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

磁共振成像（MRI）继续受到受试者的非预期和生理运动的影响。这是因为图像测量很慢，单个图像的测量时间在2到10分钟之间。运动对图像的破坏导致了昂贵的扫描时间的损失，降低了手术效率。已经提出了许多解决方案;然而，每个都被调整到特定的采集或特定类型的运动（刚体/心脏）的测量。最近的发展使用作为MRI硬件的一部分存在的RF线圈（1 - 3）。这些线圈在空间上位于受试者周围，并且它们的电特性相对于附近组织的变化（受试者运动）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：使用射频线圈预测的头部运动，展示了运动信息的高时间分辨率。这是通过15分钟的受试者特定校准实现的。然而，校准每个受试者的这些测量值是不可能的或不切实际的。其他最近的工作已经证明，原始k空间采集可以用于使用来自多个接收通道的冗余信息来量化对象运动（4）。该方法仅限于存在足够并行成像冗余的3D采集，因此还将探索基于替代模型的解决方案。受试者运动的典型特征是在相对较长的静态位置之间几乎没有突然变化。压缩感知建立了这样的稀疏创新可以准确地确定从测量的数量成比例的突变的数量。该项目将联合收割机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采集的数据来提高整体诊断质量。该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传感器与运动之间的关系来实现。这项工作是在学术兴趣之间的接口-赫斯与RF传感器的运动，坦纳与压缩传感和深度学习，包括少数拍摄学习技术，和Mailhe/西门子在MRI采集的优化。

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.