To develop MRI- or CT-based global and regional dcCLs, the study cohort included 63 subjects; 20 young controls (YCs), 18 old controls (OCs), and 25 individuals with Alzheimer’s disease dementia (ADD). The subjects underwent paired FMM and FBB PET-CT and three-dimensional (3D) T1 MRI. Healthy YCs were under 40 years of age with normal cognitive function and no history of neurological or psychiatric disorders. OCs were over 65 years of age with normal cognitive function determined using neuropsychological tests and no history of neurological or psychiatric disorders. Participants diagnosed with MCI had to meet Petersen’s criteria  and show objective memory impairment one standard deviation (SD) below the norm in at least one memory test. ADD was diagnosed based on the National Institute on Aging-Alzheimer’s Association (NIA-AA) research criteria for probable AD .
All participants underwent clinical interviews, neurological and neuropsychological examinations, and laboratory tests including complete blood count, blood chemistry, thyroid function tests, syphilis serology, and vitamin B12/folate levels. The absence of structural lesions including cerebral infarctions, brain tumors, vascular malformations, and hippocampal sclerosis was confirmed based on brain MRI.
The Institutional Review Board of Samsung Medical Center approved the study protocol and all methods were performed according to the approved guidelines. Written consent was obtained from each participant.
MRI data acquisition
Standardized 3D T1 turbo field echo images were acquired from all participants at Samsung Medical Center using the same 3.0 T MRI scanner (Philips Achieva; Philips Healthcare, Andover, MA, USA). The detailed parameters are described in Additional file 1, Supplementary Methods 1.
Aβ PET-CT data acquisition
Participants underwent FMM and FBB PET at Samsung Medical Center using a Discovery STe PET/CT scanner (GE Medical Systems, Milwaukee, WI, USA) in 3D scanning mode that examined 47 slices 3.3-mm in thickness spanning the entire brain [12, 13]. Paired FMM and FBB PET images were acquired on two separate days; the mean interval time (4.0 ± 2.5 months across all groups) was not different among the three groups (p = 0.89). Among the 63 head-to-head dataset, FMM PET was performed first in half of the patients (total 36: 19 ADD, 6 OCs, and 11 YCs) and FBB PET first in the other half of the participants (total 27: 6 ADD, 12 OCs, and 9 YCs). According to the protocols for the ligands proposed by the manufacturers, a 20-min emission PET scan with dynamic mode (consisting of 4 × 5 min frames) was performed 90 min after injection of a mean dose of 185 MBq of FMM or 311.5 MBq of FBB. 3D PET images were reconstructed in a 128 × 128 × 47 matrix with a voxel size of 2 mm × 2 mm × 3.27 mm using the ordered-subsets expectation maximization algorithm (FMM iterations = 4 and subset = 20; FBB iterations = 4 and subset = 20).
CT images were acquired using a 16-slice helical CT (140 KeV, 80 mA; 3.75-mm section width) for attenuation correction and reconstructed in a 512 x 512 matrix with a voxel size of 0.5 mm x 0.5 mm x 3.27 mm.
Regional visual assessment and regional dcCL scales
Two experienced neurologists visually quantified FMM and FBB images  in our head-to-head dataset. Each doctor scored the frontal, parietal, posterior cingulate/precuneus, lateral temporal, and striatum as positive or negative and recorded the overall amyloid status. Inter-rater agreement was excellent for FMM (Fleiss k = 0.86-0.97) and FBB (Fleiss k = 0.9-1.0) for five regions. After individual ratings were performed, the final visual positivity was determined based on the majority of agreement regarding visual reading results.
Development of MRI-based global and regional CTX VOI
The method overview is shown in Fig. 1. To develop MRI-based dcCLs, we followed CL process for preprocessing (i.e., creating SUVR parametric PET images on MNI space) to MRI, FMM, and FBB PET images as described in Klunk et al. The processing details are provided in the original CL manuscript . Briefly, individual MR images were co-registered onto the MNI-152 template and then individual PET images were co-registered onto the corresponding MRI images. The PET and MRI images were spatially normalized (Fig. 1b) using transformation parameters of SPM8 unified segmentation method of T1-weighted MRIs. The whole cerebellum (WC) mask was used as the reference region from the GAAIN website and SUVR parametric PET images of FMM and FBB using the WC mask created and used for global and regional dcCLs. To define the common cortical target region with amyloid accumulation distributed for FMM and FBB PET, 25 Aβ PET-positive (+) ADD patients and 18 Aβ PET-negative (-) OCs were included for all PET ligands in head-to-head cohort . The method in the original publication was used to create a common cortical target VOI (FMM-FBB global CTX VOI) for both FMM and FBB PET and the details are described in the original publication (Fig. 1d1) . Individual dcSUVR values in the FMM-FBB global CTX VOI were calculated in all PET images. MRI-based regional VOIs were defined by overlapping MRI-based FMM-FBB global CTX VOI and AAL atlas (Fig. 1e1) . The sub-regions of AAL in the MRI-based global CTX VOI were merged into six regions (frontal, PC, parietal, striatum, occipital, and temporal). Regional dcSUVR values were calculated using the six regional VOIs.
