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...

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

You might also read

Related Articles

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

Sort by

Same author

Deep learning-based dose prediction for MR-guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection.

Medical physics·2026

Same author

Impact of Monaco sequencing parameters on monitor units, plan quality, and optimization time for Elekta Unity liver SBRT plans.

Journal of applied clinical medical physics·2026

Same author

Extraction of Macranthoidin B and Dipsacoside B from <i>Lonicera macranthoides</i> Utilizing Ultrasound-Assisted Deep Eutectic Solvent: Optimization of Conditions and Extraction Mechanism.

ACS omega·2025

Same author

Evaluation of the system accuracy of frameless stereotactic radiosurgery using a combination of cone beam CT, six degrees of freedom couch, and surface image-guided systems.

Journal of applied clinical medical physics·2025

Same author

Patient setup variation on Elekta Unity and its impact on adaptive planning.

Journal of applied clinical medical physics·2025

Same author

MR-linac MLC positioning QA by digitally stitching dual double-exposed films.

Journal of applied clinical medical physics·2024

Search research articles

Related Experiment Video

Updated: Dec 25, 2025

Analyzing Dendritic Morphology in Columns and Layers

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

Evaluation of a neural network-based photon beam profile deconvolution method.

Karl Mund¹, Jian Wu¹, Chihray Liu¹

¹Department of Radiation Oncology, University of Florida, Gainesville, FL, USA.

Journal of Applied Clinical Medical Physics

|April 1, 2020

Summary

This summary is machine-generated.

An artificial neural network (ANN) effectively eliminates the volume average effect (VAE) in scanning ionization chambers (ICs). This method accurately deconvolves beam profiles across various energies and modalities, improving radiation therapy precision.

Keywords:

artificial neural network deconvolution detector response function volume averaging effect

More Related Videos

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Published on: December 7, 2017

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Dec 25, 2025

Analyzing Dendritic Morphology in Columns and Layers

Analyzing Dendritic Morphology in Columns and Layers

Published on: March 23, 2017

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Published on: December 7, 2017

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

Area of Science:

Medical Physics
Radiation Oncology
Artificial Intelligence in Medicine

Background:

Scanning ionization chambers (ICs) suffer from the volume averaging effect (VAE), which can distort radiation beam profile measurements.
Artificial neural networks (ANNs) offer a potential solution for deconvolution, but their efficacy across different beam parameters needs validation.

Purpose of the Study:

To evaluate an ANN-based deconvolution method for eliminating the VAE in IC measurements.
To assess the method's performance with varying beam energies (6 and 10 MV) and modalities (flattened and unflattened).

Main Methods:

A three-layer ANN processed transverse photon beam profiles using a sliding window approach.
Measurements were performed using three ICs (CC04, CC13, FC65-P) and an EDGE diode across diverse field sizes, depths, energies, and modalities.
ANNs were trained and tested using EDGE diode profiles as reference data, with separate and combined models developed.

Main Results:

The ANN deconvolution achieved excellent agreement between deconvolved and reference beam profiles across all tested conditions.
The average penumbra width difference (PWD) significantly decreased from 1-3 mm to under 0.15 mm (separate ANNs) and 0.20 mm (combined ANNs).

Conclusions:

The ANN deconvolution method is effective for correcting VAE in IC measurements for various beam energies, modalities, and IC sizes.
Separate ANNs provided slightly superior results, but combined ANNs also offer clinically acceptable accuracy with comprehensive training data.