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Basic Science Investigation |
1 Division of Neuroscience, Department of Clinical Neuroscience, Imperial College London, London, United Kingdom; 2 Hammersmith Imanet, Hammersmith Hospital, London, United Kingdom; 3 PET Neurology Group, MRC CSC, Hammersmith Hospital, London, United Kingdom; 4 Division of Neuroscience, Department of Neuropathology, Imperial College London, London, United Kingdom; 5 PET Epilepsy Group, Hammersmith Hospital, London, United Kingdom; and 6 Department of Psychiatry, University of Mainz, Germany
Correspondence: For correspondence or reprints contact: Federico E. Turkheimer, PhD, Division of Neuroscience and Mental Health, Department of Clinical Neuroscience, DuCane Rd., London W12 0NN, U.K. E-mail: federico.turkheimer{at}imperial.ac.uk
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Key Words: PET • PK11195 • microglia • supervised clustering
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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In diseases of the central nervous system, a higher density of PBBS has been observed in macrophages and activated microglia, the intrinsic immune defense of the brain (3). Significant microglial activation occurs after mild-to-severe neuronal damage resulting from traumatic, inflammatory, degenerative, and neoplastic disease (3). Microglia, therefore, act as sensors for pathologic events, including subtle ones without any obvious structural damage (4), and are activated not only in the surroundings of focal lesions but also in the distant, anterograde and retrograde projection areas of the lesioned neural pathway and even in structurally normal transsynaptic areas (5,6).
The high selectivity for the PBBS has made PK11195 the ligand of choice for the in vivo imaging of activated microglia with PET. PET imaging with the molecular marker [11C]-(R)-PK11195 (7) now provides an indicator of active disease in the brain with wide applicability (3).
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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1.6 in cortical gray matter in controls) and those calculated with a tissue reference (BP 0.07–0.46) (9). This difference could have resulted from the presence of significant nonspecific binding in brain tissue (7). However, BP values obtained from plasma input and corrected for nonspecific binding calculated from the reference region were still higher than SRTM BP values indicating, among several possible data and model deficiencies, the presence of specific binding in the reference used (10). The use of a tissue input function may provide practical advantages, but the selection of a reference region devoid of PBBS is a challenging task. Microglia are distributed ubiquitously throughout the brain and their activation may occur along projections into healthy-appearing tissue (3). Furthermore, activation of microglia is associated with aging even in the normal brain (11).
The use of postmortem data for the selection of appropriate reference regions devoid of active microglia may be valuable in specific diseases and can increase [11C]-(R)-PK11195 sensitivity even with small sample sizes (12). When an informed choice is not possible, an alternative approach is the use of cluster analysis that segments voxels into classes on the basis of their time courses and selects as reference the class of voxels that exhibits the kinetic behavior closest to that of gray matter in healthy controls (13).
Improved Reference Region Extraction: Scope and Rationale
Here we present an improved clustering algorithm for the automatic extraction of a reference tissue region for the quantification of [11C]-(R)-PK11195 PET studies. Improvement was sought on 3 different grounds. The first aim was to increase the reliability of current clustering methodology that is based on unsupervised tissue classification. An unsupervised clustering algorithm defines tissue classes in a data-dependent manner according to a very generic model (e.g., a mixture of gaussian distributions) that may not accurately describe the underlying physiology and, therefore, introduce instability or inaccuracies in the grouping.
Second, we considered an algorithmic design that aimed at the extraction of a proper reference region, whereby "proper" meant that its use as input function would produce BP values comparable with those obtained with a blood input function.
Third, we considered further methodologic developments in the BP calculations that could accommodate the improved modeling of the reference region and produce robust and reproducible BP estimates.
Supervised Clustering of [11C]-(R)-PK11195 Studies
In the normal brain, immunocytochemical staining suggests the presence of PBBS in muscle cells of small- and medium-sized intraparenchymal arteries and in the bigger leptomeningeal arteries; in perivascular macrophages, lymphocytes, and neutrophils; in the choroid plexus; and in the ependyma (Fig. 2). Other regions with a rich density of receptors include the meninges, olfactory bulbs, and the pituitary gland (3). Given the limited spatial resolution of PET images, specific binding in these areas is a likely source of diffuse low-level [11C]-(R)-PK11195–specific signal that may easily affect the reference region even in healthy control subjects.
