Jove
Visualize
Contact Us
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 Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

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

Sort by
Same author

Blurring evidence with advocacy: a systematic review of policy recommendations for net zero.

npj environmental social sciences·2026
Same author

Evidence Communication Rules for Policy (ECR-P) critical appraisal tool.

Systematic reviews·2025
Same author

Recent methodological advances in federated learning for healthcare.

Patterns (New York, N.Y.)·2024
Same author

Gradient synchronization for multivariate functional data, with application to brain connectivity.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2024
Same author

Common methodological pitfalls in ICI pneumonitis risk prediction studies.

Frontiers in immunology·2023
Same author

The impact of imputation quality on machine learning classifiers for datasets with missing values.

Communications medicine·2023
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2026

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
09:03

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Published on: October 22, 2019

Smoothing dynamic positron emission tomography time courses using functional principal components.

Ci-Ren Jiang1, John A D Aston, Jane-Ling Wang

  • 1Department of Statistics, University of California, Davis, CA 95616, USA. crjiang@wald.ucdavis.edu

Neuroimage
|April 7, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a functional smoothing method to reduce noise in Positron Emission Tomography (PET) time course data. The approach improves subsequent analyses, like Spectral Analysis, by enhancing data accuracy and reducing errors.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
07:28

Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level

Published on: January 24, 2025

Related Experiment Videos

Last Updated: Jun 24, 2026

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
09:03

Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET

Published on: October 22, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
07:28

Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level

Published on: January 24, 2025

Area of Science:

  • Medical Imaging Analysis
  • Statistical Modeling
  • Functional Data Analysis

Background:

  • Positron Emission Tomography (PET) generates time course data that is often noisy.
  • Existing analysis methods can be sensitive to noise, impacting results.
  • Preprocessing PET data is crucial for accurate downstream analysis.

Purpose of the Study:

  • To present a functional smoothing approach for PET time course data.
  • To reduce noise in PET data by borrowing information across space.
  • To introduce a new statistical model for improved data variation accounting.

Main Methods:

  • Utilizing a nonparametric covariate adjustment for pooled spatial information.
  • Introducing the Multiplicative Nonparametric Random Effects Model.
  • Employing a locally adaptive bandwidth for precise smoothing at each time point.

Main Results:

  • The functional smoothing approach effectively reduces noise in PET time course data.
  • The Multiplicative Nonparametric Random Effects Model accurately accounts for data variation.
  • Improved mean squared error for subsequent analyses like Spectral Analysis.

Conclusions:

  • The proposed functional smoothing method is a valuable preprocessing step for PET data.
  • This technique enhances the reliability and accuracy of PET data analysis.
  • The new statistical model offers a more robust way to handle PET data variability.