Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Magnetic Resonance Imaging

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...

You might also read

Related Articles

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

Sort by

Same author

Respiratory Motion Management in Abdominal MRI: Revisiting the Gap Between Technical Advances and Clinical Translation.

Magnetic resonance in medicine·2026

Same author

Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI.

Physics in medicine and biology·2026

Same author

Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.

Nature aging·2026

Same author

On the Utility of Foundation Models for Fast MRI: Vision-Language-Guided Image Reconstruction.

Magnetic resonance in medicine·2026

Same author

Accessible cartilage tumor malignancy prediction via vision-language foundation model adaptation.

Skeletal radiology·2026

Same author

Pole-To-Pole 3D Radial Trajectory Designs Improve Image Quality and Quantitative Parametric Mapping in the Brain and Heart.

Magnetic resonance in medicine·2026

Search research articles

Related Experiment Video

Updated: Jul 31, 2025

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

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

Published on: July 19, 2013

Rapid 3D T₁ mapping using deep learning-assisted Look-Locker inversion recovery MRI.

Haoyang Pei^1,2,3, Ding Xia¹, Xiang Xu¹

¹BioMedical Engineering and Imaging Institute (BMEII) and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Magnetic Resonance in Medicine

|May 1, 2023

Summary

This study introduces a deep learning method for faster 3D Look-Locker inversion recovery (LLIR) T1 mapping. The novel approach eliminates the need for a delay time (TD) between repetitions, significantly reducing scan duration without compromising accuracy.

Keywords:

Look-Locker MP-GRASP MRI T1 apping deep learning inversion recovery

More Related Videos

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: Jul 31, 2025

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

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

Published on: July 19, 2013

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Imaging
Artificial Intelligence in Medicine

Background:

Conventional 3D Look-Locker inversion recovery (LLIR) T1 mapping necessitates lengthy data acquisition with a delay time (TD) between repetitions to ensure B1 robustness, extending overall scan time.
The delay time (TD) is crucial for accurate T1 fitting in traditional LLIR T1 mapping but contributes significantly to prolonged scan durations.

Purpose of the Study:

To develop a novel deep learning-assisted 3D LLIR MRI approach for rapid T1 mapping that eliminates the requirement for a delay time (TD).
To accelerate 3D T1 mapping by leveraging deep learning to bypass the need for TD, thereby reducing scan time without sacrificing accuracy.

Main Methods:

Implemented a deep learning model trained on GraspT1 datasets with a 6s delay time (GraspT1-TD6), incorporating additional anatomical images.
The trained network was then applied to GraspT1 datasets acquired without a delay time (GraspT1-TD0) for T1 estimation.
Evaluated the robustness of the deep learning approach against variations in spatial resolution, imaging orientation, and scanner platform.

Main Results:

Deep learning-based T1 estimation using GraspT1-TD0 demonstrated high accuracy when compared to reference values.
The inclusion of supplementary anatomical images in the training dataset enhanced the precision of T1 estimation.
The developed technique exhibited robustness against minor alterations in spatial resolution, imaging orientation, and scanner hardware.

Conclusions:

The proposed deep learning-assisted 3D LLIR MRI method effectively removes the need for a delay time (TD) in T1 mapping.
This novel approach achieves accurate T1 estimation, representing a significant advancement in efficient and robust 3D LLIR T1 mapping.
The findings highlight the potential of deep learning to optimize MRI acquisition protocols and improve patient throughput.