Automated PET-only quantification of amyloid deposition with adaptive template and empirically pre-defined ROI

Beta-amyloid plaque (Aβ) accumulation in brain cortex is a pathological hallmark of Alzheimer's disease (AD) and is believed to begin before the onset of cognitive impairment (Klunk et al 2004, Sperling et al 2011, Johnson et al 2013). Aβ imaging with PET provides an important tool for the assessment of early and differential diagnosis of AD with the National Institute on Aging-Alzheimer's Association (NIA-AA) criteria (Ng et al 2007, McKhann et al 2011) and also used for screening of subjects and evaluation of responses to therapy in clinical trials of AD therapeutics (Doody et al 2014, Salloway et al 2014).

Quantification of amyloid deposition with standardized uptake value ratio (SUVR) has been generally performed to improve diagnosis and to monitor AD therapy, particularly in clinical research and trial (Lopresti et al 2005, Rinne et al 2010). Amyloid PET quantification typically requires an individual high-resolution MRI for spatial normalization into standard Montreal Neurological Institute (MNI) space (Evans et al 2012) and to generate a probability map of gray matter (Lundqvist et al 2013, Nordberg et al 2013). Although MRI is often used for normalization into MNI space where region-of-interest (ROI) template is defined, a high-resolution 3D-MRI is not always available in clinical practice (Lundqvist et al 2013, Bourgeat et al 2015). On the other hand, when MRI-based quantification is performed, the errors potentially occur in the process of gray matter segmentation and co-registration between MRI and PET images. Furthermore, geometric distortion and motion artifact of MRI might influence the accuracy of ROI definition (Imabayashi et al 2013). Therefore, a PET-only quantification method is desirable that does not rely on the availability of MRI.

Several previous studies have attempted to quantify amyloid deposition based on spatial normalization using PET image alone (Edison et al 2013, Zhou et al 2014). However, amyloid positive images and negative images have strikingly different activity distribution patterns, which hamper use of a single template image for all cases. Lundqvist et al and Bourgeat et al each has suggested an automated PET-only adaptive template method (Lundqvist et al 2013, Bourgeat et al 2015). These methods have reported accurate and robust quantification without the use of MRI. However, these previous studies have adopted manually defined ROI template or automatic-anatomical-labeling (AAL) ROI or FreeSurfer-based ROI (Lundqvist et al 2013, Su et al 2013, Schain et al 2014, Zhou et al 2014). These anatomical ROIs were not necessarily suitable for amyloid quantification because they were not generated voxel-wise over the regions that are apt to accumulate amyloid PET drugs in pathological conditions. On the other hand, the ROI defined manually on the co-registered MRI were also used to calculate SUVR mainly for clinical research (Lopresti et al 2005, Becker et al 2013, Lundqvist et al 2013). However, the manual ROI is less reproducible and requires a strenuous work. A ROI template characterizing voxel-wise amyloid specific regions may provide higher sensitivity and specificity.

The purpose of this study was to examine the feasibility of PET-only amyloid quantification with adaptive template and empirically determined amyloid-characteristic ROI.

2.1. Brain MRI and PiB PET scans

2.2. Visual interpretation

2.3. Amyloid positive and negative templates

We used the PMOD software ver3.4 (PMOD group, Switzerland) and MATLAB (The Mathworks, MA) for all image analyses. The PET image was rigidly registered to the MRI using the normalized mutual information method, and then was spatially normalized to the standard MNI T1 atlas using the same transformation parameters of individual MRI-based normalization. The AAL-ROI template was applied into normalized images on MNI space. All voxel values were normalized by the mean value of cerebellar cortex (AAL-ROI labeled as 'Cerebellum_Crus') to generate the SUVR-scaled images. Eleven typical positive images (4 AD and 7 MCI subjects with high PiB accumulation in all 4 cortical areas and striatum mentioned above) and eight typical negative images were averaged to generate positive and negative templates, respectively (figure 1).

