mSOUND: An Open Source Toolbox for Modeling Acoustic Wave Propagation in Heterogeneous Media

Benchmark comparison of transcranial ultrasound simulation: Comparing the CIVA Healthcare platform method with existing compressional wave models

Computational models for acoustic wave propagation play a pivotal role in transcranial ultrasound (TUS) therapy, which is commonly employed for calculating intracranial pressure fields and phase delays to correct for wave distortion during propagation through the skull. Recent collaborative work by Aubry, Bates, Boehm, Butts Pauly, Christensen, Cueto, Gélat, Guasch, Jaros, Jing, Jones, Li, Marty, Montanaro, Neufeld, Pichardo, Pinton, Pulkkinen, Stanziola, Thielscher, Treeby, and van 't Wout [J. Acoust. Soc. Am. 152(2), 1003-1019 (2022)] presented a set of numerical benchmarks (BMs) to allow comparisons between various modeling tools and techniques used across the field. In this work, these established BMs are extended to incorporate the CIVA Healthcare simulation platform, which employs a distinct ray-tracing methodology for acoustic wave modeling. The goal of this study is not only to provide a comprehensive benchmarking analysis of the CIVA Heathcare platform within the context of TUS but also to contribute to the ongoing dialogue regarding the intercomparison of different modeling techniques. By doing so, the aim is to enhance the validity and reliability of these computational models for improved applications in TUS therapy.

Overview of Therapeutic Ultrasound Applications and Safety Considerations: 2024 Update

A 2012 review of therapeutic ultrasound was published to educate researchers and physicians on potential applications and concerns for unintended bioeffects (doi: 10.7863/jum.2012.31.4.623). This review serves as an update to the parent article, highlighting advances in therapeutic ultrasound over the past 12 years. In addition to general mechanisms for bioeffects produced by therapeutic ultrasound, current applications, and the pre-clinical and clinical stages are outlined. An overview is provided for image guidance methods to monitor and assess treatment progress. Finally, other topics relevant for the translation of therapeutic ultrasound are discussed, including computational modeling, tissue-mimicking phantoms, and quality assurance protocols.

Machine Learning-Enhanced Skull-Universal Acoustic Hologram for Efficient Transcranial Ultrasound Neuromodulation Across Varied Rodent Skulls

Ultrasound neuromodulation (UNM) has gained significant interest in brain science due to its non-invasive nature, precision, and deep brain stimulation capabilities. However, the skull poses challenges along the acoustic path, leading to beam distortion and necessitating effective acoustic aberration correction. Acoustic holograms used with single-element ultrasound transducers offer a promising solution by enabling both aberration correction and multi-focal stimulation. A major limitation, however, is that hologram lenses designed for specific skulls may not perform well on other skulls, requiring multiple custom lenses for scaled studies. To address this, we introduce the Skull-Universal Acoustic Hologram (SUAH), which enables efficient transcranial UNM across various skull types. Our hologram generation framework integrates a physics-based acoustic hologram, differentiable acoustic simulation in heterogeneous media, and a gradient accumulation technique. SUAH, trained on a range of rodent skull shapes, demonstrated remarkable generalizability and robustness, even outperforming the Skull-Specific Acoustic Hologram (SSAH). Through comprehensive analyses, we showed that SUAH performs exceptionally well-even when trained on smaller datasets-significantly outperforming training based on individual skulls. In conclusion, SUAH shows promise as a scalable, versatile, and accurate tool for ultrasound neuromodulation, representing a significant advancement over conventional single-skull hologram lenses. Its ability to adapt to different skull types without the need for multiple custom lenses has the potential to greatly facilitate research in ultrasound neuromodulation.

Acoustic-holography-patterned primary hepatocytes possess liver functions

Acoustic holography (AH), a promising approach for cell patterning, emerges as a powerful tool for constructing novel invitro 3D models that mimic organs and cancers features. However, understanding changes in cell function post-AH remains limited. Furthermore, replicating complex physiological and pathological processes solely with cell lines proves challenging. Here, we employed acoustical holographic lattice to assemble primary hepatocytes directly isolated from mice into a cell cluster matrix to construct a liver-shaped tissue sample. For the first time, we evaluated the liver functions of AH-patterned primary hepatocytes. The patterned model exhibited large numbers of self-assembled spheroids and superior multifarious core hepatocyte functions compared to cells in 2D and traditional 3D culture models. AH offers a robust protocol for long-term in vitro culture of primary cells, underscoring its potential for future applications in disease pathogenesis research, drug testing, and organ replacement therapy.

