Comparison between MR and CT imaging used to correct for skull-induced phase aberrations during transcranial focused ultrasound

Robust deep learning estimation of cortical bone porosity from MR T1-weighted images for individualized transcranial focused ultrasound planning

Objective

Transcranial focused ultrasound (tFUS) is an emerging neuromodulation approach that has been demonstrated in animals but is difficult to translate to humans because of acoustic attenuation and scattering in the skull. Optimal dose delivery requires subject-specific skull porosity estimates which has traditionally been done using CT. We propose a deep learning (DL) estimation of skull porosity from T1-weighted MRI images which removes the need for radiation-inducing CT scans.

Approach

We evaluate the impact of different DL approaches, including network architecture, input size and dimensionality, multichannel inputs, data augmentation, and loss functions. We also propose back-propagation in the mask (BIM), a method whereby only voxels inside the skull mask contribute to training. We evaluate the robustness of the best model to input image noise and MRI acquisition parameters and propagate porosity estimation errors in thousands of beam propagation scenarios.

Main results

Our best performing model is a cGAN with a ResNet-9 generator with 3D 64×64×64 inputs trained with L1 and L2 losses. The model achieved a mean absolute error of 6.9% in the test set, compared to 9.5% with the pseudo-CT of Izquierdo et al. (38% improvement) and 9.4% with the generic pixel-to-pixel image translation cGAN pix2pix (36% improvement). Acoustic dose distributions in the thalamus were more accurate with our approach than with the pseudo-CT approach of both Burgos et al. and Izquierdo et al, resulting in near-optimal treatment planning and dose estimation at all frequencies compared to CT (reference).

Significance

Our DL approach porosity estimates with ~7% error, is robust to input image noise and MRI acquisition parameters (sequence, coils, field strength) and yields near-optimal treatment planning and dose estimates for both central (thalamus) and lateral brain targets (amygdala) in the 200-1000 kHz frequency range.

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.

Numerical Investigation of Layered Homogeneous Skull Model for Simulations of Transcranial Focused Ultrasound

BACKGROUND AND OBJECTIVES

The influence of the intracranial pressure field must be discussed with the development of a single-element transducer for low-intensity transcranial focused ultrasound because the skull plays a significant role in blocking and dispersing ultrasound wave propagation. Ultrasound propagation is mainly affected by the structure and acoustic properties of the skull; thus, we aimed to investigate the impact of simplifying the acoustic properties of the skull on the simulation of the transcranial pressure field to present guidance for efficient skull modeling in full-wave simulations.

MATERIALS AND METHODS

We constructed a three-dimensional computational model for ultrasound transmission with the same structure but varying acoustic properties of the skull. The structural information and heterogeneous acoustic properties of the skull were acquired from computed tomography images, and we segmented the skull into three layers (3 L), including spongy and compact bones. We then assigned homogeneous acoustic properties to a single layer (1 L) or 3 L of the skull. In addition, we investigated the influence of different types of transducers and different ultrasound frequencies (1.1 MHz, 0.5 MHz, and 0.25 MHz) on the intracranial pressure field to provide a comparison of the heterogenous and homogeneous models.

RESULTS

We indicated the importance of numerical simulations in estimating the intracranial pressure field of the skull owing to beam distortions. When we simplified the skull model, both the 1 L and 3 L models showed contours of the acoustic focus comparable to those of the heterogeneous model. When we evaluated the peak pressure and volume of the acoustic focus, the 1 L model produced a better estimation of peak pressure with a difference <10%, and the 3 L model is suitable to obtain smaller errors in the volume of the acoustic focus.

CONCLUSIONS

In conclusion, we examined the possibility of simplification of skull models using 1 L and 3 L homogeneous properties in the numerical simulation for focused ultrasound. The results show that the layered homogeneous model can provide characteristics comparable to those of the acoustic focus in heterogeneous models.

