AIA-UltraNeRF:Acoustic-Impedance-Aware Neural Radiance Field with Hash Encodings for Robotic Ultrasound Reconstruction and Localization

TL;DR

AIA-UltraNeRF introduces an acoustic-impedance-aware neural radiance field with hash encodings for faster ultrasound reconstruction and accurate localization in robotic systems, effectively separating scanning from diagnosis.

Contribution

It presents a novel acoustic-impedance-aware NeRF with hash encoding, enabling rapid reconstruction and localization in robotic ultrasound imaging, and introduces a dual-supervised network for pose estimation.

Findings

Achieves 9.9 times faster inference than vanilla NeRF.

Effectively characterizes ultrasound image color via acoustic impedance.

Demonstrates successful reconstruction and localization on phantom and human data.

Abstract

Neural radiance field (NeRF) is a promising approach for reconstruction and new view synthesis. However, previous NeRF-based reconstruction methods overlook the critical role of acoustic impedance in ultrasound imaging. Localization methods face challenges related to local minima due to the selection of initial poses. In this study, we design a robotic ultrasound system (RUSS) with an acoustic-impedance-aware ultrasound NeRF (AIA-UltraNeRF) to decouple the scanning and diagnostic processes. Specifically, AIA-UltraNeRF models a continuous function of hash-encoded spatial coordinates for the 3D ultrasound map, allowing for the storage of acoustic impedance without dense sampling. This approach accelerates both reconstruction and inference speeds. We then propose a dual-supervised network that leverages teacher and student models to hash-encode the rendered ultrasound images from the reconstructed map. AIA-UltraNeRF retrieves the most similar hash values without the need to render images again, providing an offline initial image position for localization. Moreover, we develop a RUSS with a spherical remote center of motion mechanism to hold the probe, implementing operator-independent scanning modes that separate image acquisition from diagnostic workflows. Experimental results on a phantom and human subjects demonstrate the effectiveness of acoustic impedance in implicitly characterizing the color of ultrasound images. AIAUltraNeRF achieves both reconstruction and localization with inference speeds that are 9.9 faster than those of vanilla NeRF.