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2.4. Empirically determined PiB-prone region-of-interest template (EPP-ROI)

Figure 2 shows the process of making an empirically PiB-prone ROI template (EPP-ROI). Voxels, in which PiB accumulation was higher than 1.7 in SUVR, were extracted from the positive template. Non-specific PiB accumulative voxels, which actually covers the white matter, were also extracted from the negative template based on the SUVR cutoff value of 1.7. We adopted the SUVR cutoff value of 1.7 to make non-specific PiB accumulations of the negative template fit the white matter of average MRI (figure 3) by discussions among nuclear medicine physicians and technologists. We then generated the EPP-ROI by subtracting the negative mask from the positive mask and further selecting voxels overlapping with one of the following AAL-ROIs (Tzourio-Mazoyer et al 2002) that were considered important: 'Precentral' (precentral gyrus), 'Rolandic_Oper' (rolandic operculum), 'Supp_Motor_Area' (supplementary motor area), 'Frontal_Sup' (superior frontal gyrus), 'Frontal_Mid' (middle frontal gyrus), 'Frontal_Inf' (inferior frontal gyrus), 'Rectus' (rectus gyrus), 'Cingulum_Ant' (anterior cingulate gyrus), 'Cingulum_Mid' (middle cingulate gyrus), 'Postcentral' (postcentral gyrus), 'SupraMarginal' (supramarginal gyrus), 'Angular' (angular gyrus), 'Parietal' (superior and inferior parietal gyrus), 'Cingulum_Post' (posterior cingulate gyrus), 'Precuneus' (precuneus), 'Calcarine' (calcarine fissure and surrounding cortex), 'Cuneus' (cuneus), 'Occipital' (occipital gyrus), 'Fusiform' (fusiform gyrus), 'Temporal' (superior, middle and inferior temporal gyrus), 'Putamen' (putamen) and 'Pallidum' (globus pallidus) (figure 4).

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The cerebellar cortex region, which was created by trimming the outside of AAL-ROI labeled as 'Cerebellum_Crus' areas to minimize the influence of the partial volume effect, was included in the EPP-ROI as a reference region.

2.5. MRI-based quantification method

2.6. PET-only quantification method with adaptive template method

Figure 5 shows the workflow of PET-only quantification method. First, the PET images were non-rigidly normalized to the positive and negative template, respectively. Second, the normalized cross-correlation (NCC_z) was calculated between the normalized PET image and the respective template on each axial slice (z) that contains brain tissue as follows:

$$\begin{eqnarray}&&\text{NC}{{\text{C}}_{z}}=\frac{{\sum}_{i=1}^{x}{\sum}_{j=1}^{y}\left(\left(A(i,j)-\bar{A}\right)\left(B(i,j)-\bar{B}\right)\right)}{\sqrt{\left({\sum}_{i=1}^{x}{\sum}_{j=1}^{y}{{\left(A(i,j)-\bar{A}\right)}^{2}}\right)\left({\sum}_{i=1}^{x}{\sum}_{j=1}^{y}{{\left(B(i,\,j)-\bar{B}\right)}^{2}}\right)}}\end{eqnarray}$$

where x and y are image matrix size. Then NCC_z was averaged across the slices to obtain NCC. The normalization template having higher NCC was adopted. Third, the EPP-ROI was inversely transformed to individual PET images. We calculated the SUVR and evaluated differences among three methods for the first set of subjects. Table 1 shows the property of the three methods.

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Table 1. Summary of three types of quantification methods.

Process	MRI-based AAL-ROI	MRI-based EPP-ROI	PET-based EPP-ROI
Gray matter segmentation	MRI-based	—	—
Spatially normalization	MRI-based	MRI-based	PET-based with AT
Coregistration	NMI method	NMI method	—

AT: adaptive template method. NMI: Normalized mutual information method.

2.7. Comparison of new method with visual interpretation

2.8. Statistical analysis

Table 2 shows the SUVR obtained by three methods. The higher SUVR was obtained with EPP-ROI compared with the AAL-ROI when MRI-based normalization was used. On the other hand, the SUVR were almost the same between MRI-based and PET-based methods as long as EPP-ROI was employed. Figure 6 shows the representative ROI setting of the three methods on a PiB positive subject. MRI-based AAL-ROI protruded from the cortex while the EPP-ROI was localized over the PiB accumulative cortical areas. In PET-based normalization process, the NCC value chose the 'correct' template corresponding to the actual visual classification for all negative images. For the positive images, accordance was found for 87% of the subjects (20/23 cases).

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Figure 7 shows the correlation between SUVRs obtained by MRI-based AAL-ROI method and MRI-based EPP-ROI method (A) and the correlation between SUVRs obtained by MRI-based EPP-ROI method and PET-based EPP-ROI method (B). Both showed a high correlation (R  =  0.99, 0.99). Figure 8 shows individual SUVR values computed with the three methods in relation to clinical diagnosis. Most of the patients clinically diagnosed with AD or MCI showed higher SUVR. All normal control subjects showed lower SUVR. MRI-based EPP-ROI method provided higher SUVR than MRI-based AAL-ROI, while PET-based EPP-ROI and MRI-based EPP-ROI presented almost identical SUVR values.

