Subject movement during the magnetic resonance examination is inevitable and causes not only image artefacts but also deteriorates the homogeneity of the main magnetic field (B0), which is a prerequisite for high quality data. Thus, characterization of changes to B0, e.g. induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine-tuned the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data. Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method. We therefore conclude that it is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of magnetic resonance acquisitions without the use of navigators.
翻译:摘要:磁共振检查期间的受试者运动是不可避免的,不仅会引起图像伪影,也会使主磁场(B0)的均匀度变差,这是高质量数据的前提。因此,鉴定B0场的变化,例如由于受试者运动引起的变化,对于易于B0非均匀性的MR应用很重要。我们提出了一种基于深度学习的方法,可以从头部姿势变化中预测大脑内的这种变化,以便进行回顾性或甚至实时矫正。我们在体内脑7T MRI数据上训练了一个三维的U-net。输入包括初始位置处的B0图和解剖图像,以及在不同头部位置处的解剖图像(通过在初始解剖图像上应用刚体变换获得)。输出包括在新头部位置处的B0图。我们还通过测量给定受试者的有限数量的头部位置,对网络权重进行了进一步的微调,并使用这些数据来训练U-net。我们的方法与常规的动态B0场映射方法(通过交错引导进行)进行了比较,这些方法具有有限的空间分辨率和需要进行不必要的序列修改。定性和定量比较表明,所提出的方法和交错导航器等价方法之间的表现相似。因此,我们得出结论,可以通过刚性主体运动预测B0图,当与外部跟踪硬件相结合时,可以使用此信息来提高磁共振采集的质量,而无需使用引导器。