SENSE AND GRAPPA RECONSTRUCTION OF MULTI-SHOT MULTI-ECHO EPI DATA

多镜头多回波 EPI 数据的 Sense 和 Grappa 重建

• 批准号: 7358820
• 负责人: DAVID F CLAYTON
• 金额: $ 2.49万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7358820
• 关键词: SENSE; GRAPPA; RECONSTRUCTION; MULTI; SHOT

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. One of the most promising benefits of parallel imaging is that it can reduce distortions by shortening the read-out times of fast acquisition techniques such as EPI (1,2). The trade-off for this improvement is a drop in SNR due to imperfect receiver geometry (g-factor) and fewer measurements; there is also the potential for errors due to the more complicated reconstruction algorithms which rely on estimations of additional information about the receiver coils. This additional information is typically acquired either by a separate low-resolution scan or by incorporating a segment of k-space acquired at the Nyquist sampling rate directly into the sequence of interest; both of these techniques have limitations and disadvantages. We have recently developed a multi-shot multi-echo EPI pulse sequence to measure PERfusion with Multiple Echoes and Temporal Enhancement (PERMEATE), described elsewhere in this volume. This sequence allows for greater reconstruction flexibility: each shot can be treated as a separate under-sampled k-space acquisition and reconstructed using either GRAPPA (3) or SENSE (4) with an effective reduction factor, R, equal to the number of interleaves, Ni. By borrowing information from the other interleaves, it is possible to generate the auto-calibration signals (ACS) required for GRAPPA, or the full-FOV sensitivity maps required for SENSE (4). Additional flexibility is possible for acquisitions in which Ni is large enough so that various combinations of shots can be collated into k-space sets with R < Ni. In this work, we explore the differences between the implementation and performance of basic GRAPPA and SENSE reconstructions of PERMEATE data. Materials and Methods. Images from a healthy volunteer were acquired using a 1.5 T scanner (GE Signa) with an 8-channel head array (MRI Devices). The PERMEATE pulse sequence was used with 96¿96 resolution, 15 slices, 4 interleaves, 4 echoes (TE = 12.4, 27.4, 42.4, and 57.4 ms), TR = 1.2 s, and 20 time frames. Prior to GRAPPA or SENSE reconstruction, all 4 consecutive interleaves were assembled into one fully-sampled (R = 1) k-space and EPI correction was performed: phase correction using an entropy-minimization algorithm (described elsewhere in this volume) and regridding to account for ramp sampling. Two types of phase-encoding sub-samplings could then be extracted for each slice and echo: odd and even shots were combined for an effective R = 2, or each shot was treated separately for R = 4. GRAPPA was performed using a 5¿4 kernel applied over the entire kx range; the number of ACS lines used was 2. SENSE was performed using the typical unfolding method in which a least-squares solution is found for the R pixels that are aliased into the same location in each of acquired coil component images. The matrices that get inverted for each unfolding operation are comprised of data from coil sensitivity maps derived from the fully-sampled data. The methods used here are variations on those typically used to generate maps from a separate calibration scan. The full-FOV image can be obtained by taking the Fourier transform of the entire R = 1 k-space data or of a central subset (for a low-resolution estimate). In the latter case, cubic spline interpolation to the desired resolution is performed in the image domain. Each coil image is then smoothed by convolution with a square kernel and divided by the sum-of-squares image. One more refinement is the inclusion of a binary mask that can be used to limit the number of pixels known to contribute a given pixel in the aliased image and, hence, reduce noise contribution from areas outside the anatomy of interest. Results and Discussion. Figure 1 shows examples of various R = 4 reconstructions (bottom 4 rows) with the R = 1 reconstruction show in the top row for comparison. The GRAPPA reconstruction performs well and, for the most part, can be treated as if it were a black-box operation. The initial results from the SENSE reconstruction seemed less promising. These were the results of a direct port of the algorithm being used for clinical scans in which the sensitivity maps were derived from a FGRE calibration scan. In this case, the fully-sampled images were smoothed with a 10¿10 kernel to generate the maps. This method leaves considerable residual aliasing artifacts from poor coil sensitivity estimation at the edges of the head; also, there are regions of high signal pile-up in locations where the maps do a poor job of estimating the receiver phase. To improve this reconstruction, the next step was to use only a low-resolution estimate for the maps by extracting the center 32¿32 region of the R = 1 data. This significantly reduced the regions of hyper-intensity but still left an undesirable amount of residual aliasing. Here, maps were made from the full k-space data and smoothed with a 3¿3 kernel. While this appears to be the best of the SENSE reconstructions, especially in that there is essentially no residual aliasing, there is still more signal pile-up in regions of high susceptibility variation as compared to GRAPPA. One possible explanation for this is that the susceptibility drop-outs and distortions inherent in the EPI data used to make the coil maps have a strong influence in those regions. References. (1) Bammer R, et al. MRM 2001;46:548. (2) Yang QX, et al. MRM 2004;52:1418. (3) Griswold MA, et al. MRM 2002;47:1202. (3) Pruessmann, et al. MRM 1999;42:952. (4) Skare et al. Workshop on Methods for Quantitative Diffusion MRI of Human Brain 2005:17. Acknowledgements. NIH (1R01EB002771), The Center for Advanced MR Technology at Stanford (P41RR09784), The Lucas Foundation, The Oak Foundation. Thanks to MA Griswold for sharing the opengrappa Matlab code.

