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

5.6K
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...
5.6K

You might also read

Related Articles

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

Sort by
Same author

DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue.

Magnetic resonance in medicine·2025
Same author

Multimetric MRI Captures Early Response and Acquired Resistance of Pancreatic Cancer to KRAS Inhibitor Therapy.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.

Magnetic resonance in medicine·2024
Same author

Cranial bone imaging using ultrashort echo-time bone-selective MRI as an alternative to gradient-echo based "black-bone" techniques.

Magma (New York, N.Y.)·2023
Same author

Learning ADC maps from accelerated radial k-space diffusion-weighted MRI in mice using a deep CNN-transformer model.

Magnetic resonance in medicine·2023
Same author

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

Bone·2023

Related Experiment Video

Updated: Aug 28, 2025

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.2K

Dynamic Contrast-Enhanced MRI in the Abdomen of Mice with High Temporal and Spatial Resolution Using Stack-of-Stars

Stephen Pickup1, Miguel Romanello1, Mamta Gupta1

  • 1Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.

Tomography (Ann Arbor, Mich.)
|September 22, 2022
PubMed
Summary

This study presents an optimized dynamic contrast-enhanced (DCE) MRI protocol for abdominal cancer mouse models. The novel method overcomes motion and RF inhomogeneity challenges, enabling robust perfusion parameter estimation in pancreatic tumors.

Keywords:
dynamic contrast enhancedperfusionradialrespiratory motionstack of stars

More Related Videos

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
09:08

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

Published on: February 27, 2011

15.9K
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

10.4K

Related Experiment Videos

Last Updated: Aug 28, 2025

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model
06:24

Dynamic Contrast Enhanced Magnetic Resonance Imaging of an Orthotopic Pancreatic Cancer Mouse Model

Published on: April 18, 2015

15.2K
Multiple-mouse Neuroanatomical Magnetic Resonance Imaging
09:08

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

Published on: February 27, 2011

15.9K
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

10.4K

Area of Science:

  • Biomedical Imaging
  • Radiology
  • Oncology Research

Background:

  • Quantitative dynamic contrast-enhanced (DCE) MRI in abdominal cancer mouse models faces challenges including RF inhomogeneity, respiratory motion artifacts, and the need for high spatial and temporal resolution.
  • Existing DCE MRI protocols often struggle to provide accurate and reliable data in small animal models due to these technical limitations.

Purpose of the Study:

  • To develop and validate an optimized DCE MRI protocol for quantitative imaging in abdominal cancer mouse models.
  • To address challenges of motion artifacts, RF inhomogeneity, and resolution requirements in preclinical abdominal cancer imaging.

Main Methods:

  • A novel DCE MRI protocol incorporating actual flip-angle B1 mapping, variable flip-angle T1 mapping, and a motion-robust radial acquisition with k-space weighted image contrast (KWIC) reconstruction.
  • Utilized spoiled radial imaging with stack-of-stars (SoS) sampling and golden-angle increments for motion artifact minimization and view-sharing reconstruction.
  • Demonstrated the protocol in a genetically engineered mouse model of pancreatic ductal adenocarcinoma.

Main Results:

  • The optimized protocol produced artifact-free DCE MRI images with good signal-to-noise ratio (SNR) in mouse models.
  • Successfully minimized artifacts from respiratory motion using the radial acquisition and KWIC reconstruction strategy.
  • Yielded robust estimation of perfusion parameters, with mean values of K = 0.23 ± 0.14 min⁻¹ and v = 0.31 ± 0.17 for pancreatic tumors (n=22).

Conclusions:

  • The developed DCE MRI protocol effectively overcomes common imaging challenges in abdominal cancer mouse models.
  • This optimized method enables robust and accurate quantitative perfusion parameter estimation, crucial for preclinical cancer research.
  • The stack-of-stars sampled DCE MRI approach provides artifact-free imaging and reliable DCE parameter quantification.