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

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

You might also read

Related Articles

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

Sort by

Same author

Intuitively tuned elastic bias correction of atmospheric inversion using Gaussian process prior: Application to accidental radioactive emissions.

Journal of hazardous materials·2026

Same author

Spatial-temporal source term estimation using deep neural network prior and its application to Chernobyl wildfires.

Journal of hazardous materials·2025

Same author

Hygrothermal Degradation of Epoxy Electrical Insulating Material-Testing and Mathematical Modeling.

Polymers·2024

Same author

Vertical Excitation Energies and Lifetimes of the Two Lowest Singlet Excited States of Cytosine, 5-Aza-cytosine, and the Triazine Family: Quantum Mechanics-Molecular Mechanics Studies.

Journal of chemical theory and computation·2023

Same author

Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors.

Sensors (Basel, Switzerland)·2022

Same author

Sources and fate of atmospheric microplastics revealed from inverse and dispersion modelling: From global emissions to deposition.

Journal of hazardous materials·2022

Search research articles

Related Experiment Video

Updated: Apr 24, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Bayesian blind separation and deconvolution of dynamic image sequences using sparsity priors.

Ondrej Tichy, Vaclav Smidl

IEEE Transactions on Medical Imaging

|September 3, 2014

Summary

This summary is machine-generated.

This study introduces a novel blind source separation method for dynamic imaging, improving signal extraction from overlapping structures. The probabilistic approach enhances accuracy in separating tissue signals, outperforming existing methods.

More Related Videos

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Related Experiment Videos

Last Updated: Apr 24, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Published on: June 16, 2014

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

Medical Imaging
Signal Processing
Computational Biology

Background:

Superposition of projected structures in 3-D imaging complicates signal extraction from dynamic organs and tissues.
This overlap issue persists in dynamic tomography, leading to mixed signals within voxels.
Separating signals from dynamic structures is a challenging blind source separation problem.

Purpose of the Study:

To develop a novel blind source separation method for dynamic image sequences.
To address the underdetermined nature of signal separation with a probabilistic model.
To improve the accuracy of extracting signals specific to individual dynamic structures.

Main Methods:

Proposed a probabilistic model for dynamic image sequences.
Modeled source dynamics as a convolution of an input function and a source-specific kernel.
Utilized a Bayesian model with a hierarchical prior, solved via the Variational Bayes method.
Incorporated a prior distribution favoring sparse source images and convolution kernels.

Main Results:

Demonstrated the method's relevance for dynamic renal scintigraphy tasks.
Achieved superior accuracy in tissue separation compared to previous methods using simulated and clinical data.
Outperformed existing methods in mean square and mean absolute errors for simulated source estimation.

Conclusions:

The proposed probabilistic blind source separation method effectively separates signals from dynamic structures in medical imaging.
The method shows significant improvements in accuracy for tasks like dynamic renal scintigraphy.
A MATLAB implementation is available, facilitating further research and application.