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Scanning Protocol
All [11C]-(R)-PK11195 studies were performed on an ECAT EXACT 3D (CTI/Siemens) PET camera with 23.4-cm axial field of view, 95 transaxial planes (15). To reduce the effect of activity outside the direct field of view in brain scans, the tomograph was equipped with annular side shielding. A transmission scan was acquired before every emission using a single rotating photon point source of 150 MBq of 137Cs for subsequent attenuation correction and scatter correction. The spatial resolution of the images reconstructed using the reprojection algorithm with the ramp and Colsher filters set to Nyquist frequency is close to isotropic: 5.1-mm (full width at half maximum [FWHM]) transaxially and 5.9-mm FWHM axially (15).
Subjects consisted of 12 healthy control subjects injected with 185 MBq without arterial blood sampling, 6 healthy controls injected with 296 MBq for whom an arterial input function was available, 3 patients with Huntington's disease (HD) (296 MBq injected) (16), and 4 patients with Alzheimer's disease (AD) scanned twice (296 MBq injected for each scan) with a maximum time interval of 6 wk (no blood sampling available for the patient group). [11C]-(R)-PK11195 was provided by Hammersmith Imanet plc.
Thirty seconds after the start of the emission scan, [11C]-(R)-PK11195 was infused intravenously over 10 s in 5 mL physiologic saline. Emission data were then acquired over 60 min in list mode and rebinned as 18 time frames (30-s background frame, 1 x 15-s frame, 1 x 5-s frame, 1 x 10-s frame, 1 x 30-s frame, 4 x 60-s frames, 7 x 300-s frames, and 2 x 600-s frames). Subjects were placed in the scanner oriented parallel to the orbitomeatal line, and head positioning was monitored throughout the scan. Volumetric T1-weighted MR images were obtained on a 1.0-T Picker HPQ scanner (Picker International) at the Robert Steiner MR Unit, Hammersmith Hospital, London.
Ethical approval was granted by the Hammersmith Hospitals Trust Ethics Committee, and permission to administer radioisotopes was granted by the Administration of Radioactive Substances Advisory Committee of the Department of Health, U.K. Informed written consent was obtained from all patients and healthy volunteers.
Blood Sampling Protocol
Blood input data were available for 6 control subjects. For these subjects, arterial whole-blood activity was monitored continuously for the first 15 min of the scan with a bismuth germanate coincidence detector at a flow rate of 5 mL/min (17). Eight discrete arterial blood samples were taken at 5, 10, 15, 20, 30, 40, 50, and 60 min into heparinized syringes. The activity concentrations of the whole blood and plasma were measured.
Five plasma samples per scan (at 5, 10, 20, 40, and 60 min) were analyzed for metabolites using a semiautomated system with online solid-phase extraction followed by reverse-phase chromatography with online radioactivity and ultraviolet detectors and integration system (18).
For the generation of the plasma input functions, the time course of the plasma-to-blood ratio obtained from the 8 discrete arterial samples was fitted to a model. On average, the ratio started at 1.4 and steadily decreased to 1.3 at 60 min, and the function of choice was the straight line:
 | (Eq. 1) |
Next, the measurement of the arterial whole-blood activity obtained from the continuous detector system was multiplied with that ratio to obtain a total plasma activity curve for the first 15 min of the scan. This curve was then combined with the discrete plasma activity concentration measurements at 20, 30, 40, 50, and 60 min to form an input function describing the total plasma activity concentration for the entire scan.
Finally, the input function of the activity concentration due to unmetabolized [11C]-(R)-PK11195 in plasma was created by multiplying the total plasma activity input function with the function obtained from the fit of the model for the parent fraction in plasma with the 5 measurements of the parent compound during the scan. The mathematic model for the description of the amount of parent compound in plasma was the following equation:
 | (Eq. 2) |
This function describes an exponential approach to a falling straight line, beginning at 1 for t = 0.
Data Processing
The clustering code described in the following sections was implemented using Matlab (The Mathworks Inc.) on a SUN Ultra10 workstation (Sun Microsystems, Inc.). Statistical parametric mapping SPM2 (Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, University College London, London) was used for PET/PET and PET/MRI coregistration, normalization to the MNI/ICBM152 space (MNI is Montreal Neurological Institute. ICBM is International Consortium for Brain Mapping), as well as MRI segmentation. An in-house package for tracer kinetic modeling written in Matlab was used for the kinetic analysis of the time–activity curves and blood data processing. Parametric maps of BP values using SRTM were calculated using the software package RPM written in Matlab (19). Statistical analysis of region-of-interest (ROI) values was performed in SPSS version 13 (SPSS Inc.).
