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

Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET

You might also read

Related Articles

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

Sort by
Same author

Arrhythmic Myocarditis and Prevention of Sudden Cardiac Death: Current Evidence, Risk Stratification, and Management.

Hellenic journal of cardiology : HJC = Hellenike kardiologike epitheorese·2026
Same author

Stroke and its consequences: protocol and pilot data of the observational Berlin Long-term Observation of Vascular Events (BeLOVE) stroke stratum.

BMJ neurology open·2026
Same author

Opportunistic screening for incident cardiometabolic disease in metabolically healthy non-obese individuals: a prospective cohort study.

Cardiovascular diabetology·2026
Same author

Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026
Same author

[Inflammatory Heart Disease: The Role of Multimodality Cardiac Imaging in Myocarditis and Pericarditis].

Therapeutische Umschau. Revue therapeutique·2026
Same author

High resolution, 3D isotropic late gadolinium enhanced imaging for the quantification of left atrial fibrosis and post-ablation scarring.

European heart journal. Imaging methods and practice·2026

Related Experiment Video

Updated: Jun 30, 2026

Assessment of Cardiac Function and Myocardial Morphology Using Small Animal Look-locker Inversion Recovery SALLI MRI in Rats
08:41

Assessment of Cardiac Function and Myocardial Morphology Using Small Animal Look-locker Inversion Recovery SALLI MRI in Rats

Published on: July 19, 2013

13.1K

Zero-Shot Unsupervised Motion Estimation for Motion-Corrected Cardiac T1 Mapping.

Mara Guastini, Jeanette Schulz-Menger, Tobias Schaeffter

    IEEE Transactions on Bio-Medical Engineering
    |October 22, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep learning method for fast cardiac quantitative MRI (qMRI) by correcting motion in continuously acquired data. The novel approach significantly improves T1 mapping accuracy and image sharpness, reducing scan times.

    More Related Videos

    Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
    06:56

    Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

    Published on: January 7, 2021

    2.8K
    Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
    12:54

    Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

    Published on: October 2, 2021

    3.6K

    Related Experiment Videos

    Last Updated: Jun 30, 2026

    Assessment of Cardiac Function and Myocardial Morphology Using Small Animal Look-locker Inversion Recovery SALLI MRI in Rats
    08:41

    Assessment of Cardiac Function and Myocardial Morphology Using Small Animal Look-locker Inversion Recovery SALLI MRI in Rats

    Published on: July 19, 2013

    13.1K
    Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
    06:56

    Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

    Published on: January 7, 2021

    2.8K
    Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
    12:54

    Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo

    Published on: October 2, 2021

    3.6K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence in Medicine
    • Cardiovascular Research

    Background:

    • Cardiac quantitative MRI (qMRI) is vital for diagnosing conditions like myocardial fibrosis.
    • Cardiac motion during MRI acquisition necessitates synchronization, leading to prolonged scan times.
    • Motion artifacts are a significant challenge in cardiac qMRI, impacting diagnostic accuracy.

    Purpose of the Study:

    • To develop a novel deep learning-based image registration method for cardiac qMRI.
    • To enable non-rigid motion correction for continuously acquired data over multiple cardiac cycles.
    • To reduce scan times in cardiac qMRI while maintaining or improving image quality.

    Main Methods:

    • A zero-shot, U-Net-based deep learning architecture for non-rigid motion estimation.
    • Utilizes the physical qMRI signal model and exploits motion smoothness for accurate estimation.
    • Robust to undersampling artifacts, enabling motion estimation from sparsely sampled k-space data.

    Main Results:

    • Achieved a 61.64% improvement in T1 accuracy on numerical simulations.
    • Demonstrated a 45.13% improvement in T1 map sharpness on in-vivo data.
    • Improved temporal image alignment of motion-corrected dynamics by an average of 11.78%.

    Conclusions:

    • The method provides accurate non-rigid motion correction for undersampled cardiac qMRI data.
    • Enables faster data acquisition by handling continuously acquired data.
    • The scan-specific optimization allows easy adaptation to various cardiac qMRI techniques without large training datasets.