论文标题
使用时间序列分类基于图像功能的MRI硬件故障预测
Prediction of MRI Hardware Failures based on Image Features using Time Series Classification
论文作者
论文摘要
在系统故障之前，人们必须知道硬件组件是否会在不久的将来失败才能及时抵消。因此，应该避免计划外的停机时间。在医学成像中，最大化系统的正常运行时间对于患者的健康和医疗保健提供者的日常业务至关重要。我们旨在通过训练随着时间的时间收集的顺序数据来训练磁共振成像（MRI）中使用的头部/颈部线圈的故障。由于图像特征取决于线圈的状况，因此它们从正常范围已经暗示了未来故障的偏差。因此，我们使用图像特征及其随着时间的变化来预测线圈损伤。在比较了不同的时间序列分类方法之后，我们发现长期记忆（LSTMS）达到最高的F-评分为86.43％，如果要更换硬件，则以98.33％的准确性分辨。
Already before systems malfunction one has to know if hardware components will fail in near future in order to counteract in time. Thus, unplanned downtime is ought to be avoided. In medical imaging, maximizing the system's uptime is crucial for patients' health and healthcare provider's daily business. We aim to predict failures of Head/Neck coils used in Magnetic Resonance Imaging (MRI) by training a statistical model on sequential data collected over time. As image features depend on the coil's condition, their deviations from the normal range already hint to future failure. Thus, we used image features and their variation over time to predict coil damage. After comparison of different time series classification methods we found Long Short Term Memorys (LSTMs) to achieve the highest F-score of 86.43% and to tell with 98.33% accuracy if hardware should be replaced.
