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: 7955355
• 负责人: MURAT AKSOY
• 金额: $ 1.8万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7955355
• 关键词: Accounting; Back; Childhood; Computer Retrieval of Information on Scientific Projects Database; Data; Detection; Elderly; Foundations; Funding; Grant; Head; Image; Individual; Institution; Magnetic Resonance Imaging; Maps; Medical; Methods; Morphologic artifacts; Motion; Patients; Phase; Positioning Attribute; Relative (related person); Research; Research Personnel; Resolution; Resources; Rotation; Sampling; Scanning; Scheme; Seizures; Simulate; Slice; Source; Stroke; Technology; Thick; Translations; Tremor; United States National Institutes of Health; Validation; Variant; Work; base; 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. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 介绍。患者的无意识运动仍然是 MRI 中的一个巨大挑战。具体而言，对于老年人和儿童患者群体，或者由于身体状况（震颤、癫痫、中风）而无法保持静止的患者，有效的运动补偿策略至关重要。在这项研究中，引入了一种并行成像的变体，可以纠正由刚体运动（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程度，相应地校正k空间数据，然后采用基于增强共轭梯度的迭代图像重建来合成k空间中的丢失数据。该方法在模拟交错 EPI 和螺旋图像扫描以及使用双密度螺旋扫描的体内进行了描述和验证。 材料和方法：重建通常，图像空间中的对象旋转与 k 空间数据的类似旋转并行，而平移则由线性相位滚动反映。如果这些运动分量已知，则可以校正 k 空间数据，但通常会导致 k 空间碎片。这反过来又会在最终图像中产生明显的重影伪影。我们的校正建立在迭代 SENSE 重建的增强版本之上，并按如下方式执行：1）通过在网格化之前将相应的旋转矩阵应用于每个轮廓/交错的 k 空间轨迹坐标点来反向旋转 k 空间数据。 2) 旋转进入每个轮廓/交错的编码矩阵E1的线圈灵敏度图。这种旋转是必要的，因为即使将对象旋转回其所需位置，在采集过程中对象的不同区域也会暴露于不同的线圈灵敏度。 3) 校正旋转后改变的采样密度。在本研究中，Voronoi 曲面细分已用于从旋转的 k 空间轨迹导出新的采样密度。 4) 通过将校正项 pcorr(?) = exp{-j(2??x/FOVx) (kx(?)/[kx,max-kx,min]) j(2??y/FOVy)(ky(?)/[ky,max-ky,min])} 应用于网格化之前的原始 k 空间数据，对数据进行定相以考虑平移。 运动检测 存在多种方法可以从 MR 数据中得出平移和旋转运动的程度。在这项研究中，运动信息是从导航回波中提取的。导航器信息可以从扫描轨迹本身（即自导航轨迹）中导出，或者从提供低分辨率图像的单独采集中导出。这里，使用多网格配准方法来找到参考图像和各个导航器图像之间的最大皮尔逊相关性，并提供相对于参考图像的旋转和平移量的可靠估计（所有图像的平均值）。为了提高鲁棒性并提高共同配准的准确性，该步骤至少重复两次。 实验 通过对运动损坏的体模使用逆网格操作2，生成交错螺旋和 EPI 采集（8 个交错）的合成数据。对于八个交错中的每一个，都会生成随机头部旋转（范围“30”）和平移（范围“15mm”）。在逆网格化步骤之前，将每个单独旋转和移动的图像乘以线圈灵敏度，模拟来自连接在物体圆周上的六个线圈的接收器线圈灵敏度。使用具有交错螺旋入/螺旋出读数和 8 通道头部线圈的 T2w 自旋回波扫描在 3 名健康志愿者中进行体内验证。螺旋进入部分（3-5ms持续时间）为每个插页数据提供低分辨率导航图像（322）。螺旋出部分为正常的交错螺旋采集：TR/TE=4,000ms/85ms，层厚/间隙=4/1mm，17层，FOV=24cm，矩阵=256，交错=32，NEX=1。螺旋采集的接收器带宽为 +/- 125kHz。在每次实验中，志愿者被要求以三种逐渐增加的运动水平旋转和/或移动头部（无、轻度[〜15¿]和中度[〜25¿]运动）。 参考文献：1Pruessmann K 等人。 MRM 46：638-51，2001； 2Rasche V 等人。 IEEE TMI 18：385-92，1999。 致谢：这项工作得到了 NIH (1R01EB002771)、斯坦福大学高级 MR 技术中心 (P41RR09784)、卢卡斯基金会的部分支持。