Comparison of bootstrap resampling methods for 3-D PET imaging

Autor: Irène Buvat, J.-B Aubin, Carole Lartizien
Přispěvatelé: Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Imagerie et Modélisation en Neurobiologie et Cancérologie (IMNC (UMR_8165)), Université Paris-Sud - Paris 11 (UP11)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Institut Camille Jordan [Villeurbanne] ( ICJ ), École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Centre National de la Recherche Scientifique ( CNRS ), Centre de Recherche et d'Application en Traitement de l'Image et du Signal ( CREATIS ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -École Supérieure Chimie Physique Électronique de Lyon-Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Imagerie et Modélisation en Neurobiologie et Cancérologie ( IMNC ), Université Paris-Sud - Paris 11 ( UP11 ) -Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Université Paris Diderot - Paris 7 ( UPD7 ) -Centre National de la Recherche Scientifique ( CNRS )
Rok vydání: 2010
Předmět:
Computer science
Monte Carlo method
Probability density function
Method of moments (statistics)
Poisson distribution
Sensitivity and Specificity
030218 nuclear medicine & medical imaging
03 medical and health sciences
symbols.namesake
0302 clinical medicine
Resampling
Statistics
Image Interpretation
Computer-Assisted
Humans
Electrical and Electronic Engineering
Bootstrapping (statistics)
Parametric statistics
[STAT.AP]Statistics [stat]/Applications [stat.AP]
Radiological and Ultrasound Technology
Noise measurement
business.industry
Estimation theory
[ STAT.AP ] Statistics [stat]/Applications [stat.AP]
Nonparametric statistics
Statistical parameter
Reproducibility of Results
Pattern recognition
Signal Processing
Computer-Assisted
Image Enhancement
Computer Science Applications
Sample size determination
030220 oncology & carcinogenesis
Positron-Emission Tomography
Sample Size
symbols
Analysis of variance
Artificial intelligence
business
Software
Algorithms
Zdroj: IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers, 2010, 29 (7), pp.1442-1454
ISSN: 1558-254X
0278-0062
Popis: International audience; Two groups of bootstrap methods have been proposed to estimate the statistical properties of positron emission tomography (PET) images by generating multiple statistically equivalent data sets from few data samples. The first group generates resampled data based on a parametric approach assuming that data from which resampling is performed follows a Poisson distribution while the second group consists of nonparametric approaches. These methods either require a unique original sample or a series of statistically equivalent data that can be list-mode files or sinograms. Previous reports regarding these bootstrap approaches suggest different results. This work compares the accuracy of three of these bootstrap methods for 3-D PET imaging based on simulated data. Two methods are based on a unique file, namely a list-mode based nonparametric (LMNP) method and a sinogram based parametric (SP) method. The third method is a sinogram-based nonparametric (SNP) method. Another original method (extended LMNP) was also investigated, which is an extension of the LMNP methods based on deriving a resampled list-mode file by drawings events from multiple original list-mode files. Our comparison is based on the analysis of the statistical moments estimated on the repeated and resampled data. This includes the probability density function and the moments of order 1 and 2. Results show that the two methods based on multiple original data (SNP and extended LMNP) are the only methods that correctly estimate the statistical parameters. Performances of the LMNP and SP methods are variable. Simulated data used in this study were characterized by a high noise level. Differences among the tested strategies might be reduced with clinical data sets with lower noise.
Databáze: OpenAIRE
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