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      Whole‐heart T 1 mapping using a 2D fat image navigator for respiratory motion compensation

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          Abstract

          Purpose

          To combine a 3D saturation‐recovery‐based myocardial T 1 mapping (3D SASHA) sequence with a 2D image navigator with fat excitation (fat‐iNAV) to allow 3D T 1 maps with 100% respiratory scan efficiency and predictable scan time.

          Methods

          Data from T 1 phantom and 10 subjects were acquired at 1.5T. For respiratory motion compensation, a 2D fat‐iNAV was acquired before each 3D SASHA k‐space segment to correct for 2D translational motion in a beat‐to‐beat fashion. The effect of the fat‐iNAV on the 3D SASHA T1 estimation was evaluated on the T 1 phantom. For 3 representative subjects, the proposed free‐breathing 3D SASHA with fat‐iNAV was compared to the original implementation with the diaphragmatic navigator. The 3D SASHA with fat‐iNAV was compared to the breath‐hold 2D SASHA sequence in terms of accuracy and precision.

          Results

          In the phantom study, the Bland‐Altman plot shows that the 2D fat‐iNAVs does not affect the T 1 quantification of the 3D SASHA acquisition (0 ± 12.5 ms). For the in vivo study, the 2D fat‐iNAV permits to estimate the respiratory motion of the heart, while allowing for 100% scan efficiency, improving the precision of the T 1 measurement compared to non‐motion‐corrected 3D SASHA. However, the image quality achieved with the proposed 3D SASHA with fat‐iNAV is lower compared to the original implementation, with reduced delineation of the myocardial borders and papillary muscles.

          Conclusions

          We demonstrate the feasibility to combine the 3D SASHA T 1 mapping imaging sequence with a 2D fat‐iNAV for respiratory motion compensation, allowing 100% respiratory scan efficiency and predictable scan time.

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          Most cited references27

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          Compressed sensing reconstruction for magnetic resonance parameter mapping.

          Compressed sensing (CS) holds considerable promise to accelerate the data acquisition in magnetic resonance imaging by exploiting signal sparsity. Prior knowledge about the signal can be exploited in some applications to choose an appropriate sparsifying transform. This work presents a CS reconstruction for magnetic resonance (MR) parameter mapping, which applies an overcomplete dictionary, learned from the data model to sparsify the signal. The approach is presented and evaluated in simulations and in in vivo T(1) and T(2) mapping experiments in the brain. Accurate T(1) and T(2) maps are obtained from highly reduced data. This model-based reconstruction could also be applied to other MR parameter mapping applications like diffusion and perfusion imaging.
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            Whole-heart coronary MR angiography with 2D self-navigated image reconstruction.

            Several self-navigation techniques have been proposed to improve respiratory motion compensation in coronary MR angiography. In this work, we implemented a 2D self-navigation method by using the startup profiles of a whole-heart balanced Steady-state free precession sequence, which are primarily used to catalyze the magnetization towards the steady-state. To create 2D self-navigation images (2DSN), we added phase encoding gradients to the startup profiles. With this approach we calculated foot-head and left-right motion and performed retrospective translational motion correction. The 2DSN images were reconstructed from 10 startup profiles acquired at the beginning of each shot. Nine healthy subjects were scanned, and the proposed method was compared to a 1D self-navigation (1DSN) method with foot-head correction only. Foot-head correction was also performed with the diaphragmatic 1D pencil beam navigator (1Dnav) using a tracking factor of 0.6. 2DSN shows improved motion correction compared to 1DSN and 1Dnav for all coronary arteries and all subjects for the investigated diaphragmatic gating window of 10 mm. The visualized vessel length of the right coronary artery could be significantly improved with a multiple targeted 2D self-navigation approach, compared to 2DSN method. Copyright © 2011 Wiley Periodicals, Inc.
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              Highly efficient respiratory motion compensated free-breathing coronary MRA using golden-step Cartesian acquisition.

              To develop an efficient 3D affine respiratory motion compensation framework for Cartesian whole-heart coronary magnetic resonance angiography (MRA).
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                Author and article information

                Contributors
                giovanna.nordio@kcl.ac.uk
                Journal
                Magn Reson Med
                Magn Reson Med
                10.1002/(ISSN)1522-2594
                MRM
                Magnetic Resonance in Medicine
                John Wiley and Sons Inc. (Hoboken )
                0740-3194
                1522-2594
                09 August 2019
                January 2020
                : 83
                : 1 ( doiID: 10.1002/mrm.v83.1 )
                : 178-187
                Affiliations
                [ 1 ] School of Biomedical Engineering and Imaging Sciences King's College of London London United Kingdom
                [ 2 ] Philips Healthcare Guildford United Kingdom
                [ 3 ] Escuela de Ingeniería Pontificia Universidad Católica de Chile Santiago Chile
                Author notes
                [*] [* ] Correspondence

                Giovanna Nordio, School of Biomedical Engineering and Imaging Sciences, King's College London, 3rd Floor, Lambeth Wing, St Thomas' Hospital, London SE1 7EH, United Kingdom.

                Email: giovanna.nordio@ 123456kcl.ac.uk

                Author information
                https://orcid.org/0000-0002-7835-2992
                https://orcid.org/0000-0002-1606-9550
                https://orcid.org/0000-0002-2845-8617
                https://orcid.org/0000-0001-6142-3005
                Article
                MRM27919
                10.1002/mrm.27919
                6791811
                31400054
                b3efc1b6-ab0b-4435-b55b-2888c9f36bd5
                © 2019 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 September 2018
                : 01 July 2019
                : 05 July 2019
                Page count
                Figures: 5, Tables: 0, Pages: 10, Words: 11669
                Funding
                Funded by: King's College London & Imperial College London EPSRC Centre for Doctoral Training in Medical Imaging
                Award ID: EP/L015226/1
                Funded by: EPSRC , open-funder-registry 10.13039/501100000266;
                Award ID: EP/P001009/1
                Award ID: EP/P007619
                Funded by: Wellcome EPSRC Centre for Medical Engineering
                Award ID: NS/A000049/1
                Award ID: WT 203148/Z/16/Z
                Categories
                Note
                Notes—Imaging Methodology
                Custom metadata
                2.0
                January 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.1 mode:remove_FC converted:13.11.2019

                Radiology & Imaging
                fat image navigator,myocardial t1 mapping,respiratory motion compensation

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