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

Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

You might also read

Related Articles

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

Sort by
Same author

TractoMFormer: A novel streamline-level tractography analysis framework for group classification using deep graph and multi-scale ViT.

NeuroImage·2026
Same author

Study of sex differences in the whole brain white matter using diffusion MRI tractography and suprathreshold fiber cluster statistics.

NeuroImage. Clinical·2026
Same author

Reproducibility and Reliability of Free-Water-Corrected Diffusion Tensor Imaging of the Brain: Revisited.

Human brain mapping·2026
Same author

Mapping brain tumor microstructure: A multimodal study of diffusion MRI, intraoperative fluorescence, and neuropathology in navigated biopsies.

NeuroImage. Clinical·2025
Same author

Effect of a consistent reconstruction algorithm on inter-scanner reproducibility in diffusion MRI.

Medical physics·2025
Same author

Study of Sex Differences in the Whole Brain White Matter Using Diffusion MRI Tractography and Suprathreshold Fiber Cluster Statistics.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: May 28, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Probabilistic ODF estimation from reduced HARDI data with sparse regularization.

Antonio Tristán-Vega1, Carl-Fredrik Westin

  • 1Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Boston, USA. atriveg@bwh.harvard.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 15, 2011
PubMed
Summary

High Angular Resolution Diffusion Imaging (HARDI) can now be computed faster using fewer measurements. This new method represents the Orientation Distribution Function (ODF) using Spherical Wavelets for efficient processing.

More Related Videos

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Related Experiment Videos

Last Updated: May 28, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
06:50

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Signal Processing

Background:

  • Diffusion Tensor Imaging (DTI) provides limited angular information.
  • High Angular Resolution Diffusion Imaging (HARDI) offers richer data but requires extensive measurements, limiting clinical application.
  • The Orientation Distribution Function (ODF) is crucial for detailed microstructural analysis in diffusion MRI.

Purpose of the Study:

  • To develop a computationally efficient method for HARDI data acquisition and processing.
  • To enable accurate reconstruction of the probabilistic ODF from a reduced dataset.
  • To overcome the data acquisition bottleneck in HARDI.

Main Methods:

  • Representing the probabilistic ODF in the Spherical Wavelet (SW) domain, exploiting its inherent sparsity.
  • Formulating the ODF estimation as an inverse problem with sparsity regularization.
  • Utilizing a reduced subset of HARDI measurements (approx. 4x less than standard).

Main Results:

  • Successful fast computation of positive, unit-mass, probabilistic ODFs.
  • Demonstrated accuracy using both synthetic diffusion signals and real HARDI data.
  • Achieved reliable ODF reconstruction from as few as 14-16 samples.

Conclusions:

  • Spherical Wavelet-based ODF representation significantly reduces HARDI data requirements.
  • The proposed method enhances the practical feasibility of HARDI in clinical settings.
  • This approach facilitates faster and more efficient diffusion MRI microstructure analysis.