Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data

• Published in: Frontiers in neuroscience Vol. 14; p. 569706
• Main Authors: Ando, Takahiro, Kemp, Bradley, Warnock, Geoffrey, Sekine, Tetsuro, Kaushik, Sandeep, Wiesinger, Florian, Delso, Gaspar
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.11.2020; Frontiers Media S.A
• Subjects: Accuracy; ADNI database; Alzheimer's disease; attenuation correction; Auditory discrimination; Brain mapping; Cancer; Dementia; Dementia disorders; Diagnosis; Magnetic resonance imaging; Medical imaging; Neurodegenerative diseases; Neuroimaging; Neuroscience; Patients; PET/MR; Positron emission tomography; Scanners; Simulation; Statistical analysis; Tomography; ZTE MRI; PET/MR; Alzheimer’s disease; attenuation correction; ADNI database; dementia; atlas-based MRAC; ZTE MRI; statistical analysis
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2020.569706

Attenuation 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. We 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 -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. Positron 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, < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group ( : 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). For 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.