Innovative aberration correction in ultrasound diagnostics with direct phase estimation for enhanced image quality

The paper addresses a crucial challenge in medical radiology and introduces a novel general approach, which utilises applied mathematics and information technology techniques, for aberration correction in ultrasound diagnostics. Ultrasound imaging of inhomogeneous media inherently suffers from variations in ultrasonic speed between tissue. The characteristics of aberrations are unique to each patient due to tissue morphology. This study proposes a new phase aberration correction method based on the Fourier transform and leveraging of the synthetic aperture mode. The proposed method enables correction after the emission and reception of ultrasonic wave, allowing for the estimation of aberration profiles for different parts of the sonogram. To demonstrate the method's performance, this study included the conducting of experiments using a commercially available quality control phantom, an ex-vivo temporal human bone, and specially designed distortion layers. At a frequency of 2 MHz, the experiments demonstrated an increase of two-and-three-quarters in echo signal intensity and a decrease of nearly two-fold in the width of the angular distribution compared to the pre-correction state. However, it is important to note that the implementation of the method has a limitation, as it requires an aperture synthesis mode and access to raw RF data, which restricts use in common scanners. To ensure the reproducibility of the results, this paper provides public access to an in-house C +  + code for aberration correction following the proposed method, as well as the dataset used in this study.

Generating Patient-Specific Acoustic Simulations for Transcranial Focused Ultrasound Procedures Based on Optical Tracking Information

Optical tracking is a real-time transducer positioning method for transcranial focused ultrasound (tFUS) procedures, but the predicted focus from optical tracking typically does not incorporate subject-specific skull information. Acoustic simulations can estimate the pressure field when propagating through the cranium but rely on accurately replicating the positioning of the transducer and skull in a simulated space. Here, we develop and characterize the accuracy of a workflow that creates simulation grids based on optical tracking information in a neuronavigated phantom with and without transmission through an ex vivo skull cap. The software pipeline could replicate the geometry of the tFUS procedure within the limits of the optical tracking system (transcranial target registration error (TRE): 3.9 ± 0.7 mm). The simulated focus and the free-field focus predicted by optical tracking had low Euclidean distance errors of 0.5±0.1 and 1.2±0.4 mm for phantom and skull cap, respectively, and some skull-specific effects were captured by the simulation. However, the TRE of simulation informed by optical tracking was 4.6±0.2, which is as large or greater than the focal spot size used by many tFUS systems. By updating the position of the transducer using the original TRE offset, we reduced the simulated TRE to 1.1 ± 0.4 mm. Our study describes a software pipeline for treatment planning, evaluates its accuracy, and demonstrates an approach using MR-acoustic radiation force imaging as a method to improve dosimetry. Overall, our software pipeline helps estimate acoustic exposure, and our study highlights the need for image feedback to increase the accuracy of tFUS dosimetry.

Evaluation of synthetically generated computed tomography for use in transcranial focused ultrasound procedures

Purpose

Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under magnetic resonance imaging (MRI) guidance. CT imaging is used to estimate the acoustic properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients to potentially harmful radiation. A method to estimate acoustic parameters in the skull without the need for CT is desirable.

Approach

We synthesized CT images from routinely acquired T1-weighted MRI using a 3D patch-based conditional generative adversarial network and evaluated the performance of synthesized CT (sCT) images for treatment planning with tFUS. We compared the performance of sCT with real CT (rCT) images for tFUS planning using Kranion and simulations using the acoustic toolbox, k-Wave. Simulations were performed for 3 tFUS scenarios: (1) no aberration correction, (2) correction with phases calculated from Kranion, and (3) phase shifts calculated from time reversal.

Results

From Kranion, the skull density ratio, skull thickness, and number of active elements between rCT and sCT had Pearson's correlation coefficients of 0.94, 0.92, and 0.98, respectively. Among 20 targets, differences in simulated peak pressure between rCT and sCT were largest without phase correction (12.4 % ± 8.1 % ) and smallest with Kranion phases (7.3 % ± 6.0 % ). The distance between peak focal locations between rCT and sCT was < 1.3    mm for all simulation cases.

Conclusions

Real and synthetically generated skulls had comparable image similarity, skull measurements, and acoustic simulation metrics. Our work demonstrated similar results for 10 testing cases comparing MR-sCTs and rCTs for tFUS planning. Source code and a docker image with the trained model are available at https://github.com/han-liu/SynCT_TcMRgFUS.