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: 7601890
• 负责人: MURAT AKSOY
• 金额: $ 1.73万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2008-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7601890
• 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的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 导论.患者的不自主运动仍然是MRI中的一个巨大挑战。具体而言，在老年和儿科患者人群中或在其医学状况（震颤、癫痫发作、中风）使其无法保持静止的患者中，补偿运动的有效策略至关重要。在这项研究中，一个变种的平行成像，可以纠正k空间的不一致性所产生的刚体运动（旋转或平移）。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。该方法进行了描述和验证，在模拟交错EPI和螺旋图像扫描，以及在体内使用双密度螺旋扫描。 材料与方法：重建  通常，图像空间中的对象旋转由k空间数据的类似旋转来补偿，而平移由线性相位滚动来反映。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。这又在最终图像中产生显著的重影伪影。我们的校正建立在迭代SENSE重建的增强版本1上，并且如下执行：1）通过在网格化之前将对应的旋转矩阵应用于每个轮廓/交织的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，因为即使对象被旋转回到其期望位置，对象的不同区域在采集期间已经暴露于不同的线圈灵敏度。3)校正旋转后改变的采样密度。在这项研究中，Voronoi曲面细分已被用于从旋转的k空间轨迹中导出新的采样密度。4)通过应用校正项pcorr（？）= exp{-j（2？x/FOVx）（kx（？）/ [kx，max-kx，min]）  j（2？y/FOVy）（ky（？）/ [ky.，max-ky，min]）}到网格化之前的原始k空间数据。 运动检测  存在各种方法来从MR数据导出平移和旋转运动的程度。在这项研究中，运动信息提取导航回波。导航器信息可以从扫描轨迹本身（即，自导航轨迹）或者可替代地从提供低分辨率图像的单独采集导出。这里，使用了多网格配准方法，该方法找到参考图像和各个导航图像之间的最大Pearson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）。为了增加鲁棒性并提高配准的准确性，该步骤至少重复两次。 实验  通过在运动损坏的体模上使用逆网格操作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，生成随机头部旋转（范围<$30 <$$>）和平移（范围<$15 mm）。在逆网格化步骤之前，将每个单独旋转和移位的图像乘以线圈灵敏度，该线圈灵敏度模拟来自围绕对象的圆周附接的六个线圈的接收器线圈灵敏度。在3名健康志愿者中使用T2 w自旋回波扫描进行了体内确认，该扫描具有交错螺旋输入/螺旋输出读数和8通道头部线圈。螺旋部分（3- 5 ms持续时间）为每个隔片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= 4,000 ms/85 ms，层厚/间隙=4/1 mm，17层，FOV= 24 cm，矩阵=256，交错= 32，NEX=1。螺旋采集的接收器带宽为+/-125 kHz。在每个实验中，志愿者被要求旋转和/或移动他们的头在三个增加的运动水平（没有，轻度[~]，和中度[~]运动）。 参考文献：1 Pruessmann K等人，MRM 46：638-51，2001; 2 Rasche V等人，IEEE TMI 18：385-92，1999。 鸣谢：这项工作得到了NIH（1 R 01 EB 002771）、斯坦福大学先进MR技术中心（P41 RR 09784）和卢卡斯基金会的部分支持。