HFUS-NeRF: Hybrid Representation for Fast Ultrasound Reconstruction in Robotic Ultrasound System

Abstract

Telemedicine is promising in digital healthcare management, such as supporting the coronavirus disease 2019 (COVID-19) pandemic. Three-dimensional (3D) ultrasound reconstruction and new view image synthesis, which can assist in diagnosis and reexamine, have significant potential in teleultrasound, especially integrating robotic ultrasound systems (RUSS). Neural Radiance Field (NeRF), an impressive reconstruction method, requires long training times, limiting its practicality in ultrasound. Despite NeRF variants achieving faster optimization, their performance remains confined to natural scene reconstructions. To address this limitation, we propose HFUS-NeRF, a hybrid representation method designed for fast and accurate ultrasound reconstruction. HFUS-NeRF integrates multi-resolution hash-grid and tri-plane representations to represent each sampling point of the ultrasonic wave. A unified model for sampling points from different ultrasonic probes is presented to simulate the wave's propagation through tissues, and the final ultrasound image is rendered using volume rendering. Compared with NeRF-based ultrasound reconstruction, both the hash grid and triplane resolutions can be scaled up more efficiently, improving reconstruction speed. Experimental results demonstrate that HFUS-NeRF enhances reconstruction quality while significantly reducing reconstruction time to mere minutes. Furthermore, we validated HFUS-NeRF's adaptability by reconstruction using images from different types of ultrasound probes, and real-world experiments confirmed its feasibility and transferability, enabling fast ultrasound reconstruction on human subjects.