发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在开发评估高分辨率3D MRI运动伪影模拟准确性的方案，提出APHABAMAS方案，利用数字体模解析表达图像和傅里叶域表示，量化三种算法采样误差并排名，为算法选择提供依据。
AI 中文摘要
目的：运动影响了高分辨率3D MRI（定量神经成像研究中的既定工具）的效用。基于深度学习的方法在减轻运动诱导伪影方面显示出前景，但其开发通常需要模拟的运动损坏数据。有几种开源工具用于此任务，各实现不同算法。然而，目前不存在评估这些模拟准确性的方案，这使得用户难以选择最合适的工具。本研究旨在开发这样一种方案。方法：所需方案的关键要素是一个不受采样误差影响的真实参考模拟。为满足此要求，所提出的APHABAMAS方案利用一种数字体模，其在图像和傅里叶域中的表示在任意刚体变换下都可以解析表达。结果：APHABAMAS用于量化三种现有模拟算法的采样误差，建立了它们基于准确性的首个明确排名。结论：APHABAMAS为评估高分辨率3D MRI运动伪影模拟的准确性提供了一个严格的工具。它允许基于准确性对现有模拟算法进行排名，从而能够明智地选择最合适的算法来合成运动损坏数据。
英文摘要
Purpose: Motion compromises the utility of high-resolution 3D MRI, an established tool in quantitative neuroimaging research. Deep learning-based methods have shown promise for mitigating motion-induced artifacts, but their development typically requires simulated motion-corrupted data. Several open-source tools exist for this task, each implementing different algorithms. However, no scheme currently exists for evaluating the accuracy of these simulations, making it difficult for users to choose the most suitable tool. This study aims to develop such a scheme. Methods: The essential ingredient of the desired scheme is a ground-truth reference that does not suffer from sampling-induced error. To meet this requirement, the proposed scheme, APHABAMAS, leverages a digital phantom whose image- and Fourier-domain representations can be expressed analytically under arbitrary rigid-body transformations. APHABAMAS is used to quantify sampling-induced errors of three existing algorithms under synthetic and tracking-data-derived motion trajectories at different motion severity levels. Results: All three algorithms produce distinct but visually plausible artifacts, demonstrating the need for a ground-truth reference. APHABAMAS shows that the algorithm utilizing the uniform-to-non-uniform (Type-2) NUFFT provides the most consistent agreement with the ground-truth reference across motion types and severity levels. The non-uniform-to-uniform (Type-1) NUFFT-based algorithm, which does not mimic the MR acquisition process, deteriorates substantially with increasing motion severity. Conclusions: APHABAMAS provides a rigorous tool for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations. It enables accuracy-based assessment of three existing algorithms, thereby facilitating informed selection of the most suitable one for synthesizing motion-corrupted data.
