Klein Jan, Gerken Annika, Agethen Niklas, Rothlübbers Sven, Upadhyay Neeraj, Purrer Veronika, Schmeel Carsten, Borger Valeri, Kovalevsky Maya, Rachmilevitch Itay, Shapira Yeruham, Wüllner Ullrich, Jenne Jürgen
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany.
Front Neuroimaging. 2023 Oct 26;2:1272061. doi: 10.3389/fnimg.2023.1272061. eCollection 2023.
Transcranial focused ultrasound therapy (tcFUS) offers precise thermal ablation for treating Parkinson's disease and essential tremor. However, the manual fine-tuning of fiber tracking and segmentation required for accurate treatment planning is time-consuming and demands expert knowledge of complex neuroimaging tools. This raises the question of whether a fully automated pipeline is feasible or if manual intervention remains necessary.
We investigate the dependence on fiber tractography algorithms, segmentation approaches, and degrees of automation, specifically for essential tremor therapy planning. For that purpose, we compare an automatic pipeline with a manual approach that requires the manual definition of the target point and is based on FMRIB software library (FSL) and other open-source tools.
Our findings demonstrate the high feasibility of automatic fiber tracking and the automated determination of standard treatment coordinates. Employing an automatic fiber tracking approach and deep learning (DL)-supported standard coordinate calculation, we achieve anatomically meaningful results comparable to a manually performed FSL-based pipeline. Individual cases may still exhibit variations, often stemming from differences in region of interest (ROI) segmentation. Notably, the DL-based approach outperforms registration-based methods in producing accurate segmentations. Precise ROI segmentation proves crucial, surpassing the importance of fine-tuning parameters or selecting algorithms. Correct thalamus and red nucleus segmentation play vital roles in ensuring accurate pathway computation.
This study highlights the potential for automation in fiber tracking algorithms for tcFUS therapy, but acknowledges the ongoing need for expert verification and integration of anatomical expertise in treatment planning.
经颅聚焦超声治疗（tcFUS）为帕金森病和特发性震颤的治疗提供了精确的热消融。然而，精确治疗计划所需的纤维追踪和分割的手动微调既耗时，又需要复杂神经成像工具的专业知识。这就引发了一个问题，即全自动流程是否可行，或者是否仍需要人工干预。
我们研究了对纤维束成像算法、分割方法和自动化程度的依赖性，特别是针对特发性震颤的治疗计划。为此，我们将一种自动流程与一种手动方法进行了比较，该手动方法需要手动定义目标点，并且基于牛津大学功能磁共振成像脑分析软件库（FSL）和其他开源工具。
我们的研究结果证明了自动纤维追踪和标准治疗坐标自动确定的高度可行性。采用自动纤维追踪方法和深度学习（DL）支持的标准坐标计算，我们获得了与基于FSL的手动流程相当的具有解剖学意义的结果。个别病例可能仍会出现差异，这通常源于感兴趣区域（ROI）分割的不同。值得注意的是，基于DL的方法在产生准确分割方面优于基于配准的方法。精确的ROI分割被证明至关重要，其重要性超过了微调参数或选择算法。正确的丘脑和红核分割在确保准确的路径计算中起着至关重要的作用。
本研究强调了tcFUS治疗中纤维追踪算法自动化的潜力，但也认识到在治疗计划中仍需要专家验证和整合解剖学专业知识。
