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

Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

6.7K
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...
6.7K
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
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
Elements and Compounds01:27

Elements and Compounds

103.3K
Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond.
Elements
Elements are classified as atomic or molecular based on the nature of their basic units. They are unique forms of matter with specific chemical and physical properties that cannot break down into smaller substances by ordinary chemical reactions. There...
103.3K
Diffusion01:12

Diffusion

216.8K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
216.8K
Elements: Chemical Symbols and Isotopes02:31

Elements: Chemical Symbols and Isotopes

125.3K
A chemical symbol is an abbreviation used to indicate an element or an atom of an element. For example, the symbol for mercury is Hg. The same symbol is used to indicate one atom of mercury (microscopic domain) or to label a container of many atoms of the element mercury (macroscopic domain).
Some symbols are derived from the common English name of the element; others are abbreviations of the name in another language — Latin, Greek or German. For example, the symbol for aluminum (common name)...
125.3K

You might also read

Related Articles

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

Sort by
Same author

Application of Epicardial Fat Volume for Identifying Hemodynamically Significant Coronary Artery Disease: A Retrospective Study.

International journal of general medicine·2026
Same author

Adaptive distribution-aware transformer for multi-scale visual representation learning on imbalanced and low-resolution data.

Medical image analysis·2026
Same author

Staging-dependent dysbiosis of plaque microbiota in early childhood caries.

Journal of dentistry·2026
Same author

Screening of aggregation properties of cyclic peptides by protein nanopores.

Analytical methods : advancing methods and applications·2026
Same author

Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.

Nature cardiovascular research·2026
Same author

From pixels to polygons: A survey of deep learning approaches for medical image-to-mesh reconstruction.

Medical image analysis·2026

Related Experiment Video

Updated: Jan 22, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K

Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography.

Wenqi Lu1, Jinming Duan1, David Orive-Miguel2,3

  • 1School of Computer Science, University of Birmingham, UK.

Biomedical Optics Express
|July 2, 2019
PubMed
Summary

Total variation (TV) regularization improves diffuse optical tomography (DOT) image reconstruction. Novel methods handle complex DOT challenges, accurately reconstructing images without over-smoothing or over-sparsifying.

More Related Videos

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage
07:57

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Published on: April 23, 2017

6.6K
Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques
07:16

Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques

Published on: October 20, 2023

1.8K

Related Experiment Videos

Last Updated: Jan 22, 2026

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K
An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage
07:57

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Published on: April 23, 2017

6.6K
Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques
07:16

Author Spotlight: Development of a Novel Finite Element Analysis Model for Improved Orthognathic Surgical Techniques

Published on: October 20, 2023

1.8K

Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Total variation (TV) regularization is effective for imaging but challenging for diffuse optical tomography (DOT) due to complex geometries and non-linear, non-differentiable terms.
  • Existing methods struggle with DOT's inverse problem, leading to issues like over-smoothing or over-sparsifying in reconstructions.

Purpose of the Study:

  • To develop and evaluate novel approaches for applying TV regularization to DOT image reconstruction.
  • To overcome limitations of existing methods in handling complex DOT geometries and non-linear/non-differentiable terms.

Main Methods:

  • Defined discrete differential operators for TV regularization using finite element (FEM) and graph representations.
  • Developed an alternating direction method of multipliers (ADMM) algorithm for the non-linear, non-differentiable minimization problem.
  • Investigated isotropic and anisotropic TV variants and compared FEM and graph-based implementations.

Main Results:

  • Both FEM and graph-based TV regularization accurately reconstructed sparse and non-sparse distributions in simulated and phantom data.
  • Achieved reconstructions without the over-smoothing of Tikhonov or over-sparsifying of L1 regularization.
  • Graph-based methods excelled in low-resolution meshes, while FEM methods were more accurate in high-resolution meshes.

Conclusions:

  • The developed TV regularization approaches effectively address challenges in DOT image reconstruction.
  • Both FEM and graph-based methods offer viable alternatives to traditional regularization techniques for DOT.
  • The choice between FEM and graph-based TV regularization depends on mesh resolution for optimal DOT image reconstruction.