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

Expected Value01:15

Expected Value

7.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
7.8K
What is Variation?01:14

What is Variation?

18.6K
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...
18.6K
Variation01:19

Variation

8.1K
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...
8.1K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.6K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.6K
Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.9K
Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
6.9K
Variation of Atmospheric Pressure01:18

Variation of Atmospheric Pressure

4.2K
Change in atmospheric pressure with height is particularly interesting. The decrease in atmospheric pressure with increasing altitude is due to the decreasing gravitational force per unit area as we move away from the surface of the earth.
Assuming the air temperature is constant at a given altitude and that the ideal gas law of thermodynamics describes the atmosphere to a good approximation, one can find the variation of atmospheric pressure with height.
Let p(y) be the atmospheric pressure at...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Development and validation of a 3D printed phantom for image quality assessment in fluoroscopy.

Journal of applied clinical medical physics·2026
Same author

Deep learning-based identification of aberrant anterior tibial artery on knee MRI: a brazilian multicenter study.

Skeletal radiology·2026
Same author

Pixel Tampering: Does Face Redaction Harm Medical AI Performance?

Journal of imaging informatics in medicine·2025
Same author

Perinatal hardship and infant neurodevelopment: insights from a global pandemic.

Journal of child psychology and psychiatry, and allied disciplines·2025
Same author

Progressive X-ray tube degradation detected via daily mammography quality control.

Journal of medical engineering & technology·2025
Same author

Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI From X-Ray: Integrating External Radiographic Feature Information.

IEEE journal of biomedical and health informatics·2025
Same journal

The bMRI-QUAL scoring system: an important first step toward standardizing breast MRI quality.

European radiology·2026
Same journal

Spectral CT-based habitat analysis for predicting pathologic response to neoadjuvant therapy in gastric cancer.

European radiology·2026
Same journal

MR-guided microwave ablation of liver tumors: outcomes in local tumor control and determinants of treatment success.

European radiology·2026
Same journal

AI integration in pediatric radiology: perspectives from international academic leaders.

European radiology·2026
Same journal

Association of hypertension and blood pressure control with aneurysm wall enhancement in unruptured intracranial aneurysms: a multicenter propensity score-matched study.

European radiology·2026
Same journal

Conservative management of < 3cm anterior mediastinal lesions in lung cancer screening is safe.

European radiology·2026
See all related articles

Related Experiment Video

Updated: Feb 12, 2026

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke
11:32

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke

Published on: January 3, 2025

1.6K

Ischemic stroke enhancement using a variational model and the expectation maximization method.

Allan Felipe Fattori Alves1, Rachid Jennane2, José Ricardo Arruda de Miranda1

  • 1Instituto de Biociências de Botucatu, Departamento de Física e Biofísica, UNESP-Universidade Estadual Paulista, P.O. BOX 510, Distrito de Rubião Junior S/N, Botucatu, São Paulo, 18618-000, Brazil.

European Radiology
|April 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel computational algorithm to enhance early ischemic stroke detection in CT scans. The image enhancement improved physician diagnostic accuracy, increasing sensitivity and specificity for stroke identification.

Keywords:
AlgorithmsBrainEarly diagnosisStrokeTomography

More Related Videos

PET Imaging of Neuroinflammation Using [11C]DPA-713 in a Mouse Model of Ischemic Stroke
12:01

PET Imaging of Neuroinflammation Using [11C]DPA-713 in a Mouse Model of Ischemic Stroke

Published on: June 14, 2018

13.3K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.7K

Related Experiment Videos

Last Updated: Feb 12, 2026

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke
11:32

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke

Published on: January 3, 2025

1.6K
PET Imaging of Neuroinflammation Using [11C]DPA-713 in a Mouse Model of Ischemic Stroke
12:01

PET Imaging of Neuroinflammation Using [11C]DPA-713 in a Mouse Model of Ischemic Stroke

Published on: June 14, 2018

13.3K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.7K

Area of Science:

  • Medical Imaging
  • Radiology
  • Computational Neuroscience

Background:

  • Early detection of ischemic stroke is critical to prevent irreversible cerebral damage.
  • Non-enhanced computed tomography (CT) can present challenges for inexperienced physicians in identifying subtle early stroke signs.
  • Existing diagnostic methods require high efficiency and accuracy, especially in time-sensitive neurological emergencies.

Purpose of the Study:

  • To develop and evaluate a novel computational approach for enhancing the visual perception of ischemic stroke in non-enhanced CT scans.
  • To improve the diagnostic performance of physicians, particularly those with less experience, in detecting early stroke indicators.
  • To assess the impact of image enhancement on observer sensitivity and specificity in a clinical setting.

Main Methods:

  • A retrospective analysis of 39 CT scans (23 ischemic stroke, 16 normal) was conducted.
  • Image processing involved slice selection, noise reduction via image averaging, and variational decomposition to isolate relevant image components.
  • The expectation maximization method was employed to generate enhanced CT images for improved stroke visualization.

Main Results:

  • Observer sensitivity for detecting ischemic stroke increased from 64.5% to 89.6% with the aid of enhanced images.
  • Observer specificity improved from 83.3% to 91.7% when utilizing the enhanced CT visualizations.
  • The computational tool demonstrated a significant improvement in diagnostic performance in a simulated clinical environment.

Conclusions:

  • The proposed computational algorithm effectively enhances the visual perception of ischemic stroke on non-enhanced CT.
  • Image enhancement significantly boosts the diagnostic accuracy of physicians, aiding in critical neuroradiology decisions.
  • This technology offers a valuable tool to support clinical judgment and improve patient outcomes in stroke diagnosis.