ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation
This addresses the problem of accurate 3D medical imaging without external sensors for clinicians, though it is incremental as it builds on implicit representation techniques.
The paper tackles sensorless 3D reconstruction from 2D freehand ultrasound images by using a deep implicit representation, achieving over 30% better visual quality in novel cross-sectional views compared to existing methods.
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.