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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...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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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Related Experiment Video

Updated: May 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Enhancing low-dose CT denoising via multi-view knowledge transfer without paired data.

Yueyang You1, Li Xu2, Gen Wei1

  • 1The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

Frontiers in Radiology
|May 25, 2026
PubMed
Summary

This study introduces a novel framework for low-dose computed tomography (LDCT) denoising using unpaired data. The method effectively transfers multi-view information to improve single-view denoising performance without needing paired images.

Keywords:
CT denoisingknowledge transferlow-dose CTmulti-view learningunpaired data

Related Experiment Videos

Last Updated: May 26, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Deep learning for low-dose computed tomography (LDCT) denoising faces challenges with paired image requirements and limited view information.
  • Existing methods often overlook complementary data from sagittal and coronal views, hindering denoising performance.

Purpose of the Study:

  • To develop an effective framework for unpaired LDCT denoising by leveraging multi-view information.
  • To overcome the limitations of single-axial view training in deep learning-based CT denoising.

Main Methods:

  • Proposed a Multi-view-to-Single Knowledge Transfer (MvSKT) framework for unsupervised, unpaired LDCT denoising.
  • Split 3D CT data into axial, sagittal, and coronal views to train independent 2D Generative Adversarial Network (GAN) models.
  • Utilized pseudo-supervision and cycle-consistency weighting to transfer multi-view knowledge to a single-view model.

Main Results:

  • MvSKT demonstrated superior performance compared to existing unpaired LDCT denoising methods.
  • The proposed framework achieved results comparable to supervised approaches on the AAPM-Mayo dataset.

Conclusions:

  • The MvSKT framework successfully enhances LDCT denoising by utilizing multi-view information from unpaired data.
  • This approach eliminates the need for paired low-dose and high-dose computed tomography (LDCT/HDCT) images in clinical settings.