论文标题
使用空间和时间隐式神经表示学习（stinr）的动态锥束CT重建
Dynamic Cone-beam CT Reconstruction using Spatial and Temporal Implicit Neural Representation Learning (STINR)
论文作者
论文摘要
目的：在图像引导的放射疗法中高度期望动态锥束CT（CBCT）成像，以提供具有较高空间和时间分辨率的体积图像，以实现应用应用，包括肿瘤运动跟踪/预测和降低剂量内剂量计算/积累。但是，由于每个CBCT重建的投影样本极为有限（一个CBCT体积的一个投影），动态CBCT重建是一个实质上具有挑战性的时空逆问题。方法：我们为动态CBCT重建提供了同时的空间和时间隐式神经表示（Stinr）方法。 Stinr将未知图像及其运动的演变映射到空间和时间多层感知器（MLP）中，并通过获得的投影迭代优化了MLP的神经元加权，以表示动态CBCT系列。除MLP外，我们还以主成分分析（PCA）的特定患者特异性运动模型的形式引入了先验知识，以降低时间INRS的复杂性，以解决不良条件的动态CBCT重建问题。我们使用扩展的心脏躯干（XCAT）幻影来模拟不同的肺运动/解剖场景来评估STINR。该方案包含运动变化，包括运动基线移位，运动振幅/频率变化和运动非畸形性。这些方案还包含扫描间解剖变化，包括肿瘤收缩和肿瘤位置变化。主要结果：与传统的基于PCA的方法以及基于多项式拟合的神经表示方法相比，STINR显示出始终更高的图像重建和运动跟踪精度。 STINR将肺肿瘤跟踪到平均的质量误差<2 mm，相应的重建动态CBCT <10％的相对相对误差。
Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in image-guided radiation therapy to provide volumetric images with high spatial and temporal resolutions to enable applications including tumor motion tracking/prediction and intra-delivery dose calculation/accumulation. However, the dynamic CBCT reconstruction is a substantially challenging spatiotemporal inverse problem, due to the extremely limited projection sample available for each CBCT reconstruction (one projection for one CBCT volume). Approach: We developed a simultaneous spatial and temporal implicit neural representation (STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image and the evolution of its motion into spatial and temporal multi-layer perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended cardiac torso (XCAT) phantom to simulate different lung motion/anatomy scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The scenarios also contain inter-scan anatomical variations including tumor shrinkage and tumor position change. Main results: STINR shows consistently higher image reconstruction and motion tracking accuracy than a traditional PCA-based method and a polynomial-fitting based neural representation method. STINR tracks the lung tumor to an averaged center-of-mass error of <2 mm, with corresponding relative errors of reconstructed dynamic CBCTs <10%.
