IMPROVING RIGID HEAD MOTION CORRECTION USING PARALLEL IMAGING

使用并行成像改进刚性头运动校正

• 批准号: 8362897
• 负责人: MURAT AKSOY
• 金额: $ 1.3万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8362897
• 关键词: Accounting; Annual Reports; Back; Childhood; Data; Detection; Elderly; Foundations; Funding; Grant; Head; Image; Individual; Magnetic Resonance; Magnetic Resonance Imaging; Maps; Medical; Methods; Morphologic artifacts; Motion; National Center for Research Resources; Patients; Phase; Positioning Attribute; Principal Investigator; Reading; Relative (related person); Research; Research Infrastructure; Resolution; Resources; Rotation; Sampling; Scanning; Scheme; Seizures; Simulate; Slice; Source; Stroke; Technology; Thick; Translations; Tremor; United States National Institutes of Health; Validation; Variant; Visit; Work; abstracting; base; cost; data space; density; healthy volunteer; image reconstruction; improved; in vivo; patient population; reconstruction; research study; volunteer

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)

该副本是利用资源的众多研究子项目之一 由NIH/NCRR资助的中心赠款提供。对该子弹的主要支持 而且，副投影的主要研究员可能是其他来源提供的 包括其他NIH来源。列出的总费用可能 代表subproject使用的中心基础架构的估计量， NCRR赠款不直接向子弹或副本人员提供的直接资金。 介绍。非自愿的患者运动仍然是MRI的巨大挑战。具体而言，在年长和儿科患者中 人口或医疗状况（震颤，癫痫发作，中风）的患者无法保持静止，有效 补偿运动的策略至关重要。在这项研究中，引入了一种平行成像的变体，可以 正确的K空间不一致是由刚体运动（旋转或翻译）引起的。该运动校正方案 首先确定运动程度，相应地纠正K空间数据，然后员工增加 基于共轭梯度的迭代图像重建，以合成K空间中缺少的数据。描述了该方法 并在模拟的交织EPI和螺旋图像中进行了验证，并使用双密度螺旋扫描在体内进行了验证。 材料和方法：通常重建，图像空间中的对象旋转与类似的旋转相似 k空间数据，而翻译则由线性相位卷反映。如果这些运动组件已知，则K空间 数据可以纠正，但通常会导致k空间的碎片化。反过来，这产生了重要的鬼魂 最终图像中的文物。我们的更正是基于迭代意义重建1和 如下所示：1）通过将相应的旋转矩阵应用于k空间来反向旋转k空间数据 磨削之前，每个轮廓/交织的轨迹坐标点。 2）旋转进入的线圈灵敏度图 每个配置文件/交织的编码矩阵E1。这种旋转是必要的，因为即使物体向后旋转 达到所需的位置，该物体的不同区域已暴露于不同的线圈灵敏度 获得。 3）校正旋转后改变的采样密度。在这项研究中，voronoi tessellation已用于 从旋转的K空间轨迹中得出新的采样密度。 4）逐步数据以说明翻译 应用校正术语pcorr（？）= exp {-j（2 ?? x/fovx）（kx（？）/[kx，max-kx，min]）j（2 ?? y/fovy）（ky（ky（？）/[ky，max-- ky，min]）}到原始的k空间数据之前。 运动检测存在各种方法，可从MR数据得出转化和旋转运动的程度。在 这项研究，从导航器回波中提取运动信息。导航器信息可以从 扫描轨迹本身（即自动散开的轨迹）或从提供低的单独采集中 分辨率图像。在这里，使用了多机登记方法，可以找到最大的皮尔森相关性 在参考图像和单个导航器图像之间，并提供了可靠的旋转量 和相对于参考图像的翻译（所有图像的平均值）。提高鲁棒性并改善 共同注册的精度至少重复两次。 通过使用逆，生成了用于交织的螺旋和EPI采集（8个交织）的实验合成数据（8个交织） 网格操作2在动作损坏的幻影上。对于八个中的每一个，都会交织一个随机旋转（范围 €30€）和翻译（范围15mm）。在逆向步骤之前，每个单独旋转 并将移位的图像乘以线圈灵敏度，以模拟接收器线圈灵敏度的六个线圈 附着在物体的圆周围。使用T2W在3名健康志愿者中进行体内验证 旋转回声扫描，带有交织的螺旋式插入/螺旋出读数和8通道头线圈。螺旋形部分（3-5ms） 为每个交流数据提供的持续时间）一个低分辨率导航器图像（322）。螺旋式零件是正常的 交织的螺旋采集：TR/TE = 4,000ms/85ms，切片厚度/GAP = 4/1mm，17片，FOV = 24厘米，矩阵= 256， 交叉= 32，nex = 1。螺旋恢复的接收器带宽为+/- 125kHz。在每个期间 实验志愿者被要求旋转和/或以三个增加的运动水平旋转和/或移动头部（否，轻度[〜 �15…]，中等[〜»二十五]运动）。 参考文献：1Pruessmann K等。 MRM 46：638-51，2001; 2rasche V等。 IEEE TMI 18：385-92，1999。 致谢：这项工作得到了高级MR中心NIH（1R01EB002771）的部分支持 卢卡斯基金会的斯坦福大学技术（P41RR09784）。 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摘要）