Algorithm Implementation
The supervised clustering algorithm developed for the analysis of dynamic [11C]-(R)-PK11195 data consisted of 3 elements:
Dynamic studies were normalized by subtracting from each frame its mean and dividing it by its SD to create a unit input (14). This created for each pixel i the normalized kinetic 
for the N pixels in the PET volume. Six kinetic classes were predefined: nonspecific gray matter, nonspecific white matter, pathologic PBBS binding, blood pool, skull, and muscle. If 
is the normalized kinetic for class j, where j = 1, ..., 6, the supervised clustering algorithm modeled the kinetic of each pixel as a weighted linear combination of the class kinetics as:
 | (Eq. 3) |
0.
Because the kinetic classes are not orthogonal, the weights wij were constrained to be positive by solving Equation 3 with the nonnegative least-squares algorithm (20).
Solution of Equation 3 created a volumetric map of weights wij for each class j. The reference region time–activity curve R1(t) (where j = 1 refers to the gray matter class) was finally calculated as a weighted average of the (unnormalized) pixel time–activity curves as:
 | (Eq. 4) |
Definition of Classes 1 and 2: Normal Gray and White Matter
Gray and white matter kinetics in healthy brain were extracted from 12 control subjects belonging to the Unit's normal database. Gray and white matter maps were obtained from the segmented MRI volume and then thresholded (only map values > 90% of maximum value were retained) to minimize the effect of partial volume. These maps were then coregistered to the dynamic scans and multiplied with them to obtain the normalized time courses that were then averaged to obtain the class average normalized time–activity curve.
Definition of Class 3: Pathologic PBBS Binding
To define the kinetic class specific for tissue with intense microglia activation we considered 3 symptomatic patients with HD. All 3 patients had genetically proven disease with an expanded CAG repeat in the IT15 gene on chromosome 4.
Time–activity curves were defined on the striatum and globus pallidus that have well-documented microglia activity in the disease (16,21) and that were hyperintense on the PET scans (weighted summed average radioactivity images) of these subjects.
Definition of Classes 4, 5, and 6: Blood Pool, Muscle, and Skull
The average time–activity curve specific for the blood fraction was obtained by manually drawing an ROI on the venous sinus of the 12 healthy subjects. Intense signal was identified in muscle and a class kinetic for this tissue was obtained by drawing an ROI on the sternomastoid muscle. A class was also defined for the kinetic of the skull by manually drawing ROIs directly on the PET image.
Validation: Comparison with Blood Input Modeling
The first part of the validation consisted of the comparison between the BP values obtained with blood modeling and those obtained with the reference region extracted by the supervised clustering. Six control subjects were used for whom arterial sampling of the input function was available.
Whole gray and white matter were extracted by segmentation of the MRI volume and then coregistered to the PET. The kinetics of these regions, which have a high signal-to-noise ratio, were inspected using exponential spectral analysis (ESA) (22). ESA basis functions spanned the range from 10 s to 60 min. Regions were also manually drawn on the MR image on cerebellum, thalamus, and parietal cortex and the respective time–activity curve extracted from the matched PET volumes.
BP estimates for both sets of regions were calculated using the plasma input function with the formula:
 | (Eq. 5) |
 | (Eq. 6) |
is the volume of distribution in the target region calculated with RS-ESA using the input extracted by the clustering algorithm. Note that RS-ESA incorporated a blood time–activity curve in the functional base when plasma input was used but obviously this was not possible using a reference tissue input. However, this is not expected to affect the BP estimate significantly. The fraction of signal coming from blood in PK11195 studies is no greater than in any other tracer even at late times because, although the first-pass extraction in brain is low, there is very large uptake of the tracer in peripheral organs (lung, heart, liver, and kidneys). Finally, BP estimates were calculated using SRTM and the reference region extracted by the supervised algorithm for comparison.
Validation: Test–Retest Reproducibility
The second part of the validation consisted of the assessment of the reproducibility of supervised clustering in comparison with the previous unsupervised approach. We used a set of test–retest data that consisted of 4 subjects with AD scanned twice at an interval of <6 wk. Arterial input data were not available for this cohort. To reproduce a current processing protocol of the Unit, parametric maps were obtained first and ROIs were placed later after normalization into MNI space and application of the latest version of an ROI maximum-probability brain atlas (25). The atlas was individualized for each subject by convolving it with the subject's thresholded gray matter map. ROI sampling included hippocampus, amygdala, cerebellum, lateral occipital lobe, anterior and posterior cingulate gyrus, middle frontal gyrus, posterior temporal lobe, parietal cortex, putamen, thalamus (all sampled separately on the left and right), and brain stem.
