruction, removing the need for individual source definitions based on hydrophone measurements. Finally, data from a phantom experiment were successfully reconstructed. These results demonstrate that the open-UST system is accessible for users and is suitable for UST imaging research.

Transcranial Ultrasound Imaging With Decomposition Descent Learning-Based Full Waveform Inversion

Noninvasive brain diagnosis is extremely important because of its efficiency, low cost, and painless nature in the prediction of stroke, cerebral hemorrhage, and other brain research. At present, achieving full 3-D quantitative ultrasonic imaging of the human brain is a cutting-edge challenge due to the complex structures of the human brain and the strong scattering caused by the skulls. In this article, we achieved quantitative ultrasonic imaging of inside-brain anomalies with our proposed method, the decomposition descent learning-based full waveform inversion (DDL-FWI). The proposed method adopts a linear residual decomposing technique to greatly alleviate the computation burden in fast inversion tomography (FIT) with enhanced convergence guaranteed by residual functions. Testing results in both simulation and laboratory experiments demonstrated that our method can achieve high-quality quantitative imaging of brain soft tissues and skulls even starting from homogeneous water background in 2-D, and this method is capable of reconstructing both complex brain tissues and clots in 2-D and 3-D cases using either clean or noisy signals, with a robust 3-D clot resolution as small as 18 mm and 2-D reconstruction speed in 11.20 s. Combined with advanced ultrasonic hardware, DDL-FWI can be easily trained and used for brain imaging efficiently that frees patients from harmful influences from traditional imaging techniques, e.g., ionization radiations from X-ray computed tomography (CT).

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.

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.