RoCoSDF: Row-Column Scanned Neural Signed Distance Fields for Freehand 3D Ultrasound Imaging Shape Reconstruction
Journal:
arXiv
Published Date:
Aug 14, 2024
Abstract
The reconstruction of high-quality shape geometry is crucial for developing
freehand 3D ultrasound imaging. However, the shape reconstruction of multi-view
ultrasound data remains challenging due to the elevation distortion caused by
thick transducer probes. In this paper, we present a novel learning-based
framework RoCoSDF, which can effectively generate an implicit surface through
continuous shape representations derived from row-column scanned datasets. In
RoCoSDF, we encode the datasets from different views into the corresponding
neural signed distance function (SDF) and then operate all SDFs in a normalized
3D space to restore the actual surface contour. Without requiring pre-training
on large-scale ground truth shapes, our approach can synthesize a smooth and
continuous signed distance field from multi-view SDFs to implicitly represent
the actual geometry. Furthermore, two regularizers are introduced to facilitate
shape refinement by constraining the SDF near the surface. The experiments on
twelve shapes data acquired by two ultrasound transducer probes validate that
RoCoSDF can effectively reconstruct accurate geometric shapes from multi-view
ultrasound data, which outperforms current reconstruction methods. Code is
available at https://github.com/chenhbo/RoCoSDF.
