Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).
Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).
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DOI：
10.1088/1361-6560/acb30d
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发表时间：
2023-02-06
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
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作者：
Objective.动态锥形束CT（CBCT）成像在图像引导的放射治疗中是高度期望的，以提供具有高空间和时间分辨率的体积图像，从而实现包括肿瘤运动跟踪/预测和输送内剂量计算/累积的应用。然而，由于可用于每个CBCT重建的投影样本极其有限（一个投影用于一个CBCT体积），动态CBCT重建是一个实质上具有挑战性的时空逆问题。Approach.我们开发了一种用于动态CBCT重建的同时空间和时间隐式神经表征（STINR）方法。STINR将未知图像及其运动演变映射到空间和时间多层感知器（MLP）中，并通过获取的投影迭代优化MLP的神经元权重以表示动态CBCT系列。除了MLP之外，我们还以基于主成分分析（PCA）的患者特定运动模型的形式引入了先验知识，以降低时间映射的复杂性，从而解决病态动态CBCT重建问题。我们使用扩展的心脏躯干（XCAT）体模和患者4D-CBCT数据集来模拟不同的肺部运动场景，以评估STINR。场景包含运动变化，包括运动基线偏移、运动幅度/频率变化和运动非周期性。XCAT场景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。STINR显示出比传统的基于PCA的方法和基于多项式拟合的神经表示方法更高的图像重建和运动跟踪精度。STINR跟踪肺部靶点的平均质心误差为1-2 mm，重建动态CBCT的相应相对误差约为10%。意义STINR提供了一个通用框架，允许图像引导放射治疗的准确动态CBCT重建。它是一种一次性学习方法，不依赖于预训练，并且不易受泛化问题的影响。它还允许自然的超分辨率。它也可以很容易地应用于其他成像模式。
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, 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 weightings of the MLPs via acquired projections to represent the dynamic CBCT series. In addition to the MLPs, we also introduced prior knowledge, in the form of principal component analysis (PCA)-based patient-specific motion models, to reduce the complexity of the temporal mapping to address the ill-conditioned dynamic CBCT reconstruction problem. We used the extended-cardiac-torso (XCAT) phantom and a patient 4D-CBCT dataset to simulate different lung motion scenarios to evaluate STINR. The scenarios contain motion variations including motion baseline shifts, motion amplitude/frequency variations, and motion non-periodicity. The XCAT 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 target to an average center-of-mass error of 1–2 mm, with corresponding relative errors of reconstructed dynamic CBCTs around 10%. Significance. STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.
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来自患者特异性PCA运动模型的荧光镜3D图像产生来自4D-CBCT患者数据集的：可行性研究。
影响因子：
3.2
作者：
Dhou S;Alkhodari M;Ionascu D;Williams C;Lewis JH
通讯作者：
Lewis JH
影响因子：
3.8
作者：
Gao H;Zhang Y;Ren L;Yin FF
通讯作者：
Yin FF
影响因子：
3.8
作者：
Chen X;Ouyang L;Yan H;Jia X;Li B;Lyu Q;Zhang Y;Wang J
通讯作者：
Wang J
影响因子：
3.8
作者：
Huang, Xiaokun;Zhang, You;Wang, Jing
通讯作者：
Wang, Jing
DOI：
10.1016/j.ijrobp.2007.01.014
发表时间：
2007-06-01
影响因子：
7
作者：
Borst, Gerben R.;Sonke, Jan-Jakob;Lebesque, Joos V.
通讯作者：
Lebesque, Joos V.