Literature DB >> 26597832

A model-based reconstruction technique for fast dynamic T1 mapping.

Johannes Tran-Gia1, Sotirios Bisdas2, Herbert Köstler3, Uwe Klose4.   

Abstract

PURPOSE: To present a technique for dynamic T1 mapping.
MATERIALS AND METHODS: A recently proposed model-based reconstruction entitled IR-MAP allows T1 mapping of a single slice from a single radial inversion recovery Look-Locker FLASH acquisition. To enable dynamic T1 mapping, multiple of these acquisitions are consecutively performed, each followed by a waiting period of 3s for relaxation. Next, IR-MAP is used to reconstruct an individual T1 map for each of these acquisitions. Finally, T1 errors caused by insufficient relaxation between subsequent IR pulses are iteratively corrected.
RESULTS: The functionality of the proposed setup was validated in a phantom and in seven healthy volunteers. Systematic deviations between subsequent T1 maps originating from insufficient relaxation periods were effectively corrected. Additionally, the approach was successfully applied to monitor the T1 dynamic in a patient with primary lymphoma after the intravenous injection of contrast agent.
CONCLUSION: The proposed setup enables dynamic T1 mapping of a single slice with a spatial resolution of 1.6 mm × 1.6 mm × 3 mm and a temporal resolution of one parameter map every 9 s. It therefore represents a new opportunity to track changes in T1 over time, as it is desirable in many applications such as dynamic contrast-enhanced MRI.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Dynamic T(1) mapping; Dynamic contrast-enhanced MRI; Dynamic parameter mapping; Inversion recovery Look-Locker T(1) mapping; Model-based reconstruction

Mesh:

Substances:

Year:  2015        PMID: 26597832     DOI: 10.1016/j.mri.2015.10.016

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  4 in total

1.  Single-breath-hold abdominal [Formula: see text]  mapping using 3D Cartesian Look-Locker with spatiotemporal sparsity constraints.

Authors:  Felix Lugauer; Jens Wetzl; Christoph Forman; Manuel Schneider; Berthold Kiefer; Joachim Hornegger; Dominik Nickel; Andreas Maier
Journal:  MAGMA       Date:  2018-01-25       Impact factor: 2.310

Review 2.  Physics-based reconstruction methods for magnetic resonance imaging.

Authors:  Xiaoqing Wang; Zhengguo Tan; Nick Scholand; Volkert Roeloffs; Martin Uecker
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2021-05-10       Impact factor: 4.226

3.  Improvement of Fast Model-Based Acceleration of Parameter Look-Locker T1 Mapping.

Authors:  Michał Staniszewski; Uwe Klose
Journal:  Sensors (Basel)       Date:  2019-12-05       Impact factor: 3.576

4.  A review and experimental evaluation of deep learning methods for MRI reconstruction.

Authors:  Arghya Pal; Yogesh Rathi
Journal:  J Mach Learn Biomed Imaging       Date:  2022-03-11
  4 in total

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