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

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

469
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
469
Computed Tomography01:10

Computed Tomography

5.2K
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...
5.2K
Downsampling01:20

Downsampling

235
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
235
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.2K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Tunnel Sign: Pathognomonic Radiologic Feature of <i>Paragonimus westermani</i>.

Radiology·2026
Same author

Mechanistic insight into polysaccharides and ultrasound synergistic enhancement of the quality of low-salt surimi gels.

Food chemistry: X·2026
Same author

Preoperative Imaging of Ascending Aortic Aneurysm with Stanford Type A Dissection.

Radiology·2026
Same author

Deciphering the modulatory role of short-chain fatty acids in Parkinson's disease <i>via</i> phosphorylation-dependent signaling mechanisms.

PeerJ·2026
Same author

Effect of Abnormal Calcium Dynamics on Heart Failure based on a Biophysical Modeling Study<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Exploring the gut microbiota-Parkinson's disease link: preliminary insights from metagenomics and Mendelian randomization.

Frontiers in microbiology·2025

Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.3K

DFSNE-Net: Deviant feature sensitive noise estimate network for low-dose CT denoising.

Jiaji Liu1, Huiyan Jiang2, Fuzhen Ning1

  • 1Software College, Northeastern University, No. 195, Chuangxin Road, Hunnan District, Shenyang, 110169, Liaoning, China.

Computers in Biology and Medicine
|September 9, 2022
PubMed
Summary

This study introduces a novel method for low-dose computed tomography (CT) denoising by correlating noise with deviant features. The proposed approach effectively reduces radiation exposure while maintaining image quality in CT scans.

Keywords:
AttentionDeep learningDenoiseGenerative adversarial networksLow-dose CTMulti-scale convolution

More Related Videos

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

492
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

2.9K

Related Experiment Videos

Last Updated: Aug 29, 2025

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

1.3K
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

492
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

2.9K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Signal Processing

Background:

  • Computed tomography (CT) poses radiation risks, necessitating low-dose scanning.
  • High-quality CT imaging often involves harmful radiation doses.
  • Low-dose CT denoising is a critical area of research for patient safety.

Purpose of the Study:

  • To propose a novel method for estimating and reducing noise in low-dose CT scans.
  • To establish a correlation between image noise and deviant features in CT data.
  • To improve the quality of CT images obtained with reduced radiation exposure.

Main Methods:

  • Introduced the concept of deviant features to hypothesize a link with noise.
  • Developed a deviant feature perception and downsampling method using multi-scale convolutional cooperative (MSC-DFPM) and self-information space attention (SISA-FM) modules.
  • Constructed the deviant feature sensitive noise estimate network (DFSNE-Net) with a balanced loss function and training strategy.
  • Implemented noise distribution normalization (SK-NDN) and confidence interval-based noise suppression (CI-LCNS) for noise optimization.

Main Results:

  • The proposed denoising method demonstrated superior performance compared to state-of-the-art techniques across various evaluation metrics.
  • Experimental results validated the strong correlation between noise and deviant features.
  • The method proved effective for denoising CT images acquired at different radiation doses.

Conclusions:

  • The study successfully developed and validated an effective low-dose CT denoising technique.
  • The findings confirm the significant relationship between image noise and deviant features in CT.
  • The proposed method offers a promising solution for enhancing CT image quality while minimizing radiation exposure.