A Coded Excitation Framework for High SNR Transcranial Ultrasound Imaging

Displacement and functional ultrasound (fUS) imaging of displacement-guided focused ultrasound (FUS) neuromodulation in mice

Focused ultrasound (FUS) stimulation is a promising neuromodulation technique with the merits of non-invasiveness, high spatial resolution, and deep penetration depth. However, simultaneous imaging of FUS-induced brain tissue displacement and the subsequent effect of FUS stimulation on brain hemodynamics has proven challenging thus far. In addition, earlier studies lack in situ confirmation of targeting except for the magnetic resonance imaging-guided FUS system-based studies. The purpose of this study is 1) to introduce a fully ultrasonic approach to in situ target, modulate neuronal activity, and monitor the resultant neuromodulation effect by respectively leveraging displacement imaging, FUS, and functional ultrasound (fUS) imaging, and 2) to investigate FUS-evoked cerebral blood volume (CBV) response and the relationship between CBV and displacement. We performed displacement imaging on craniotomized mice to confirm the in situ targeting for neuromodulation site. We recorded hemodynamic responses evoked by FUS while fUS imaging revealed an ipsilateral CBV increase that peaks at 4 s post-FUS. We report a stronger hemodynamic activation in the subcortical region than cortical, showing good agreement with a brain elasticity map that can also be obtained using a similar methodology. We observed dose-dependent CBV responses with peak CBV, activated area, and correlation coefficient increasing with the ultrasonic dose. Furthermore, by mapping displacement and hemodynamic activation, we found that displacement colocalized and linearly correlated with CBV increase. The findings presented herein demonstrated that FUS evokes ipsilateral hemodynamic activation in cortical and subcortical depths while the evoked hemodynamic responses colocalize and correlate with FUS-induced displacement. We anticipate that our findings will help consolidate accurate targeting as well as shedding light on one of the mechanisms behind FUS modulation, i.e., how FUS mechanically displaces brain tissue affecting cerebral hemodynamics and thereby its associated connectivity.

Coded Excitation for Ultrasonic Testing: A Review

Originating in the early 20th century, ultrasonic testing has found increasingly extensive applications in medicine, industry, and materials science. Achieving both a high signal-to-noise ratio and high efficiency is crucial in ultrasonic testing. The former means an increase in imaging clarity as well as the detection depth, while the latter facilitates a faster refresh of the image. It is difficult to balance these two indicators with a conventional short pulse to excite the probe, so in general handling methods, these two factors have a trade-off. To solve the above problems, coded excitation (CE) can increase the pulse duration and offers great potential to improve the signal-to-noise ratio with equivalent or even higher efficiency. In this paper, we first review the fundamentals of CE, including signal modulation, signal transmission, signal reception, pulse compression, and optimization methods. Then, we introduce the application of CE in different areas of ultrasonic testing, with a focus on industrial bulk wave single-probe detection, industrial guided wave detection, industrial bulk wave phased array detection, and medical phased array imaging. Finally, we point out the advantages as well as a few future directions of CE.

Transcranial Functional Ultrasound Imaging Detects Focused Ultrasound Neuromodulation Induced Hemodynamic Changes in Mouse and Nonhuman Primate Brains In Vivo

Focused ultrasound (FUS) is an emerging noinvasive technique for neuromodulation in the central nervous system (CNS). To evaluate the effects of FUS-induced neuromodulation, many studies used behavioral changes, functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, behavioral readouts are often not easily mapped to specific brain activity, EEG has low spatial resolution limited to the surface of the brain and fMRI requires a large importable scanner that limits additional readouts and manipulations. In this context, functional ultrasound imaging (fUSI) holds promise to directly monitor the effects of FUS neuromodulation with high spatiotemporal resolution in a large field of view, with a comparatively simple and flexible setup. fUSI uses ultrafast Power Doppler Imaging (PDI) to measure changes in cerebral blood volume, which correlates well with neuronal activity and local field potentials. We designed a setup that aligns a FUS transducer with a linear array to allow immediate subsequent monitoring of the hemodynamic response with fUSI during and after FUS neuromodulation. We established a positive correlation between FUS pressure and the size of the activated area, as well as changes in cerebral blood volume (CBV) and found that unilateral sonications produce bilateral hemodynamic changes with ipsilateral accentuation in mice. We further demonstrated the ability to perform fully noninvasive, transcranial FUS-fUSI in nonhuman primates for the first time by using a lower-frequency transducer configuration.

