Conventional Computed Tomography (CT) methods require large numbers of noise-free projections for accurate density reconstructions, limiting their applicability to the more complex class of Cone Beam Geometry CT (CBCT) reconstruction. Recently, deep learning methods have been proposed to overcome these limitations, with methods based on neural fields (NF) showing strong performance, by approximating the reconstructed density through a continuous-in-space coordinate based neural network. Our focus is on improving such methods, however, unlike previous work, which requires training an NF from scratch for each new set of projections, we instead propose to leverage anatomical consistencies over different scans by training a single conditional NF on a dataset of projections. We propose a novel conditioning method where local modulations are modeled per patient as a field over the input domain through a Neural Modulation Field (NMF). The resulting Conditional Cone Beam Neural Tomography (CondCBNT) shows improved performance for both high and low numbers of available projections on noise-free and noisy data.

翻译：传统计算机断层成像（CT）方法需要大量无噪声投影才能实现精确的密度重建，这限制了其在更复杂的锥束几何CT（CBCT）重建场景中的适用性。近年来，深度学习方法被提出以克服这些限制，其中基于神经场（NF）的方法通过利用连续空间坐标的神经网络近似重建密度，展现出优异性能。我们的研究重点在于改进此类方法，但与先前需为每组新投影从头训练神经场的工作不同，我们提出利用不同扫描间解剖结构的一致性，通过在投影数据集上训练单一条件神经场。我们提出一种新型条件化方法，通过神经调制场（NMF）将每位患者的局部调制建模为输入域上的场。由此产生的条件锥束神经断层成像（CondCBNT）在无噪声及含噪声数据中，无论可用投影数量多寡，均展现出更优的重建性能。