Article Figures & Data
Figures
- FIGURE 1.
Agreement between spatially normalized images with standard template in terms of normalized mutual information: internal validation dataset (n = 65) (A) and external validation dataset (n = 78) (B). SNUH = Seoul National University Hospital; SPM12 = SPM version 12.
- FIGURE 2.
18F-FDG PET images of 13-y-old boy with epilepsy, included in internal dataset. Small brain in individual space was not adequately resized by SPM, as indicated by red arrows.
- FIGURE 3.
18F-FDG PET images of 72-y-old woman from internal dataset, exhibiting striatum atrophy. Although SPM did not perfectly reshape region (red arrows), proposed method showed significant improvement, also outperforming SPM in occipital cortex (indicated by yellow arrows).
- FIGURE 4.
18F-FDG PET images of 67-y-old woman selected from an external dataset, exhibiting relatively small brain size, high noise level, and poor spatial resolution.
- FIGURE 5.
Internal validation. Comparison between MRI-based SUVR quantification in individual space using FreeSurfer (x-axis) and spatial normalization–based SUVR quantification in template space using proposed method or SPM with MRI (y-axis). Proposed method is represented by orange symbols and regression lines, whereas SPM with MRI is represented by blue symbols and regression lines: global cerebral cortex (A), frontal (B), parietal (C), temporal (D), occipital (E), and subcortex (F).
- FIGURE 6.
External validation. Comparison between MRI-based SUVR quantification in individual space using FreeSurfer (x-axis) and spatial normalization–based SUVR quantification in template space using proposed method or SPM with MRI (y-axis). Proposed method is represented by orange symbols and regression lines, whereas SPM with MRI is represented by blue symbols and regression lines: global cerebral cortex (A), frontal (B), parietal (C), temporal (D), occipital (E), and subcortex (F).
- FIGURE 7.
18F-FDG PET images of 21-y-old man with hypointense areas that are well preserved in terms of relative size and shape after spatial normalization. Images are shown in individual space before spatial normalization (A) and standard space after spatial normalization (B) using proposed method.
Tables
Dataset n Age (y) M:F Internal 65 47.15 ± 21.5 32:33 External 78 61.31 ± 7.39 26:52 - TABLE 2.
Pearson Correlation and ICC Analysis for SUVR of Internal 18F-FDG PET Dataset (n = 65) Relative to FreeSurfer Approach
SPM with MRI SPM without MRI Proposed Region Slope y-intercept R2 ICC Slope y-intercept R2 ICC Slope y-intercept R2 ICC Cerebral cortex 1.074 −0.166 0.835 0.768 0.872 0.079 0.886 0.743 1.011 −0.043 0.962 0.955 Frontal 1.057 −0.161 0.819 0.764 0.873 0.055 0.895 0.690 1.016 −0.043 0.958 0.968 Parietal 1.040 −0.188 0.782 0.519 0.855 0.109 0.829 0.768 0.957 0.000 0.931 0.885 Temporal 1.097 −0.126 0.816 0.878 0.855 0.112 0.882 0.829 1.006 −0.018 0.959 0.974 Occipital 0.901 0.018 0.716 0.610 0.727 0.277 0.820 0.752 0.932 0.052 0.941 0.936 Subcortex 1.069 −0.052 0.763 0.841 0.896 −0.017 0.855 0.491 0.966 0.027 0.922 0.956 - TABLE 3.
Pearson Correlation and ICC Analysis for SUVR of External 18F-FDG PET Dataset (n = 78) Relative to FreeSurfer Approach
SPM with MRI SPM without MRI Proposed Region Slope y-intercept R2 ICC Slope y-intercept R2 ICC Slope y-intercept R2 ICC Cerebral cortex 0.784 0.175 0.689 0.455 0.894 0.074 0.872 0.730 0.986 −0.003 0.950 0.938 Frontal 0.849 0.101 0.631 0.486 0.948 −0.008 0.868 0.628 0.997 −0.013 0.934 0.945 Parietal 0.617 0.312 0.425 −0.124 0.837 0.139 0.842 0.690 0.943 0.025 0.927 0.819 Temporal 0.896 0.094 0.766 0.852 0.885 0.101 0.854 0.879 0.996 0.009 0.958 0.975 Occipital 0.795 0.155 0.591 0.292 0.781 0.248 0.802 0.879 0.958 0.028 0.909 0.910 Subcortex 1.123 −0.112 0.859 0.891 1.049 −0.153 0.812 0.282 0.950 0.051 0.914 0.956
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