Cardiac diffusion tensor imaging (cDTI) is highly prone to image corruption, yet robust-fitting methods are rarely used. Single voxel outlier detection (SVOD) can overlook corruptions that are visually obvious, perhaps causing reluctance to replace whole-image shot-rejection (SR) despite its own deficiencies. SVOD’s deficiencies may be relatively unimportant: corrupted signals that are not statistical outliers may not be detrimental. Multiple voxel outlier detection (MVOD), using a local myocardial neighbourhood, may overcome the shared deficiencies of SR and SVOD for cDTI while keeping the benefits of both. Here, robust fitting methods using M-estimators are derived for both non-linear least squares and weighted least squares fitting, and outlier detection is applied using (i) SVOD; and (ii) SVOD and MVOD. These methods, along with non-robust fitting with/without SR, are applied to cDTI datasets from healthy volunteers and hypertrophic cardiomyopathy patients. Robust fitting methods produce larger group differences with more statistical significance for MD, FA, and E2A, versus non-robust methods, with MVOD giving the largest group differences for MD and FA. Visual analysis demonstrates the superiority of robust-fitting methods over SR, especially when it is difficult to partition the images into good and bad sets. Synthetic experiments confirm that MVOD gives lower root-mean-square-error than SVOD.

中文翻译：

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心脏弥散张量成像中的异常值检测：抛射还是稳健拟合？


心脏弥散张量成像 （cDTI） 极易发生图像损坏，但很少使用稳健拟合方法。单一体素异常值检测 （SVOD） 可能会忽略视觉上明显的损坏，这可能会导致人们不愿意替换全图像拍摄拒绝 （SR），尽管它本身存在缺陷。SVOD 的缺陷可能相对不重要：不是统计异常值的损坏信号可能不是有害的。使用局部心肌邻域的多体素异常值检测 （MVOD） 可以克服 cDTI 的 SR 和 SVOD 的共同缺陷，同时保留两者的好处。在这里，使用 M 估计器的稳健拟合方法被推导出用于非线性最小二乘法和加权最小二乘法拟合，并使用 （i） SVOD 应用异常值检测;以及 （ii） SVOD 和 MVOD。这些方法以及有/没有 SR 的非稳健拟合应用于来自健康志愿者和肥厚型心肌病患者的 cDTI 数据集。与非稳健方法相比，稳健拟合方法产生更大的组差异，MD、FA 和 E2A 具有更大的统计显著性，其中 MVOD 给出的 MD 和 FA 组差异最大。视觉分析证明了稳健拟合方法优于 SR，尤其是当难以将图像划分为好集和坏集时。合成实验证实，MVOD 的均方根误差低于 SVOD。