NerT-CA: Efficient Dynamic Reconstruction from Sparse-view X-ray Coronary Angiography
This work addresses a domain-specific problem for clinical procedures in cardiology by enabling faster and more accurate dynamic reconstructions from sparse data, though it builds incrementally on prior NeRF-based approaches.
The paper tackled the problem of 3D and 4D reconstruction of coronary arteries from sparse-view X-ray angiography, which is challenging due to sparsity and motion, by proposing NerT-CA, a hybrid neural and tensorial representation method that reduces training time and improves accuracy, achieving reasonable reconstructions from as few as three views.
Three-dimensional (3D) and dynamic 3D+time (4D) reconstruction of coronary arteries from X-ray coronary angiography (CA) has the potential to improve clinical procedures. However, there are multiple challenges to be addressed, most notably, blood-vessel structure sparsity, poor background and blood vessel distinction, sparse-views, and intra-scan motion. State-of-the-art reconstruction approaches rely on time-consuming manual or error-prone automatic segmentations, limiting clinical usability. Recently, approaches based on Neural Radiance Fields (NeRF) have shown promise for automatic reconstructions in the sparse-view setting. However, they suffer from long training times due to their dependence on MLP-based representations. We propose NerT-CA, a hybrid approach of Neural and Tensorial representations for accelerated 4D reconstructions with sparse-view CA. Building on top of the previous NeRF-based work, we model the CA scene as a decomposition of low-rank and sparse components, utilizing fast tensorial fields for low-rank static reconstruction and neural fields for dynamic sparse reconstruction. Our approach outperforms previous works in both training time and reconstruction accuracy, yielding reasonable reconstructions from as few as three angiogram views. We validate our approach quantitatively and qualitatively on representative 4D phantom datasets.