  Four-dimensional Digital Subtraction Angiography (4D DSA) plays a critical
role in the diagnosis of many medical diseases, such as Arteriovenous
Malformations (AVM) and Arteriovenous Fistulas (AVF). Despite its significant
application value, the reconstruction of 4D DSA demands numerous views to
effectively model the intricate vessels and radiocontrast flow, thereby
implying a significant radiation dose. To address this high radiation issue, we
propose a Time-aware Attenuation Voxel (TiAVox) approach for sparse-view 4D DSA
reconstruction, which paves the way for high-quality 4D imaging. Additionally,
2D and 3D DSA imaging results can be generated from the reconstructed 4D DSA
images. TiAVox introduces 4D attenuation voxel grids, which reflect attenuation
properties from both spatial and temporal dimensions. It is optimized by
minimizing discrepancies between the rendered images and sparse 2D DSA images.
Without any neural network involved, TiAVox enjoys specific physical
interpretability. The parameters of each learnable voxel represent the
attenuation coefficients. We validated the TiAVox approach on both clinical and
simulated datasets, achieving a 31.23 Peak Signal-to-Noise Ratio (PSNR) for
novel view synthesis using only 30 views on the clinically sourced dataset,
whereas traditional Feldkamp-Davis-Kress methods required 133 views. Similarly,
with merely 10 views from the synthetic dataset, TiAVox yielded a PSNR of 34.32
for novel view synthesis and 41.40 for 3D reconstruction. We also executed
ablation studies to corroborate the essential components of TiAVox. The code
will be publically available.