Machine Learning-Empowered Real-Time Acoustic Trapping: An Enabling Technique for Increasing MRI-Guided Microbubble Accumulation

Acoustic trap, using ultrasound interference to ensnare bioparticles, has emerged as a versatile tool for life sciences due to its non-invasive nature. Bolstered by magnetic resonance imaging's advances in sensing acoustic interference and tracking drug carriers (e.g., microbubble), acoustic trap holds promise for increasing MRI-guided microbubbles (MBs) accumulation in target microvessels, improving drug carrier concentration. However, accurate trap generation remains challenging due to complex ultrasound propagation in tissues. Moreover, the MBs' short lifetime demands high computation efficiency for trap position adjustments based on real-time MRI-guided carrier monitoring. To this end, we propose a machine learning-based model to modulate the transducer array. Our model delivers accurate prediction of both time-of-flight (ToF) and pressure amplitude, achieving low average prediction errors for ToF (-0.45 µs to 0.67 µs, with only a few isolated outliers) and amplitude (-0.34% to 1.75%). Compared with the existing methods, our model enables rapid prediction (<10 ms), achieving a four-order of magnitude improvement in computational efficiency. Validation results based on different transducer sizes and penetration depths support the model's adaptability and potential for future ultrasound treatments.

Low-intensity focused ultrasound to the posterior insula reduces temporal summation of pain

BACKGROUND

The insula and dorsal anterior cingulate cortex (dACC) are core brain regions involved in pain processing and central sensitization, a shared mechanism across various chronic pain conditions. Methods to modulate these regions may serve to reduce central sensitization, though it is unclear which target may be most efficacious for different measures of central sensitization.

OBJECTIVE/HYPOTHESIS

Investigate the effect of low-intensity focused ultrasound (LIFU) to the anterior insula (AI), posterior insula (PI), or dACC on conditioned pain modulation (CPM) and temporal summation of pain (TSP).

METHODS

N = 16 volunteers underwent TSP and CPM pain tasks pre/post a 10 min LIFU intervention to either the AI, PI, dACC or Sham stimulation. Pain ratings were collected pre/post LIFU.

RESULTS

Only LIFU to the PI significantly attenuated pain ratings during the TSP protocol. No effects were found for the CPM task for any of the LIFU targets. LIFU pressure modulated group means but did not affect overall group differences.

CONCLUSIONS

LIFU to the PI reduced temporal summation of pain. This may, in part, be due to dosing (pressure) of LIFU. Inhibition of the PI with LIFU may be a future potential therapy in chronic pain populations demonstrating central sensitization. The minimal effective dose of LIFU for efficacious neuromodulation will help to translate LIFU for therapeutic options.

Model-based navigation of transcranial focused ultrasound neuromodulation in humans: Application to targeting the amygdala and thalamus

BACKGROUND

Transcranial focused ultrasound (tFUS) neuromodulation has shown promise in animals but is challenging to translate to humans because of the thicker skull that heavily scatters ultrasound waves.

OBJECTIVE

We develop and disseminate a model-based navigation (MBN) tool for acoustic dose delivery in the presence of skull aberrations that is easy to use by non-specialists.

METHODS

We pre-compute acoustic beams for thousands of virtual transducer locations on the scalp of the subject under study. We use the hybrid angular spectrum solver mSOUND, which runs in ∼4 s per solve per CPU yielding pre-computation times under 1 h for scalp meshes with up to 4000 faces and a parallelization factor of 5. We combine this pre-computed set of beam solutions with optical tracking, thus allowing real-time display of the tFUS beam as the operator freely navigates the transducer around the subject' scalp. We assess the impact of MBN versus line-of-sight targeting (LOST) positioning in simulations of 13 subjects.

RESULTS

Our navigation tool has a display refresh rate of ∼10 Hz. In our simulations, MBN increased the acoustic dose in the thalamus and amygdala by 8-67 % compared to LOST and avoided complete target misses that affected 10-20 % of LOST cases. MBN also yielded a lower variability of the deposited dose across subjects than LOST.

CONCLUSIONS

MBN may yield greater and more consistent (less variable) ultrasound dose deposition than transducer placement with line-of-sight targeting, and thus could become a helpful tool to improve the efficacy of tFUS neuromodulation.

Enabling strain imaging in realistic Eulerian ultrasound simulation methods

Cardiovascular strain imaging is continually improving due to ongoing advances in ultrasound acquisition and data processing techniques. The phantoms used for validation of new methods are often burdensome to make and lack flexibility to vary mechanical and acoustic properties. Simulations of US imaging provide an alternative with the required flexibility and ground truth strain data. However, the current Lagrangian US strain imaging models cannot simulate heterogeneous speed of sound distributions and higher-order scattering, which limits the realism of the simulations. More realistic Eulerian modelling techniques exist but have so far not been used for strain imaging. In this research, a novel sampling scheme was developed based on a band-limited interpolation of the medium, which enables accurate strain simulation in Eulerian methods. The scheme was validated in k-Wave using various numerical phantoms and by a comparison with Field II. The method allows for simulations with a large range in strain values and was accurate with errors smaller than -60 dB. Furthermore, an excellent agreement with the Fourier theory of US scattering was found. The ability to perform simulations with heterogeneous speed of sound distributions was demonstrated using a pulsating artery model. The developed sampling scheme contributes to more realistic strain imaging simulations, in which the effect of heterogenous acoustic properties can be taken into account.

