Jove
Visualize
Contact Us

Related Concept Videos

Bootstrapping01:24

Bootstrapping

863
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
863

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Metabolic tumor volume predicts outcome in patients with advanced stage follicular lymphoma from the RELEVANCE trial.

Annals of oncology : official journal of the European Society for Medical Oncology·2023
Same author

Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT<sup>†</sup>.

Annals of oncology : official journal of the European Society for Medical Oncology·2020
Same author

Experimental characterisation of a proton kernel model for pencil beam scanning techniques.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)·2019
Same author

Assessment of a fully 3D Monte Carlo reconstruction method for preclinical PET with iodine-124.

Physics in medicine and biology·2015
Same author

Monte-Carlo simulations of clinically realistic respiratory gated (18)F-FDG PET: application to lesion detectability and volume measurements.

Computer methods and programs in biomedicine·2014
Same author

Partial volume effect estimation and correction in the aortic vascular wall in PET imaging.

Physics in medicine and biology·2013
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 6, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.3K

Comparison of bootstrap resampling methods for 3-D PET imaging.

C Lartizien1, J-B Aubin, I Buvat

  • 1CREATIS, CNRS UMR 5220, INSERMU630, INSA-Lyon, Lyon University, 69621 Lyon, France. carole.lartizien@creatis.insa-lyon.fr

IEEE Transactions on Medical Imaging
|April 23, 2010
PubMed
Summary

Two bootstrap methods using multiple positron emission tomography (PET) data sets accurately estimate statistical parameters for 3-D PET imaging. Methods using single data sets showed variable performance, especially with high noise levels.

More Related Videos

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
11:09

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

4.4K
Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.7K

Related Experiment Videos

Last Updated: Mar 6, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

1.3K
High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
11:09

High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

Published on: December 16, 2022

4.4K
Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images
09:21

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

12.7K

Area of Science:

  • Medical Imaging
  • Statistical Modeling
  • Computational Science

Background:

  • Bootstrap methods are used to estimate statistical properties of positron emission tomography (PET) images.
  • Existing methods include parametric and nonparametric approaches, requiring either unique or multiple data samples.
  • Previous studies on bootstrap methods for PET imaging have yielded conflicting results.

Purpose of the Study:

  • To compare the accuracy of three bootstrap methods for 3-D PET imaging using simulated data.
  • To evaluate both parametric and nonparametric approaches, including those based on single and multiple data files.
  • To identify reliable bootstrap methods for PET image analysis.

Main Methods:

  • Comparison of list-mode based nonparametric (LMNP), sinogram based parametric (SP), and sinogram-based nonparametric (SNP) methods.
  • Investigation of an extended LMNP method using multiple original list-mode files.
  • Analysis of statistical moments (probability density function, moments of order 1 and 2) on resampled data.

Main Results:

  • Only the SNP and extended LMNP methods, which utilize multiple original data sets, accurately estimated statistical parameters.
  • The LMNP and SP methods, relying on single data files, demonstrated variable performance.
  • The study used simulated data with high noise levels, suggesting potential differences with lower-noise clinical data.

Conclusions:

  • Bootstrap methods leveraging multiple data sources (SNP and extended LMNP) are recommended for accurate statistical parameter estimation in 3-D PET imaging.
  • Single-data-file bootstrap methods (LMNP and SP) may be less reliable, particularly under noisy conditions.
  • Further validation with clinical PET data is warranted to confirm findings and assess performance across different noise levels.