Dataset Information

Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods.

ABSTRACT: This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [(11)C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); and (3) the Logan graphical plot. Nonphysiologic VT estimates were eliminated and the remaining ones were compared with the reference values, i.e., those obtained with a voxelwise 2TCM solved with a nonlinear estimator. With regard to voxelwise VT estimates, H-BFM showed the best agreement with weighted nonlinear least square (WNLLS) values and the lowest percentage of mean relative difference (1±1%). All methods showed comparable variability in the relative differences. H-BFM provided the best correlation with WNLLS (y=1.034x-0.013; R(2)=0.973). Despite a slight bias, the other three methods also showed good agreement and high correlation (R(2)>0.96). H-BFM yielded the most reliable voxelwise quantification of [(11)C](R)-rolipram as well as the complete description of the tracer kinetic. The Logan plot represents a valid alternative if only VT estimation is required. Its marginally higher bias was outweighed by a low computational time, ease of implementation, and robustness.

SUBMITTER: Rizzo G

PROVIDER: S-EPMC3705428 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods.

Rizzo Gaia G Veronese Mattia M Zanotti-Fregonara Paolo P Bertoldo Alessandra A

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20130320 7

This study compared model-based and data-driven methods to assess the best methodology for generating precise and accurate parametric maps of the parameters of interest in [(11)C](R)-rolipram brain positron-emission tomography studies. Parametric images were generated using (1) a two-tissue compartmental model (2TCM) solved with the hierarchical basis function method (H-BFM) linear estimator; (2) data-driven spectral-based methods: standard spectral analysis (std SA) and rank-shaping SA (RS); an ...[more]

PMID: 23512132

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Project description:Head motion degrades image quality and causes erroneous parameter estimates in tracer kinetic modeling in brain PET studies. Existing motion correction methods include frame-based image registration (FIR) and correction using real-time hardware-based motion tracking (HMT) information. However, FIR cannot correct for motion within 1 predefined scan period, and HMT is not readily available in the clinic since it typically requires attaching a tracking device to the patient. In this study, we propose a motion correction framework with a data-driven algorithm, that is, using the PET raw data itself, to address these limitations. Methods: We propose a data-driven algorithm, centroid of distribution (COD), to detect head motion. In COD, the central coordinates of the line of response of all events are averaged over 1-s intervals to generate a COD trace. A point-to-point change in the COD trace in 1 direction that exceeded a user-defined threshold was defined as a time point of head motion, which was followed by manually adding additional motion time points. All the frames defined by such time points were reconstructed without attenuation correction and rigidly registered to a reference frame. The resulting transformation matrices were then used to perform the final motion-compensated reconstruction. We applied the new COD framework to 23 human dynamic datasets, all containing large head motion, with 18F-FDG (n = 13) and 11C-UCB-J ((R)-1-((3-(11C-methyl-11C)pyridin-4-yl)methyl)-4-(3,4,5-trifluorophenyl)pyrrolidin-2-one) (n = 10) and compared its performance with FIR and with HMT using Vicra (an optical HMT device), which can be considered the gold standard. Results: The COD method yielded a 1.0% ± 3.2% (mean ± SD across all subjects and 12 gray matter regions) SUV difference for 18F-FDG (3.7% ± 5.4% for 11C-UCB-J) compared with HMT, whereas no motion correction (NMC) and FIR yielded -15.7% ± 12.2% (-20.5% ± 15.8%) and -4.7% ± 6.9% (-6.2% ± 11.0%), respectively. For 18F-FDG dynamic studies, COD yielded differences of 3.6% ± 10.9% in K i value as compared with HMT, whereas NMC and FIR yielded -18.0% ± 39.2% and -2.6% ± 19.8%, respectively. For 11C-UCB-J, COD yielded 3.7% ± 5.2% differences in V T compared with HMT, whereas NMC and FIR yielded -20.0% ± 12.5% and -5.3% ± 9.4%, respectively. Conclusion: The proposed COD-based data-driven motion correction method outperformed FIR and achieved comparable or even better performance than the Vicra HMT method in both static and dynamic studies.

Project description:The choice of the most appropriate unsupervised machine-learning method for "heterogeneous" or "mixed" data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of "ready-to-use" tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

Dataset Information

Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods.

Publications

Voxelwise quantification of [(11)C](R)-rolipram PET data: a comparison between model-based and data-driven methods.

Similar Datasets

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