Bie Chongxue, Liang Yuhua, Zhang Lihong, Zhao Yingcheng, Chen Yanrong, Zhang Xueru, He Xiaowei, Song Xiaolei
School of Information Science and Technology, Northwest University, Xi'an 710127, China.
Quant Imaging Med Surg. 2019 Oct;9(10):1697-1713. doi: 10.21037/qims.2019.09.14.
Chemical exchange saturation transfer (CEST) MRI requires the acquisition of multiple saturation-weighted images and can last several minutes. Misalignments among these images, which are often due to the inevitable motion of the subject, will corrupt CEST contrast maps and result in large quantification errors. Therefore, the registration of the CEST series is critical. However, registration is challenging since common intensity-based registration algorithms may fail to differentiate CEST signals from motion artifacts. Herein, we studied how different patterns of motion affect CEST quantification and proposed a cascaded two-step registration scheme by utilizing features extracted from the entire Z-spectral image series instead of direct registration to a single image.
The proposed approach is conducted in two stages: during the first coarse registration, the Z-spectral image series is decomposed by robust principal component analysis (RPCA) to separate CEST contrast from motion. The recomposed image series using only the low-rank component, which contains minimized motion, are averaged to generate a reference for the alignment of the image series. To further remove residual misalignments, the coarse registration is followed by a refinement stage, which uses PCA iteratively to generate motionless synthetic reference series with the first few principal components (PCs) that correspond to CEST contrast. In the end, the quality check is performed to exclude the images with unsuccessful registration.
The proposed registration scheme (RPCA + PCA_R) was assessed by both phantom experiments and data of tumor-bearing mouse brain, with simulated random rigid motion in different patterns applied to the acquired static Z-spectral image series. For comparison, previous correction schemes using an explicit image [either S or S(∆ω)] as registration reference were also performed, named as SR and S_R respectively. To illustrate the advantage of combination of RPCA and PCA, registration was also exploited using either only the RPCA-based method (RPCA_R) or only the PCA-based one (PCA_R). Compared with the above four methods, RPCA + PCA_R allowed for more accurate correction of the corrupted Z-spectral images, exhibiting smaller MTR(∆ω) error maps and lower residual Z-spectra referring to the static data. Among all the five correction methods, the corrected Z-spectral image series by RPCA + PCA_R and the resulting MTR(∆ω) maps achieved the highest correlation coefficients (CC) with respect to the static ones.
The registration scheme of RPCA + PCA_R provides robust motion correction between two specific Z-spectral images and among an entire image series, through extraction of the static component from the entire Z-spectra set and inclusion of a PCA-based refinement step. Therefore, this method can help improve CEST acquisition and quantification.
化学交换饱和转移（CEST）磁共振成像需要采集多个饱和加权图像，可能持续几分钟。这些图像之间的错位通常是由于受试者不可避免的运动导致的，这会破坏CEST对比图并导致较大的量化误差。因此，CEST系列图像的配准至关重要。然而，配准具有挑战性，因为常见的基于强度的配准算法可能无法区分CEST信号和运动伪影。在此，我们研究了不同运动模式如何影响CEST量化，并提出了一种级联两步配准方案，该方案利用从整个Z谱图像系列中提取的特征，而不是直接配准到单个图像。
所提出的方法分两个阶段进行：在第一次粗配准期间，通过稳健主成分分析（RPCA）对Z谱图像系列进行分解，以将CEST对比与运动分离。仅使用包含最小运动的低秩分量重新组合的图像系列进行平均，以生成用于图像系列对齐的参考。为了进一步消除残余错位，在粗配准之后进行细化阶段，该阶段使用主成分分析（PCA）迭代地生成与CEST对比对应的前几个主成分（PC）的静止合成参考系列。最后，进行质量检查以排除配准不成功的图像。
所提出的配准方案（RPCA + PCA_R）通过体模实验和荷瘤小鼠脑数据进行评估，将不同模式的模拟随机刚体运动应用于采集的静态Z谱图像系列。为了进行比较，还执行了以前使用显式图像[要么是S要么是S(∆ω)]作为配准参考的校正方案，分别命名为SR和S_R。为了说明RPCA和PCA组合的优势，还分别仅使用基于RPCA的方法（RPCA_R）或仅使用基于PCA的方法（PCA_R）进行配准。与上述四种方法相比，RPCA + PCA_R能够更准确地校正受损的Z谱图像，相对于静态数据，其显示出更小的MTR(∆ω)误差图和更低的残余Z谱。在所有五种校正方法中，RPCA + PCA_R校正后的Z谱图像系列和所得的MTR(∆ω)图与静态图像的相关系数（CC）最高。
RPCA + PCA_R配准方案通过从整个Z谱集提取静态分量并包含基于PCA的细化步骤，在两个特定的Z谱图像之间以及整个图像系列中提供稳健的运动校正。因此，该方法有助于改善CEST采集和量化。