该子项目是利用 NIH/NCRR 资助的中心拨款提供的资源的众多研究子项目之一。子项目和研究者 (PI) 可能已从另一个 NIH 来源获得主要资金，因此可以在其他 CRISP 条目中得到体现。列出的机构是中心的机构，不一定是研究者的机构。介绍。并行成像最有前途的好处之一是它可以通过缩短 EPI 等快速采集技术的读出时间来减少失真 (1,2)。这种改进的代价是由于接收器几何形状（g 因子）不完善和测量次数较少而导致信噪比下降；由于更复杂的重建算法依赖于有关接收器线圈的附加信息的估计，因此也可能出现错误。这些附加信息通常通过单独的低分辨率扫描或通过将以奈奎斯特采样率获取的一段 k 空间直接合并到感兴趣的序列中来获取；这两种技术都有局限性和缺点。我们最近开发了一种多发射多回波 EPI 脉冲序列，用于通过多重回波和时间增强 (PERMEATE) 测量 PERfusion，如本卷其他部分所述。该序列提供了更大的重建灵活性：每个镜头都可以被视为单独的欠采样 k 空间采集，并使用 GRAPPA (3) 或 SENSE (4) 进行重建，有效缩减因子 R 等于交错数 Ni。通过借用其他交错的信息，可以生成 GRAPPA 所需的自动校准信号 (ACS)，或 SENSE (4) 所需的全 FOV 灵敏度图。对于采集来说，额外的灵活性是可能的，其中 Ni 足够大，以便可以将不同的镜头组合整理到 R < Ni 的 k 空间集中。在这项工作中，我们探讨了 PERMEATE 数据的基本 GRAPPA 和 SENSE 重建的实施和性能之间的差异。材料和方法。使用带有 8 通道头部阵列（MRI Devices）的 1.5 T 扫描仪（GE Signa）获取健康志愿者的图像。使用的 PERMEATE 脉冲序列具有 96¿96 分辨率、15 个切片、4 个交错、4 个回波（TE = 12.4、27.4、42.4 和 57.4 ms）、TR = 1.2 s 和 20 个时间帧。在 GRAPPA 或 SENSE 重建之前，将所有 4 个连续交错组装到一个完全采样 (R = 1) k 空间中，并执行 EPI 校正：使用熵最小化算法（本卷其他地方描述）进行相位校正，并重新网格化以考虑斜坡采样。然后可以为每个切片和回波提取两种类型的相位编码子采样：将奇数和偶数镜头组合起来以获得有效的 R = 2，或者将每个镜头单独处理以获得 R = 4。GRAPPA 使用应用于整个 kx 范围的 5¿4 内核进行；使用的 ACS 线数为 2。使用典型的展开方法执行 SENSE，其中为每个采集的线圈组件图像中混叠到同一位置的 R 像素找到最小二乘解。每次展开操作求逆的矩阵由来自完全采样数据的线圈灵敏度图的数据组成。这里使用的方法是通常用于从单独的校准扫描生成地图的方法的变体。全 FOV 图像可以通过对整个 R = 1 k 空间数据或中心子集（用于低分辨率估计）进行傅里叶变换来获得。在后一种情况下，在图像域中执行三次样条插值以达到所需的分辨率。然后通过与平方核卷积来平滑每个线圈图像并除以平方和图像。另一项改进是包含二元掩模，该二元掩模可用于限制已知在混叠图像中贡献给定像素的像素数量，从而减少来自感兴趣解剖结构之外的区域的噪声贡献。 结果和讨论。图 1 显示了各种 R = 4 重建的示例（底部 4 行），其中 R = 1 重建显示在顶部行中以进行比较。 GRAPPA 重建表现良好，并且在大多数情况下，可以将其视为黑盒操作。 SENSE 重建的初步结果似乎不太乐观。这些是直接用于临床扫描的算法的结果，其中灵敏度图源自 FGRE 校准扫描。在这种情况下，使用 10¿10 内核对完全采样的图像进行平滑以生成地图。由于磁头边缘的线圈灵敏度估计不佳，该方法留下了相当大的残留混叠伪影；此外，地图在估计接收器相位方面表现不佳的位置也存在高信号堆积区域。为了改进这种重建，下一步是通过提取 R = 1 数据的中心 32¿32 区域，仅对地图使用低分辨率估计。这显着减少了高强度区域，但仍然留下了不期望的残留混叠量。此处，地图是根据完整的 k 空间数据制作的，并使用 3¿3 内核进行平滑。虽然这似乎是最好的 SENSE 重建，特别是基本上没有残留混叠，但与 GRAPPA 相比，在高磁敏度变化的区域中仍然存在更多信号堆积。对此的一种可能的解释是，用于制作线圈图的 EPI 数据中固有的磁化率丢失和失真对这些区域有很大的影响。 参考。 (1) Bammer R 等人。 MRM 2001；46：548。 (2) 杨庆新，等。 MRM 2004；52：1418。 (3) 格里斯沃尔德·马等人。 MRM 2002；47：1202。 (3)普鲁斯曼等人。 MRM 1999；42：952。 (4)斯卡雷等人。人脑定量扩散 MRI 方法研讨会 2005：17。 致谢。 NIH (1R01EB002771)、斯坦福大学先进 MR 技术中心 (P41RR09784)、卢卡斯基金会、橡树基金会。感谢 MA Griswold 分享 opengrappa Matlab 代码。