Image processing approaches to enhance perivascular space visibility and quantification using MRI
Image processing approaches to enhance perivascular space visibility and quantification using MRI
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DOI：
10.1038/s41598-019-48910-x
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发表时间：
2019-08-26
影响因子：
4.6
通讯作者：
Toga, Arthur W.
中科院分区：
文献类型：
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作者：
Sepehrband, Farshid;Barisano, Giuseppe;Toga, Arthur W.
血管周围间隙（PVS）成像，也被称为Virchow-Robin空间，具有重要的临床价值，但仍需要神经影像学技术来改善PVS的制图和量化。目前的PVS评估技术是一种基于感兴趣区域可见PVS的视觉阅读的评分系统，并且通常仅限于大口径的PVS。增强PVS的可见性可以支持医学诊断并使新的神经科学研究成为可能。提高MRI分辨率是提高PVS可见性的一种方法，但受采集时间和物理条件的限制。或者，可以利用图像处理方法来提高pv与周围组织之间的对比度。这里我们结合T1和t2加权图像来增强PVS的对比度，增强PVS的可见性。增强PVS对比度（Enhanced PVS Contrast， EPC）是通过结合T1和t2加权图像进行自适应滤波以去除非结构化高频空间噪声来实现的。通过将健康年轻人的EPC提交给两位专家读者并通过自动量化来评估他们。我们发现EPC提高了PVS的显著性，有助于分辨更多的PVS。我们还提出了一种高度可靠的自动化pv量化方法，该方法使用专家读数进行了优化。
Imaging the perivascular spaces (PVS), also known as Virchow-Robin space, has significant clinical value, but there remains a need for neuroimaging techniques to improve mapping and quantification of the PVS. Current technique for PVS evaluation is a scoring system based on visual reading of visible PVS in regions of interest, and often limited to large caliber PVS. Enhancing the visibility of the PVS could support medical diagnosis and enable novel neuroscientific investigations. Increasing the MRI resolution is one approach to enhance the visibility of PVS but is limited by acquisition time and physical constraints. Alternatively, image processing approaches can be utilized to improve the contrast ratio between PVS and surrounding tissue. Here we combine T1- and T2-weighted images to enhance PVS contrast, intensifying the visibility of PVS. The Enhanced PVS Contrast (EPC) was achieved by combining T1- and T2-weighted images that were adaptively filtered to remove non-structured high-frequency spatial noise. EPC was evaluated on healthy young adults by presenting them to two expert readers and also through automated quantification. We found that EPC improves the conspicuity of the PVS and aid resolving a larger number of PVS. We also present a highly reliable automated PVS quantification approach, which was optimized using expert readings.