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: 7722869
• 负责人: MURAT AKSOY
• 金额: $ 1.68万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7722869
• 关键词: Accounting; Back; Childhood; Computer Retrieval of Information on Scientific Projects Database; Condition; Data; Detection; Elderly; Erythrocyte Ghost; Foundations; Funding; Grant; Head; Image; Individual; Institution; Magnetic Resonance Imaging; Maps; Medical; Methods; Morphologic artifacts; Motion; Patients; Phase; Population; Positioning Attribute; Range; 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; desire; healthy volunteer; image reconstruction; improved; in vivo; 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.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 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。具体地说，在老年人和儿科患者群体中，或者在医疗条件(震颤、癫痫、中风)阻止他们保持不动的患者中，有效的运动补偿策略是至关重要的。在这项研究中，引入了一种并行成像的变体，可以校正由于刚体运动(旋转或平移)而产生的k空间不一致性。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，然后采用基于增广共轭梯度的迭代图像重建来合成k空间中的缺失数据。该方法被描述并在模拟交错EPI和螺旋图像扫描以及使用双密度螺旋扫描的活体图像扫描中得到验证。 材料和方法：重建通常情况下，物体在图像空间的旋转与k空间数据的类似旋转是平行的，而平移是由线性相位卷曲反映的。如果这些运动分量是已知的，则可以校正k空间数据，但通常会导致k空间的碎片化。这反过来又会在最终图像中产生重要的鬼影伪影。我们的校正建立在迭代感测重建的增强版本1上，并且如下执行：1)通过将对应的旋转矩阵应用于栅格化之前每个轮廓/交错的k空间轨迹坐标点来反旋转k空间数据。2)为每个轮廓/交织旋转进入编码矩阵E1的线圈敏感度图。这种旋转是必要的，因为即使对象被旋转回其所需位置，对象的不同区域在采集期间也暴露于不同的线圈灵敏度。3)对旋转后改变的采样密度进行校正。在这项研究中，Voronoi细分被用来从旋转的k空间轨迹中推导出新的采样密度。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，对数据进行阶段化以考虑平移。 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。在本研究中，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)中获得，或者可替换地从提供低分辨率图像的单独采集中获得。在这里，使用了多网格配准方法，该方法找到了参考图像和单个导航图像之间的最大皮尔逊相关性，并提供了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)。为了增加稳健性和提高联合配准的准确性，这一步骤至少重复了两次。 实验通过在运动损坏的体模上使用反向网格化操作2来生成交错螺旋和EPI采集(8个交错)的合成数据。对于八个交错的每一个，产生随机的磁头旋转(范围30)和平移(范围15 mm)。在反向网格化步骤之前，将每个单独旋转和移位的图像乘以线圈灵敏度，该线圈灵敏度模拟了六个绕物体圆周连接的线圈的接收器线圈灵敏度。对3名健康志愿者进行了体内验证，使用T2w自旋回波扫描，采用交错螺旋输入/螺旋输出读出和8通道磁头线圈。螺旋部分(持续时间3-5ms)为每个交错数据提供低分辨率导航图像(322)。螺旋输出部分为正常交错螺旋采集：TR/TE=4000ms/85ms，层厚/间隙=4/1 mm，17层，FOV=24 cm，矩阵=256，交织=32，NEX=1。螺旋采集的接收带宽为+/-125 kHz。在每个实验中，志愿者被要求以三种递增的运动水平(不、轻度运动和中度运动)旋转和/或移动他们的头部。 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)、斯坦福高级磁共振技术中心(P41RR09784)、卢卡斯基金会的部分支持。