Motion detection and correction in neuro MRI using RF sensors and learning from k-space data

磁共振成像（MRI）继续受到受试者无意识和生理运动的影响。这是因为图像测量很慢，每张图像需要2到10分钟。运动对图像的破坏造成了昂贵的扫描时间损失，降低了操作效率。已经提出了若干解决办法；然而，每一个都被调整到特定的获取或测量特定类型的运动（刚体/心脏）。最近的一项发展使用了作为MRI硬件一部分的射频线圈（1-3）。这些线圈在空间上位于受试者周围，其电特性随附近组织（受试者运动）的变化而变化。我们的初步工作表明，使用专用的校准扫描，这些数据可以测量头部运动。[图1：使用射频线圈预测头部运动，展示了运动信息的高时间分辨率。]这是通过15分钟受试者特定校准来实现的]然而，为每个受试者校准这些测量是不可能的或不切实际的。最近的其他工作表明，原始k空间获取可以使用来自多个接收通道的冗余信息来量化主体运动(4)。该方法仅限于存在足够并行成像冗余的3D采集，因此也将探索基于模型的替代解决方案。主体运动的典型特征是在相对较长的静态位置中很少有突然变化。压缩感知表明，这种稀疏创新可以从与突变数量成比例的一些测量中精确确定。该项目将压缩感知技术与深度学习技术相结合，实现运动检测和补偿的自动化，从而优化扫描速率效率，减少重新扫描的需要，并通过尽可能多地利用获取的数据来提高整体诊断质量。这项工作提出了应用机器学习方法来量化运动，具有高精度和高时间分辨率。这可以通过使用原始k空间数据中可用的部分信息来学习RF传感器和运动之间的关系来实现。这项工作是学术利益之间的接口- Hess的运动射频传感器，Tanner的压缩传感和深度学习，包括少量学习技术，以及Mailhe/Siemens的MRI采集优化。

Magnetic Resonance Imaging (MRI) continues to be affected by unintended and physiological movement of the subject. This is because image measurement is slow, tacking between 2 and 10 minutes for a single image. Images corrupted by motion lead to loss of expensive scan time, and reduced operation efficacity. A number of solutions have been proposed; however, each are tuned to specific acquisitions or to the measurement of specific types of motion (rigid body / cardiac). A recent development uses the RF coils that exist as part of the MRI hardware (1-3). These coils are spatially located around the subject and their electrical characteristics change in relation to changes in nearby tissue (subject motion). Our preliminary work has shown that using a dedicated calibration scan, this data can measure head motion. [Figure 1: head motion predicted using rf coils, demonstrating a high temporal resolution for motion information. This was achieved with a 15 minute subject specific calibration]However, it is not possible or practical to calibrate these measurements for each subject. Other recent work has demonstrated that raw k-space acquisitions can be used to quantify subject motion(4) using redundant information from multiple receive channels. This method is limited to 3D acquisitions where sufficient parallel imaging redundancy exists, therefore alternative model based solutions will also be explored. Subject motion is typically characterized by few abrupt changes amongst relatively lengthy static positions. Compressed sensing established that such sparse innovations can be accurately determined from a number of measurements proportional to the number of abrupt changes. This project will combine techniques from compressed sensing with deep learning in order to automate motion detection and compensation so as to optimize the scan rate efficiency, reduce the need for rescanning, and improve overall diagnostic quality by making use of as much acquired data as possible.This work proposes the application of machine learning methods to quantify motion, with high accuracy and high temporal resolution. This can be achieved by learning the relationship between the RF sensors and motion using the partial information available in the raw k-space data. This work is at the interface between the academic interests - Hess with RF sensors for motion, Tanner with compressed sensing and deep learning including few-shot learning techniques, and Mailhe/Siemens in optimisation of MRI acquisitions.