Finite difference-embedded UNet for solving transcranial ultrasound frequency-domain wavefield

Transcranial ultrasound imaging assumes a growing significance in the detection and monitoring of intracranial lesions and cerebral blood flow. Accurate solution of partial differential equation (PDE) is one of the prerequisites for obtaining transcranial ultrasound wavefields. Grid-based numerical solvers such as finite difference (FD) and finite element methods have limitations including high computational costs and discretization errors. Purely data-driven methods have relatively high demands on training datasets. The fact that physics-informed neural network can only target the same model limits its application. In addition, compared to time-domain approaches, frequency-domain solutions offer advantages of reducing computational complexity and enabling stable and accurate inversions. Therefore, we introduce a framework called FD-embedded UNet (FEUNet) for solving frequency-domain transcranial ultrasound wavefields. The PDE error is calculated using the optimal 9-point FD operator, and it is integrated with the data-driven error to jointly guide the network iterations. We showcase the effectiveness of this approach through experiments involving idealized skull and brain models. FEUNet demonstrates versatility in handling various input scenarios and excels in enhancing prediction accuracy, especially with limited datasets and noisy information. Finally, we provide an overview of the advantages, limitations, and potential avenues for future research in this study.

Transcranial Phase Correction Using Pulse-Echo Ultrasound and Deep Learning: A 2-D Numerical Study

Phase aberration caused by human skulls severely degrades the quality of transcranial ultrasound images, posing a major challenge in the practical application of transcranial ultrasound techniques in adults. Aberration can be corrected if the skull profile (i.e., thickness distribution) and speed of sound (SOS) are known. However, accurately estimating the skull profile and SOS using ultrasound with a physics-based approach is challenging due to the complexity of the interaction between ultrasound and the skull. A deep learning approach is proposed herein to estimate the skull profile and SOS using ultrasound radiofrequency (RF) signals backscattered from the skull. A numerical study was performed to test the approach's feasibility. Realistic numerical skull models were constructed from computed tomography (CT) scans of five ex vivo human skulls in this numerical study. Acoustic simulations were performed on 3595 skull segments to generate array-based ultrasound backscattered signals. A deep learning model was developed and trained to estimate skull thickness and SOS from RF channel data. The trained model was shown to be highly accurate. The mean absolute error (MAE) was 0.15 mm (2% error) for thickness estimation and 13 m/s (0.5% error) for SOS estimation. The Pearson correlation coefficient between the estimated and ground-truth values was 0.99 for thickness and 0.95 for SOS. Aberration correction performed using deep-learning-estimated skull thickness and SOS values yielded significantly improved beam focusing (e.g., narrower beams) and transcranial imaging quality (e.g., improved spatial resolution and reduced artifacts) compared with no aberration correction. The results demonstrate the feasibility of the proposed approach for transcranial phase aberration correction.

Rail fracture monitoring based on ultrasonic-guided wave technology with multivariate coded excitation

Steel lay the basis of railroad train traffic. Rail fracture is the most serious injury to the rail, which should be monitored in time. The conventional mono-pulse exciting ultrasonic guide wave (UGW) has low energy, the conventional Barker code has limited coding sequence length and the orthogonal complementary Golay code has the problem of low monitoring efficiency. This study proposes multivariate coded excitation (MCE) to excite UGW to monitor rail fracture, and the feasibility of coding and decoding the method is theoretically derived and verified. Ideally, the MCE generated based on the 3-bit Barker code (B3: 1,1, -1) and 4-bit orthogonal complementary Golay code (GA4: 1,1,1, -1; GB4: 1,1, -1,1) is calculated to have a main-lobe power level (MPL) gain of 8.1020 dB, which is significantly higher than the MPL of Barker and Golay codes. For the proposed method, finite element modeling simulation and experimental study are carried out respectively. Analyze and process the data, and calculate the gain of the difference in amplitude (DIA) between the amplitude of echo caused by rail fracture and the amplitude in the healthy rail. The gain of the DIA of the echoes in the MCE is above 50 dB (experimental data, the value of simulation data is 5 dB) under different degrees of rail fracture, while the gain of the DIA of the echoes caused by the other three excitation methods is below 40 dB (experimental data, the value of simulation data is 1 dB). Simulation and experimental results show that the MCE makes up for the shortcomings of the conventional Barker code and Golay code, improves the excitation energy of the monitoring system, and the high gain of the DIA of the echoes is more conducive to the identification of rail fracture damage.

Decoding motor plans using a closed-loop ultrasonic brain-machine interface

Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.