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

8.1K
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...
8.1K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

308
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...
308
Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

27.0K
In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
27.0K
What is Variation?01:14

What is Variation?

17.6K
Apart from the measures of central tendency, distribution, outliers, and the changing characteristics of data with time, an important characteristic of any data set is its variation or spread. In some data sets, the data values are concentrated closely near the mean; in others, the data values are more widely spread out from the mean.
The range, standard deviation, standard error, and variance are the different measures of variation.
Range: The range is the difference between its maximum and...
17.6K
Variation01:19

Variation

7.7K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.7K
Fischer Projections02:18

Fischer Projections

16.4K
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
16.4K

You might also read

Related Articles

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

Sort by
Same author

Prognostic nomogram for surgery of lung cancer in HIV-infected patients.

Journal of thoracic disease·2021
Same author

High Triglyceride-Glucose Index is Associated with Poor Cardiovascular Outcomes in Nondiabetic Patients with ACS with LDL-C below 1.8 mmol/L.

Journal of atherosclerosis and thrombosis·2021
Same author

Results of Arthroscopic Treatment of Acute Posterior Cruciate Ligament Avulsion Fractures With Suspensory Fixation.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association·2021
Same author

Is abnormal function with troponin T elevation definitely myocardial infarction?

European heart journal·2021
Same author

Preparation, Biosafety, and Cytotoxicity Studies of a Newly Tumor-Microenvironment-Responsive Biodegradable Mesoporous Silica Nanosystem Based on Multimodal and Synergistic Treatment.

Oxidative medicine and cellular longevity·2021
Same author

Contributing Factors Affecting the Severity of Metro Escalator Injuries in the Guangzhou Metro, China.

International journal of environmental research and public health·2021

Related Experiment Video

Updated: Jan 22, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K

Projection data smoothing through noise-level weighted total variation regularization for low-dose computed

Xiaojuan Deng1,2, Yunsong Zhao1,2, Hongwei Li1,2

  • 1School of Mathematical Sciences, Capital Normal University, Beijing, China.

Journal of X-Ray Science and Technology
|July 9, 2019
PubMed
Summary

This study introduces a novel noise-level weighted total variation (NWTV) method to reduce radiation dose in computed tomography (CT) imaging. NWTV effectively balances noise reduction and edge preservation, minimizing artifacts in low-dose scans.

Keywords:
Denoisinglow-dose ctnon-stationary gaussian noiseweighted total variation

More Related Videos

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data
09:37

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Published on: April 26, 2016

9.0K
Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography
11:45

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

Published on: May 26, 2015

10.3K

Related Experiment Videos

Last Updated: Jan 22, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.3K
Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data
09:37

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Published on: April 26, 2016

9.0K
Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography
11:45

Generation and 3-Dimensional Quantitation of Arterial Lesions in Mice Using Optical Projection Tomography

Published on: May 26, 2015

10.3K

Area of Science:

  • Medical Imaging
  • Image Processing
  • Radiology

Background:

  • Computed tomography (CT) imaging faces challenges in reducing radiation dose while preserving image quality.
  • Non-stationary Gaussian noise distribution in low-dose CT complicates image reconstruction and denoising.

Purpose of the Study:

  • To develop and evaluate a novel denoising method for low-dose CT projection data.
  • To address the limitations of existing methods in handling spatially varying noise levels.

Main Methods:

  • A noise-level weighted total variation (NWTV) regularization term was incorporated into a denoising model.
  • The NWTV method adapts regularization based on spatially varying noise levels, unlike edge-weighted total variation.

Main Results:

  • The proposed NWTV regularization achieved competitive results on both simulated and real CT imaging data.
  • NWTV demonstrated superior performance in balancing noise reduction and edge preservation for sinograms with sharp edges.
  • Stair-casing artifacts, common in total variation regularization, were significantly reduced in low-dose CT images using NWTV.

Conclusions:

  • The NWTV method offers an effective approach for denoising low-dose CT projection data.
  • NWTV provides a better balance between noise removal and edge preservation compared to traditional methods.
  • The proposed method shows promise for improving image quality in low-dose CT applications.