Transcranial Acoustic Metamaterial Parameters Inverse Designed by Neural Networks

Abstract

Objective: The objective of this work is to investigate the mapping relationship between transcranial ultrasound image quality and transcranial acoustic metamaterial parameters using inverse design methods. Impact Statement: Our study provides insights into inverse design methods and opens the route to guide the preparation of transcranial acoustic metamaterials. Introduction: The development of acoustic metamaterials has enabled the exploration of cranial ultrasound, and it has been found that the influence of the skull distortion layer on acoustic waves can be effectively eliminated by adjusting the parameters of the acoustic metamaterial. However, the interaction mechanism between transcranial ultrasound images and transcranial acoustic metamaterial parameters is unknown. Methods: In this study, 1,456 transcranial ultrasound image datasets were used to explore the mapping relationship between the quality of transcranial ultrasound images and the parameters of transcranial acoustic metamaterials. Results: The multioutput parameter prediction model of transcranial metamaterials based on deep back-propagation neural network was built, and metamaterial parameters under transcranial image evaluation indices are predicted using the prediction model. Conclusion: This inverse big data design approach paves the way for guiding the preparation of transcranial metamaterials.

A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging

“…The field of deep learning in medical ultrasound imaging is rapidly expanding with many applications such as clutter suppression in Doppler [1], super-resolution imaging [2,3], anomaly detection methods for breast ultrasound images [4], beamforming pre-steered, subsampled data [5], transcranial ultrasound imaging [6,7], minimum variance beamforming [8,9], and image reconstruction from raw channel data [10][11][12][13]. In the context of image reconstruction from raw channel data, CNNs have proven their capability to learn the reconstruction process without requiring explicit input information regarding receiver array geometry, a medium speed of sound, or the spatial discretization of the imaged region of interest (parameters which are typically essential for the standard delay and sum algorithm).…”