School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin, Ireland.
School of Electrical and Electronic Engineering, University College Dublin, Belfield, Dublin, Ireland.
Magn Reson Imaging. 2022 Nov;93:3-10. doi: 10.1016/j.mri.2022.07.007. Epub 2022 Jul 26.
Gibbs ringing creates artefacts in magnetic resonance images that can mislead clinicians. Reconstruction algorithms attempt to suppress Gibbs ringing, or an additional ringing suppression algorithm may be applied post reconstruction. Novel reconstruction algorithms are often compared with filtered Fourier reconstruction, but the choices of filters and filter parameters can be arbitrary and sub-optimal. Evaluation of different reconstruction and post-processing algorithms is difficult to automate or subjective: many metrics have been used in the literature. In this paper, we evaluate twelve of those metrics and demonstrate that none of them are fit for purpose. We propose a novel metric and demonstrate its efficacy in 1D and 2D simulations. We use our new metric to optimise and compare 17 smoothing filters for suppression of Gibbs artefacts. We examine the transfer functions of the optimised filters, with counter-intuitive results regarding the highest-performing filters. Our results will simplify and improve the comparison of novel MRI reconstruction and post-processing algorithms, and lead to the automation of ringing suppression in MRI. They also apply more generally to other applications in which data is captured in the Fourier domain.
吉布斯环带会在磁共振图像中产生伪影，从而误导临床医生。重建算法试图抑制吉布斯环带，或者在重建后应用额外的环带抑制算法。新的重建算法通常与滤波傅里叶重建进行比较，但滤波器和滤波器参数的选择可能是任意的和次优的。不同重建和后处理算法的评估很难自动化或主观化：文献中已经使用了许多指标。在本文中，我们评估了其中的 12 个指标，证明它们都不适用。我们提出了一个新的指标，并在 1D 和 2D 模拟中证明了它的有效性。我们使用我们的新指标来优化和比较 17 种用于抑制吉布斯伪影的平滑滤波器。我们检查了优化滤波器的传递函数，得到了关于性能最高的滤波器的反直觉结果。我们的结果将简化和改进新型 MRI 重建和后处理算法的比较，并导致 MRI 中的环带抑制自动化。它们也更普遍地适用于其他在傅里叶域中捕获数据的应用。
