The impact of atlas-based MR attenuation correction on the diagnosis of FDG-PET/MR for Alzheimer’s diseases— A simulation study combining multi-center data and ADNI-data

Sekine, Tetsuro; Buck, Alfred; Delso, Gaspar; Kemp, Bradley J.; Voert, Edwin E. G. W. ter; Huellner, Martin W.; Veit-Haibach, Patrick; Kaushik, Sandeep; Warnock, Geoffrey

doi:10.1371/journal.pone.0233886

Cited by 6 publications

(7 citation statements)

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“…From ADNI-1 data, we extracted 107 participants (48 healthy and 59 AD participants). The inclusion criteria were completeness of date of birth and diagnosis (healthy or AD) who visit 24 months after 1st PET scan, which inclusion criteria was the same as our previous simulation study focusing on Atlas-based MRAC (Sekine et al, 2020). All raw PET images were of sufficient quality for visual scoring and for softwarebased analysis using PALZ (PMOD Alzheimer's Discrimination tool, Zurich, Switzerland).…”

Section: Alzheimer Disease Neuroimaging Initiative (Adni) Datamentioning

confidence: 99%

“…We recruited 27 patients who underwent both PET/CT and PET/MR for oncologic staging from our previous study (Delso et al, 2018;Sekine et al, 2020). The 27 patients (15 males and 12 females, 60.0 ± 13.0 years) with lymphoma, pheochromocytoma, myeloma, melanoma, Merkel-cell cancer, lung cancer, pancreatic cancer, breast cancer and dementia were collected from another previous study, after excluding 3 patients.…”

Section: Patientsmentioning

confidence: 99%

“…The whole steps were done according to the previous report (Sekine et al, 2020). All simulation steps were performed in MNI space with same spatial resolution (2 mm isotropic voxels).…”

Section: Creation Of Simulated Data: Adni-data With Atlas-ac or Zte-acmentioning

confidence: 99%

“…In our previous simulation study, the diagnostic accuracy of FDG-PET on PET/MR was tested by using normal database of FDG-PET on PET/CT (Sekine et al, 2020). That study combined real patients' FDG-PET images from PET/MR and well-controlled large cohort FDG-PET data from PET/CT in the same space (spatially normalized to the same brain template with the same voxel size).…”

Section: Introductionmentioning

confidence: 99%

“…In the study, FDG-PET from PET/MR was generated based on atlas-based MRAC (Atlas) which is one of the commercially available MRAC methods installed in the GE SIGNA PET/MR (Sekine et al, 2016a;Yang et al, 2017a). The results showed that Atlas had similar diagnostic accuracy to the gold-standard, CT-based attenuation correction (CTAC), for the diagnosis of AD, although it slightly impaired sensitivity (Sekine et al, 2020). Currently, Atlas is rarely used for brain PET/MR because the vendor already developed a more accurate MRAC by using zero-echo time MRI (ZTE) which estimates head skull bone by capturing subtle proton density (Delso et al, 2015;Wiesinger et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Alzheimer Disease Neuroimaging Initiative (Adni) Datamentioning

confidence: 99%

Section: Patientsmentioning

confidence: 99%

“…The whole steps were done according to the previous report (Sekine et al, 2020). All simulation steps were performed in MNI space with same spatial resolution (2 mm isotropic voxels).…”

Section: Creation Of Simulated Data: Adni-data With Atlas-ac or Zte-acmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%