Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain.
Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain.
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DOI：
10.1016/j.jmr.2017.05.004
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发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
Chen Z
中科院分区：
文献类型：
--
作者：
Fang J;Bao L;Li X;van Zijl PCM;Chen Z
背景场去除是定量磁化率绘图 (QSM) 的重要 MR 相位预处理步骤。它将组织磁化率源感应的局部场与感兴趣区域之外的源产生的背景场分开，例如，磁化率源。大脑，例如空气组织界面。在空气-组织边界附近，例如颅骨和鼻旁窦存在较大的磁敏感度变化，目前的背景场去除方法通常是不够的，并且这些区域通常需要通过脑罩侵蚀来排除，但代价是丢失局部场的信息，从而丢失这些区域的磁敏感度测量。在本文中，我们提出了使用区域自适应核（R-SHARP）对相位数据的可变核复杂谐波伪影减少（V-SHARP）背景场去除方法的扩展，其中采用可扩展的球形高斯核（SGK），其核半径和权重可根据反映场变化幅度的能量“函数”进行调整。这种能量泛函是根据轮廓和两个包含正则化项的拟合函数来定义的，从中导出水平集形成中的曲线演化模型以实现能量最小化。我们利用它来检测由强磁化率变化引起的大场梯度区域。在这些区域中，SGK 将具有小半径和高权重，以适应场扰动的体素能量。使用所提出的方法，可以有效地去除外部源产生的背景场，从而更准确地估计局部场，从而更准确地估计 QSM 偶极子反演，以绘制局部组织磁化率源。数值模拟、模型和体内人脑数据表明，即使整个鼻旁窦区域保留在脑罩中，与 V-SHARP 和 RESHARP（启用正则化的 SHARP）方法相比，R-SHARP 的性能也有所提高。使用 R-SHARP 方法也可以在很大程度上消除由于导出的 QSM 图中强烈的磁敏度变化而产生的阴影伪影，从而实现更准确的 QSM 重建。
Background field removal is an important MR phase preprocessing step for quantitative susceptibility mapping (QSM). It separates the local field induced by tissue magnetic susceptibility sources from the background field generated by sources outside a region of interest, e.g. brain, such as air-tissue interface. In the vicinity of air-tissue boundary, e.g. skull and paranasal sinuses, where large susceptibility variations exist, present background field removal methods are usually insufficient and these regions often need to be excluded by brain mask erosion at the expense of losing information of local field and thus susceptibility measures in these regions. In this paper, we propose an extension to the variable-kernel sophisticated harmonic artifact reduction for phase data (V-SHARP) background field removal method using a region adaptive kernel (R-SHARP), in which a scalable spherical Gaussian kernel (SGK) is employed with its kernel radius and weights adjustable according to an energy “functional” reflecting the magnitude of field variation. Such an energy functional is defined in terms of a contour and two fitting functions incorporating regularization terms, from which a curve evolution model in level set formation is derived for energy minimization. We utilize it to detect regions of with a large field gradient caused by strong susceptibility variation. In such regions, the SGK will have a small radius and high weight at the sphere center in a manner adaptive to the voxel energy of the field perturbation. Using the proposed method, the background field generated from external sources can be effectively removed to get a more accurate estimation of the local field and thus of the QSM dipole inversion to map local tissue susceptibility sources. Numerical simulation, phantom and in vivo human brain data demonstrate improved performance of R-SHARP compared to V-SHARP and RESHARP (regularization enabled SHARP) methods, even when the whole paranasal sinus regions are preserved in the brain mask. Shadow artifacts due to strong susceptibility variations in the derived QSM maps could also be largely eliminated using the R-SHARP method, leading to more accurate QSM reconstruction.
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