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

Radiation: Applications

Radiation: Applications

The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
The average...

Radiation Pressure: Problem Solving

Radiation Pressure: Problem Solving

The radiation pressure applied by an electromagnetic wave on a perfectly absorbing surface equals the energy density of the wave. The wave's momentum also gets transferred to the surface when an electromagnetic wave is entirely absorbed by it. The rate at which momentum is transmitted to an absorbing surface perpendicular to the propagation direction equals the force on the surface.
The average value of the rate of momentum transfer divided by the absorbing area represents the average force...

You might also read

Related Articles

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

Sort by

Same author

Strategies for enhancing delivery efficiency on MR-Linac: A dosimetric study and historical plan review.

Journal of applied clinical medical physics·2026

Same author

Nuclear KRT19 links the NF-κB-FSCN1 signaling to gastric cancer metastasis.

Communications biology·2026

Same author

Adapt-on-demand: Enabling flexible and scalable adaptive radiotherapy through workflow automation.

Journal of applied clinical medical physics·2026

Same author

Fluorinated Zr-MOF supported Cu(I) for synergistic four-component CO<sub>2</sub> reactions.

Chemical communications (Cambridge, England)·2026

Same author

Development of an indirect enzyme-linked immunosorbent assay based on the nucleocapsid protein of bovine parainfluenza virus type 3.

Frontiers in cellular and infection microbiology·2026

Same author

Comparative analysis of viral biological characteristics and pathogenicity of representative prevalent avian reovirus strains from genotypes I to V.

Virulence·2026

Same journal

Deep learning-based dose prediction to enhance planning efficiency in cervical brachytherapy with hybrid applicators.

Physics in medicine and biology·2026

Same journal

Corrigendum: Referenceless MR thermometry-a comparison of five methods (2017<i>Phys. Med. Biol</i>.<b>62</b>1-16).

Physics in medicine and biology·2026

Same journal

Corrigendum: Measured and Monte Carlo simulated electron backscatter to the monitor chamber for the varian TrueBeam linac (2016<i>Phys. Med. Biol</i>.<b>61</b>8779).

Physics in medicine and biology·2026

Same journal

Corrigendum: 3D range-modulator for scanned particle therapy: development, Monte Carlo simulations and experimental evaluation (2017<i>Phys. Med. Biol</i>.<b>62</b>7075).

Physics in medicine and biology·2026

Same journal

Recent progress in applications of computing to radiotherapy (ICCR 2016).

Physics in medicine and biology·2026

Same journal

Novel TMS coils designed using an inverse boundary element method.

Physics in medicine and biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 25, 2025

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Latent space arc therapy optimization.

Noah Bice¹, Mohamad Fakhreddine¹, Ruiqi Li¹

¹Department of Radiation Oncology, UT Health San Antonio, San Antonio, TX, United States of America.

Physics in Medicine and Biology

|August 5, 2021

Summary

This summary is machine-generated.

This study introduces unsupervised deep learning to reduce the complexity of volumetric modulated arc therapy (VMAT) planning. This approach enables faster optimization of radiation treatment plans by creating lower-dimensional representations.

Keywords:

VMAT deep learning optimization

More Related Videos

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Published on: May 8, 2018

Related Experiment Videos

Last Updated: Oct 25, 2025

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Dynamic Lung Tumor Tracking for Stereotactic Ablative Body Radiation Therapy

Published on: June 7, 2015

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Treatment of Liver Metastases Using an Internal Target Volume Method for Stereotactic Body Radiotherapy

Published on: May 8, 2018

Area of Science:

Medical Physics
Radiation Oncology
Computational Optimization

Background:

Volumetric modulated arc therapy (VMAT) planning involves complex, high-dimensional, non-convex optimization.
Current methods use heuristics for initialization, leading to slower optimization and favoring local optima due to plan overparameterization.

Purpose of the Study:

To address VMAT plan overparameterization and accelerate treatment planning.
To reduce the effective dimensionality of VMAT treatment plans.

Main Methods:

Utilized unsupervised deep learning to reduce the dimensionality of VMAT treatment plans.
Developed an optimization engine based on these low-dimensional arc representations.

Main Results:

Successfully reduced the effective dimension of VMAT treatment plans.
Facilitated faster radiation therapy planning times through optimized low-dimensional representations.

Conclusions:

Unsupervised deep learning offers a viable method to overcome VMAT planning challenges.
This approach enhances optimization efficiency, leading to quicker and potentially improved radiation treatment plans.