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

You might also read

Related Articles

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

Sort by

Same author

Comprehensive evaluation of a deep learning-based synthetic CT model for MR-only radiotherapy across multiple anatomical sites.

Physics and imaging in radiation oncology·2026

Same author

Artificial Intelligence in Image Assisted Radiation Oncology.

Cancers·2026

Same author

Toward universal dose prediction: A multi-scale, multi-objective framework for sequential boost radiotherapy.

Medical physics·2026

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

RAPID-LC: rapid evidence-to-practice uptake of large core thrombectomy across a stroke consortium.

Journal of neurology·2026

Same author

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

Journal of applied clinical medical physics·2026

Same journal

Pulse‑level characterization of low monitor unit deliveries on a modern linear accelerator using a plastic scintillation detector.

Journal of applied clinical medical physics·2026

Same journal

Improving image quality in terbium-161 phantom imaging: Quantitative evaluation of DEW and TEW scatter correction methods.

Journal of applied clinical medical physics·2026

Same journal

Latent density discrepancies in commercial lung-equivalent inserts and their clinical dosimetric impact.

Journal of applied clinical medical physics·2026

Same journal

Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.

Journal of applied clinical medical physics·2026

Same journal

Conditional sparing in FLASH radiotherapy: Conformal dosimetry and ALARA remain essential.

Journal of applied clinical medical physics·2026

Same journal

Dual-scan conformal cone-beam CT for targeted image-quality improvement using dynamic collimation.

Journal of applied clinical medical physics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 18, 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

Boosting radiotherapy dose calculation accuracy with deep learning.

Yixun Xing¹, You Zhang¹, Dan Nguyen¹

¹Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Journal of Applied Clinical Medical Physics

|June 20, 2020

Summary

This summary is machine-generated.

Deep learning enhances radiotherapy dose calculations by boosting accuracy. A U-Net model improved low-accuracy Anisotropic Analytic Algorithm (AAA) doses to match high-accuracy Acuros XB (AXB) levels, achieving better results efficiently.

Keywords:

AAA AXB CT deep learning dose calculation inhomogeneous regions

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

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

Related Experiment Videos

Last Updated: Dec 18, 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

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant

Published on: October 6, 2023

Area of Science:

Medical Physics
Radiotherapy
Computational Imaging

Background:

Radiotherapy dose calculation involves a trade-off between speed and accuracy.
Faster methods like pencil-beam convolution are less accurate than Monte-Carlo simulations.
Dose differences are linked to tissue density and electronic disequilibrium.

Purpose of the Study:

To develop a deep learning framework to improve the accuracy of radiotherapy dose calculations.
To convert lower-accuracy Anisotropic Analytic Algorithm (AAA) doses to higher-accuracy Acuros XB (AXB) levels.
To leverage computed tomography (CT) intensity data for dose accuracy enhancement.

Main Methods:

A hierarchically dense U-Net deep learning model was developed.
The model used CT images and AAA doses as input to predict AXB doses.
Trained and tested on 120 lung radiotherapy plans with varying parameters.

Main Results:

Boosted AAA doses showed significantly improved agreement with AXB 'ground-truth' doses.
Average gamma passing rate (1 mm/1%) improved from 87.8% to 97.6%.
Mean squared error (MSE) decreased from 0.31 to 0.11.

Conclusions:

Deep learning can effectively capture discrepancies between dose calculation algorithms.
A combination of a less accurate algorithm and a deep learning model can achieve high accuracy and efficiency.
This approach offers a potential pathway to optimize radiotherapy planning and delivery.