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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

You might also read

Related Articles

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

Sort by
Same author

Multicenter Automated Central Vein Sign Detection Performs as Well as Manual Assessment for the Diagnosis of Multiple Sclerosis.

AJNR. American journal of neuroradiology·2024
Same author

Phenotyping Down syndrome: discovery and predictive modelling with electronic medical records.

Journal of intellectual disability research : JIDR·2024
Same author

Exploratory Multisite MR Spectroscopic Imaging Shows White Matter Neuroaxonal Loss Associated with Complications of Type 1 Diabetes in Children.

AJNR. American journal of neuroradiology·2023
Same author

MTT and Blood-Brain Barrier Disruption within Asymptomatic Vascular WM Lesions.

AJNR. American journal of neuroradiology·2021
Same author

Intensity warping for multisite MRI harmonization.

NeuroImage·2020
Same author

Evaluation of cell transplant-mediated attenuation of diffuse injury in experimental autoimmune encephalomyelitis using onVDMP CEST MRI.

Experimental neurology·2020
Same journal

LEARNABLE HIERARCHICAL VISUAL CONTEXTS FOR TUMOR SEGMENTATION IN COMPUTED TOMOGRAPHY IMAGES.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

DUAL CROSS-ATTENTION SIAMESE TRANSFORMER FOR RECTAL TUMOR REGROWTH ASSESSMENT IN WATCH-AND-WAIT ENDOSCOPY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

LUMEN: LONGITUDINAL MULTI-MODAL RADIOLOGY MODEL FOR PROGNOSIS AND DIAGNOSIS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

OVERVIEW OF THE CXR-LT 2026 CHALLENGE: MULTI-CENTER LONG-TAILED AND ZERO SHOT CHEST X-RAY CLASSIFICATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

CROSS-MODAL FINE-TUNING OF 3D CONVOLUTIONAL FOUNDATION MODELS FOR ADHD CLASSIFICATION WITH LOW-RANK ADAPTATION.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same journal

AN IN SILICO STUDY OF LOW-INTENSITY FOCUSED ULTRASOUND DISPLACEMENT MAPPING WITH A 220 KHZ CLINICAL PHASED-ARRAY TRANSDUCER.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

ROBUST MAXIMUM LIKELIHOOD ESTIMATION IN Q-SPACE MRI.

B A Landman1, J A D Farrell, S A Smith

  • 1Johns Hopkins University School of Medicine and Kennedy Krieger Institute Biomedical Engineering, Biophysics, Neurology, Radiology, and the F.M. Kirby Center Baltimore, Maryland, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Q-space imaging estimates molecular diffusion probability density functions (PDFs) without Gaussian assumptions. The new QEMRL method improves PDF reliability and tissue contrast in diffusion MRI, enabling higher resolution imaging.

More Related Videos

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Related Experiment Videos

Last Updated: Jun 12, 2026

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
09:30

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
08:51

Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla

Published on: February 19, 2021

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Neuroscience

Background:

  • Diffusion weighted magnetic resonance imaging (DW-MRI) is crucial for studying tissue microstructure.
  • Estimating molecular diffusion probability density functions (PDFs) offers a more comprehensive understanding of diffusion than traditional methods.
  • Current PDF estimation techniques often rely on Gaussian distribution assumptions and are sensitive to imaging artifacts.

Purpose of the Study:

  • To introduce a novel, robust M-estimator for diffusion PDF estimation in Q-space imaging.
  • To address the limitations of existing methods by mitigating the impact of imaging artifacts.
  • To enhance the reliability and tissue contrast of diffusion PDF estimations.

Main Methods:

  • Development of Q-space Estimation by Maximizing Rician Likelihood (QEMRL), a maximum likelihood-based M-estimator.
  • Modeling of diffusion PDFs using constrained Gaussian mixtures.
  • Validation through simulations and in vivo human spinal cord imaging.

Main Results:

  • QEMRL demonstrated improved reliability of estimated diffusion PDFs compared to prior approaches.
  • The method significantly increased tissue contrast in diffusion MRI.
  • Robust likelihood measures effectively mitigated the impact of imaging artifacts on PDF estimation.

Conclusions:

  • QEMRL provides a robust and reliable method for estimating diffusion PDFs in Q-space imaging.
  • The technique enhances tissue contrast and allows for more detailed exploration of PDF properties.
  • QEMRL holds potential for enabling higher spatial resolution in diffusion MRI acquisitions.