目前不可切除肝癌的护理标准是经动脉化疗栓塞术 (TACE)，其中涉及使用化疗颗粒选择性栓塞供应肝脏肿瘤的动脉。复杂的细血管的准确体积识别对于选择性栓塞至关重要。三维成像，特别是锥形束 CT (CBCT)，有助于在这种高度可变的解剖结构中可视化和定位小血管，但较长的图像采集时间会导致扫描内患者运动，从而扭曲血管结构和组织边界。为了提高血管解剖结构的清晰度和程序内实用性，这项工作提出了一种有针对性的运动估计和补偿框架，消除了对任何先验信息或外部跟踪以及用户交互的需要。运动估计分两个阶段进行：(i) 目标识别阶段，使用多视图卷积神经网络在投影域中分割动脉和导管，以构建粗略的 3D 血管掩模； (ii)目标运动估计阶段，通过优化在目标血管掩模上计算的血管增强目标函数，迭代地求解时变运动场。血管增强目标是通过局部图像 Hessian 的特征值导出的，以强调明亮的管状结构。运动补偿是通过空间变换算子实现的，该算子将与时间相关的变形应用于部分角度重建，从而通过梯度反向传播实现有效的最小化。该框架在解剖学上真实的模拟运动损坏 CBCT 中进行训练和评估，模拟肝肿瘤的 TACE，在中等（3.0 毫米）和大（6.0 毫米）运动幅度。运动补偿在模拟病例中显着改善了中位血管 DICE 评分（大运动从 0.30 提高到 0.59）、图像 SSIM（大运动从 0.77 提高到 0.93）和血管清晰度（大运动从 0.189 mm-1 提高到 0.233 mm-1） 。临床介入 CBCT 中，运动补偿还显示血管清晰度增加（之前为 0.188 mm-1，之后为 0.205 mm-1）和重建血管长度（中值从 37.37 增加到 41.00 mm）。所提出的解剖感知运动补偿框架提出了一种有前景的方法，可提高 CBCT 在术中血管成像中的实用性，促进选择性栓塞手术。版权所有 © 2024。由 Elsevier B.V. 出版。

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries. To improve clarity of vascular anatomy and intra-procedural utility, this work proposes a targeted motion estimation and compensation framework that removes the need for any prior information or external tracking and for user interaction. Motion estimation is performed in two stages: (i) a target identification stage that segments arteries and catheters in the projection domain using a multi-view convolutional neural network to construct a coarse 3D vascular mask; and (ii) a targeted motion estimation stage that iteratively solves for the time-varying motion field via optimization of a vessel-enhancing objective function computed over the target vascular mask. The vessel-enhancing objective is derived through eigenvalues of the local image Hessian to emphasize bright tubular structures. Motion compensation is achieved via spatial transformer operators that apply time-dependent deformations to partial angle reconstructions, allowing efficient minimization via gradient backpropagation. The framework was trained and evaluated in anatomically realistic simulated motion-corrupted CBCTs mimicking TACE of hepatic tumors, at intermediate (3.0 mm) and large (6.0 mm) motion magnitudes. Motion compensation substantially improved median vascular DICE score (from 0.30 to 0.59 for large motion), image SSIM (from 0.77 to 0.93 for large motion), and vessel sharpness (0.189 mm-1 to 0.233 mm-1 for large motion) in simulated cases. Motion compensation also demonstrated increased vessel sharpness (0.188 mm-1 before to 0.205 mm-1 after) and reconstructed vessel length (median increased from 37.37 to 41.00 mm) on a clinical interventional CBCT. The proposed anatomy-aware motion compensation framework presented a promising approach for improving the utility of CBCT for intra-procedural vascular imaging, facilitating selective embolization procedures.Copyright © 2024. Published by Elsevier B.V.