Parametric maps were produced using both RS-ESA and SRTM. Reproducibility of the 2 clustering methods was assessed by calculating the test–retest variability and the intraclass correlation coefficient (ICC).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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Comparison Between Reference Input and Plasma Input Modeling
Blood input data were used initially to confirm the presence of a slowly equilibrating kinetic component in brain tissues. Figure 5 shows the kinetic components extracted by ESA on the whole gray matter of healthy volunteers. The result is shown for 2 subjects to illustrate its consistency. Very similar results were obtained in all other control subjects. It is clear from Figure 5 that ESA detects a slowly increasing kinetic component that corresponds to 75% of the total radioactivity at 60 min. Furthermore, Figure 6 illustrates for the same 2 subjects how—by subtracting from the whole gray matter time–activity curve the slow component extracted by ESA—one obtains a time–activity curve that is comparable with the one of the reference regions obtained by the supervised clustering algorithm. This suggests that the supervised clustering algorithm selects as reference region gray matter voxels that are distant from the vasculature, where the slow kinetic component is prominent. This kinetic component is much slower than the one we apportion to microglia but is very similar to the kinetic of PK11195 previously reported in the heart (26). This is consistent with either different transport rates of the tracer or, more likely, different affinities of the PBBS in endothelium and muscle.
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In this ROI analysis, the simple modeling structure of SRTM lacked the degrees of freedom to describe time–activity curves with high signal-to-noise ratios and the fits were poor. Consequently, there was little correspondence between SRTM-derived BPs and plasma-derived BPs (Table 1), and correlation between the 2 measures was lower (r = 0.507, P < 0.004; Fig. 7B). In particular, SRTM overestimated BPs in white matter (Table 1), due to the inability of the model to cope with the irreducible spillover of signal from gray matter caused by imperfect segmentation. Note that percentage errors for the estimates of the volumes of distributions were low for both RS-ESA and SRTM (1.5% for large regions such as cortex and cerebellum, 3% for thalamus).
Validation: Test–Retest Reproducibility
The final validation consisted of the comparison of the reproducibility of the new reference region extraction with that obtained through the unsupervised algorithm (13). Both SRTM and RS-ESA were used to generate parametric maps. RS-ESA performed poorly in the pixel-by-pixel estimation, producing maps with high variability (data not shown). SRTM's simpler structure instead performed well with the high noise levels and the less heterogeneous signal at the pixel level and was selected as the better compromise for the estimation of parametric maps. Therefore, only SRTM results are reported.
Tables 2 and 3 show results for the 4 AD subjects injected with 296 MBq for whom test–retest data were available in terms of mean value, mean of the differences between the first and second scan, percentage mean difference, and the ICC.
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Regional ICCs were 0.596, on average, for the BPs obtained using the unsupervised clustering methodology. This value must be compared with the average ICC obtained using the supervised reference extraction—that is, 0.878—as shown in Table 3. In this table, given that BP values are higher, fractional mean differences are more informative and equal to 10.6% on average.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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A relevant finding of the study is the presence of a slowly equilibrating kinetic component in the tissue time–activity curves. Evidence from immunohistochemistry suggests that this signal is specific for PBBS binding in the vasculature, and its kinetic, although different from that of specific binding to activated microglia, resembles closely the [11C]-(R)-PK11195 kinetic in heart (26).
The presence of this additional component introduced another level of complexity in the kinetic modeling of ROI time–activity curves. For this task we abandoned compartmental modeling and adopted RS-ESA for the calculation of BP estimates for both plasma input and reference tissue input analyses. The effective extraction of a reference region combined with parameter estimation through RS-ESA provided an excellent agreement between plasma input and reference tissue input–derived BPs that were also highly correlated (r = 0.811, P < 10–5). This validates further the use of reference region modeling for the quantification of [11C]-(R)-PK11195 and allows direct comparison with the plasma input counterpart.
Finally, we investigated the reliability of the new reference extraction when BP parametric maps for [11C]-(R)-PK11195 are produced on a test–retest dataset. In this application, given the generally low signal-to-noise ratio in [11C]-(R)-PK11195 studies, SRTM was the method of choice for kinetic analysis. Results confirmed a substantial increase in the reliability of the estimates with the new supervised approach (mean ICC = 0.878) compared with the unsupervised approach (mean ICC = 0.596) and low test–retest variability (10.6%).
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CONCLUSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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References |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONReferences |
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