A rapid element pressure field simulation method for transcranial phase correction in focused ultrasound therapy

Transcranial focused ultrasound ablation has emerged as a promising technique for treating neurological disorders. The clinical system exclusively employed the ray tracing method to compute phase aberrations induced by the human skull, taking into account computational time constraints. However, this method compromises slightly on accuracy compared to simulation-based methods. This study evaluates a fast simulation method that simulates the time-harmonic pressure field within the region of interest for effective phase correction. Experimental validation was carried out using a 512-element, 670 kHz hemispherical transducer for fourex vivoskulls. The ray tracing method achieved a restoration ratio of 64.81% ± 4.33% of acoustic intensity normalized to hydrophone measurements. In comparison, the rapid simulation method demonstrated improved results with a restoration ratio of 73.10% ± 7.46%, albeit slightly lower than the full-wave simulation which achieved a restoration ratio of 75.87% ± 5.40%. The rapid simulation methods exhibited computational times that were less than five minutes for parallel computation with 8 threads. The incident angle was calculated, and a maximum difference of 6.8 degrees was found when the fixed position of the skull was changed. Meanwhile, the restoration ratio of acoustic intensity was validated to be above 70% for different target positions away from the geometrical focus of the transducer. The favorable balance between time consumption and correction accuracy makes this method valuable for clinical treatment applications.

Nonlinear Acoustic Holography With Adaptive Sampling

Accurate and efficient numerical simulation of highly nonlinear ultrasound propagation is essential for a wide range of therapeutic and physical ultrasound applications. However, due to large domain sizes and the generation of higher harmonics, such simulations are computationally challenging, particularly in 3-D problems with shock waves. Current numerical methods are based on computationally inefficient uniform meshes that resolve the highest harmonics across the entire spatial domain. To address this challenge, we present an adaptive numerical algorithm for computationally efficient nonlinear acoustic holography. At each propagation step, the algorithm monitors the harmonic content of the acoustic signal and adjusts its discretization parameters accordingly. This enables efficient local resolution of higher harmonics in areas of high nonlinearity while avoiding unnecessary resolution elsewhere. Furthermore, the algorithm actively adapts to the signal's nonlinearity level, eliminating the need for prior reference simulations or information about the spatial distribution of the harmonic content of the acoustic field. The proposed algorithm incorporates an upsampling process in the frequency domain to accommodate the generation of higher harmonics in forward propagation and a downsampling process when higher harmonics are decimated in backward propagation. The efficiency of the algorithm was evaluated for highly nonlinear 3-D problems, demonstrating a significant reduction in computational cost with a nearly 50-fold speedup over a uniform mesh implementation. Our findings enable a more rapid and efficient approach to modeling nonlinear high-intensity focused ultrasound (HIFU) wave propagation.

BabelBrain: An Open-Source Application for Prospective Modeling of Transcranial Focused Ultrasound for Neuromodulation Applications

BabelBrain is an open-source standalone graphic-user-interface application designed for studies of neuromodulation using transcranial-focused ultrasound (FUS). It calculates the transmitted acoustic field in the brain tissue, taking into account the distortion effects caused by the skull barrier. The simulation is prepared using scans from magnetic resonance imaging (MRI) and, if available, computed tomography (CT) and zero-echo time MRI scans. It also calculates the thermal effects based on a given ultrasound regime, such as the total duration of exposure, the duty cycle, and acoustic intensity. The tool is designed to work in tandem with neuronavigation and visualization software, such as 3-DSlicer. It uses image processing to prepare domains for ultrasound simulation and uses the BabelViscoFDTD library for transcranial modeling calculations. BabelBrain supports multiple GPU backends, including Metal, OpenCL, and CUDA, and works on all major operating systems including Linux, macOS, and Windows. This tool is particularly optimized for Apple ARM64 systems, which are common in brain imaging research. The article presents the modeling pipeline used in BabelBrain and a numerical study where different methods of acoustic properties mapping were tested to select the best method that can reproduce the transcranial pressure transmission efficiency reported in the literature.

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.

Corrections to "mSOUND: An Open Source Toolbox for Modeling Acoustic Wave Propagation in Heterogeneous Media"

In the above article [1]>, the authors regret that there were some mistakes pertaining to (1)-(3) and (5).