Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
Deepwise AI Lab, Beijing, 100080, China.
Int J Comput Assist Radiol Surg. 2023 Dec;18(12):2213-2221. doi: 10.1007/s11548-023-02921-2. Epub 2023 May 5.
Preprocedural planning is a key step in radiofrequency ablation (RFA) treatment for liver tumors, which is a complex task with multiple constraints and relies heavily on the personal experience of interventional radiologists, and existing optimization-based automatic RFA planning methods are very time-consuming. In this paper, we aim to develop a heuristic RFA planning method to rapidly and automatically make a clinically acceptable RFA plan.
First, the insertion direction is heuristically initialized based on tumor long axis. Then, the 3D RFA planning is divided into insertion path planning and ablation position planning, which are further simplified into 2D by projections along two orthogonal directions. Here, a heuristic algorithm based on regular arrangement and step-wise adjustment is proposed to implement the 2D planning tasks. Experiments are conducted on patients with liver tumors of different sizes and shapes from multicenter to evaluate the proposed method.
The proposed method automatically generated clinically acceptable RFA plans within 3 min for all cases in the test set and the clinical validation set. All RFA plans of our method achieve 100% treatment zone coverage without damaging the vital organs. Compared with the optimization-based method, the proposed method reduces the planning time by dozens of times while generating RFA plans with similar ablation efficiency.
The proposed method demonstrates a new way to rapidly and automatically generate clinically acceptable RFA plans with multiple clinical constraints. The plans of our method are consistent with the clinical actual plans on almost all cases, which demonstrates the effectiveness of the proposed method and can help reduce the burden on clinicians.
术前规划是肝脏肿瘤射频消融（RFA）治疗的关键步骤，这是一项复杂的任务，具有多种约束条件，严重依赖介入放射科医生的个人经验，而现有的基于优化的自动 RFA 规划方法非常耗时。在本文中，我们旨在开发一种启发式 RFA 规划方法，以快速自动生成临床可接受的 RFA 计划。
首先，根据肿瘤长轴启发式初始化插入方向。然后，将 3D RFA 规划分为插入路径规划和消融位置规划，并通过沿两个正交方向的投影进一步简化为 2D。这里，提出了一种基于规则排列和逐步调整的启发式算法来实现 2D 规划任务。我们通过多中心实验评估了来自不同大小和形状的肝脏肿瘤患者对所提出方法的评估。
所提出的方法在测试集和临床验证集中的所有病例中都能在 3 分钟内自动生成临床可接受的 RFA 计划。我们方法的所有 RFA 计划都能达到 100%的治疗区域覆盖，而不会损害重要器官。与基于优化的方法相比，该方法将规划时间缩短了数十倍，同时生成了具有相似消融效率的 RFA 计划。
该方法为快速自动生成具有多种临床约束的临床可接受的 RFA 计划提供了一种新方法。我们方法的计划与几乎所有病例的临床实际计划一致，这证明了该方法的有效性，可以帮助减轻临床医生的负担。
