Accelerated sequences of 4D flow MRI using GRAPPA and compressed sensing: A comparison against conventional MRI and computational fluid dynamics.

Morgane Garreau, Thomas Puiseux, Solenn Toupin, Daniel Giese, Simon Mendez, Franck Nicoud, Ramiro Moreno
Author Information
  1. Morgane Garreau: University of Montpellier, CNRS, Montpellier, France. ORCID
  2. Thomas Puiseux: Spin Up, ALARA Group, Strasbourg, France. ORCID
  3. Solenn Toupin: Siemens Healthcare France, Saint-Denis, France.
  4. Daniel Giese: Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany. ORCID
  5. Simon Mendez: University of Montpellier, CNRS, Montpellier, France. ORCID
  6. Franck Nicoud: University of Montpellier, CNRS, Montpellier, France. ORCID
  7. Ramiro Moreno: I2MC, INSERM/UPS UMR 1297, Toulouse, France.

Abstract

PURPOSE: To evaluate hemodynamic markers obtained by accelerated GRAPPA (R = 2, 3, 4) and compressed sensing (R = 7.6) 4D flow MRI sequences under complex flow conditions.
METHODS: The accelerated 4D flow MRI scans were performed on a pulsatile flow phantom, along with a nonaccelerated fully sampled k-space acquisition. Computational fluid dynamics simulations based on the experimentally measured flow fields were conducted for additional comparison. Voxel-wise comparisons (Bland-Altman analysis, -norm metric), as well as nonderived quantities (velocity profiles, flow rates, and peak velocities), were used to compare the velocity fields obtained from the different modalities.
RESULTS: 4D flow acquisitions and computational fluid dynamics depicted similar hemodynamic patterns. Voxel-wise comparisons between the MRI scans highlighted larger discrepancies at the voxels located near the phantom's boundary walls. A trend for all MR scans to overestimate velocity profiles and peak velocities as compared to computational fluid dynamics was noticed in regions associated with high velocity or acceleration. However, good agreement for the flow rates was observed, and eddy-current correction appeared essential for consistency of the flow rates measurements with respect to the principle of mass conservation.
CONCLUSION: GRAPPA (R = 2, 3) and highly accelerated compressed sensing showed good agreement with the fully sampled acquisition. Yet, all 4D flow MRI scans were hampered by artifacts inherent to the phase-contrast acquisition procedure. Computational fluid dynamics simulations are an interesting tool to assess these differences but are sensitive to modeling parameters.

