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用于脑正电子发射断层扫描中图像配准和部分容积校正的迭代框架。

Iterative framework for image registration and partial volume correction in brain positron emission tomography.

作者信息

Matsubara Keisuke, Ibaraki Masanobu, Shidahara Miho, Kinoshita Toshibumi

机构信息

Department of Radiology and Nuclear Medicine, Research Institute for Brain and Blood Vessels, Akita Cerebrospinal and Cardiovascular Center, 6-10 Senshu-Kubota-machi, Akita, 010-0874, Japan.

Department of Quantum Science and Energy Engineering, Graduate School of Engineering, Tohoku University, Sendai, Japan.

出版信息

Radiol Phys Technol. 2020 Dec;13(4):348-357. doi: 10.1007/s12194-020-00591-2. Epub 2020 Oct 19.

Abstract

Imprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration. We applied PoR to the [C]PiB PET data of 92 participants obtained from the Alzheimer's Disease Neuroimaging Initiative database and compared the registration results, PV-corrected standardized uptake value (SUV) and its ratio to the cerebellum (SUVR), and intra-region coefficient of variation (CoV) between PoR and conventional registration. Significant differences in registration of as much as 2.74 mm and 3.02° were observed between the two methods (effect size <  - 0.8 or > 0.8), which resulted in considerable SUVR differences throughout the brain, reaching a maximal difference of 62.3% in the sensory motor cortex. Intra-region CoV was significantly reduced using the PoR throughout the brain. These results suggest that PoR reduces error as a result of imprecise registration in PVC and is a useful method for accurately quantifying the amyloid burden in PET.

摘要

正电子发射断层扫描(PET)与解剖磁共振(MR)图像之间的配准不准确是MR成像引导的部分容积校正(MR-PVC)中误差的关键来源。在此,我们提出了一种用于图像配准和部分容积校正的新框架,我们将其称为PVC优化配准(PoR),以解决配准不准确的问题。PoR框架在未校正的PET和平滑后的PV校正图像之间迭代进行PVC和配准,以获得精确配准。我们将PoR应用于从阿尔茨海默病神经影像倡议数据库获得的92名参与者的[C]PiB PET数据,并比较了PoR与传统配准之间的配准结果、PV校正的标准化摄取值(SUV)及其与小脑的比值(SUVR)以及区域内变异系数(CoV)。两种方法之间在配准上观察到高达2.74毫米和3.02°的显著差异(效应大小< -0.8或> 0.8),这导致整个大脑的SUVR存在相当大的差异,在感觉运动皮层中最大差异达到62.3%。使用PoR时,整个大脑的区域内CoV显著降低。这些结果表明,PoR减少了PVC中因配准不准确导致的误差,是一种准确量化PET中淀粉样蛋白负荷的有用方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8672/7688593/63367c3f6ce5/12194_2020_591_Fig1_HTML.jpg

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