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

The Nucleosome Core Particle02:10

The Nucleosome Core Particle

14.5K
Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
The paradox
Nucleosomes, paradoxically, perform two opposite functions simultaneously. On the one hand, their main responsibility is to protect the delicate DNA strands from physical damage and help achieve a higher compaction ratio. While on the other hand, they must allow polymerase enzymes to access DNA...
14.5K
The Nucleosome Core Particle01:12

The Nucleosome Core Particle

2.4K
Nucleosomes are the DNA-histone complex, where the DNA strand is wound around the histone core. The histone core is an octamer containing two copies of H2A, H2B, H3, and H4 histone proteins.
Nucleosomes, paradoxically, perform two opposite functions simultaneously. On the one hand, their primary aim is to protect the delicate DNA strands from physical damage and help achieve a higher compaction ratio. On the other hand, they must allow polymerase enzymes to access histone-bound DNA during...
2.4K
What are Estimates?01:06

What are Estimates?

8.8K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.8K
One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

887
The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
887
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

246
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
246
Constant Volume Calorimetry02:41

Constant Volume Calorimetry

30.8K
Calorimeters are useful to determine the heat released or absorbed by a chemical reaction. Coffee cup calorimeters are designed to operate at constant (atmospheric) pressure and are convenient to measure heat flow (or enthalpy change) accompanying processes that occur in solution at constant pressure. A different type of calorimeter that operates at constant volume, colloquially known as a bomb calorimeter, is used to measure the energy produced by reactions that yield large amounts of heat and...
30.8K

You might also read

Related Articles

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

Sort by
Same author

Low-Field Neuroimaging: Opportunities and Limitations.

Journal of computer assisted tomography·2026
Same author

7 Tesla MRI links poorer cognitive function to higher perivascular space burden in neuroPASC.

medRxiv : the preprint server for health sciences·2026
Same author

Machine learning framework for depression subtype grouping: integrating high-resolution imaging and clinical symptom analysis via correlation and clustering.

Frontiers in psychiatry·2026
Same author

Natural Language Processing to Automate Cerebrovascular Event Identification in Stroke Alerts.

Stroke (Hoboken, N.J.)·2026
Same author

Prehospital Stroke Triage to Route Patients Directly to a Thrombectomy Center: New York City First-Year Experience.

Stroke (Hoboken, N.J.)·2026
Same author

Predicting Acute Cerebrovascular Events in Stroke Alerts Using Large-Language Models and Structured Data.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Feb 3, 2026

Osmotic Drug Delivery to Ischemic Hindlimbs and Perfusion of Vasculature with Microfil for Micro-Computed Tomography Imaging
10:50

Osmotic Drug Delivery to Ischemic Hindlimbs and Perfusion of Vasculature with Microfil for Micro-Computed Tomography Imaging

Published on: June 29, 2013

12.8K

Estimation of Ischemic Core Volume Using Computed Tomographic Perfusion.

Yu Sakai1, Bradley N Delman1, Johanna T Fifi2,3

  • 1From the Department of Radiology (Y.S., B.N.D., A.H.D., K.N.), Icahn School of Medicine at Mount Sinai, New York City, NY.

Stroke
|October 26, 2018
PubMed
Summary
This summary is machine-generated.

The Bayesian method more accurately estimates ischemic core volume in stroke patients compared to traditional deconvolution techniques, reducing variability and improving diagnostic precision for computed tomographic perfusion (CTP) imaging.

Keywords:
computed tomographyfollow-up studieshumansinfarctionmagnetic resonance imagingperfusion imaging

More Related Videos

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.7K
Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
07:59

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Published on: December 6, 2018

8.6K

Related Experiment Videos

Last Updated: Feb 3, 2026

Osmotic Drug Delivery to Ischemic Hindlimbs and Perfusion of Vasculature with Microfil for Micro-Computed Tomography Imaging
10:50

Osmotic Drug Delivery to Ischemic Hindlimbs and Perfusion of Vasculature with Microfil for Micro-Computed Tomography Imaging

Published on: June 29, 2013

12.8K
Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
06:56

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis

Published on: September 22, 2023

1.7K
Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors
07:59

Estimating Sediment Denitrification Rates Using Cores and N2O Microsensors

Published on: December 6, 2018

8.6K

Area of Science:

  • Neurology
  • Radiology
  • Medical Imaging

Background:

  • Computed tomographic perfusion (CTP) imaging is crucial for estimating infarction volume in stroke patients.
  • CTP data is often affected by noise, oscillations, and tracer delay, challenging accurate infarction volume estimation.
  • The Bayesian method offers a robust probabilistic approach to mitigate these CTP data artifacts.

Purpose of the Study:

  • To compare the accuracy of ischemic core volume estimation between the Bayesian method and the block-circulant singular value deconvolution (SVD) technique using CTP data.
  • To evaluate the performance of different CTP-derived methods (cerebral blood flow and cerebral blood volume) in conjunction with both deconvolution techniques against MRI-determined infarct volumes.

Main Methods:

  • A cohort of 35 patients with anterior circulation ischemic stroke and successful recanalization were included.
  • CTP data were processed using both Bayesian and circulant SVD deconvolution methods.
  • Ischemic core volumes were calculated using cerebral blood flow (CBF) and cerebral blood volume (CBV) CTP methods and compared with final infarct volumes determined by MRI.

Main Results:

  • The Bayesian CBF method showed the smallest mean difference (-4 mL) and narrowest limits of agreement (-28 to 19 mL) when compared to MRI-derived infarct volumes.
  • Circulant SVD methods, particularly the CBV method, demonstrated significantly larger differences and wider limits of agreement compared to MRI.
  • Bayesian postprocessing generally resulted in more accurate and less variable CTP-estimated ischemic core volumes.

Conclusions:

  • The Bayesian method enhances the accuracy and reduces variability in CTP-based ischemic core volume estimation for stroke patients.
  • While variabilities exist between CTP postprocessing methods, the Bayesian approach demonstrates superior performance over traditional circulant SVD techniques.
  • Accurate estimation of ischemic core volume is critical for guiding treatment decisions in acute ischemic stroke.