# Cerebrovascular Reactivity Assessment during Carbon Dioxide Inhalation Using SPECT
## 2 Materials and Methods
### SPECT CVR Processing  
A quantitative analysis was performed using the attenuation corrected from the 1 st and 2 nd SPECT data with voxel-based calculations to generate a three-dimensional CVR map, which was performed using an automated processing pipeline comprising the following preprocessing steps: realignment, spatial normalization, decay correction (subtraction), count scaling, image intensity normalization, and spatial smoothing (Figure 2). In particular, the realignment, spatial normalization, and spatial smoothing steps were processed using algorithms in the Statistical Parametric Mapping (SPM) software package (Wellcome Trust Centre for Neuroimaging). In detail, first, since two images were acquired per day, image displacement may have occurred due to the movement of the subject's head. To correct this, realignment was performed to estimate the rigid-body transformation of the 2 nd SPECT image from the 1 st SPECT image. Second, since all subjects had brains of different sizes, spatial normalization was performed to normalize the realigned SPECT image to the Montreal Neurological Institute (MNI) template space at 2 mm resolution. 99 mTc-tHMPAO was injected twice in total so that the first-injected 99 mTc-tHMPAO remained at the time of the second injection, causing a problem of reducing the sensitivity of the 2 nd SPECT image. Third, therefore, the spatial-normalized image from the 1 st SPECT image underwent radioactive decay signal correction (decay correction) to measure the amount of the 1 st SPECT image signal. Then, the corrected image was obtained by subtracting the decay-corrected 1 st SPECT image from the spatial-normalized 2 nd SPECT image (subtraction) and correcting the signal intensity of the 2 nd SPECT image due to residual radiation of the 1 st SPECT. Fourth, count scaling was used to divide the signal intensity in each voxel in the spatial-normalized 2 nd SPECT image by the ratio of radiation counts in each SPECT. Then, to solve the problem where 99 mTc-tHMPAO affects the increase or decrease in blood flow in the brain, the 1 st and 2 nd SPECT data were normalized to the average signal strength of the brain (image intensity normalization). Finally, the intensity-normalized SPECT data were smoothed with an isotropic three-dimensional Gaussian kernel of 8 mm in FWHM so that the error distribution of the given data was smoothed sufficiently for statistical inference (spatial smoothing). The CVR value (%) was subsequently calculated from each voxel in each SPECT image as shown in Equation (1) and then estimated to generate a three-dimensional voxel-wise CVR map. The formula was described as follows:  
\[\text{CVR (\%)}=[(\text{2 nd }-\text{1 st})/\text{1 st}]\times 100 \tag{1}\]  
The positive CVR value could be derived from the site of normal perfusion pressure following the administration of a vasodilator such as CO2, whereas the negative CVR value could be derived from the site where blood flow decreased. The AAL template consisted of 116 ROIs covering cortical and subcortical structures of the entire brain (54 symmetrical ROIs for each hemisphere including the frontal, temporal, parietal, and occipital cortices; the insula; striatum; thalamus; cerebellum; and 8 vermis ROIs in the medial plane). This template has been recently used for SPECT analysis studies [20, 21]. We reconstructed a voxel-based CVR map from the voxel-wise CVR map (Figure 3) to average the CVR values in all voxels in each ROI. Furthermore, 13 subgroup CVR ROIs (ROI #01: left temporal cortex; ROI #02: right temporal cortex; ROI #03: vermis, medial; ROI #04: left insula/cingulate; ROI #05: right insula/cingulate; ROI #06: left frontal cortex; ROI #07: right frontal cortex; ROI #08: left occipital cortex; ROI #09: right occipital cortex; ROI #10: left parietal cortex; ROI #11: right parietal cortex; ROI #12: left central cortex; and ROI #13: right central cortex) were obtained with the voxel averages for all subjects (Figure 4 and Table 2).