Analysis of [11C]PIB PET data

Pittsburgh Compound B (PIB, PiB, or 6-OH-BTA-1) is a derivative of thioflavin T, and both bind to amyloid-β; [11C]PIB has been successfully used as a PET ligand to in vivo visualization and quantification of extracellular amyloid-β deposits, especially in Alzheimer’s disease (AD) patients. Cortical PIB uptake is associated with age, APOE genotype, and gender even in “healthy ageing” (Scheinin et al., 2014). Individuals with memory impairment but negative PIB PET do not exhibit AD pathology upon postmortem examination (Scheinin et al., 2018).

[11C]PIB binding to amyloid-β in the grey brain matter is specific and reversible. [11C]PIB binding in white matter has a non-specific and non-saturable component (Fodero-Tavoletti et al., 2009), possibly due to the high lipid content of the white matter, and a specific component due to affinity to the β-sheet structure present in the myelin. [11C]PIB PET allows longitudinal evaluation of white matter in healthy individuals (Veronese et al., 2015), and can be used to quantify myelin loss and regeneration in the white matter of MS patients (Stankoff et al., 2011; Bodini et al., 2016).

The uptake in white matter is much lower than in grey matter, but due to slower kinetics in white matter, the uptake is prominent at later time points, which may impede the quantification of amyloid-β deposits in the grey matter in case of considerable partial volume effect. Partial volume correction decreases [11C]PIB binding estimates in cortical grey matter more in healthy subjects than in patients with high amyloid-β deposition (Matsubara et al., 2016).

The recommended analysis methods for quantification of amyloid load in the brain are

Cardiac amyloidosis PET studies have been analyzed using FUR (Antoni et al., 2013; Kero et al., 2016). SUV calculation has been used in analysis of PIB uptake in gastrocnemius muscle in inclusion body myositis (Maetzler et al., 2011).

Dynamic PET study is required to apply MTGA and SRTM, while only a short late-scan is needed for SUVR method. Dynamic study is also required to estimate BPND using Washout Allometric Reference Method (WARM); WARM is not affected by regional perfusion differences. All methods can be calculated pixel-by-pixel, providing parametric maps for further analysis. Sato et al. (2013) have also proposed a method for estimating k3 using reference tissue model and 40-min dynamic PET scan.

In addition, changes in perfusion may be measured from dynamic [11C]PIB PET data (Gjedde et al., 2013; Rodell et al., 2013); see below.

If cerebellum cannot be used as a reference region, either arterial sampling or an image-derived arterial input function (Su et al, 2015), or supervised clustering procedure (Ikoma et al., 2013) is required.

Logan plot with [11C]PIB

Distribution volume ratio (DVR), or binding potential (BPND = DVR-1), can be calculated with Logan plot without arterial plasma data sampling, using cerebellar cortex as input (Mintun et al. 2006; Li et al. 2008). Reference region k2 was set to 0.2 min-1, but this value had only minimal impact on the results (Mintun et al. 2006). However, it may advance the time when Logan plot reaches linearity, thus reducing the required total scan length.

Supervised clustering method is recommended to extract the white matter TAC for usage as the reference region input for quantification of myelin binding (Veronese et al., 2015; Bodini et al., 2016).

Regional analysis

Estimate the regional DVR using logan with option -k2=0.2 and set fit time from 20 minutes to a end time common to all PET studies.

Pixel-by-pixel analysis

To produce DVR images use imgdv with option -k2=0.2 and set fit start time to 20. BPND images (Mikhno et al., 2008) can be achieved by subtracting 1 from DVR images.

SRTM

Simplified reference tissue model (SRTM) has also been used to compute DVR or BPND images. The original SRTM has been enhanced by applying constraint for reference region k2 (Yaqub et al., 2008; Zwan et al., 2014; Sojkova et al., 2015).

Tissue-to-cerebellum ratio

Amyloid load can be quantified by computing region-to-cerebellum ratio over 60 to 90 minutes (Lopresti et al., 2005; Kemppainen et al. 2006; Kemppainen et al., 2014), either regionally with dftratio or pixel-by-pixel with imgratio. Optimal time range was thoroughly studied by McNamee et al. (2009); their suggestion was to use 40-60-min period in studies limited by low injected dose, but otherwise the 50-70-min period because of greater measurement stability, especially for longitudinal multisite studies.

Advantages of the ratio approach are 1) large effect sizes for Alzheimer’s disease (AD) and control group differences (Lopresti et al., 2005), and 2) possibility to obtain the required data from a single relatively short scan. However, ratio is dependent on the uptake period and sensitive to changes in perfusion (van Berckel et al., 2013).

In cognitively intact individuals cerebellar grey matter is preferred reference tissue compared to pons (Adamczuk et al., 2016). AD patients may have also cerebellar plaques, which may render cerebellum vulnerable as a reference area. Therefore, it may be necessary to calculated results also by using pons as a reference area (Koivunen et al., 2008). In data-driven diagnostic classification, statistical analysis suggests that normalization by cerebellar grey matter and pons yields identical classification accuracy of AD (96% accuracy, 96% sensitivity, 95% specificity), while normalization by white matter performed less well, not outperforming CSF biomarkers (Oliveira et al., 2018).

With another amyloid-β tracer, [18F]florbetapir, cerebral white matter was found to be better reference region than cerebellum or pons (Chen et al., 2015).

Dual-phase amyloid PET

Amyloid tracers have high lipophilicity, which makes them good perfusion surrogates. Cerebral perfusion is lower in Alzheimer’s disease than in healthy volunteers, but permeability-surface area product is unchanged, supporting the use of the unidirectional blood-brain clearance of [11C]PIB in tracking blood flow changes (Gjedde et al., 2013).

