PDF(Pubmed)
Abstract：
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.
摘要：
目前治疗不可切除肝癌的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地栓塞供肝肿瘤的动脉。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），在这种高度可变的解剖结构中，有助于小血管的可视化和靶向，但是较长的图像采集时间会导致扫描中的患者运动，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手术中的实用性，这项工作提出了一个有针对性的运动估计和补偿框架，消除了任何先验信息或外部跟踪和用户交互的需要。在两个阶段中执行运动估计:(i)目标识别阶段,其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3D血管掩模;以及(ii)目标运动估计阶段,其经由在目标血管掩模上计算的血管增强目标函数的优化来迭代求解时变运动场。血管增强目标是通过局部图像Hessian的特征值得出的，以强调明亮的管状结构。运动补偿是通过空间变换器算子实现的，这些算子将时间相关的变形应用于部分角度重建，允许通过梯度反向传播有效的最小化。该框架在模拟肝肿瘤TACE的解剖学逼真的模拟运动破坏的CBCT中进行了训练和评估，在中等（3.0毫米）和大（6.0毫米）的运动幅度。运动补偿显著改善了中位血管DICE评分（对于大运动，从0.30到0.59），图像SSIM（大运动从0.77到0.93），和血管锐度（0.189mm-1到0.233mm-1的大运动）在模拟情况下。在临床介入性CBCT上，运动补偿还显示出血管锐度增加（之前为0.188mm-1，之后为0.205mm-1）和重建的血管长度（中位数从37.37mm增加到41.00mm）。提出的解剖感知运动补偿框架提出了一种有希望的方法，用于提高CBCT用于过程中血管成像的实用性。促进选择性栓塞程序。
