Deep Learning-Based Framework for Fast and Accurate Acoustic Hologram Generation

Abstract

Acoustic holography has been gaining attention for various applications such as non-contact particle manipulation, non-invasive neuromodulation, and medical imaging. However, only a few studies on how to generate acoustic holograms have been conducted, and even conventional acoustic hologram algorithms show limited performance in the fast and accurate generation of acoustic holograms, thus hindering the development of novel applications. We here propose a deep learning-based framework to achieve fast and accurate acoustic hologram generation. The framework has an autoencoder-like architecture; thus the unsupervised training is realized without any ground truth. For the framework, we demonstrate a newly developed hologram generator network, the Holographic Ultrasound generation Network (HU-Net), which is suitable for unsupervised learning of hologram generation, and a novel loss function that is devised for energy-efficient holograms. Furthermore, for considering various hologram devices (i.e., ultrasound transducers), we propose a physical constraint layer. Simulation and experimental studies were carried out for two different hologram devices such as a 3D printed lens, attached to a single element transducer, and a 2D ultrasound array. The proposed framework was compared with the iterative angular spectrum approach (IASA) and the state-of-the-art iterative optimization method, Diff-PAT. In the simulation study, our framework showed a few hundred times faster generation speed, along with comparable or even better reconstruction quality, than those of IASA and Diff-PAT. In the experimental study, the framework was validated with 3D-printed lenses fabricated based on different methods, and the physical effect of the lenses on the reconstruction quality was discussed. The outcomes of the proposed framework in various cases (i.e., hologram generator networks, loss functions, hologram devices) suggest that our framework may become a very useful alternative tool for other existing acoustic hologram applications and it can expand novel medical applications. IEEE