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.

翻译：四维数字减影血管造影（4D DSA）在脑动静脉畸形（AVM）和动静脉瘘（AVF）等众多医学疾病的诊断中具有关键作用。尽管其应用价值显著，但4D DSA的重建需要大量视角来有效建模复杂的血管和造影剂流动模式，进而导致较高的辐射剂量。为解决这一高辐射问题，我们提出了一种时间感知衰减体素（TiAVox）方法用于稀疏视角4D DSA重建，该方法为高质量四维成像开辟了新路径。此外，通过重建的4D DSA图像可生成二维和三维DSA成像结果。TiAVox引入四维衰减体素网格，该网格同时反映空间和时间维度的衰减特性，通过最小化渲染图像与稀疏二维DSA图像之间的差异进行优化。该方法无需任何神经网络参与，具有明确的物理可解释性，每个可学习体素的参数直接代表衰减系数。我们在临床数据集和模拟数据集上验证了TiAVox方法：在临床来源数据集上，仅使用30个视角即可实现31.23的峰值信噪比（PSNR）用于新视角合成，而传统Feldkamp-Davis-Kress方法需要133个视角；在合成数据集中，仅利用10个视角，TiAVox在新视角合成上达到34.32的PSNR，在三维重建上达到41.40的PSNR。我们还通过消融实验验证了TiAVox的关键组件。相关代码将开源发布。