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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

You might also read

Related Articles

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

Sort by
Same author

Kernel-based Maximum likelihood reconstruction of attenuation and activity (MLAA) in SPECT imaging for improved attenuation correction and activity quantification: Simulation, phantom and patient validation studies.

Physics in medicine and biology·2026
Same author

EXPRESS: Single-tracer [<sup>18</sup>F]FDG PET quantification of blood-brain barrier permeability and cerebral blood flow: Validation using dual-tracer PET.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism·2026
Same author

Cyanobacterial extracellular polymeric substances empowered biological aqua crust formation via selective mineral adsorption for sustainable metal(loid) bioremediation.

Journal of hazardous materials·2026
Same author

Oxygenic photogranules in wastewater treatment: Formation mechanisms, engineering optimization and prospects.

Bioresource technology·2026
Same author

Sharing a whole-/total-body [<sup>18</sup>F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

Scientific data·2026
Same author

Identifying genes related to olfaction in the antennae of Samia cynthia Drury (Lepidoptera: Saturniidae) via de novo transcriptome assembly.

Journal of economic entomology·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
Same journal

SynReEM: Synapse Reconstruction via Instance Structure Encoding in Anisotropic Electron Microscopic Volumes.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

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

An optimization transfer algorithm for nonlinear parametric image reconstruction from dynamic PET data.

Guobao Wang1, Jinyi Qi

  • 1Department of Biomedical Engineering, University of California, Davis, CA 95616, USA. gbwang@ucdavis.edu

IEEE Transactions on Medical Imaging
|August 16, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new, fast algorithm for directly reconstructing kinetic parameters from dynamic positron emission tomography (PET) data. The method improves bias-variance performance and speeds up convergence for molecular imaging applications.

More Related Videos

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

Related Experiment Videos

Last Updated: May 19, 2026

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

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking
14:21

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

Area of Science:

  • Molecular Imaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Direct reconstruction of kinetic parameters from dynamic positron emission tomography (PET) data is complex.
  • Existing methods may lack efficiency or ease of use.

Purpose of the Study:

  • To present a novel optimization transfer algorithm for penalized likelihood direct reconstruction of nonlinear parametric images.
  • To demonstrate improved performance and faster convergence compared to existing methods.

Main Methods:

  • Developed a three-step iterative algorithm: EM-like image update, image smoothing, and time activity curve fitting.
  • Utilized computer simulations to evaluate performance against indirect reconstruction.
  • Applied the algorithm to real 4-D PET data.

Main Results:

  • The proposed direct reconstruction algorithm shows superior bias-variance performance over indirect methods.
  • Achieved a substantially faster convergence rate compared to previous algorithms.
  • Successfully applied to real-world dynamic PET data.

Conclusions:

  • The new algorithm offers an efficient and user-friendly approach for direct kinetic parameter reconstruction in dynamic PET.
  • It provides a promising tool for molecular imaging research and clinical applications.