From a dynamic PET scan with arterial blood sampling the compartmental model parameter K1 can be estimated, and it reflects cerebral blood flow (Blomquist et al., 2008; Chen et al., 2015). In clinical setting the blood sampling is usually omitted; then the first phase of the scan, for example the first 6 min p.i., can be used to calculate SUVR between the regions of interest and cerebellum (Forsberg et al., 2012), or preferably R1 from SRTM analysis is used as marker of relative perfusion changes (Chen et al., 2015; Sojkova et al., 2015).



References:

Adamczuk K, Schaeverbeke J, Nelissen N, Neyens V, Vandenbulcke M, Goffin K, Lilja J, Hilven K, Dupont P, Van Laere K, Vandenberghe R. Amyloid imaging in cognitively normal older adults: comparison between 18F-flutemetamol and 11C-Pittsburgh compound B. Eur J Nucl Med Mol Imaging 2016; 43(1): 142-151.

Blomquist G, Engler H, Nordberg A, Ringheim A, Wall A, Forsberg A, Estrada S, Frändberg P, Antoni G, Långström B. Unidirectional influx and net accumulation of PIB. Open Neuroimag J. 2008; 2: 114-125.

Chen K, Roontiva A, Thiyyagura P, Lee W, Liu X, Ayutyanont N, Protas H, Luo JL, Bauer R, Reschke C, Bandy D, Koeppe RA, Fleisher AS, Caselli RJ, Landau S, Jagust WJ, Weiner MW, Reiman EM. Improved power for characterizing longitudinal amyloid-β PET changes and evaluating amyloid-modifying treatments with a cerebral white matter reference region. J Nucl Med. 2015; 56(4): 560-566.

Chen YJ, Nasrallah IM. Brain amyloid PET interpretation approaches: from visual assessment in the clinic to quantitative pharmacokinetic modeling. Clin Transl Imaging 2017; 5(6): 561-573. doi: 10.1007/s40336-017-0257-4.

Fodero-Tavoletti MT, Rowe CC, McLean CA, Leone L, Li Q-X, Masters CL, Cappai R, Villemagne VL. Characterization of PiB binding to white matter in Alzheimer disease and other dementias. J Nucl Med. 2009; 50: 198-204.

Kemppainen NM, Aalto S, Wilson IA, Någren K, Helin S, Brück A, Oikonen V, Kailajärvi M, Scheinin M, Viitanen M, Parkkola R, Rinne JO. Voxel-based analysis of PET amyloid ligand [11C]PIB uptake in Alzheimer disease. Neurology 2006; 67(9): 1575-1580.

Kemppainen NM, Aalto S, Karrasch M, Någren K, Savisto N, Oikonen V, Viitanen M, Parkkola R, Rinne JO. Cognitive reserve hypothesis: Pittsburgh Compound B and fluorodeoxyglucose positron emission tomography in relation to education in mild AD. Ann Neurol. 2008; 63(1): 112-118.

Koivunen J, Verkkoniemi A, Aalto S, Paetau A, Ahonen JP, Viitanen M, Någren K, Rokka J, Haaparanta M, Kalimo H, Rinne JO. PET amyloid ligand [11C]PIB uptake shows predominantly striatal increase in variant Alzheimer’s disease. Brain 2008; 131(7): 1845-1853.

Li Y, Rinne JO, Mosconi L, Pirraglia E, Rusinek H, DeSanti S, Kemppainen N, Någren K, Kim B-C, Tsui W, de Leion MJ. Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2008; 35: 2169–2181.

Lopresti BJ, Klunk WE, Mathis CA, Hoge JA, Ziolko SK, Lu X, Meltzer CC, Schimmel K, Tsopelas ND, DeKosky ST, Price JC. Simplified quantification of Pittsburgh compound B amyloid imaging PET studies: a comparative analysis. J Nucl Med. 2005; 46(12): 1959-1972.

McNamee RL, Yee SH, Price JC, Klunk WE, Rosario B, Weissfeld L, Ziolko S, Berginc M, Lopresti B, Dekosky S, Mathis CA. Consideration of optimal time window for Pittsburgh Compound B PET summed uptake measurements. J Nucl Med. 2009; 50(3): 348-355.

Mikhno A, Devanand D, Pelton G, Cuasay K, Gunn R, Upton N, Lai RY, Libri V, Mann JJ, Parsey RV. Voxel-based analysis of 11C-PIB scans for diagnosing Alzheimer’s Disease. J Nucl Med. 2008; 49: 1262-1269.

Mintun MA, LaRossa GN, Sheline YI, Dence CS, Lee SY, Mach RH, Klunk WE, Mathis CA, DeKosky ST, Morris JC. [11C]PIB in a nondemented population: Potential antecedent marker of Alzheimer disease. Neurology 2006; 67: 446-452.

Scheinin NM, Tolvanen TK, Wilson IA, Arponen EM, Någren KA, Rinne JO. Biodistribution and radiation dosimetry of the amyloid imaging agent 11C-PIB in humans. J Nucl Med. 2007; 48(1): 128-133.

Su Y, Blazey TM, Snyder AZ, Raichle ME, Hornbeck RC, Aldea P, Morris JC, Benzinger TLS. Quantitative amyloid imaging using image-derived arterial input function. PLoS One 2015; 10(4): e0122920.

Ziolko SK, Weissfeld LA, Klunk WE, Mathis CA, Hoge JA, Lopresti BJ, DeKosky ST, Price JC. Evaluation of voxel-based methods for the statistical analysis of PIB PET amyloid imaging studies in Alzheimer’s disease. Neuroimage 2006; 33: 94-102.



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Created at: 2008-08-04
Updated at: 2018-09-03
Written by: Vesa Oikonen