Objectives

Despite their life-saving capabilities, cerebrospinal fluid (CSF) shunts exhibit high failure rates, with a large fraction of failures attributed to the regulating valve. Due to a lack of methods for the detailed analysis of valve malfunctions, failure mechanisms are not well understood, and valves often have to be surgically explanted on the mere suspicion of malfunction.

The presented pilot study aims to demonstrate radiological methods for comprehensive analysis of CSF shunt valves, considering both the potential for failure analysis in design optimization, and for future clinical in-vivo application to reduce the number of required shunt revision surgeries. The proposed method could also be utilized to develop and support in situ repair methods (e.g. by lysis or ultrasound) of malfunctioning CSF shunt valves.

Materials and methods

The primary methods described are contrast-enhanced radiographic time series of CSF shunt valves, taken in a favorable projection geometry at low radiation dose, and the machine-learning-based diagnosis of CSF shunt valve obstructions. Complimentarily, we investigate CT-based methods capable of providing accurate ground truth for the training of such diagnostic tools. Using simulated test and training data, the performance of the machine-learning diagnostics in identifying and localizing obstructions within a shunt valve is evaluated regarding per-pixel sensitivity and specificity, the Dice similarity coefficient, and the false positive rate in the case of obstruction free test samples.

Results

Contrast enhanced subtraction radiography allows high-resolution, time-resolved, low-dose analysis of fluid transport in CSF shunt valves. Complementarily, photon-counting micro-CT allows to investigate valve obstruction mechanisms in detail, and to generate valid ground truth for machine learning-based diagnostics.

Machine-learning-based detection of valve obstructions in simulated radiographies shows promising results, with a per-pixel sensitivity >70%, per-pixel specificity >90%, a median Dice coefficient >0.8 and <10% false positives at a detection threshold of 0.5.

Conclusions

This ex-vivo study demonstrates obstruction detection in cerebro-spinal fluid shunt valves, combining radiological methods with machine learning under conditions compatible to future in-vivo application.

Results indicate that high-resolution contrast-enhanced subtraction radiography, possibly including time-series data, combined with machine-learning image analysis, has the potential to strongly improve the diagnostics of CSF shunt valve failures. The presented method is in principle suitable for in-vivo application, considering both measurement geometry and radiological dose. Further research is needed to validate these results on real-world data and to refine the employed methods.

In combination, the presented methods enable comprehensive analysis of valve failure mechanisms, paving the way for improved product development and clinical diagnostics of CSF shunt valves.

中文翻译：

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用于分析脑脊液 (CSF) 分流阀的高分辨率放射学方法的初步研究

 目标

尽管脑脊液 (CSF) 分流器具有挽救生命的功能，但其故障率很高，其中很大一部分故障归因于调节阀。由于缺乏对瓣膜故障进行详细分析的方法，人们对瓣膜故障的机制还没有很好的了解，并且常常仅仅因为怀疑存在故障就必须通过手术移植瓣膜。

所提出的试点研究旨在展示用于综合分析脑脊液分流阀的放射学方法，同时考虑设计优化中失效分析的潜力，以及未来临床体内应用以减少所需分流翻修手术的数量。所提出的方法还可用于开发和支持故障脑脊液分流阀的原位修复方法（例如通过裂解或超声）。

 材料和方法

所描述的主要方法是在低辐射剂量下以有利的投影几何形状拍摄的脑脊液分流阀的对比增强放射线摄影时间序列，以及基于机器学习的脑脊液分流阀阻塞诊断。作为补充，我们研究了基于 CT 的方法，能够为此类诊断工具的培训提供准确的地面事实。使用模拟测试和训练数据，评估机器学习诊断在识别和定位分流阀内障碍物方面的性能，包括每像素灵敏度和特异性、Dice 相似系数以及无障碍物情况下的误报率测试样品。

 结果

对比增强减影射线照相可以对脑脊液分流阀中的流体输送进行高分辨率、时间分辨、低剂量的分析。作为补充，光子计数微型 CT 可以详细研究瓣膜阻塞机制，并为基于机器学习的诊断生成有效的基本事实。

基于机器学习的模拟射线照相中瓣膜阻塞检测显示出良好的结果，每像素灵敏度 >70%，每像素特异性 >90%，中值 Dice 系数 >0.8，检测阈值误报率 <10% 0.5。

 结论

这项离体研究展示了脑脊液分流阀的阻塞检测，在与未来体内应用兼容的条件下将放射学方法与机器学习相结合。

结果表明，高分辨率对比增强减影射线照相（可能包括时间序列数据）与机器学习图像分析相结合，有可能极大地改善脑脊液分流阀故障的诊断。考虑到测量几何形状和放射剂量，所提出的方法原则上适合体内应用。需要进一步的研究来验证现实世界数据的这些结果并完善所采用的方法。

结合起来，所提出的方法可以对瓣膜失效机制进行全面分析，为改进脑脊液分流阀的产品开发和临床诊断铺平道路。