Imaging of Single Transducer-Harmonic Motion Imaging-Derived Displacements at Several Oscillation Frequencies Simultaneously

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 targeting for neuromodulation site. We recorded hemodynamic responses evoked by FUS and fUS revealed an ipsilateral CBV increase that peaks at 4 s post-FUS. We saw a stronger hemodynamic activation in the subcortical region than cortical, showing good agreement with the brain elasticity map that can also be obtained using a similar methodology. We observed dose-dependent CBV response with peak CBV, activated area, and correlation coefficient increasing with ultrasonic dose. Furthermore, by mapping displacement and hemodynamic activation, we found that displacement colocalizes and linearly correlates with CBV increase. The findings presented herein demonstrated that FUS evokes ipsilateral hemodynamic activation in cortical and subcortical depths and the evoked hemodynamic responses colocalized and correlate with FUS-induced displacement. We anticipate that our findings will help consolidate accurate targeting as well as an understanding of how FUS displaces brain tissue and affects cerebral hemodynamics.

Harmonic Motion Imaging-Guided Focused Ultrasound Ablation: Comparison of Three Focused Ultrasound Interference Filtering Methods

OBJECTIVE

Harmonic motion imaging (HMI) is an acoustic radiation force-based elasticity imaging technique, which can be used to monitor changes in tissue mechanical properties caused by focused ultrasound (FUS)-induced thermal ablation. In conventional HMI, the amplitude-modulated FUS sequence and imaging pulse are transmitted simultaneously. With this method, the high-amplitude FUS signal must be separated from the imaging data for tissue displacement estimation. Frequency domain notch filtering and bandpass filtering were previously used to filter FUS interference from imaging data. However, FUS interference becomes more pronounced at the increased intensities and durations necessary for thermal ablation, rendering frequency domain filtering challenging. In this study, three methods for FUS interference filtering during HMI-guided FUS ablation (HMIgFUS) were compared. The methods were notch filtering; interleaved HMIgFUS, with interleaved FUS and imaging pulses; and FUS-net, a convolutional neural network-based U-Net autoencoder developed by our group for FUS interference filtering in radiofrequency data. FUS-net was applied here for the first time for the purpose of ablation displacement monitoring.

METHODS

The three filtering methods were tested during 20 HMIgFUS acquisitions in an ex vivo canine liver using a range of peak positive pressures from 11 to 18 MPa and durations ranging from 60 to 180 seconds. The B-mode mean squared errors (MSEs) and displacement amplitude contrast-to-noise ratios (CNRs) of the three methods were calculated and compared.

RESULTS

The interleaved method for HMIgFUS was found to be significantly robust in avoiding FUS interference in all tested cases for FUS ablation monitoring, especially cases with high FUS pressure and long durations, as opposed to traditional notch filtering and FUS-net filtering. CNRs obtained from displacement amplitude maps in the interleaved data set were significantly higher in all cases than those obtained from the notch filtered and FUS-net data sets. There was not a significant trend in displacement CNR between the FUS-net and notch filtered data sets. However, B-mode MSE was found to be significantly higher when comparing the FUS-net and interleaved data sets as opposed to the notch filtered and interleaved data sets, suggesting further potential of FUS-net as an FUS interference filtering method.

CONCLUSION

These findings indicate the robustness of interleaved HMIgFUS in avoiding FUS interference during HMIgFUS monitoring and the advantages, limitations and future potential of FUS-net and notch filtering.

Optimization of the Tracking Beam Sequence in Harmonic Motion Imaging

Harmonic motion imaging (HMI) is an ultrasound elastography technique that estimates the viscoelastic properties of tissues by inducing localized oscillatory motion using focused ultrasound (FUS). The resulting displacement, assumed to be inversely proportional to the tissue local stiffness, is estimated using an imaging array based on RF speckle tracking. In conventional HMI, this is accomplished with plane-wave (PW) imaging, which inherently suffers from low lateral resolution. Coherent PW compounding (PWC) leverages spatial and temporal resolution using synthetic focusing in transmit. In this study, we introduced focused imaging with parallel tracking in HMI and compared parallel tracking of various transmit F-numbers (F/2.6, 3, 4, and 5) qualitatively and quantitatively with PW and PWC imaging at various compounded angle ranges (6°, 12°, and 18°). An in silico model of a 56-kPa spherical inclusion (diameter: 3.6 mm) embedded in a 5.3-kPa background and a 5.3-kPa elastic phantom with cylindrical inclusions (Young's moduli: 22-56 kPa, diameters: 2.0-8.6 mm) were imaged to assess different tracking beam sequences. Speckle biasing in displacement estimation associated with parallel tracking was also investigated and concluded to be negligible in HMI. Parallel tracking in receive (Rx) resulted in 2%-7% and 8%-12% increase compared to PW imaging ( ) in HMI contrast and contrast-to-noise ratio in silico and phantoms. Focused imaging with parallel tracking in Rx was concluded to be most robust among PW and PWC imaging for displacement estimation, and its preclinical feasibility was demonstrated in postsurgical human cancerous breast tissue specimens and in vivo murine models of breast cancer.

Breast stiffness, a risk factor for cancer and the role of radiology for diagnosis

Over the last five decades, breast density has been associated with increased risk of developing breast cancer. Mammographically dense breasts are considered those belonging to the heterogeneously dense breasts, and extremely dense breasts subgroups according to the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). There is a statistically significant correlation between the increased mammographic density and the presence of more glandular tissue alone. However, the strength of this correlation is weak. Although the mechanisms driving breast density-related tumor initiation and progression are still unknown, there is evidence suggesting that certain molecular pathways participating in epithelial-stromal interactions may play a pivotal role in the deposition of fibrillar collagen, increased matrix stiffness, and cell migration that favor breast density and carcinogenesis. This article describes these molecular mechanisms as potential "landscapers" for breast density-related cancer. We also introduce the term "Breast Compactness" to reflect collagen density of breast tissue on chest CT scan and the use of breast stiffness measurements as imaging biomarkers for breast cancer screening and risk stratification.