Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Assessment of Diffusion and Perfusion

Assessment of Diffusion and Perfusion

Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

You might also read

Related Articles

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

Sort by

Same author

Enantioselective Radical Ring-Opening Cyanation of Oxime Esters by Dual Photoredox and Copper Catalysis.

Organic letters·2019

Same author

ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING.

Proceedings. IEEE International Symposium on Biomedical Imaging·2019

Same author

Technical note: Development and application of KASP assays for rapid screening of 8 genetic defects in Holstein cattle.

Journal of dairy science·2019

Same author

Sesquiterpenes and diterpenes from Euphorbia thymifolia.

Fitoterapia·2019

Same author

Glechomanamides A-C, Germacrane Sesquiterpenoids with an Unusual Δ<sup>8</sup>-7,12-Lactam Moiety from <i>Salvia scapiformis</i> and Their Antiangiogenic Activity.

Journal of natural products·2019

Same author

Parameter optimization framework on wave gradients of Wave-CAIPI imaging.

Magnetic resonance in medicine·2019

Search research articles

Related Experiment Video

Updated: Oct 7, 2025

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

Inferring CT perfusion parameters and uncertainties using a Bayesian approach.

Tao Sun¹, Roger Fulton^2,3, Zhanli Hu¹

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

Quantitative Imaging in Medicine and Surgery

|January 7, 2022

Summary

This summary is machine-generated.

A new Bayesian inference algorithm improves computed tomography perfusion imaging by providing accurate cerebral blood flow (CBF) and mean transit time (MTT) estimates with uncertainty quantification. This method reduces bias and assesses scan condition impacts for better stroke assessment.

Keywords:

Bayesian inference Stroke computed tomography perfusion (CT perfusion)

More Related Videos

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Published on: May 30, 2011

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

Related Experiment Videos

Last Updated: Oct 7, 2025

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Published on: August 9, 2024

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

Published on: May 30, 2011

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

Area of Science:

Medical Imaging
Radiology
Computational Neuroscience

Background:

Computed tomography (CT) perfusion imaging is vital for acute stroke assessment.
Perfusion parameters like cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) aid stroke diagnosis.
Conventional singular value decomposition (SVD) methods for parameter calculation have known limitations.

Purpose of the Study:

To develop a Bayesian inference algorithm for deriving CT perfusion parameters and their uncertainties.
To address limitations of conventional methods in calculating perfusion parameters.

Main Methods:

A Bayesian inference algorithm using variational techniques and expectation-maximization was developed.
The algorithm estimates perfusion parameters and their probability distributions (mean and variance).
Uncertainty is quantified using the coefficient of variation, evaluated via simulations and patient studies.

Main Results:

Simulations demonstrated significantly reduced bias compared to conventional methods.
The algorithm assessed the impact of scan conditions (frame interval, truncation, motion) on parameter estimates.
Patient studies showed accurate CBF and MTT maps for lesion identification and feasibility with motion-corrected data.

Conclusions:

The proposed Bayesian method enhances confidence in parameter estimation and aids scan protocol design.
Further clinical evaluation is recommended to fully validate the method's utility in stroke diagnosis.