MRI-Driven PET Image Optimization for Neurological Applications
MRI-Driven PET Image Optimization for Neurological Applications
复制标题
用于神经学应用的 MRI 驱动 PET 图像优化
DOI：
10.3389/fnins.2019.00782
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发表时间：
2019
影响因子：
4.3
通讯作者：
Zhu Xiaohua
中科院分区：
文献类型：
--
作者：
Zhu Yuankai;Zhu Xiaohua
正电子发射断层扫描（PET）和磁共振成像（MRI）是研究癫痫、痴呆、精神疾病等神经系统疾病的常用影像学检查方法，由于这两种方法在成像原理和物理性能上各不相同，因此各有优缺点。为了获得相互补充的信息并相互加强，需要将PET和MRI融合。这种组合的双模态（顺序或同时）可以产生更好的脑组织软组织对比度，灵活的采集参数，并最大限度地减少辐射暴露。PET/MRI最独特的优越性主要表现在基于MRI对PET固有局限性的改进，如运动伪影、部分容积效应（PVE）和定量分析中的侵入性操作。扫描过程中头部的运动会严重影响PET图像的有效分辨率，特别是对于长时间的动态扫描。混合PET/MRI设备可以通过同时采集的MRI信息为PET数据提供运动校正（MC）。关于与有限空间分辨率相关联的PVE，PET数据的处理和重建可以通过顺序地或同时地使用采集的MRI来进一步优化。动态PET数据的定量分析主要依赖于有创动脉血液采样程序来获取动脉输入函数（AIF）。一种不需要动脉插管的图像导出输入函数（IDIF）方法可以作为AIF的潜在替代估计。与仅使用PET数据相比，结合MRI的解剖或功能信息可以提高IDIF方法的准确性。然而，由于两种模式之间的干扰和固有的差异，这些方法用于优化PET图像的MRI的基础上仍然有许多技术挑战。本文综述了MRI驱动的PET数据优化的最新进展，当前的挑战和未来的发展方向，神经系统的应用，无论是顺序或同时采集方法。
Positron emission tomography (PET) and magnetic resonance imaging (MRI) are established imaging modalities for the study of neurological disorders, such as epilepsy, dementia, psychiatric disorders and so on. Since these two available modalities vary in imaging principle and physical performance, each technique has its own advantages and disadvantages over the other. To acquire the mutual complementary information and reinforce each other, there is a need for the fusion of PET and MRI. This combined dual-modality (either sequential or simultaneous) could generate preferable soft tissue contrast of brain tissue, flexible acquisition parameters, and minimized exposure to radiation. The most unique superiority of PET/MRI is mainly manifested in MRI-based improvement for the inherent limitations of PET, such as motion artifacts, partial volume effect (PVE) and invasive procedure in quantitative analysis. Head motion during scanning significantly deteriorates the effective resolution of PET image, especially for the dynamic scan with lengthy time. Hybrid PET/MRI device can offer motion correction (MC) for PET data through MRI information acquired simultaneously. Regarding the PVE associated with limited spatial resolution, the process and reconstruction of PET data can be further optimized by using acquired MRI either sequentially or simultaneously. The quantitative analysis of dynamic PET data mainly relies upon an invasive arterial blood sampling procedure to acquire arterial input function (AIF). An image-derived input function (IDIF) method without the need of arterial cannulization, can serve as a potential alternative estimation of AIF. Compared with using PET data only, combining anatomical or functional information from MRI for improving the accuracy in IDIF approach has been demonstrated. Yet, due to the interference and inherent disparity between the two modalities, these methods for optimizing PET image based on MRI still have many technical challenges. This review discussed upon the most recent progress, current challenges and future directions of MRI-driven PET data optimization for neurological applications, with either sequential or simultaneous acquisition approach.