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.

该副本是使用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这是调查员的机构。 介绍。非自愿的患者运动仍然是MRI的巨大挑战。具体而言，在年龄较大的儿科患者人群或医疗状况（震颤，癫痫发作，中风）的患者中，他们静止不动，有效弥补运动的有效策略至关重要。在这项研究中，引入了一种平行成像的变体，该变体可以纠正刚体运动（旋转或翻译）引起的K空间不一致。该运动校正方案首先识别运动程度，相应地纠正K空间数据，然后员工进行增强的基于共轭梯度的迭代图像重建，以使K-Space中缺少的数据合成丢失的数据。在模拟的交织EPI和螺旋图像扫描以及使用双密度螺旋扫描中描述和验证该方法。 材料和方法：通常，重建图像空间中的对象旋转与K空间数据的类似旋转平行，而翻译则由线性相位滚动反映。如果已知这些运动组件，则可以校正K空间数据，但通常会导致K空间的碎片化。反过来，这在最终图像中产生了重要的幽灵文物。我们的校正基于迭代意义重建1的增强版本，并如下执行：1）通过将相应的旋转矩阵应用于每种配置文件/Interleave的K-Space轨迹坐标点，然后在研磨之前将相应的旋转矩阵应用于K-Space轨迹坐标点。 2）旋转对每个轮廓/交织的编码矩阵E1进入编码矩阵E1的线圈灵敏度图。这种旋转是必要的，因为即使物体旋转回到所需的位置，该物体的不同区域在采集过程中已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 Tessellation已被用来从旋转的K空间轨迹中得出新的采样密度。 4）通过将校正术语pcorr（？）= exp {-j（2？x/fovx）（kx（？）/[kx，max-kx，min]）施加逐渐解释数据以说明翻译。 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。在这项研究中，运动信息是从导航器回波中提取的。导航器信息可以源自扫描轨迹本身（即自散开轨迹），也可以从提供低分辨率图像的单独采集中得出。在这里，使用了多机格式注册方法，该方法找到了参考图像和单个导航器图像之间的最大Pearson相关性，并提供了相对于参考图像的旋转和翻译量的可靠估计（所有图像的平均值）。为了提高鲁棒性并提高共同注册的准确性，至少重复了两次。 通过在运动损坏的幻影上使用逆磨操作2，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）。对于八个交织中的每一个，都会生成随机的头部旋转（范围€30）和翻译（范围€15mm）。在进行反向磨削步骤之前，将每个单独旋转和移位的图像乘以线圈灵敏度，从而模拟接收器的线圈灵敏度，这些线圈灵敏度来自附着在物体圆圈周围的六个线圈。使用T2W自旋回声扫描和交织的螺旋式插入/螺旋出读数和8频道头部线圈在3名健康志愿者中进行体内验证。为每个Interleaf数据提供了一个低分辨率导航器图像（322）提供的螺旋形零件（3-5ms持续时间）。螺旋形部分是正常的交织螺旋采集：TR/TE = 4,000ms/85ms，切片厚度/GAP = 4/1mm，17片，FOV = 24厘米，矩阵= 256，Interleaves = 32和Nex = 1。螺旋恢复的接收器带宽为+/- 125kHz。在每个实验过程中，要求志愿者以三个增加的运动水平（不，轻度[〜€15€]和中等[〜¿25]运动以三个增加的运动水平旋转和/或移动头部。 参考文献：1Pruessmann K等。 MRM 46：638-51，2001; 2rasche V等。 IEEE TMI 18：385-92，1999。 致谢：这项工作得到了卢卡斯基金会（Lucas Foundation）斯坦福大学高级技术中心（P41RR09784）的高级MR技术中心NIH（1R01EB002771）的部分支持。