IMPROVING RIGID HEAD MOTION CORRECTION USING PARALLEL IMAGING

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 导言。患者的非自主运动在磁共振成像中仍然是一个巨大的挑战。具体而言，在老年人和儿科患者中 对于身体状况(震颤、癫痫、中风)不能保持静止的人群或患者，有效 补偿运动的策略是最重要的。在这项研究中，引入了一种并行成像的变体，它可以 更正由刚体运动(旋转或平移)引起的k空间不一致。该运动校正方案 首先识别运动的程度，相应地校正k空间数据，然后使用增强的 基于共轭梯度的迭代图像重建在k-空间合成缺失数据。描述了该方法 并在模拟交错EPI和螺旋图像扫描中以及在体内使用双密度螺旋扫描进行验证。 材料和方法：重建通常，物体在图像空间中的旋转与物体在图像空间中的旋转是平行的 K空间数据，而平移由线性相位滚动反映。如果这些运动分量是已知的，则k空间 数据可以被校正，但通常会导致k空间的碎片化。这反过来又产生了重要的幽灵。 最终图像中的瑕疵。我们的纠正建立在迭代感觉重建的增强版本1上 被执行如下：1)通过将对应的旋转矩阵应用于k空间来对k空间数据进行反旋转 网格化前每个轮廓/交错的轨迹坐标点。2)旋转进入的线圈敏感度贴图 每个简档/交织的编码矩阵E1。这种旋转是必要的，因为即使对象向后旋转 至其所需位置时，对象的不同区域已在 收购。3)对旋转后改变的采样密度进行校正。在本研究中，Voronoi细分已被用于 从旋转的k-空间轨迹推导出新的采样密度。4)通过以下方式分阶段处理数据以进行转换 应用修正项pcorr=exp{-j(2？x/FOVx)(kx？/[kx，max-kx，min])j(2？y/FOVy)(Ky？/[Ky，max- Ky，Min]))}转换为网格化之前的原始k空间数据。 运动检测存在各种方法来从MR数据中得出平移和旋转运动的程度。在……里面 本研究从导航回波中提取运动信息。导航器信息可以从 扫描轨迹本身(即，自导航轨迹)或者可替换地来自提供低 分辨率图像。在这里，使用了多格网配准方法来查找最大皮尔逊相关性 在参考图像和各个导航器图像之间，并提供旋转的可靠估计 以及相对于参考图像的平移(在所有图像上平均)。以增加健壮性并改进 联合登记的准确性这一步骤至少重复了两次。 实验通过使用逆生成交错螺旋和EPI采集(8个交错)的合成数据 对运动损坏的幻影执行的网格操作2。对于八个交错中的每一个，随机地旋转头部(范围 30)和平移(范围15 mm)。在反向网格化步骤之前，每个单独旋转的 并将移位图像乘以线圈灵敏度，模拟来自六个线圈的接收器线圈灵敏度 附着在物体的圆周上。使用T2w对3名健康志愿者进行了体内验证 使用交错螺旋输入/螺旋输出读出和8通道磁头线圈的自旋回波扫描。螺旋输入部分(3-5毫秒 持续时间)为每个插页数据提供低分辨率导航器图像(322)。螺旋状的部分是正常的 交错螺旋采集：TR/TE=4000ms/85ms，层厚/间隙=4/1 mm，17层，FOV=24 cm，矩阵=256， 交织=32，NEX=1。螺旋捕获的接收机带宽为/-125 kHz。在每个 实验志愿者被要求以三种递增的运动水平(不，轻微[~])旋转和/或移动他们的头部 [15]和适度的[25]运动)。 参考文献：1Pruessmann K，et al.MRM 46：638-51,2001；2 Rasche V，et al.IEEE TMI 18：385-92,1999。 致谢：这项工作得到了美国国立卫生研究院(1R01EB002771)，高级磁共振中心的部分支持 技术在斯坦福大学(P41RR09784)，卢卡斯基金会。 要了解卢卡斯中心正在进行的其他项目，请访问http://rsl.stanford.edu/(卢卡斯年度报告 和ISMRM 2011年摘要)

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Introduction. Involuntary patient motion is still a great challenge in MRI. Specifically, in the elderly and pediatric patient population or in patients whose medical conditions (tremor, seizure, stroke) preclude them to hold still, effective strategies to compensate for motion are paramount. In this study, a variant of parallel imaging is introduced that can correct k-space inconsistencies arising from rigid body motion (rotation or translation). This motion correction scheme first identifies the degree of motion, corrects the k-space data accordingly and thereafter employs an augmented conjugate gradient based iterative image reconstruction to synthesize missing data in k-space. The method is described and verified in simulated interleaved EPI and spiral images scans as well as in vivo using bi-density spiral scanning. Materials and Methods: Reconstruction  Generally, an object rotation in image space is paralleled by a similar rotation of k-space data, whereas translations are reflected by linear phase rolls. If these motion components are known, k-space data can be corrected for but usually leading to a fragmentation of k-space. This, in turn, gives rise to significant ghost artifacts in the final image. Our correction builds upon an augmented version of an iterative SENSE reconstruction1 and is performed as follows: 1) counter-rotating k-space data by applying the corresponding rotation matrix to the k-space trajectory coordinate points of each profile/interleave prior to gridding. 