Abstract

AimAttenuation correction using zero-echo time (ZTE) – magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer’s disease (AD) by performing simulation study.MethodsWe recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient’s specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference.ResultsPositron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (−0.0796 ± 0.0938 vs. −0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R2: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8–84.5%), respectively).ConclusionFor FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.

Highlights

  • The results showed that atlas-based MRAC (Atlas) had similar diagnostic accuracy to the gold-standard, computed tomography (CT)-based attenuation correction (CTAC), for the diagnosis of Alzheimer’s disease (AD), it slightly impaired sensitivity (Sekine et al, 2020)

  • The primary goal of Alzheimer Disease Neuroimaging Initiative (ADNI) has been to test whether serial MR imaging (MRI), Positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD

  • The results show the PETscore based on both Atlas and zero-echo time MRI (ZTE) were underestimated compared with the original PETscore based on CTAC

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Summary

Introduction

Positron emission tomography (PET)/magnetic resonance (MR) has been distributed worldwide and started to be used for the evaluation of dementia both in the clinical and research setting (Drzezga et al, 2014; Barthel et al, 2015; Fendler et al, 2016; Henriksen et al, 2016; Mainta et al, 2017; Zhang et al, 2017; Hope et al, 2019; Prato et al, 2019; Yan et al, 2020). The multimodal evaluation combining functional images such as PET and morphological images such as MR imaging (MRI) is optimal because each of them provides complementary information (Teipel et al, 2015; Kaltoft et al, 2019). One of the fundamental limitations of PET/MR systems is attenuation correction (AC) derived from MRI (MRAC). With conventional MRI sequences, bone has subtle or no signal intensity because of fast T2∗ decay. This results in a difficulty to discriminate bone from other components. This is relevant in brain parenchyma which is covered entirely by the skull. Neglecting attenuation correction from bone causes large and spatially varying error (Andersen et al, 2014)

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