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      Pseudo‐spiral sampling and compressed sensing reconstruction provides flexibility of temporal resolution in accelerated aortic 4D flow MRI: A comparison with k‐t principal component analysis

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          Abstract

          Introduction

          Time‐resolved three‐dimensional phase contrast MRI (4D flow) of aortic blood flow requires acceleration to reduce scan time. Two established techniques for highly accelerated 4D flow MRI are k‐t principal component analysis (k‐t PCA) and compressed sensing (CS), which employ either regular or random k‐space undersampling. The goal of this study was to gain insights into the quantitative differences between k‐t PCA‐ and CS‐derived aortic blood flow, especially for high temporal resolution CS 4D flow MRI.

          Methods

          The scan protocol consisted of both k‐t PCA and CS accelerated 4D flow MRI, as well as a 2D flow reference scan through the ascending aorta acquired in 15 subjects. 4D flow scans were accelerated with factor R = 8. For CS accelerated scans, we used a pseudo‐spiral Cartesian sampling scheme, which could additionally be reconstructed at higher temporal resolution, resulting in R = 13. 4D flow data were compared with the 2D flow scan in terms of flow, peak flow and stroke volume. A 3D peak systolic voxel‐wise velocity and wall shear stress (WSS) comparison between k‐t PCA and CS 4D flow was also performed.

          Results

          The mean difference in flow/peak flow/stroke volume between the 2D flow scan and the 4D flow CS with R = 8 and R = 13 was 4.2%/9.1%/3.0% and 5.3%/7.1%/1.9%, respectively, whereas for k‐t PCA with R = 8 the difference was 9.7%/25.8%/10.4%. In the voxel‐by‐voxel 4D flow comparison we found 13.6% and 3.5% lower velocity and WSS values of k‐t PCA compared with CS with R = 8, and 15.9% and 5.5% lower velocity and WSS values of k‐t PCA compared with CS with R = 13.

          Conclusion

          Pseudo‐spiral accelerated 4D flow acquisitions in combination with CS reconstruction provides a flexible choice of temporal resolution. We showed that our proposed strategy achieves better agreement in flow values with 2D reference scans compared with using k‐t PCA accelerated acquisitions.

          Abstract

          The goal of this study was to gain insights in the quantitative differences between compressed sensing and k‐t PCA‐derived aortic blood flow, especially for high temporal resolution compressed sensing 4D flow MRI. Random pseudo‐spiral sampling with compressed sensing reconstruction provides a more flexible choice of temporal resolution compared with regular sampling like k‐t PCA. We showed that our proposed strategy achieves better agreement in peak flow values with 2D reference scans compared with using k‐t PCA accelerated acquisitions.

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          Most cited references34

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          Comparing methods of measurement: why plotting difference against standard method is misleading.

          When comparing a new method of measurement with a standard method, one of the things we want to know is whether the difference between the measurements by the two methods is related to the magnitude of the measurement. A plot of the difference against the standard measurement is sometimes suggested, but this will always appear to show a relation between difference and magnitude when there is none. A plot of the difference against the average of the standard and new measurements is unlikely to mislead in this way. We show this theoretically and by a practical example.
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            An optimal radial profile order based on the Golden Ratio for time-resolved MRI.

            In dynamic magnetic resonance imaging (MRI) studies, the motion kinetics or the contrast variability are often hard to predict, hampering an appropriate choice of the image update rate or the temporal resolution. A constant azimuthal profile spacing (111.246 degrees), based on the Golden Ratio, is investigated as optimal for image reconstruction from an arbitrary number of profiles in radial MRI. The profile order is evaluated and compared with a uniform profile distribution in terms of signal-to-noise ratio (SNR) and artifact level. The favorable characteristics of such a profile order are exemplified in two applications on healthy volunteers. First, an advanced sliding window reconstruction scheme is applied to dynamic cardiac imaging, with a reconstruction window that can be flexibly adjusted according to the extent of cardiac motion that is acceptable. Second, a contrast-enhancing k-space filter is presented that permits reconstructing an arbitrary number of images at arbitrary time points from one raw data set. The filter was utilized to depict the T1-relaxation in the brain after a single inversion prepulse. While a uniform profile distribution with a constant angle increment is optimal for a fixed and predetermined number of profiles, a profile distribution based on the Golden Ratio proved to be an appropriate solution for an arbitrary number of profiles.
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              XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.

              To develop a novel framework for free-breathing MRI called XD-GRASP, which sorts dynamic data into extra motion-state dimensions using the self-navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing.
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                Author and article information

                Contributors
                lukas.gottwald@outlook.com
                Journal
                NMR Biomed
                NMR Biomed
                10.1002/(ISSN)1099-1492
                NBM
                Nmr in Biomedicine
                John Wiley and Sons Inc. (Hoboken )
                0952-3480
                1099-1492
                20 January 2020
                April 2020
                : 33
                : 4 ( doiID: 10.1002/nbm.v33.4 )
                : e4255
                Affiliations
                [ 1 ] Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers University of Amsterdam the Netherlands
                [ 2 ] Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers University of Amsterdam the Netherlands
                Author notes
                [*] [* ] Correspondence

                Lukas M. Gottwald, MSc, Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers – location AMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands.

                Email: lukas.gottwald@ 123456outlook.com

                Lukas M. Gottwald and Eva S. Peper made equal contributions to this study.

                Author information
                https://orcid.org/0000-0002-7394-821X
                https://orcid.org/0000-0001-6571-3341
                https://orcid.org/0000-0002-1777-8164
                https://orcid.org/0000-0003-3946-653X
                https://orcid.org/0000-0001-6700-5058
                https://orcid.org/0000-0002-5477-973X
                https://orcid.org/0000-0001-8755-0192
                Article
                NBM4255 NBM-19-0127.R4
                10.1002/nbm.4255
                7079056
                31957927
                a5759821-7066-4cd2-ac29-e54730f68f7e
                © 2020 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd

                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
                : 27 June 2019
                : 16 December 2019
                : 17 December 2019
                Page count
                Figures: 7, Tables: 3, Pages: 13, Words: 7417
                Funding
                Funded by: Stichting voor de Technische Wetenschappen , open-funder-registry 10.13039/501100003958;
                Award ID: 13928
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                April 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.8 mode:remove_FC converted:18.03.2020

                Radiology & Imaging
                cardiovascular,compressed sensing,flow quantitation,sampling strategies
                Radiology & Imaging
                cardiovascular, compressed sensing, flow quantitation, sampling strategies

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