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首页> 外文期刊>Acta Neurochirurgica >Machine learning-aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET
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Machine learning-aided personalized DTI tractographic planning for deep brain stimulation of the superolateral medial forebrain bundle using HAMLET

机译：机器学习辅助个性化DTI牵引规划，用于使用哈姆雷特的超薄内侧前脑束的深脑刺激

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BackgroundGrowing interest exists for superolateral medial forebrain bundle (slMFB) deep brain stimulation (DBS) in psychiatric disorders. The surgical approach warrants tractographic rendition. Commercial stereotactic planning systems use deterministic tractography which suffers from inherent limitations, is dependent on manual interaction (ROI definition), and has to be regarded as subjective. We aimed to develop an objective but patient-specific tracking of the slMFB which at the same time allows the use of a commercial surgical planning system in the context of deep brain stimulation.MethodsThe HAMLET (Hierarchical Harmonic Filters for Learning Tracts from Diffusion MRI) machine learning approach was introduced into the standardized workflow of slMFB DBS tractographic planning on the basis of patient-specific dMRI. Rendition of the slMFB with HAMLET serves as an objective comparison for the refinement of the deterministic tracking procedure. Our application focuses on the tractographic planning of DBS (N=8) for major depression and OCD.ResultsPrevious results have shown that only fibers belonging to the ventral tegmental area to prefrontal/orbitofrontal axis should be targeted. With the proposed technique, the deterministic tracking approach, that serves as the surgical planning data, can be refined, over-sprouting fibers are eliminated, bundle thickness is reduced in the target region, and thereby probably a more accurate targeting is facilitated. The HAMLET-driven method is meant to achieve a more objective surgical fiber display of the slMFB with deterministic tractography.ConclusionsThe approach allows overlying the results of patient-specific planning from two different approaches (manual deterministic and machine learning HAMLET). HAMLET shows the slMFB as a volume and thus serves as an objective tracking corridor. It helps to refine results from deterministic tracking in the surgical workspace without interfering with any part of the standard software solution. We have now included this workflow in our daily clinical experimental work on slMFB DBS for psychiatric indications.
机译：对精神病疾病中的超薄内侧前脑束（SLMFB）深脑刺激（DBS）存在的背景兴趣存在。外科手术方法认证左右交流。商业立体定向规划系统使用遭受内在局限性的确定性牵引，取决于手动交互（ROI定义），并且必须被视为主观。我们旨在开发一个客观但特定于SLMFB的患者特定的跟踪，同时允许在深脑刺激的背景下使用商业手术计划系统。哈姆雷特（来自扩散MRI的分层谐波过滤器）基于患者特异性DMRI将学习方法引入SLMFB DBS杂志规划的标准化工作流程。使用哈姆雷特的SLMFB渲染是用于改进确定性跟踪程序的客观比较。我们的应用重点介绍了对重大抑郁和OCD的DBS（n = 8）的左右术语。结果已经表明，只有属于腹侧/眶内轴的侧面的纤维应该靶向。利用所提出的技术，可以精制用作外科手术计划数据的确定性跟踪方法，消除过萌发纤维，在目标区域中减小束厚度，从而便于更准确的靶向。哈姆雷特驱动方法旨在实现具有确定性牵引的SLMFB的更客观的外科纤维显示。结论方法可以从两种不同的方法（手动确定性和机器学习哈姆雷特）覆盖患者特定规划的结果。哈姆雷特显示SLMFB作为体积，因此用作客观跟踪走廊。它有助于在手术工作空间中的确定性跟踪中改进结果，而不会干扰标准软件解决方案的任何部分。我们现在在我们的日常临床实验工作中包含了此工作流程，用于SLMFB DBS进行精神病毒症。

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