Development of CT-based global and regional CTX VOI
For constructing the brain CT template, 139 CT scans were collected from normal controls (NCs) in another dataset who underwent FBB-PET CT. In the PET-CT scanner, the CT image was low-dose CT to reduce patient exposure to radiation. Brain CT template was created using corrected Hounsfield units (HU) of brain tissues in the CT images. The details of the HU correction approach are described in the original methodology paper . Briefly, as shown in Fig. 1a, total CT images were reoriented. Intensities of the images were scaled to boost HU of brain tissues. The HU-corrected CT images were co-registered onto corresponding T1 MR images. Individual T1 MR images were spatially normalized on MNI space and spatial normalization parameters of T1 MR images were applied to corresponding HU-corrected CT images. The normalized CT images were flipped to create a symmetric template and the mean image was created using the normalized CT images. Gaussian smoothing at 8 mm was applied to the template to remove template noise.
HU correction processing was performed to individual CT images. FMM and FBB PET images were co-registered onto corresponding HU-corrected CT images and the PET images were spatially normalized using normalization parameters of each HU-corrected CT image onto MNI space by the created brain CT template (Fig. 1c). Using the normalized FMM and FBB PET images with WC mask as the reference region, CT-based SUVR parametric PET images were created. The SUVR PET images were used to generate a FMM-FBB global CTX VOI in the same manner as described above for the MRI-based FMM-FBB method (Fig. 1d2). In addition, individual dcSUVR values were calculated using the CT-based FMM-FBB global CTX VOI. CT-based regional VOIs were also defined by overlapping CT-based FMM-FBB global CTX VOI and AAL atlas (Fig. 1e2). The sub-regions of AAL in the CT-based global CTX VOI were merged into six regions (frontal, PC, parietal, striatum, occipital, and temporal). Regional dcSUVR values were calculated using the six regional VOIs.
Development of MRI and CT-based global and regional dcCL
The dcCL method was used to derive equations from global and regional dcSUVR values using created FMM-FBB VOIs for direct conversion . Fig. 1f1and 1f2 show the summary of methods; each method shows the process of regression equations derived from dcSUVR and dcCL of MRI-based and CT-based methods globally and in six regions, respectively.
The FMM-FBB VOIs of the CT-based method were applied to FMM and FBB PET to acquire dcSUVRs and FMM-FBB VOIs of MRI-based method were used to validate the CT-based method. First, the equations of dcCL conversion from MRI and CT-based dcSUVR and dcCL values were derived using the CL formula globally and in six regions. Second, dcCL scales of MRI and CT-based methods were calculated using the dcCL conversion equations globally and for the six regions.
Validation of the clinical efficacy of CT-based regional dcCLs in the independent cohort
To validate the clinical efficacy of CT-based regional dcCLs, 2,245 FMM and FBB PET scans in ADD, aMCI, and cognitive normal (CN) groups were recruited. Gaussian mixture model was performed to determine dcCL cutoffs in 547 NCs 55 years of age or older. Global and six regional cutoffs including frontal, PC, parietal, striatum, occipital, and temporal, were determined using a machine learning technique and the cutoffs were 18.96, 22.32, 28.06, 21.57, 27.02, 25.57, and 23.07, respectively. The group was classified into four groups based on global and striatal dcCL cutoffs. First, based on global Aβ dcCL scales, the cohort was classified as global (-) and global (+). The global (-) group was further classified into regional (-) and regional (+) groups based on regional cutoffs for at least one or more regions. In addition, the global (+) group was further classified into striatal (-) and striatal (+) groups based on striatal cutoffs. Thus, the cohort was classified into four groups: global (-) and regional (-) Aβ: G(-)R(-); global (-) and regional (+) Aβ: G(-)R(+); global (+) and striatal (-) Aβ: G(+)Str(-); global (+) and striatal (+) Aβ: G(+)Str(+).
All participants underwent neuropsychological testing using the Seoul Neuropsychological Screening Battery 2nd edition (SNSB-II) including the Seoul Verbal Learning Test (SVLT) delayed recall and clinical dementia rating scale-sum of box (CDR-SOB) [17, 18]. The detailed items are described in Additional file 1, Supplementary Methods 2.
In the head-to-head cohort, group difference and ROC analysis were performed between regional visual positivity and MRI- and CT-based regional dcCL scales in order to validate regional dcCL scales. Regression analysis was performed for reliability between FMM and FBB PET ligands or between MRI-based and CT-based methods using dcSUVR and dcCL scales globally and regionally. The regression was also performed to derive global and regional dcCL formulas from the head-to-head cohort. For precision, the differences in dcCL scales between FMM and FBB ligands or between MRI-based and CT-based methods were investigated using Bland-Altman plots . The absolute value differences between dcCL scales of MRI-based and CT-based methods or between dcCL scales derived based on FMM and FBB ligands were compared using a generalized estimating equation (GEE).
In an independent cohort for clinical validation, the chi-square test for categorical variables and analysis of covariance (ANCOVA) for continuous variables were used to compare the demographics and frequency of APOE4 genotype and MMSE scores among the four groups. To investigate the neuropsychological results among the four groups, ANCOVA was performed after controlling for age and apolipoprotein E ε4 (APOE4-ε4) carrier.
SPSS version 24.0 (SPSS Inc., Chicago, IL, USA) was used for GEE and MedCalc Statistical Software version 17.9.2 (Ostend, Belgium; 2017) for correlation, linear regression, ANCOVA, and Bland-Altman analyses.