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首页> 外文期刊>Magnetic resonance imaging: An International journal of basic research and clinical applications >Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares
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Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares

机译：自动伪像检测和去除，使用局部二进制模式和2D局部最小二乘相结合，改进了运动受损DTI数据集中的张量估计

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摘要

Signal variation in diffusion-weighted images (DWIs) is influenced both by thermal noise and by spatially and temporally varying artifacts, such as rigid-body motion and cardiac pulsation. Motion artifacts are particularly prevalent when scanning difficult patient populations, such as human infants. Although some motion during data acquisition can be corrected using image coregistration procedures, frequently individual DWIs are corrupted beyond repair by sudden, large amplitude motion either within or outside of the imaging plane. We propose a novel approach to identify and reject outlier images automatically using local binary patterns (LBP) and 2D partial least square (2D-PLS) to estimate diffusion tensors robustly. This method uses an enhanced LBP algorithm to extract texture features from a local texture feature of the image matrix from the DWI data. Because the images have been transformed to local texture matrices, we are able to extract discriminating information that identifies outliers in the data set by extending a traditional one-dimensional PLS algorithm to a two-dimension operator. The class-membership matrix in this 2D-PLS algorithm is adapted to process samples that are image matrix, and the membership matrix thus represents varying degrees of importance of local information within the images. We also derive the analytic form of the generalized inverse of the class-membership matrix. We show that this method can effectively extract local features from brain images obtained from a large sample of human infants to identify images that are outliers in their textural features, permitting their exclusion from further processing when estimating tensors using the DWIs. This technique is shown to be superior in performance when compared with visual inspection and other common methods to address motion-related artifacts in DWI data. This technique is applicable to correct motion artifact in other magnetic resonance imaging (MRI) techniques (e.g., the bootstrapping estimation) that use univariate or multivariate regression methods to fit MRI data to a pre-specified model.
机译：扩散加权图像（DWI）中的信号变化既受热噪声的影响，也受时空变化的伪影（例如刚体运动和心脏搏动）的影响。当扫描困难的患者群体（例如人类婴儿）时，运动伪影特别普遍。尽管可以使用图像合并过程来纠正数据采集过程中的某些运动，但是由于成像平面内或成像平面外突然的大幅度运动，单个DWI经常会损坏而无法修复。我们提出了一种新颖的方法来使用局部二进制模式（LBP）和2D偏最小二乘（2D-PLS）自动识别和剔除离群值图像，从而可靠地估计扩散张量。该方法使用增强的LBP算法从DWI数据中从图像矩阵的局部纹理特征中提取纹理特征。因为图像已被转换为局部纹理矩阵，所以我们能够通过将传统的一维PLS算法扩展到二维算子来提取识别数据集中异常值的区分信息。此2D-PLS算法中的类成员矩阵适用于处理作为图像矩阵的样本，因此，成员矩阵表示图像中局部信息的重要性的变化程度。我们还推导了类成员矩阵的广义逆的解析形式。我们表明，该方法可以从大量人类婴儿样本获得的大脑图像中有效提取局部特征，以识别出其纹理特征中存在异常的图像，从而在使用DWI估计张量时可以将它们排除在进一步的处理之外。与目视检查和解决DWI数据中与运动有关的伪像的其他常用方法相比，该技术在性能上表现出优势。该技术适用于纠正其他磁共振成像（MRI）技术（例如自举估计）中的运动伪影，这些技术使用单变量或多变量回归方法将MRI数据拟合到预先指定的模型中。

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