Thomas Benjamin A, Cuplov Vesna, Bousse Alexandre, Mendes Adriana, Thielemans Kris, Hutton Brian F, Erlandsson Kjell
Agency for Science Technology and Research, National University of Singapore Clinical Imaging Research Centre, Singapore, Singapore. Institute of Nuclear Medicine, University College London Hospital, University College London, London, UK.
Phys Med Biol. 2016 Nov 21;61(22):7975-7993. doi: 10.1088/0031-9155/61/22/7975. Epub 2016 Oct 25.
Positron emission tomography (PET) images are degraded by a phenomenon known as the partial volume effect (PVE). Approaches have been developed to reduce PVEs, typically through the utilisation of structural information provided by other imaging modalities such as MRI or CT. These methods, known as partial volume correction (PVC) techniques, reduce PVEs by compensating for the effects of the scanner resolution, thereby improving the quantitative accuracy. The PETPVC toolbox described in this paper comprises a suite of methods, both classic and more recent approaches, for the purposes of applying PVC to PET data. Eight core PVC techniques are available. These core methods can be combined to create a total of 22 different PVC techniques. Simulated brain PET data are used to demonstrate the utility of toolbox in idealised conditions, the effects of applying PVC with mismatched point-spread function (PSF) estimates and the potential of novel hybrid PVC methods to improve the quantification of lesions. All anatomy-based PVC techniques achieve complete recovery of the PET signal in cortical grey matter (GM) when performed in idealised conditions. Applying deconvolution-based approaches results in incomplete recovery due to premature termination of the iterative process. PVC techniques are sensitive to PSF mismatch, causing a bias of up to 16.7% in GM recovery when over-estimating the PSF by 3 mm. The recovery of both GM and a simulated lesion was improved by combining two PVC techniques together. The PETPVC toolbox has been written in C++, supports Windows, Mac and Linux operating systems, is open-source and publicly available.
正电子发射断层扫描（PET）图像会因一种称为部分容积效应（PVE）的现象而退化。人们已经开发出一些方法来减少部分容积效应，通常是通过利用其他成像模态（如MRI或CT）提供的结构信息。这些方法被称为部分容积校正（PVC）技术，通过补偿扫描仪分辨率的影响来减少部分容积效应，从而提高定量准确性。本文所述的PETPVC工具箱包含了一系列方法，既有经典方法，也有最新方法，用于将部分容积校正应用于PET数据。有八种核心PVC技术可供使用。这些核心方法可以组合起来，总共创建22种不同的PVC技术。使用模拟脑PET数据来证明该工具箱在理想条件下的效用、应用点扩散函数（PSF）估计不匹配的部分容积校正的效果以及新型混合部分容积校正方法在改善病变定量方面的潜力。在理想条件下执行时，所有基于解剖结构的部分容积校正技术都能在皮质灰质（GM）中实现PET信号的完全恢复。应用基于反卷积的方法会由于迭代过程的过早终止而导致恢复不完全。部分容积校正技术对PSF不匹配很敏感，当PSF高估3毫米时，会导致GM恢复中出现高达16.7%的偏差。将两种部分容积校正技术结合在一起可改善GM和模拟病变的恢复情况。PETPVC工具箱是用C++编写的，支持Windows、Mac和Linux操作系统，是开源且公开可用的。
