Marchetto Elisa, Flassbeck Sebastian, Mao Andrew, Assländer Jakob
Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, NY, USA.
Center for Advanced Imaging Innovation and Research (CAIR), Dept. of Radiology, NYU School of Medicine, NY, USA.
ArXiv. 2024 Dec 27:arXiv:2412.19552v1.
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.
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.
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.
The proposed contrast-optimized subspace improves the accuracy of the motion estimation.
定量磁共振成像（MRI）技术的长扫描时间使得运动伪影更易出现。对于类似磁共振指纹识别的方法，这个问题可以通过基于奇异值分解（SVD）子空间重建的自导航回顾性运动校正来解决。然而，SVD会增强所有组织中的高信号强度，这限制了组织类型之间的对比度，并最终降低了配准的准确性。本文的目的是旋转子空间，以实现两种组织类型之间的最大对比度，并提高运动估计的准确性。
通过平均自相关矩阵的广义特征分解，导出一个能增强脑实质与脑脊液之间对比度的子空间，随后进行Gram-Schmidt过程以保持正交性。我们使用针对定量磁化传递成像优化的3D混合状态序列，对85次不同运动水平的扫描测试了我们的运动校正方法。
对比分析表明，对比度优化后的基显著提高了实质-脑脊液对比度，从而使运动估计更平滑，定量图中的伪影减少。
所提出的对比度优化子空间提高了运动估计的准确性。
