The objective of this work is to achieve sensorless reconstruction of a 3D volume from a set of 2D freehand ultrasound images with deep implicit representation. In contrast to the conventional way that represents a 3D volume as a discrete voxel grid, we do so by parameterizing it as the zero level-set of a continuous function, i.e. implicitly representing the 3D volume as a mapping from the spatial coordinates to the corresponding intensity values. Our proposed model, termed as ImplicitVol, takes a set of 2D scans and their estimated locations in 3D as input, jointly refining the estimated 3D locations and learning a full reconstruction of the 3D volume. When testing on real 2D ultrasound images, novel cross-sectional views that are sampled from ImplicitVol show significantly better visual quality than those sampled from existing reconstruction approaches, outperforming them by over 30% (NCC and SSIM), between the output and ground-truth on the 3D volume testing data. The code will be made publicly available.
@article{arxiv.2109.12108,
title = {ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation},
author = {Pak-Hei Yeung and Linde Hesse and Moska Aliasi and Monique Haak and the INTERGROWTH-21st Consortium and Weidi Xie and Ana I. L. Namburete},
journal= {arXiv preprint arXiv:2109.12108},
year = {2021}
}