A Long Short-Term Memory Network for Vessel Reconstruction Based on Laser Doppler Flowmetry via a Steerable Needle
A Long Short-Term Memory Network for Vessel Reconstruction Based on Laser Doppler Flowmetry via a Steerable Needle
复制标题
基于可操纵针激光多普勒血流测量的血管重建长短期记忆网络
作者：
Vani Virdyawan;F. Baena
出血是脑内经皮介入治疗的风险之一，可能危及生命。由于其能够遵循曲线路径，可控针可以避开血管，尽管需要了解血管姿势。为了实现这一目标，我们提出部署激光多普勒流量计（LDF）传感器作为可操纵针的原位血管检测方法。由于LDF系统的灌注值不直接提供位置信息，因此我们建议使用基于长短期记忆（LSTM）网络的机器学习技术在线执行血管重建。首先，LSTM用于基于单个LDF探头的连续测量来预测接近血管的直径和位置。其次，基于嵌入在可操纵针内的四个LDF探头的测量结果预测“禁入”区域，这说明了完整的血管姿态。该网络使用模拟数据进行训练，并在实验数据上进行测试，单探头网络的直径预测精度为75%，位置均方根（RMS）误差为0.27 mm，4探头设置的血管体积重叠为77%。
Hemorrhage is one risk of percutaneous intervention in the brain that can be life-threatening. Steerable needles can avoid blood vessels thanks to their ability to follow curvilinear paths, although knowledge of vessel pose is required. To achieve this, we present the deployment of laser Doppler flowmetry (LDF) sensors as an in-situ vessel detection method for steerable needles. Since the perfusion value from an LDF system does not provide positional information directly, we propose the use of a machine learning technique based on a Long Short-term Memory (LSTM) network to perform vessel reconstruction online. Firstly, the LSTM is used to predict the diameter and position of an approaching vessel based on successive measurements of a single LDF probe. Secondly, a “no-go” area is predicted based on the measurement from four LDF probes embedded within a steerable needle, which accounts for the full vessel pose. The network was trained using simulation data and tested on experimental data, with 75% diameter prediction accuracy and 0.27 mm positional Root Mean Square (RMS) Error for the single probe network, and 77% vessel volume overlap for the 4-probe setup.