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Related Concept Videos

Computed Tomography01:10

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...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...

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Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans
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Published on: September 27, 2020

Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review.

Alzahra Altalib1,2, Chunhui Li1, Alessandro Perelli3

  • 1School of Science and Engineering, University of Dundee, Dundee DD1 4HN, UK.

Tomography (Ann Arbor, Mich.)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Conditional diffusion models show promise for synthetic CT generation from Cone Beam Computed Tomography (CBCT) data, improving image quality. Further validation is needed for clinical use.

Keywords:
Cone-beam computed tomographyX-ray computedcomputer assistedconditional diffusion modelsdenoising diffusion probabilistic modelsradiotherapy planningsynthetic CTtomography

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Cone Beam Computed Tomography (CBCT) offers low-dose volumetric imaging for radiotherapy but suffers from image quality issues like noise and artifacts, limiting its use for synthetic CT (sCT) generation.
  • Conditional Diffusion Models (CDMs) are emerging deep learning techniques that may overcome these limitations due to their iterative denoising capabilities, potentially preserving anatomical details and modeling uncertainty better than previous methods.

Purpose of the Study:

  • This systematic review critically evaluates the application of CDMs for CBCT-to-CT synthesis.
  • It focuses on identifying architectural strategies, analyzing reported quantitative outcomes, and assessing the clinical relevance of these models.
  • The review addresses the types of CDMs used, their performance metrics, and their discussed clinical implications.

Main Methods:

  • A comprehensive systematic literature search was performed across major scientific databases (PubMed, Web of Science, Scopus, IEEE Xplore, Google Scholar).
  • Studies published between 2013 and 2024 were screened for eligibility.
  • Eleven studies meeting the inclusion criteria were analyzed to answer the review's core questions.

Main Results:

  • Conditional Diffusion Models demonstrated promising image quality in CBCT-to-CT synthesis, particularly when enhanced with anatomical priors, spatial-frequency guidance, hierarchical refinement, or latent representations.
  • However, the current evidence base is limited by heterogeneity in anatomy, dimensionality, supervision, and evaluation metrics, restricting definitive comparative claims.
  • The reviewed studies suggest CDMs are a promising avenue for sCT generation, but require further dose-aware validation, standardized reporting, and multicenter evaluation.

Conclusions:

  • Conditional Diffusion Models represent a significant advancement in the field of CBCT-to-CT synthesis, offering improved image quality over traditional methods.
  • Despite promising results, the heterogeneity and limitations of current studies necessitate further research before widespread clinical adoption.
  • Future work should focus on robust validation, standardization of evaluation, and multicenter trials to establish the clinical utility of CDMs for synthetic CT generation.