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

Computed Tomography01:10

Computed Tomography

7.9K
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...
7.9K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

221
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...
221
Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

Imaging Studies I: Kidney, Ureter, and Bladder Studies

195
Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...
195
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

206
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...
206
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

715
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...
715

You might also read

Related Articles

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

Sort by
Same author

WDK-Net: Lightweight Wavelet Diffusion with Kolmogorov-Arnold Network for Limited-angle Cardiac CT Reconstruction.

IEEE transactions on medical imaging·2026
Same author

MCEPANet: A connectivity-edge guided attention network for robust medical image segmentation with multi-scale boundary preservation.

Biomedical physics & engineering express·2026
Same author

Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction for Sparse-View CT.

IEEE transactions on medical imaging·2026
Same author

Clinical Metadata Guided Limited-Angle CT Image Reconstruction.

IEEE transactions on medical imaging·2026
Same author

An interpretable cascaded residual iterative network for sparse-view spectral CT imaging.

Quantitative imaging in medicine and surgery·2026
Same author

Machine learning-driven nanoparticle-enhanced paper chromogenic array sensor approach for detecting sub-lethally injured Salmonella in low moisture food.

Food research international (Ottawa, Ont.)·2026

Related Experiment Video

Updated: Dec 27, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.3K

Improving Low-Dose Pediatric Abdominal CT by Using Convolutional Neural Networks.

Robert D MacDougall1, Yanbo Zhang1, Michael J Callahan1

  • 1Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (R.D.M., M.J.C., J.P.R., M.B., P.R.J.); Department of Biomedical Engineering (R.D.M.) and Department of Electrical and Computer Engineering (Y.Z., H.Y.), University of Massachusetts Lowell, Lowell, Mass; and Ping An Technology, US Research Laboratory, Palo Alto, Calif (Y.Z.).

Radiology. Artificial Intelligence
|February 25, 2020
PubMed
Summary
This summary is machine-generated.

Convolutional neural networks (CNNs) significantly improved low-dose pediatric CT image quality by reducing noise by 31%. This AI-driven enhancement offers potential for dose reduction or better image quality on older CT scanners.

More Related Videos

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

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

704

Related Experiment Videos

Last Updated: Dec 27, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.3K
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

3.3K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

704

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Low-dose pediatric CT scans are crucial for minimizing radiation exposure.
  • Traditional image reconstruction methods like filtered back projection (FBP) can result in suboptimal image quality.
  • Iterative reconstruction (IR) algorithms improve image quality but are not always available on all scanners.

Purpose of the Study:

  • To evaluate the efficacy of convolutional neural networks (CNNs) in enhancing the image quality of low-dose pediatric abdominal CT scans.
  • To determine if CNN-based postprocessing can simulate the results of iterative reconstruction (IR) on images reconstructed with filtered back projection (FBP).

Main Methods:

  • A residual CNN was trained to predict image noise reduction by comparing FBP and IR reconstructed images.
  • CNN-based postprocessing was applied to low-dose pediatric CT datasets acquired on a scanner limited to FBP reconstruction.
  • Objective noise measurements and subjective image reviews by two pediatric radiologists were performed to assess image quality.

Main Results:

  • CNN-enhanced images showed a 31% reduction in image noise compared to FBP images (P < .001).
  • Radiologists preferred CNN images for overall image quality, citing improvements in low contrast, image noise, and artifacts.
  • While spatial resolution was comparable, CNNs effectively improved other key image quality metrics.

Conclusions:

  • Well-trained CNNs can significantly improve image quality in the image space for FBP reconstructed CT scans.
  • This AI-driven approach may enable radiation dose reduction or enhanced image quality on scanners limited to FBP reconstruction.
  • CNNs offer a promising solution for improving diagnostic confidence in pediatric CT imaging.