利用图像平滑性提升MRI干扰消除效果，尤其适用于动态干扰场景。
Smooth Total variation Regularization for Interference Detection and Elimination (STRIDE) for MRI
- 结合MR图像的总变差特性优化干扰检测
- 在0.5T扫描仪上实现更高信噪比与更低误差
- 适合动态电磁干扰环境下的高精度MRI成像
MRI正越来越多地需要在存在潜在动态电磁干扰（EMI）的电子设备附近运行。为此，本文提出STRIDE方法，通过利用磁共振图像固有的平滑性（基于总变差正则化），改进了以往依赖外部传感器的EMI去除方法。STRIDE同时采集干扰探测器与主成像线圈的数据，将数据转换至图像域，并对生成图像阵列的每一列，通过优化总变差平滑性，组合并相减来自干扰探测器的信息。在0.5T扫描仪上的体模和活体数据集上测试表明，相较于标准实现，STRIDE实现了更优的视觉效果、更高的时间信噪比（temporal SNR）、更大的干扰去除率及更低的均方根误差（RMSE）。该方法利用磁共振图像的内在特性，在时变噪声源下显著优于传统算法。
原文摘要 · Abstract (English)
MRI is increasingly desired to function near electronic devices that emit potentially dynamic electromagnetic interference (EMI). To accommodate for this, we propose the STRIDE method, which improves on previous external-sensor-based EMI removal methods by exploiting inherent MR image smoothness in its total variation. STRIDE measures data from both EMI detectors and primary MR imaging coils, transforms this data into the image domain, and for each column of the resulting image array, combines and subtracts data from the EMI detectors in a way that optimizes for total-variation smoothness. Performance was tested on phantom and in-vivo datasets with a 0.5T scanner. STRIDE resulted in visually better EMI removal, higher temporal SNR, larger EMI removal percentage, and lower RMSE than standard implementations. STRIDE is a robust technique that leverages inherent MR image properties to provide improved EMI removal performance over standard algorithms, particularly for time-varying noise sources.
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