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Figure 9 shows the results of ¹¹C-PiB SUVR on the 2nd set of subjects obtained by PET-based EPP-ROI method. All subjects were correctly categorized into positive (n  =  19) and negative (n  =  15) using cutoff value of 1.7.

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In this study, we have generated an empirically determined PiB-prone ROI (EPP-ROI) and have examined PET-only quantification with adaptive template method. The PET-only amyloid quantification method provided comparable SUVR values to the MRI-based SUVR and categorized ¹¹C-PiB scans exactly the same as visual assessment.

We generated empirically PiB-prone ROI based on actual human PiB images. The results showed high correlation between SUVR obtained with MRI-based AAL-ROI and EPP-ROI (R  =  0.99). The EPP-ROI provided higher SUVR than the AAL-ROI irrespective of positive or negative images, especially in positive images. This difference is considered to arise from exclusion of the voxels adjacent to the contour of the cortical area that are included in AAL-ROI, making EPP-ROI less sensitive to partial volume effect and normalization error. In a case with an error in the MRI-based gray matter segmentation as shown in figure 6, AAL-ROI extends beyond the cortical surface, leading to underestimation of SUVR. On the other hand, the EPP-ROI template is considered to provide more accurate and reproducible SUVR because it was derived from the cortical accumulation pattern of actual PiB images and does not contain voxels on the brain surface or gray–white border. Therefore the EPP-ROI has a potential to improve quantitative accuracy of amyloid quantification.

In this study, we adopted the SUVR cutoff value of 1.7 to make the EPP-ROI cover only amyloid-specific areas. We preliminarily generated the ROI template based on the SUVR cutoff value of 2.0 and calculated the SUVR for the 20 subjects of the 1st set as a pilot experiment. Average SUVR computed with the ROI template (made from the cutoff value of 2.0) were 2.23 for the positive subjects and 1.36 for the negative subjects. On the other hand, those computed with the EPP-ROI (cutoff value of 1.7) were 2.18 and 1.30, respectively. Since the difference between these two was relatively small (<5%), SUVR may not be so sensitive to selection of the cutoff level. Conversely, if we choose a smaller cutoff value, the ROI template may become smaller and may be more localized in cortical areas. However, this may potentially trim important amyloid-specific regions, particularly if a PET camera with poor spatial resolution is used.

When the EPP-ROI was used, the PET-based spatial normalization method with adaptive template provided almost the same SUVR as the MRI-based method. This result indicates that PET-based normalization with adaptive template method exhibited equivalent performance as the MRI-based normalization. Lundqvist et al and Bourgeat et al have also evaluated the PET-based normalization using adaptive template in order to quantify amyloid deposition (Lundqvist et al 2013, Bourgeat et al 2015). While they used more than two types of templates, we adopted only two templates. We believe that two types of templates provided a sufficient accuracy for amyloid PET image normalization because our PET-based normalization was comparable to the MRI-based normalization results (table 2). Furthermore, our PET-only quantification method is simple and robust because it does not rely on MRI segmentation and that it applies the EPP-ROI to original PET images without interpolation. ROI analysis in the original PET space can minimize the risk of reducing quantitative accuracy and reproducibility (Lundqvist et al 2013). Edison et al and Saint-Aubert et al have suggested that PET-only quantification method is adequate for clinical use (Edison et al 2013, Saint-Aubert et al 2014). Our method is considered to be useful especially for clinical practice.

We tested the PET-only quantification method by applying it to a ¹¹C-PiB image set acquired with two different PET cameras. As a result, all subjects were correctly categorized into positive and negative based on the cutoff value of 1.7. Categorization of ¹¹C-PiB scans by the PET-only quantification method showed exact concordance with the visual read results. Therefore, the PET-only quantification method could help interpretation of amyloid images based on these results. This PET-only quantification method could be generalized to any other amyloid tracers if their specific templates are created, and the idea of standardizing the scaling of quantitative values may also be applicable (Klunk et al 2015).

The PET-only amyloid quantification with adaptive template and EPP-ROI provides accurate, robust, simple and automated amyloid quantification without MRI. In addition, categorization of amyloid PET scans using an SUVR threshold showed good concordance with the visual read results. The proposed quantification method could help interpretation and quantitative evaluation of amyloid PET images and be useful for clinical practice as well as for research.

This study was supported by JSPS KAKENHI Grant Number 16K19882.