Echo3D reconstructs a personalized 3D heart from six position-agnostic 2D echo-style slices via alternating pose estimation and implicit neural reconstruction, reporting high accuracy on slices generated from CT meshes.
ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation
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abstract
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.
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3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices
Echo3D reconstructs a personalized 3D heart from six position-agnostic 2D echo-style slices via alternating pose estimation and implicit neural reconstruction, reporting high accuracy on slices generated from CT meshes.