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 Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

65.3K
The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
65.3K
The Electromagnetic Spectrum01:24

The Electromagnetic Spectrum

33.7K
Electromagnetic waves are categorized according to their wavelengths and frequencies, giving the electromagnetic spectrum. These waves are classified as radio, infrared, ultraviolet, etc. Radio waves refer to electromagnetic radiation with wavelengths ranging from millimeters to kilometers. Radio waves are commonly used for audio communications (i.e., radios) and typically result from an alternating current in the wires of a broadcast antenna. They cover a broad wavelength range and are used...
33.7K
Inertia Tensor01:24

Inertia Tensor

1.1K
The concept of the inertia tensor is employed to depict the mass distribution and rotational inertia of a solid or rigid object. This tensor is expressed through a three-by-three matrix. Each component within this matrix corresponds to varying moments of inertia about specific axes.
The diagonal components of the inertia tensor matrix represent the moments of inertia concerning the principal axes of the object. These primary axes are defined as the axes where the object experiences the least...
1.1K
Diffusion01:12

Diffusion

218.6K
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...
218.6K
Diffusion01:21

Diffusion

6.4K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.4K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

368
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
368

You might also read

Related Articles

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

Sort by
Same author

Diagnostic accuracy of biparametric vs. multiparametric MRI for clinically significant prostate cancer.

World journal of urology·2025
Same author

Diffusion basis spectrum imaging detects axonal injury in the optic nerve following traumatic brain injury.

Magnetic resonance imaging·2025
Same author

Tubule-Specific Compensatory Responses to Cpt1a Deletion in Aged Mice.

Kidney360·2025
Same author

Differentiating Pathology of Acute Disseminated Encephalomyelitis From Multiple Sclerosis in Children Using Diffusion Magnetic Resonance Biomarkers.

Pediatric neurology·2025
Same author

The Role of the Glymphatic System in Cervical Spondylotic Myelopathy: Insights From Advanced Imaging.

Clinical spine surgery·2025
Same author

An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.

The Journal of urology·2025
Same journal

Injury Severity Influences Long-Term Cognitive Control in Pediatric "Mild" Traumatic Brain Injury.

Human brain mapping·2026
Same journal

Early Adulthood Signatures of Motherhood in Brain Aging.

Human brain mapping·2026
Same journal

Neural Markers of Interocular Grouping During Binocular Rivalry With MEG.

Human brain mapping·2026
Same journal

Neural Correlates of Explicit Outcome Expectation Effects: An Activation Likelihood Estimation Meta-Analysis.

Human brain mapping·2026
Same journal

Benchmarking fMRI Denoising Pipelines.

Human brain mapping·2026
Same journal

Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.

Human brain mapping·2026
See all related articles

Related Experiment Video

Updated: Feb 3, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

29.3K

Accelerated Diffusion Basis Spectrum Imaging With Tensor Computations.

Kainen L Utt1, Jacob S Blum1, Donsub Rim2

  • 1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA.

Human Brain Mapping
|February 2, 2026
PubMed
Summary
This summary is machine-generated.

This study presents a new framework for faster diffusion-weighted imaging (DWI) data processing using entire-image modeling. The method improves computational speed and signal-to-noise ratio for diffusion parameter estimation in neuroimaging.

Keywords:
diffusion MRImultitensor estimationself‐diffusionsignal processing

More Related Videos

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.2K
Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
07:00

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

9.4K

Related Experiment Videos

Last Updated: Feb 3, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

29.3K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

23.2K
Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
07:00

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Published on: May 7, 2019

9.4K

Area of Science:

  • Neuroimaging
  • Medical Physics
  • Computational Biology

Background:

  • Diffusion-weighted imaging (DWI) is crucial for understanding brain microstructure.
  • Current DWI processing methods can be computationally intensive and limited in accuracy.
  • Accurate estimation of diffusion parameters is essential for reliable neuroimaging analysis.

Purpose of the Study:

  • To introduce an advanced framework for accelerated processing of DWI data.
  • To optimize the estimation of diffusion parameters using an entire-image modeling approach.
  • To enhance computational speed and signal-to-noise ratio (SNR) in diffusion parameter mapping.

Main Methods:

  • Utilized an entire-image modeling approach to map input diffusion data to predicted signals.
  • Employed a stochastic gradient descent optimizer (Adam) for parameter value estimation.
  • Applied the framework to diffusion basis spectrum imaging (DBSI) using in vivo human and ex vivo mouse brain DWI data.

Main Results:

  • Demonstrated significant improvements in computational speed compared to standard DBSI.
  • Achieved higher signal-to-noise ratio (SNR) in estimated parameter maps.
  • Validated the framework's applicability to various diffusion signal representations.

Conclusions:

  • The developed framework enables rapid and reliable signal partitioning in complex microstructural environments.
  • This approach shows significant potential for advancing future neuroimaging research.
  • The method offers a faster and more robust alternative for DWI data analysis.