Toblerone: Surface-Based Partial Volume Estimation.
Toblerone: Surface-Based Partial Volume Estimation.
复制标题
Toblerone：基于表面的部分体积估计。
DOI：
10.1109/tmi.2019.2951080
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发表时间：
2020
影响因子：
10.6
通讯作者：
Kirk TF
中科院分区：
文献类型：
--
作者：
Kirk TF
部分容积效应（PVE）是功能成像数据分析的混杂源。PVE的校正需要估计图像中存在的部分体积（PV）。这些估计通常是通过体积分割获得的，但这种方法对于复杂的结构（如皮层）可能不准确。另一种方法是使用基于表面的分割，这在文献中是公认的。三角帆是一种新的方法，用于估计PV使用这样的表面。它使用一种纯粹的几何方法，考虑表面和图像体素之间的相交。与现有的基于表面的技术相比，三角巧克力不限于与任何特定的结构或形态一起使用。在神经影像学背景下，对模拟表面、模拟T1加权MRI图像和最后的人类连接组项目重测数据集进行了评价。已经与两种现有的基于表面的方法进行了比较;在所有分析中，三角巧克力的性能与比较方法相匹配或超过比较方法。评估结果还表明，与现有的体积方法（FSL FAST）相比，基于表面的方法与Toblerone提供了更好的鲁棒性扫描仪噪声和字段不均匀性，以及更好的会话间重复性的大脑体积。与体积方法相比，基于表面的方法不需要执行重新扫描，这在通常用于神经成像的分辨率下是有利的。
Partial volume effects (PVE) present a source of confound for the analysis of functional imaging data. Correction for PVE requires estimates of the partial volumes (PVs) present in an image. These estimates are conventionally obtained via volumetric segmentation, but such an approach may not be accurate for complex structures such as the cortex. An alternative is to use surface-based segmentation, which is well-established within the literature. Toblerone is a new method for estimating PVs using such surfaces. It uses a purely geometric approach that considers the intersection between a surface and the voxels of an image. In contrast to existing surface-based techniques, Toblerone is not restricted to use with any particular structure or modality. Evaluation in a neuroimaging context has been performed on simulated surfaces, simulated T1-weighted MRI images and finally a Human Connectome Project test-retest dataset. A comparison has been made to two existing surface-based methods; in all analyses Toblerone’s performance either matched or surpassed the comparator methods. Evaluation results also show that compared to an existing volumetric method (FSL FAST), a surface-based approach with Toblerone offers improved robustness to scanner noise and field non-uniformity, and better inter-session repeatability in brain volume. In contrast to volumetric methods, a surface-based approach negates the need to perform resampling which is advantageous at the resolutions typically used for neuroimaging.