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Related Experiment Video

Updated: Feb 28, 2026

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

Model-based T₁ mapping with sparsity constraints using single-shot inversion-recovery radial FLASH.

Xiaoqing Wang¹, Volkert Roeloffs¹, Jakob Klosowski¹

¹Biomedizinische NMR Forschungs GmbH am Max-Planck-Institut für biophysikalische Chemie, Göttingen, Germany.

Magnetic Resonance in Medicine

|June 13, 2017

Summary

This study introduces a model-based reconstruction for fast T1 mapping, improving accuracy and precision in MRI scans. The technique offers robust performance for clinical applications.

Keywords:

Look-Locker T1 mapping; parallel imaging model-based reconstruction sparsity constraint

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Last Updated: Feb 28, 2026

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

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Published on: July 19, 2013

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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 Physics
Biomedical Engineering

Background:

Accurate T1 mapping is crucial for quantitative MRI.
Existing single-shot T1 mapping methods face challenges in spatial resolution, accuracy, and precision.
Inversion-recovery (IR) fast low-angle shot (FLASH) with radial encoding offers potential for accelerated imaging.

Purpose of the Study:

To develop a model-based reconstruction technique for single-shot T1 mapping.
Achieve high spatial resolution, accuracy, and precision in T1 mapping.
Utilize IR FLASH acquisition with radial encoding for improved performance.

Main Methods:

A model-based reconstruction approach was developed.
Joint estimation of model parameters including equilibrium magnetization, steady-state magnetization, 1/T1*, and coil sensitivities.
Employed joint sparsity constraints and an iteratively regularized Gauss-Newton method.
Validated using numerical and experimental phantoms, as well as in vivo human brain and liver studies at 3T.

Main Results:

The proposed method demonstrated improved robustness against phase errors and numerical precision compared to previous techniques.
Achieved high accuracy and precision in both phantom and in vivo studies.
Outperformed real-time MRI with pixel-wise fitting and model-based approaches with pre-determined coil sensitivities.

Conclusions:

The comprehensive model-based reconstruction with L1 regularization enables rapid and robust T1 mapping.
The technique offers high accuracy and precision, suitable for clinical applications.
Warrants accelerated computing and online implementation for future clinical trials.