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Four-dimensional Digital Subtraction Angiography (4D DSA) plays a criticalrole in the diagnosis of many medical diseases, such as ArteriovenousMalformations (AVM) and Arteriovenous Fistulas (AVF). Despite its significantapplication value, the reconstruction of 4D DSA demands numerous views toeffectively model the intricate vessels and radiocontrast flow, therebyimplying a significant radiation dose. To address this high radiation issue, wepropose a Time-aware Attenuation Voxel (TiAVox) approach for sparse-view 4D DSAreconstruction, which paves the way for high-quality 4D imaging. Additionally,2D and 3D DSA imaging results can be generated from the reconstructed 4D DSAimages. TiAVox introduces 4D attenuation voxel grids, which reflect attenuationproperties from both spatial and temporal dimensions. It is optimized byminimizing discrepancies between the rendered images and sparse 2D DSA images.Without any neural network involved, TiAVox enjoys specific physicalinterpretability. The parameters of each learnable voxel represent theattenuation coefficients. We validated the TiAVox approach on both clinical andsimulated datasets, achieving a 31.23 Peak Signal-to-Noise Ratio (PSNR) fornovel 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 34.32for novel view synthesis and 41.40 for 3D reconstruction. We also executedablation studies to corroborate the essential components of TiAVox. The codewill be publically available.

TiAVox: Time-aware Attenuation Voxels for Sparse-view 4D DSA Reconstruction