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

Cueto, Carlos; Bates, Oscar; Strong, George; Cudeiro, Javier; Luporini, Fabio; Agudo, Òscar Calderón; Gorman, Gerard J.; Guasch, Lluís; Tang, Meng‐Xing

doi:10.1016/j.cmpb.2022.106855

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…Stride solves the second-order, isotropic, linear acoustic wave equation using an FDTD approximation over a rectangular Cartesian grid, 81 which is generated using the domain-specific language Devito. 82 Spatial derivatives are calculated using a 10th-order finite-difference approximation, while time integration is performed using a 4th-order time-stepping scheme optimized for increased stability.…”

Section: Modelsmentioning

confidence: 99%

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

Aubry

Bates

Boehm

et al. 2022

The Journal of the Acoustical Society of America

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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.

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Section: Modelsmentioning

confidence: 99%

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

Aubry

Bates

Boehm

et al. 2022

The Journal of the Acoustical Society of America

Self Cite

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“…In the second stage of our proposed method, we simulate the US images from the speed of the sound images. For simulating ultrasound images from the speed of sound images which we generate, we use Stride software (Cueto et al, 2022). Stride is an open-source library for ultrasound computed tomography.…”

Section: Proposed Methodsmentioning

confidence: 99%

Simulating Ultrasound images from CT Scans

Nasser

Sethi

2023

Preprint

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Anatomical information in ultrasound (US) imaging has not been exploited fully because its wave interference pattern (WIP) has been viewed as speckle noise. We tested the idea that more information can be retrieved by disentangling the WIP rather than discarding it as noise. We numerically solved the forward model of generating US images from computed tomography (CT) images by solving wave-equations using the Stride library. By doing so, we have paved the way for using deep neural networks to be trained on the data generated by the forward model to simulate the solution of the inverse problem, which is generating the CT-style and CT-quality images from a real US image. We demonstrate qualitative features of the generated images that are rich in anatomical details and realism.

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“…In particular, the ADSeismic.jl [24] library focuses on seismic wave propagation and several inversion algorithms commonly used in the seismic field, and also includes the support for neural network representation of velocity models [25]. In Python, the Devito package [26] and the recently published Stride [27] library can be used to solve acoustic optimization problems that scale over large super computing clusters, while SimPEG [28] can be used for geophysical parameter estimation. In JAX, several recent works have developed tools for simulation-based inference and differentiable simulations.…”

Section: Related Softwarementioning

confidence: 99%