2) Rotating the coil sensitivity map that enters the encoding matrix E1 for each profile/interleave. This rotation is necessary because even if the object is rotated back to its desired position, different regions of the object have been exposed to different coil sensitivities during the acquisition. 3) Correcting the altered sampling density after rotation. In this study, Voronoi tessellation has been used to derive the new sampling density from the rotated k-space trajectories. 4) Phasing the data to account for translation by applying the correction term pcorr(?) = exp{-j(2??x/FOVx) (kx(?)/[kx,max-kx,min])  j(2??y/FOVy)(ky(?)/[ky,max- ky,min])} to the original k-space data prior to gridding. Motion detection  Various methods exist to derive the extent of translational and rotational motion from MR data. In this study, the motion information was extracted from navigator echoes. The navigator information can be derived from the scan trajectory itself (i.e. self-navigating trajectories) or alternatively from a separate acquisition that provides a low resolution image. Here, a multi-grid registration approach was used that finds the maximum Pearson correlation between a reference image and individual navigator images and provided a reliable estimate of the amount of rotation and translation relative to the reference image (average over all images). To increase robustness and to improve the accuracy of co-registration this step was repeated at least twice. Experiments  Synthetic data for interleaved spiral and EPI acquisitions (8 interleaves) were generated by using inverse gridding operations2 on a motion corrupted phantom. For each of the eight interleaves a random head rotation (range ¿30¿) and translation (range ¿15mm) was generated. Prior to the inverse gridding step, each of the individually rotated and shifted images were multiplied by coil sensitivities simulating receiver coil sensitivities from six coils that were attached around the circumference of the object. In vivo validation was performed in 3 healthy volunteers using T2w spin echo scans with an interleaved spiral-in/spiral-out readout and an 8-channel head coil. The spiral-in part (3-5ms duration) provided for each interleaf data a low resolution navigator image (322). The spiral-out part was a normal interleaved spiral acquisition: TR/TE=4,000ms/85ms, slice thickness/ gap=4/1mm, 17 slices, FOV=24cm, matrix=256, interleaves = 32, and NEX=1. The receiver bandwidth for the spiral acquisition was +/- 125kHz. During each experiment the volunteers were asked to rotate and/or shift their heads at three increasing levels of motion (no, mild [~ ¿15¿], and moderate [~¿25¿] motion ). References: 1Pruessmann K, et al. MRM 46: 638-51, 2001; 2Rasche V, et al. IEEE TMI 18: 385-92, 1999. Acknowledgements: This work was supported in part by the NIH (1R01EB002771), the Center of Advanced MR Technology at Stanford (P41RR09784), Lucas Foundation. To read about other projects ongoing at the Lucas Center, please visit http://rsl.stanford.edu/ (Lucas Annual Report and ISMRM 2011 Abstracts)