Search research articles

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Development and validation of commissioning tests for volumetric modulated arc therapy on the 1.5T MR-linac.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2026

Same author

First Patient Study With Visual Biofeedback for Gating Delivery Efficiency Improvements on a 1.5 T MR-Linac.

International journal of radiation oncology, biology, physics·2026

Same author

Bowel tracking for MR-guided radiotherapy: simultaneous optimization of small bowel imaging and tracking.

Physics in medicine and biology·2025

Same author

The NCS code of practice for the quality assurance of treatment planning systems (NCS-35).

Physics in medicine and biology·2023

Same author

Dosimetric evaluation of off-axis fields and angular transmission for the 1.5 T MR-linac.

Physics in medicine and biology·2022

Same author

First experimental exploration of real-time cardiorespiratory motion management for future stereotactic arrhythmia radioablation treatments on the MR-linac.

Physics in medicine and biology·2022

Same journal

Effective contrast-enhanced preprocessing for intracranial artery segmentation in digital subtraction angiography.

Physics in medicine and biology·2026

Same journal

Improving Plan Quality in Adaptive Proton Therapy Using an Interactive Dose Modification Tool.

Physics in medicine and biology·2026

Same journal

Technical Note: Real-Time MLC Control and Latency Measurement Optimization with External Verification.

Physics in medicine and biology·2026

Same journal

Fetus-Specific Hematopoietic Stem Cell Dosimetry Framework for Leukemia-Relevant Target Cells During Prenatal Development.

Physics in medicine and biology·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

See all related 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

Search research articles

Related Experiment Video

Updated: Aug 26, 2025

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

Robust deep learning-based forward dose calculations for VMAT on the 1.5T MR-linac.

G Tsekas¹, G H Bol¹, B W Raaymakers¹

¹Department of Radiotherapy, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584CX, The Netherlands.

Physics in Medicine and Biology

|October 5, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning framework for accurate dose calculations in Volumetric Modulated Arc Therapy (VMAT) on a 1.5T MR-linac. The method ensures robust and precise treatment planning for various cancer types.

Keywords:

IMRT MR-linac VMAT deep learning dose engine online adaptive workflow

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

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

Related Experiment Videos

Last Updated: Aug 26, 2025

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Positron Emission Tomography-based Dose Painting Radiation Therapy in a Glioblastoma Rat Model using the Small Animal Radiation Research Platform

Published on: March 24, 2022

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

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

Area of Science:

Medical Physics
Radiotherapy Technology
Artificial Intelligence in Medicine

Background:

Volumetric Modulated Arc Therapy (VMAT) is a widely used radiotherapy technique.
Accurate dose calculation is crucial for effective VMAT delivery, especially with advanced systems like the 1.5T MR-linac.
Deep learning offers potential for improving computational efficiency and accuracy in dose prediction.

Purpose of the Study:

To develop and validate a robust deep learning framework for VMAT forward dose calculations on a 1.5T MR-linac.
To assess the accuracy and reliability of the proposed method for diverse tumor sites and patient data.
To establish a promising solution for VMAT plan validation in clinical workflows.

Main Methods:

A convolutional neural network (CNN) was trained using VMAT segment doses from clinical data converted to an MR-linac environment.
The CNN predicted segment doses for MR-linac-deliverable VMAT test plans, with randomized angles and shifted MLC shapes for robust learning.
Monte Carlo simulations generated ground truth dose data with 1% uncertainty.

Main Results:

The deep learning framework achieved highly accurate dose distributions for MR-linac VMAT plans, with 99.7% ± 0.5% passing the 3%/3 mm gamma criterion.
Evaluation on unseen Intensity-Modulated Radiation Therapy (IMRT) patients also met clinical requirements, achieving 99.0% ± 0.6% for the 3%/3 mm analysis.
The method demonstrated robustness across different tumor anatomies and tissue densities.

Conclusions:

The presented deep learning framework provides a robust and accurate solution for VMAT forward dose calculations on the 1.5T MR-linac.
This approach shows significant promise as a plan validation tool for both IMRT and VMAT, enhancing treatment planning safety and efficiency.
The framework's ability to handle diverse patient data and anatomical variations underscores its clinical applicability.