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

Multi-class segmentation of aortic branches and zones in computed tomography angiography: The AortaSeg24 challenge.

Medical image analysis·2026

Same author

Voxel-Wise Case-Control Analysis of Dose, Linear Energy Transfer, and Symptomatic Brainstem Necrosis in Pediatric Brain Tumors Treated With Proton Therapy.

International journal of radiation oncology, biology, physics·2026

Same author

A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations.

Frontiers in psychiatry·2026

Same author

Improving confidence in MRI-based auto-segmentation via uncertainty assessment.

Acta oncologica (Stockholm, Sweden)·2026

Same author

Development of a Patient Decision Aid for the DAHANCA 35 Proton Radiotherapy Trial: An Iterative Development Process with User-Centered Design Elements.

MDM policy & practice·2026

Same author

Understanding and leveraging uncertainties in autosegmentation for radiotherapy.

BJR artificial intelligence·2026

Search research articles

Related Experiment Video

Updated: Jun 7, 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 dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks.

Casper Dueholm Vestergaard¹, Ulrik Vindelev Elstrøm¹, Ludvig Paul Muren¹

¹Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark.

Physics and Imaging in Radiation Oncology

|November 13, 2024

Summary

Deep learning generated synthetic CT images from CBCT with improved quality and accurate proton dose calculations, making them suitable for adaptive proton therapy.

Keywords:

Adaptive proton therapy Cone-beam CT Deep-learning Image quality improvement Proton dose calculation Synthetic CT

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: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jun 7, 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: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

Area of Science:

Medical imaging
Radiotherapy physics
Artificial intelligence in medicine

Background:

Cone-beam computed tomography (CBCT) image quality limitations can impact proton dose calculations for adaptive radiotherapy.
Accurate dose assessment is crucial for evaluating anatomical changes during treatment.

Purpose of the Study:

To evaluate the image quality of synthetic CTs generated from CBCT using unsupervised 3D deep learning.
To assess the accuracy of proton dose calculations on these synthetic CTs.

Main Methods:

Trained and tested three deep learning networks (cycle-consistent generative adversarial network, contrastive unpaired translation, CycleCUT) on 102 head-and-neck cancer patients.
Generated synthetic CTs from CBCT and compared them to ground-truth CTs.
Evaluated image quality using PSNR, SSIM, mean error, and MAE; assessed dose accuracy using 3D gamma analysis and DVH parameters.

Main Results:

All synthetic CTs preserved CBCT anatomy and improved image quality.
CycleCUT showed slightly better image quality (MAE: 53 HU).
All networks achieved high proton dose calculation accuracy (gamma passing rate >97%).

Conclusions:

Deep learning-based synthetic CTs provide dose distributions comparable to conventional CT for proton therapy.
The fast generation process makes these networks viable for adaptive proton therapy.