Real-time liver tumor localization via combined surface imaging and a single x-ray projection.
Real-time liver tumor localization via combined surface imaging and a single x-ray projection.
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DOI：
10.1088/1361-6560/acb889
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发表时间：
2023-03-09
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
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Objective.实时成像是实时自适应放射治疗的一个组成部分，它提供了解剖运动的即时知识，以驱动输送适应性，从而提高患者的安全性和治疗效果。实时成像的时间约束（<500毫秒）显著限制了可以采集的成像信号，使得体积成像和3D肿瘤定位极具挑战性。实时肝脏成像特别困难，肝脏内软组织对比度低。我们提出了一个基于深度学习（DL）的框架（Surf-X-Bio），用于从组合的光学表面图像和单个机载X射线投影中实时跟踪3D肝脏肿瘤运动。Approach. Surf-X-Bio执行基于网格的可变形配准，通过三个步骤对肝脏肿瘤进行体积跟踪/定位。首先，建立DL模型来从光学表面图像估计肝脏边界运动，使用学习到的运动之间的相关性诱导的外部身体表面和肝脏边界。其次，残余的肝脏边界运动估计误差进一步校正的图形神经网络为基础的DL模型，使用从一个单一的X射线投影提取的信息。最后，应用生物力学建模驱动的DL模型来解决用于肿瘤定位的肝内运动，使用通过先前步骤导出的肝边界运动。主要结果。与仅表面图像和仅X射线模型相比，Surf-X-Bio在肿瘤定位方面表现出更高的准确性和更好的鲁棒性。通过Surf-X-Bio，平均值（±s.d.）95-肝脏边界与“地面实况”的百分位Hausdorff距离从9.8（±4.5）（运动估计前）降至2.4（±1.6）mm。肝肿瘤的质心定位误差从8.3（±4.8）mm降低到1.9（±1.6）mm。Surf-X-Bio可以通过结合表面成像和X射线成像准确跟踪肝脏肿瘤。快速的计算速度（每次推理<250毫秒）使其能够在临床上应用于实时运动管理和自适应放射治疗。
Objective. Real-time imaging, a building block of real-time adaptive radiotherapy, provides instantaneous knowledge of anatomical motion to drive delivery adaptation to improve patient safety and treatment efficacy. The temporal constraint of real-time imaging (<500 milliseconds) significantly limits the imaging signals that can be acquired, rendering volumetric imaging and 3D tumor localization extremely challenging. Real-time liver imaging is particularly difficult, compounded by the low soft tissue contrast within the liver. We proposed a deep learning (DL)-based framework (Surf-X-Bio), to track 3D liver tumor motion in real-time from combined optical surface image and a single on-board x-ray projection. Approach. Surf-X-Bio performs mesh-based deformable registration to track/localize liver tumors volumetrically via three steps. First, a DL model was built to estimate liver boundary motion from an optical surface image, using learnt motion correlations between the respiratory-induced external body surface and liver boundary. Second, the residual liver boundary motion estimation error was further corrected by a graph neural network-based DL model, using information extracted from a single x-ray projection. Finally, a biomechanical modeling-driven DL model was applied to solve the intra-liver motion for tumor localization, using the liver boundary motion derived via prior steps. Main results. Surf-X-Bio demonstrated higher accuracy and better robustness in tumor localization, as compared to surface-image-only and x-ray-only models. By Surf-X-Bio, the mean (±s.d.) 95-percentile Hausdorff distance of the liver boundary from the ‘ground-truth’ decreased from 9.8 (±4.5) (before motion estimation) to 2.4 (±1.6) mm. The mean (±s.d.) center-of-mass localization error of the liver tumors decreased from 8.3 (±4.8) to 1.9 (±1.6) mm. Significance. Surf-X-Bio can accurately track liver tumors from combined surface imaging and x-ray imaging. The fast computational speed (<250 milliseconds per inference) allows it to be applied clinically for real-time motion management and adaptive radiotherapy.
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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
DOI：
10.1016/j.ejmp.2020.02.001
发表时间：
2020-02-01
影响因子：
3.4
作者：
Hirai, Ryusuke;Sakata, Yukinobu;Mori, Shinichiro
通讯作者：
Mori, Shinichiro
影响因子：
3.8
作者：
Keall, Paul J.;Sawant, Amit;Stathakis, Sotirios
通讯作者：
Stathakis, Sotirios
DOI：
10.1016/s0360-3016(01)01649-2
发表时间：
2001-09-01
影响因子：
7
作者：
Balter, JM;Dawson, LA;Ten Haken, R
通讯作者：
Ten Haken, R
影响因子：
3.5
作者：
Keall, Paul;Poulsen, Per;Booth, Jeremy T.
通讯作者：
Booth, Jeremy T.