通过优化基函数提升MRI运动校正对比度，减少伪影。
Contrast-Optimized Basis Functions for Self-Navigated Motion Correction in Quantitative MRI
- 用广义特征分解和正交化构造高对比度子空间
- 在85组扫描中显著提升皮质与脑脊液对比度
- 适合需要精准运动校正的定量MRI研究者
定量MRI扫描时间长，易产生运动伪影。对于类似MR-Fingerprinting的方法，可通过奇异值分解（SVD）子空间中的自导航回顾性运动校正来缓解。但SVD会提高所有组织的信号强度，削弱组织间对比度，影响配准精度。本文提出旋转子空间以最大化皮质与脑脊液之间的对比度，从而提升运动估计准确性。方法基于均值自相关矩阵的广义特征分解，并经Gram-Schmidt正交化处理。在使用3D混合态序列采集的85组不同运动水平扫描数据上验证了该方法。结果表明，对比度优化后的基函数显著提升了皮质-脑脊液对比度，使运动估计更平滑，定量图中伪影减少。结论：该方法有效提高了运动估计精度。
原文摘要 · Abstract (English)
Purpose: The long scan times of quantitative MRI techniques make motion artifacts more likely. For MR-Fingerprinting-like approaches, this problem can be addressed with self-navigated retrospective motion correction based on reconstructions in a singular value decomposition (SVD) subspace. However, the SVD promotes high signal intensity in all tissues, which limits the contrast between tissue types and ultimately reduces the accuracy of registration. The purpose of this paper is to rotate the subspace for maximum contrast between two types of tissue and improve the accuracy of motion estimates. Methods: A subspace is derived that promotes contrasts between brain parenchyma and CSF, achieved through the generalized eigendecomposition of mean autocorrelation matrices, followed by a Gram-Schmidt process to maintain orthogonality. We tested our motion correction method on 85 scans with varying motion levels, acquired with a 3D hybrid-state sequence optimized for quantitative magnetization transfer imaging. Results: A comparative analysis shows that the contrast-optimized basis significantly improve the parenchyma-CSF contrast, leading to smoother motion estimates and reduced artifacts in the quantitative maps. Conclusion: The proposed contrast-optimized subspace improves the accuracy of the motion estimation.
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