Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data.
Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data.
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DOI：
10.1016/j.neuroimage.2017.12.040
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发表时间：
2018-05-01
期刊：
影响因子：
5.7
通讯作者：
Campbell J
中科院分区：
文献类型：
--
作者：
Andersson JLR;Graham MS;Drobnjak I;Zhang H;Campbell J
由于它们在相位编码（PE）方向上的低带宽，磁化率诱导的非共振场导致回波平面成像（EPI）图像中的失真。因此，在使用EPI进行扩散研究时，校正可测量性引起的失真至关重要。磁感应场是由干扰场的物体（头部）引起的，通常假设它在物体定义的框架内保持恒定（即，当物体在扫描仪中移动时，它跟随物体）。然而，这只是近似正确的。当一个非球形物体绕着一个与磁通量平行的轴（z轴）旋转时，它会改变它破坏磁场的方式，导致不同的扭曲。因此，如果使用单个场来校正失真，则在对象取向与测量场时的对象取向基本上不同的体积中将存在残余失真。在本文中，我们提出了一种后处理方法，用于估计字段，因为它在实验过程中的运动变化。它只需要一个单一的测量领域和知识的方向时，该领域被收购的主题。作为对象移动的结果的场的体积到体积的变化直接从扩散数据估计，而不需要任何额外的或特殊的采集。它使用一个生成模型，预测每个卷将如何预测场变化，并反转该模型以产生场变化的估计。仿真和实验数据均验证了该方法的有效性。结果表明，我们能够以高精度跟踪场，并且我们能够针对变化场的不利影响校正数据。
Because of their low bandwidth in the phase-encode (PE) direction, the susceptibility-induced off-resonance field causes distortions in echo planar imaging (EPI) images. It is therefore crucial to correct for susceptibility-induced distortions when performing diffusion studies using EPI. The susceptibility-induced field is caused by the object (head) disrupting the field and it is typically assumed that it remains constant within a framework defined by the object, (i.e. it follows the object as it moves in the scanner). However, this is only approximately true. When a non-spherical object rotates around an axis other than that parallel with the magnetic flux (the z-axis) it changes the way it disrupts the field, leading to different distortions. Hence, if using a single field to correct for distortions there will be residual distortions in the volumes where the object orientation is substantially different to that when the field was measured. In this paper we present a post-processing method for estimating the field as it changes with motion during the course of an experiment. It only requires a single measured field and knowledge of the orientation of the subject when that field was acquired. The volume-to-volume changes of the field as a consequence of subject movement are estimated directly from the diffusion data without the need for any additional or special acquisitions. It uses a generative model that predicts how each volume would look predicated on field change and inverts that model to yield an estimate of the field changes. It has been validated on both simulations and experimental data. The results show that we are able to track the field with high accuracy and that we are able to correct the data for the adverse effects of the changing field.
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