发表机构
Princeton University; University of California Davis; King’s College London（普林斯顿大学; 加州大学戴维斯分校; 伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该工具基于MATLAB生成随机导丝轨迹，用于射频加热风险评估，实现99.84%长度准确率并快速生成3000条轨迹，发现线圈类型无法单独预测个体轨迹风险。
AI 中文摘要
意义：血管内器械的射频感应加热限制了MRI引导的介入操作，包括针对脑肿瘤的靶向药物递送。目的：提出一种能够快速生成随机化导丝轨迹以用于射频加热风险评估的工具。方法：一个MATLAB工具将导丝建模为具有随机化控制节点和固定标称长度的样条曲线。作为用例，使用直导线传递函数以及Duke体模中64 MHz局部线圈和体线圈场（匹配B1）对1000条轨迹的尖端sSAR进行了估计。结果：长度准确率为99.84%，在11.5秒内生成了3000条轨迹。第50、95和99百分位处的体线圈与局部线圈sSAR比值分别为1.5、1.6和2.27，但部分轨迹在局部线圈下加热更严重。结论：该工具支持大样本加热评估，且仅凭线圈类型无法可靠预测个体轨迹的风险。
英文摘要
Significance: RF-induced heating of endovascular devices limits MRI-guided interventions, including targeted drug delivery to brain tumors. Purpose: To present a tool that rapidly generates randomized guidewire trajectories for RF heating risk assessment. Methods: A MATLAB tool models the guidewire as a spline with randomized control nodes and a fixed nominal length. As a use case, tip sSAR was estimated for 1000 trajectories using straight-wire transfer functions and 64 MHz local and body coil fields (matched B1) in the Duke phantom. Results: Length accuracy was 99.84%, and 3000 trajectories were generated in 11.5 s. Body-to-local sSAR ratios at the 50th, 95th, and 99th percentiles were 1.5, 1.6, and 2.27, but some trajectories heated more under the local coil. Conclusion: The tool enables large-sample heating assessment, and coil type alone did not reliably predict individual trajectory risk.
