Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)
Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)
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DOI：
10.1088/1361-6560/ad46dc
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发表时间：
2024-06-07
影响因子：
3.5
通讯作者：
Zhang，You
中科院分区：
文献类型：
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作者：
Shao，Hua-Chieh;Mengke，Tielige;Zhang，You
动态锥形束计算机断层扫描（CBCT）可以捕获高空间分辨率、时变图像，用于运动监测、患者设置和放射治疗的自适应规划。然而，动态CBCT重建是一个非常不适定的时空逆问题，因为动态序列中的每个CBCT体积仅由一个或几个X射线投影捕获，这是由于机架旋转速度慢和解剖运动快方法我们开发了一种基于机器学习的技术，无先验模型时空隐式神经表示（PMF-STINR），根据顺序采集的X射线投影重建动态CBCT。PMF-STINR采用联合图像重建和配准方法来解决采样不足的挑战，从而能够从奇异X射线投影进行动态CBCT重建。具体而言，PMF-STINR使用空间隐式神经表示来重建参考CBCT体积，并且其应用时间INR来表示参考CBCT的扫描内动态运动以产生动态CBCT。PMF-STINR将时间INR与基于学习的B样条运动模型耦合，以在重建期间捕获时变可变形运动。与以前的方法相比，PMF-STINR的空间INR、时间INR和B样条模型都是在重建过程中一次性学习的，而不使用任何患者特定的先验知识或运动分类/分箱。以及具有各种成像协议（半扇/全扇、全采样/稀疏采样、不同能量和mAs设置等）的多机构患者数据集。结果表明，基于单次学习的PMF-STINR能够准确、鲁棒地重建动态CBCT图像，捕获高度不规则的运动，具有高时间分辨率（≤ 0.1 s）和亚毫米级的精度。重要的是，PMF-STINR能够重建动态CBCT图像，并解决传统3D CBCT扫描的扫描内运动，而无需使用任何先验的解剖/运动模型或运动分类/分箱。它可以提供比传统4D-CBCT更丰富的运动信息，是一种很有前途的运动管理工具。
ObjectiveDynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (eg breathing).ApproachWe developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning.Main resultsPMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy.SignificancePMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.