title: Global-two-stage filtering of clinical PET parametric maps: application to [(11)C]-(R)-PK11195.
abstract: BACKGROUND
  In Positron Emission Tomography (PET) quantification of physiological parameters at the voxel level may result in unreliable estimates due to the high noise of voxel time activity curves. Global-Two-Stage (GTS), an estimation technique belonging to the group of "population approaches", can be used to tackle this problem. GTS was previously tested on simulated PET data and yielded substantial improvements when compared to standard estimation approaches such as Weighted NonLinear Least Squares (WNLLS) and Basis Function Method (BFM). In this work GTS performance is assessed in a clinical context using the neuroinflammation marker [(11)C]-(R)-PK11195 applied to a cohort of Huntington's disease (HD) patients with and without symptoms.
  
  
  METHODS
  Parametric maps of binding potential (BP(ND)) of 12 normal controls (NC), 9 symptomatic and 9 presymptomatic HD patients were generated by applying a modified reference tissue model that accounts for tracer vascular activity in both reference and target tissues (SRTMV). GTS was then applied to SRTMV maps and its performance compared with that of SRTMV. Three smoothed versions of SRTMV, obtained by filtering the original SRTMV maps with Gaussian filters of 3 mm, 5 mm and 7 mm Full Width Half Maximum (FWHM), were also included in the comparison. Since striatal degeneration is the hallmark of HD, sensitivity was assessed for all methods by computing the mean of z-scores in caudate, putamen and globus pallidus in the voxel-by-voxel statistical comparison of BP(ND) between HD and NC.
  
  
  RESULTS
  Application of GTS to parametric maps brought a substantial qualitative improvement to SRTMV maps to the extent that anatomical structures often became visible. In addition, most parameter estimates that were outside the physiological range with SRTMV were corrected by GTS. GTS yielded a 2.3-fold increase in sensitivity with respect to SRTMV for the symptomatic cohort (mean of striatal z-scores of 0.76 for SRTMV and 1.79 for GTS) and an even more substantial increase for the presymptomatic cohort (mean of striatal z-scores of 0.34 for SRTMV and 0.96 for GTS). The sensitivity of GTS was similar to the one obtained with a filter of 7 mm FWHM applied to the initial SRTMV maps but GTS images were not characterized by the notable loss of resolution typical of smoothed maps. GTS, additionally, does not require to change/define settings according to the tracer and level of noise, whereas the choice of the FWHM value of the Gaussian filter normally employed in the smoothing procedure is typically arbitrary.
  
  
  CONCLUSIONS
  GTS is a powerful and robust tool for improving the quality of parametric maps in PET. The method is particularly appealing in that it can be applied to any tracer and estimation method, provided that initial estimates of the parameter vector and of its covariance are available. Although the benefits of GTS are far from being exhaustively assessed, the significant improvements obtained both on real and simulated data suggest that it could become an important tool for dynamic PET in the future.
authors.affiliation: MRC Clinical Sciences Center and Division of Medicine, Imperial College, London, UK. gtomasi@imperial.ac.uk
authors.forename: Giampaolo
authors.initials: G
authors.lastname: Tomasi
authors.forename: Alessandra
authors.initials: A
authors.lastname: Bertoldo
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
authors.forename: Nicola
authors.initials: N
authors.lastname: Pavese
authors.forename: Yen F
authors.initials: YF
authors.lastname: Tai
authors.forename: Alexander
authors.initials: A
authors.lastname: Hammers
authors.forename: Federico E
authors.initials: FE
authors.lastname: Turkheimer
mesh_terms: D000328:Adult; D000465:Algorithms; D001160:Arterioles / Q000000981:diagnostic imaging; D001499:Bayes Theorem; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D002536:Cerebral Arteries / Q000000981:diagnostic imaging; D002550:Cerebral Veins / Q000000981:diagnostic imaging; D004730:Endothelium, Vascular / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D006816:Huntington Disease / Q000000981:diagnostic imaging; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007546:Isoquinolines; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009131:Muscle, Smooth, Vascular / Q000000981:diagnostic imaging; D049268:Positron-Emission Tomography / Q000379:methods / Q000706:statistics & numerical data*; D019275:Radiopharmaceuticals
chemical_list: D007546:Isoquinolines; D019275:Radiopharmaceuticals; C037850:PK 11195