In this study, we inversely registered the atlas in the template space to the original PET/MR data space and calculated the SUV—defined as registering atlas template to subjects’ images (RATSI) method—then compared the quantification with that of the traditional RSIAT method of fitting the clinical image data to the template space. Using the two automatic methods, we segmented two representative brain areas containing four regions: the left caudate (CAU_L), right caudate (CAU_R), left putamen (PUT_L), and right putamen (PUT_R) in twenty PD patients, then compared the SUVmean and SUVmax in the corresponding brain regions. The manual segmentation method was also performed and used as the ground truth. For quantitatively evaluating the two atlas-based automatic methods, the segmented results from the three methods (including the manual method) were normalized into the same MNI space in the end, then the Dice coefficient (DC) and Hausdorff distance (HD) were calculated to evaluate inter-rater variability. The RATSI method was applied to quantify the differences in 18F-FDG uptake between PD and MSA groups in multiple brain regions, including caudate, putamen, and the cerebellar gray matter.
Subjects and Data
We retrospectively studied patients who had undergone 18F-FDG PET/MR brain examinations for diagnosing or evaluating neurodegenerative diseases in our PET center (Wuhan Union Hospital, Wuhan, China). The study was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology. Patients provided written informed consent.
Twenty typical PD (60 ± 5 y) and eight MSA patients (60 ± 8 y) were involved in this study. The diagnosis was according to the diagnostic criteria for PD in China in 2016 and the MSA diagnostic criteria of a Chinese expert consensus in 2017. The exclusion criteria were as follows: (1) a clear history of stroke, with brain MRI examination revealing large cortical infarction or hemorrhagic manifestations; (2) CNS infectious disease; (3) brain tumors or history of head trauma; (4) history of craniocerebral surgery; and (5) suboptimal image quality.
Image Acquisition and Reconstruction
All patients underwent 18FDG-PET and MRI brain imaging simultaneously in a hybrid PET/MR scanner (3.0 T, SIGNA TOF-PET/MR, GE Healthcare). The 18F-FDG was produced in our center by a Minitrace cyclotron (GE Healthcare, USA) and automatic synthesizer (PAT Biotechnology Company, Beijing, China). The radiochemical purity was > 95%.
All participants fasted for at least 6 h and stopped any drugs that could affect brain glucose metabolism for at least 12 h before the 18F-FDG injection. The intravenously injected dose was 0.1 mCi/kg (3.7 MBq/kg) after ensuring the blood glucose level was ≤ 200 mg/dL. The scan began 40 min post 18F-FDG injection, during which the subject rested in a quiet and dimly-lit room. The total scanning time for PET was 15 min and the 3D T1WI (three-dimensional gradient echo sequence, flip angle = 12°, time of echo [TE]/time of repetition [TR] = 2.6/6.9 ms, bandwidth = 50 KHz, FOV = 24 cm × 24 cm, matrix = 384 × 384) sequence was simultaneously acquired.
The PET data were reconstructed using the ordered subsets expectation maximum (OSEM) algorithm with TOF technique. The parameters were as follows: FOV = 30 cm × 30 cm, matrix = 192 × 192, Filter Cutoff = 3.0 mm, Subsets = 28, Iterations = 3. The PET attenuation correction was atlas-based MRI attenuation correction, combined with Dixon water-fat separation methods .
Brain Segmentation and SUV Quantification
Automatic brain segmentation was based on an atlas template from the automated anatomical labeling atlas (http://www.gin.cnrs.fr/en/tools/aal-aal2/) shown in Figure 1. There are 70 segmented regions labeled from 1 to 70 in this brain atlas, which were used for both the two atlas-based automatic methods. By registration of 3D T1-weighted MRI to MNI space with SPM12 segmentation (http://www.fil.ion.ucl.ac.uk/spm/download/), the forward and inverse deformation fields could be produced. The RSIAT spatially fitted the 18F-FDG PET images to the atlas template with the forward deformation field directly and produced the PET images in the MNI space, which could be segmented directly with the brain atlas. In contrast, the RATSI fitted the acquired inverse deformation field to the brain atlas template, generating a personalized brain atlas for every subject, which then was used for regional 18F-FDG PET image quantification, as shown in figure 2.
For the two automatic brain segmentation, the detailed steps were as followed:
- The medicine (DICOM) format of 3D T1 MRI and 18F-FDG PET images was converted to the neuroimaging informatics technology initiative (NIfTI) format using SPM12 for following processing.
- The 3D T1 images were normalized to MNI space. The results would produce the inverse deformation field (for RATSI) and forward deformation field (for RSIAT), which extracted the information of transformation between the data acquisition space and MNI space.
- For RATSI, by utilizing the inverse deformation filed, the anatomical labeling atlas in the MNI space was transformed into the data acquisition space and produced the personalized atlas template, which was in the same space with FDG images. For RSIAT, by utilizing the forward deformation field, the FDG-PET images could be normalized to the MNI space.
- For RATSI, the regional FDG images were segmented according to the personalized atlas template and further, the mean or max SUV values in different regional brain could be calculated. For RSIAT, the normalized FDG images were registered with the atlas template, then were segmented directly according to the regions-labeled brain atlas. At last, the regional SUV values could be calculated.
Manual segmentation was performed by a clinical neuroimaging expert using ITK-SNAP (http://www.itksnap.org) section by section, using the 3D T1 structural images. As the manual method was time-consuming, only two cerebral nuclei containing four regions (left caudate, right caudate, left putamen, and right putamen) were extracted and used for evaluation of the automatic segmented results. The extracted regions based on structural images produced the corresponding binary mask, which used for 18F-FDG PET images segmentation.
The regional SUV calculations were performed with Matlab 2016a (Mathworks, Natick, MA, USA). The SUVs were calculated by  (see Equation 1 in the Supplementary Files)
where r is the radioactivity concentration [kBq/mL], is the decay-corrected amount of injected radiolabeled 18F-FDG [kBq], and w is the weight of the patient [g].
The four brain regions were segmented with the manual method on twenty PD subjects for inter-rater variability evaluation by using the parameters DC and HD. The DC evaluates the similarity between two volumes by measuring their overlap . (see Equation 2 in the Supplementary Files)
where A and B represent the segmentation volumes of the automatic methods and manual method, respectively. A∩B represents the intersection of the two volumes. A DC value of 1 represents two identical segmentations while a DC value of 0 represents no overlap between the two segmentations. HD usually measures how far two subsets of a metric space are from each other, and here, determines on average how much the two segmented volumes differ. A smaller HD represents a closer agreement between two volumes.
The differences in parameters DC and HD were analyzed by a paired t-test. The four segmented brain regions used as binary masks were overlapped on the PET images to extract the regional SUVmean and SUVmax. One-way analysis of variance (ANOVA) was used to compare the differences in quantitative SUVs among the three segmentation methods. The F-test was used to test whether the variance was homogeneous and the two-tailed t-test was used to compare the differences in SUVs in the different regions in the basal ganglia and cerebellar gray matter between the PD group and MSA group. P>0.05 was considered variance homogeneous for F-test. P < 0.05 was considered statistically significant for t-test.