Keywords

References

  1. Magn Reson Med. 2021 Apr;85(4):2174-2187 [PMID: 33107141]
  2. J Magn Reson Imaging. 2012 Dec;36(6):1450-9 [PMID: 23065951]
  3. Magn Reson Med. 2000 May;43(5):682-90 [PMID: 10800033]
  4. Radiology. 2015 Apr;275(1):245-54 [PMID: 25325326]
  5. J Magn Reson Imaging. 1991 Jul-Aug;1(4):405-13 [PMID: 1790362]
  6. Magn Reson Med. 2001 May;45(5):872-9 [PMID: 11323814]
  7. J Biomech. 2021 Dec 2;129:110793 [PMID: 34715606]
  8. Biomed Eng Online. 2021 Aug 21;20(1):84 [PMID: 34419047]
  9. Magn Reson Med. 2000 May;43(5):734-8 [PMID: 10800039]
  10. J Cardiovasc Magn Reson. 2015 Aug 10;17:72 [PMID: 26257141]
  11. J Biomech. 2017 Jan 25;51:83-88 [PMID: 27986327]
  12. JACC Cardiovasc Imaging. 2019 Feb;12(2):252-266 [PMID: 30732721]
  13. Magn Reson Med. 1998 Feb;39(2):300-8 [PMID: 9469714]
  14. Open Heart. 2020 Feb 13;7(1):e001158 [PMID: 32153789]
  15. Magn Reson Med. 2008 Nov;60(5):1169-77 [PMID: 18958854]
  16. Magn Reson Imaging. 2018 May;48:62-69 [PMID: 29223732]
  17. J Magn Reson Imaging. 2015 Feb;41(2):505-16 [PMID: 24436246]
  18. J Magn Reson Imaging. 1995 Mar-Apr;5(2):207-15 [PMID: 7766984]
  19. Magn Reson Imaging. 2020 Dec;74:232-243 [PMID: 32889090]
  20. Magn Reson Med. 2019 Jun;81(6):3675-3690 [PMID: 30803006]
  21. Comput Biol Med. 2021 Apr;131:104230 [PMID: 33545507]
  22. Magn Reson Med. 2014 Jul;72(1):33-40 [PMID: 24006013]
  23. J Biomech. 2021 Feb 12;116:110209 [PMID: 33422725]
  24. Magn Reson Med. 2010 Jun;63(6):1575-82 [PMID: 20512861]
  25. Acta Radiol. 2019 Mar;60(3):327-337 [PMID: 30479136]
  26. AJNR Am J Neuroradiol. 2014 Mar;35(3):536-43 [PMID: 24231854]
  27. NMR Biomed. 2019 May;32(5):e4063 [PMID: 30747461]
  28. Magn Reson Med. 2014 Aug;72(2):522-33 [PMID: 24006309]
  29. Front Bioeng Biotechnol. 2022 Mar 24;10:836611 [PMID: 35402418]
  30. Magn Reson Med. 2022 Dec;88(6):2432-2446 [PMID: 36005271]
  31. Comput Biol Med. 2021 Nov 23;140:105053 [PMID: 34847383]
  32. Magn Reson Med. 2003 Oct;50(4):791-801 [PMID: 14523966]
  33. Ann Biomed Eng. 2012 Mar;40(3):729-41 [PMID: 22009313]
  34. Magn Reson Med. 1995 Dec;34(6):910-4 [PMID: 8598820]
  35. Int J Numer Method Biomed Eng. 2020 Sep;36(9):e3381 [PMID: 32627366]
  36. J Magn Reson Imaging. 1997 Mar-Apr;7(2):339-46 [PMID: 9090588]
  37. Acad Radiol. 2014 Aug;21(8):1002-8 [PMID: 25018072]
  38. J Magn Reson Imaging. 2015 Aug;42(2):495-504 [PMID: 25447784]
  39. Ann Biomed Eng. 2016 Nov;44(11):3346-3358 [PMID: 27073110]
  40. Magn Reson Imaging. 1992;10(1):13-23 [PMID: 1545672]
  41. Biomed Res Int. 2019 Mar 14;2019:6074984 [PMID: 31001557]
  42. Magn Reson Med. 2011 Oct;66(4):966-75 [PMID: 21437975]
  43. J Magn Reson Imaging. 2012 Jul;36(1):128-38 [PMID: 22336966]
  44. Heart Vessels. 2017 Aug;32(8):1032-1044 [PMID: 28444501]
  45. PLoS One. 2021 Mar 26;16(3):e0248816 [PMID: 33770130]
  46. J Magn Reson Imaging. 2008 Jul;28(1):210-8 [PMID: 18581344]
  47. Magn Reson Imaging. 2015 Feb;33(2):185-93 [PMID: 25460329]

MeSH Term

Artifacts
Blood Flow Velocity
Hydrodynamics
Imaging, Three-Dimensional
Magnetic Resonance Imaging
Phantoms, Imaging

Word Cloud

Created with Highcharts 10.0.0flow4DMRIfluiddynamicscompressedscansvelocitycomputationalacceleratedGRAPPAsensingacquisitionrateshemodynamicobtainedR = 23sequencesphantomfullysampledComputationalsimulationsfieldscomparisonVoxel-wisecomparisonsprofilespeakvelocitiesgoodagreementPURPOSE:evaluatemarkers4R = 76complexconditionsMETHODS:performedpulsatilealongnonacceleratedk-spacebasedexperimentallymeasuredconductedadditionalBland-Altmananalysis-normmetricwellnonderivedquantitiesusedcomparedifferentmodalitiesRESULTS:acquisitionsdepictedsimilarpatternshighlightedlargerdiscrepanciesvoxelslocatednearphantom'sboundarywallstrendMRoverestimatecomparednoticedregionsassociatedhighaccelerationHoweverobservededdy-currentcorrectionappearedessentialconsistencymeasurementsrespectprinciplemassconservationCONCLUSION:highlyshowedYethamperedartifactsinherentphase-contrastprocedureinterestingtoolassessdifferencessensitivemodelingparametersAcceleratedusingsensing:conventionalCFD

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