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

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

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

Sort by

Same author

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

Journal of neurology·2026

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

Successful treatment of an NDM-5-producing, carbapenem-resistant Salmonella Kentucky ST198 infection with aztreonam and ceftazidime-avibactam in a patient with acute erythroid leukemia: a case report.

BMC infectious diseases·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

Search research articles

Related Experiment Video

Updated: Jan 7, 2026

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

Geometry-encoded deep learning (GeoDL) framework for real-time 3D dose verification for online adaptive radiotherapy.

Shunyu Yan¹, Austen Maniscalco¹, Biling Wang¹

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

Machine Learning. Health

|January 1, 2026

Summary

Geometry-encoded deep learning (GeoDL) enables rapid, accurate 3D dose verification for online adaptive radiotherapy (ART). This AI approach significantly enhances treatment efficiency and quality by providing real-time dose estimates crucial for clinical decision-making.

Keywords:

Secondary dose verification artificial intelligence deep learning prostate cancer real-time calculation

More Related Videos

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

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Related Experiment Videos

Last Updated: Jan 7, 2026

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

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

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

Area of Science:

Medical Physics
Radiotherapy
Artificial Intelligence in Medicine

Background:

Online adaptive radiotherapy (ART) requires fast and accurate dose verification.
Current methods are computationally intensive, hindering ART workflow efficiency.
Previous work showed potential for 2D dose estimation using geometry-encoded deep learning (GeoDL).

Purpose of the Study:

To investigate the feasibility of extending GeoDL for real-time 3D dose verification.
To address the unmet clinical need for rapid dose assessment during ART.
To evaluate GeoDL's performance across the entire 3D volume of interest.

Main Methods:

Developed a GeoDL algorithm unifying fluence maps and CT images using linear accelerator geometry.
Employed a 3D U-Net architecture trained on integrated CT-FM volumes for 3D dose estimation.
Utilized a combined loss function to optimize predictions in high and low dose regions; trained and tested on prostate cancer cases.

Main Results:

Achieved highly accurate 3D dose estimation in near real-time (~35ms per case).
Demonstrated excellent dosimetric accuracy with an average γ passing rate (3%/2mm) of 99.94% ± 0.15%.
Reported minimal mean dose differences for planning target volumes (PTV) and organs at risk (OARs).

Conclusions:

GeoDL is feasible for ultra-fast dose verification in ART.
The algorithm shows strong performance in key dosimetric metrics for the entire 3D volume.
GeoDL is a promising tool for real-world clinical application in radiotherapy.