Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

X-ray Imaging01:24

X-ray Imaging

7.7K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
7.7K
Positron Emission Tomography01:29

Positron Emission Tomography

6.2K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
6.2K
Computed Tomography01:10

Computed Tomography

7.6K
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...
7.6K
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

853
Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
853
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

893
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
893
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

1.8K
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
1.8K

You might also read

Related Articles

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

Sort by
Same author

Detecting Performance Drift in AI Models for Medical Image Analysis Using CUSUM Chart.

Journal of imaging informatics in medicine·2026
Same author

Physics-informed data augmentation to simulate low dose CT scans: Application to lung nodule detection.

Medical physics·2026
Same author

Task-Based Sampling of Patient Data for Rigorous Machine Learning/AI Performance Assessment.

Journal of imaging informatics in medicine·2026
Same author

Synthetic skin image generation using a physics-based, object-to-image computational pipeline.

International journal of computer assisted radiology and surgery·2026
Same author

Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging.

Bioengineering (Basel, Switzerland)·2026
Same author

Detection of Confounders and Potential Confounders in Computed Tomography Lung Datasets.

Journal of imaging informatics in medicine·2025

Related Experiment Video

Updated: May 2, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

2.0K

Synthetic data in radiological imaging: current state and future outlook.

Elena Sizikova1, Andreu Badal1, Jana G Delfino1

  • 1Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, United States.

BJR Artificial Intelligence
|May 1, 2026
PubMed
Summary

Synthetic data generation offers a promising solution to overcome data limitations in artificial intelligence (AI) for radiology. This approach can reduce costs and improve data quality, though further research is needed.

Keywords:
digital twinsin silico medicineradiologysimulationssynthetic data

More Related Videos

3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

41.0K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

12.8K

Related Experiment Videos

Last Updated: May 2, 2026

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
07:13

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

2.0K
3D Printing of Preclinical X-ray Computed Tomographic Data Sets
11:06

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

41.0K
Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

12.8K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Developing AI for radiology faces significant data limitations, including high costs, privacy concerns, and low disease prevalence.
  • Acquiring sufficient and representative annotated patient datasets is a major hurdle for AI deployment in medical imaging.

Purpose of the Study:

  • To summarize research trends and practical applications of synthetic data for AI in radiology.
  • To explore techniques for generating synthetic data, their applications, and quality control measures.

Main Methods:

  • Review of current research trends in synthetic data generation for radiological AI.
  • Discussion of various synthetic data generation techniques and their application areas.
  • Analysis of quality control and evaluation methods for synthetic imaging data.

Main Results:

  • Synthetic data offers advantages over patient data, including reduced harm, lower costs, and improved scalability.
  • Various techniques exist for generating synthetic radiological data, with specific applications and quality assessment challenges.
  • Current methods for evaluating synthetic imaging data are discussed.

Conclusions:

  • Synthetic data holds significant potential to address data availability gaps in radiological AI.
  • Further research and development are necessary to fully realize the benefits of synthetic data in medical imaging.