Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Reconstruction of Signal using Interpolation

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...

Partial Differential Equations

Partial Differential Equations

A stone dropped into a still pond generates waves that propagate outward in circular patterns, creating a dynamic surface whose elevation depends on both position and time. At any given location, the water level oscillates as the wave passes, while at any fixed moment, the surface exhibits smooth, curved structures extending across space. This dual dependence requires a mathematical description that accounts for variation in multiple variables simultaneously.At a fixed point on the water...

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

Wave Parameters

Wave Parameters

The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...

You might also read

Related Articles

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

Sort by

Same author

Dynamic reconfiguration of subunits from the hippocampal-amygdala complex indicate patterns of psychosis vulnerability in 22q11.2 deletion syndrome.

Scientific reports·2026

Same author

Shared and Individual Resting-State MEG Network Signatures of Tinnitus Revealed by Holistic Graph Learning.

IEEE open journal of engineering in medicine and biology·2026

Same author

The microstructure-weighted human connectome: network properties and structure-function correlations across spatial scales.

bioRxiv : the preprint server for biology·2026

Same author

A diffusion MRI-derived perivascular metric related to glymphatic-associated processes in bipolar disorder vulnerability: Multimodal correlates across emotion dysregulation patients and offspring.

Psychiatry research·2026

Same author

Spinal cord structural and functional architecture and its shared organization with the brain across the adult lifespan.

Nature communications·2026

Same author

Regional BOLD variability reflects microstructural maturation and neuronal ensheathment in the preterm infant cortex.

Nature communications·2026

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Reconstruction of dynamic PET data using spatio-temporal wavelet l(1) regularization.

Jeroen Verhaeghe¹, Dimitri Van De Ville, Ildar Khalidov

¹ELIS-MEDISIP, Ghent University, Sint Pietersnieuwstraat 41, 9000 Ghent, Belgium. jeroen.verhaeghe@ugent.be

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

This summary is machine-generated.

This study introduces exponential-spline wavelets for dynamic Positron Emission Tomography (PET) reconstruction. These novel wavelets improve spatio-temporal regularization, outperforming traditional methods for time activity curve analysis and image reconstruction.

Related Experiment Videos

Last Updated: Jul 10, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Area of Science:

Medical Imaging
Signal Processing
Computational Science

Background:

Positron Emission Tomography (PET) reconstruction is an ill-posed problem.
Regularization techniques are essential for accurate PET image reconstruction.
Existing methods often use standard wavelets, which may not optimally model PET data dynamics.

Purpose of the Study:

To extend l(1) regularization of wavelet coefficients for dynamic PET data reconstruction.
To introduce and evaluate exponential-spline (E-spline) wavelets for modeling temporal dynamics in PET.
To demonstrate the benefits of spatio-temporal regularization using E-spline wavelets.

Main Methods:

Application of l(1) regularization optimized via iterative thresholding to PET data.
Development and implementation of E-spline wavelets for the temporal dimension of PET data.
Comparative analysis using a 1-D time activity curve (TAC) fitting experiment and a tomographic reconstruction experiment.

Main Results:

E-spline wavelets effectively model time activity curves in dynamic PET.
Spatio-temporal regularization enhances PET reconstruction accuracy.
E-spline wavelets demonstrate superior performance compared to Battle-Lemarié wavelets in both TAC fitting and tomographic reconstruction.

Conclusions:

The proposed spatio-temporal regularization with E-spline wavelets is a powerful approach for dynamic PET reconstruction.
E-spline wavelets offer significant advantages over conventional wavelets for analyzing PET time-activity data.
This method holds promise for improving the quality and interpretability of dynamic PET images.