KomaMRI.jl: An open-source framework for general MRI simulations with GPU acceleration.

Carlos Castillo-Passi, Ronal Coronado, Gabriel Varela-Mattatall, Carlos Alberola-López, René Botnar, Pablo Irarrazaval
Author Information
  1. Carlos Castillo-Passi: School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. ORCID
  2. Ronal Coronado: Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. ORCID
  3. Gabriel Varela-Mattatall: Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute, Western University, London, Ontario, Canada. ORCID
  4. Carlos Alberola-López: Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain. ORCID
  5. René Botnar: School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. ORCID
  6. Pablo Irarrazaval: Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile. ORCID

Abstract

PURPOSE: To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).
METHODS: Koma was developed using the Julia programming language. Like other MRI simulators, it solves the Bloch equations with CPU and GPU parallelization. The inputs are the scanner parameters, the phantom, and the pulse sequence that is Pulseq-compatible. The raw data is stored in the ISMRMRD format. For the reconstruction, MRIReco.jl is used. A graphical user interface utilizing web technologies was also designed. Two types of experiments were performed: one to compare the quality of the results and the execution speed, and the second to compare its usability. Finally, the use of Koma in quantitative imaging was demonstrated by simulating Magnetic Resonance Fingerprinting (MRF) acquisitions.
RESULTS: Koma was compared to two well-known open-source MRI simulators, JEMRIS and MRiLab. Highly accurate results (with mean absolute differences below 0.1% compared to JEMRIS) and better GPU performance than MRiLab were demonstrated. In an experiment with students, Koma was proved to be easy to use, eight times faster on personal computers than JEMRIS, and 65% of test subjects recommended it. The potential for designing acquisition and reconstruction techniques was also shown through the simulation of MRF acquisitions, with conclusions that agree with the literature.
CONCLUSIONS: Koma's speed and flexibility have the potential to make simulations more accessible for education and research. Koma is expected to be used for designing and testing novel pulse sequences before implementing them in the scanner with Pulseq files, and for creating synthetic data to train machine learning models.

Keywords

References

  1. Magn Reson Med. 1989 Apr;10(1):135-44 [PMID: 2547135]
  2. PLoS One. 2019 May 17;14(5):e0216594 [PMID: 31100074]
  3. Magn Reson Med. 2014 Nov;72(5):1460-70 [PMID: 24323973]
  4. J Magn Reson. 2021 Aug;329:107011 [PMID: 34147025]
  5. Magn Reson Med. 2022 Dec;88(6):2395-2407 [PMID: 35968675]
  6. NMR Biomed. 2001 Apr;14(2):57-64 [PMID: 11320533]
  7. IEEE Trans Pattern Anal Mach Intell. 2022 Jan;44(1):143-153 [PMID: 32750834]
  8. EJNMMI Phys. 2016 Dec;3(1):19 [PMID: 27562024]
  9. Magn Reson Med Sci. 2020 Aug 3;19(3):247-253 [PMID: 31217368]
  10. Quant Imaging Med Surg. 2015 Dec;5(6):858-85 [PMID: 26807369]
  11. Magn Reson Med. 2017 Apr;77(4):1544-1552 [PMID: 27271292]
  12. J Magn Reson. 2000 Mar;143(1):79-87 [PMID: 10698648]
  13. Z Med Phys. 2019 May;29(2):102-127 [PMID: 30553609]
  14. Comput Methods Programs Biomed. 2020 Oct;195:105634 [PMID: 32645627]
  15. Magn Reson Med. 2015 Jun;73(6):2152-62 [PMID: 24986223]
  16. IEEE Trans Med Imaging. 2009 Sep;28(9):1354-64 [PMID: 19273001]
  17. Magn Reson Med Sci. 2019 Jul 16;18(3):208-218 [PMID: 30416180]
  18. Magn Reson Med. 2018 Jan;79(1):83-96 [PMID: 28261851]
  19. Magn Reson Med. 2017 Jan;77(1):411-421 [PMID: 26822475]
  20. Magn Reson Med. 2021 Sep;86(3):1633-1646 [PMID: 33817833]
  21. Magn Reson Med. 2021 Aug;86(2):709-724 [PMID: 33755247]
  22. J Cardiovasc Magn Reson. 2014 Jul 04;16:48 [PMID: 24996972]
  23. Magn Reson Imaging. 1984;2(2):113-20 [PMID: 6530920]
  24. J Magn Reson Imaging. 2018 Apr;47(4):875-890 [PMID: 28753225]
  25. Nature. 2013 Mar 14;495(7440):187-92 [PMID: 23486058]
  26. Neuroimage. 2006 Aug 1;32(1):138-45 [PMID: 16750398]
  27. Magn Reson Med. 2023 Jul;90(1):329-342 [PMID: 36877139]
  28. IEEE Trans Med Imaging. 1991;10(1):53-65 [PMID: 18222800]
  29. Magn Reson Med. 2016 Jun;75(6):2372-8 [PMID: 26148753]
  30. Magn Reson Med. 2010 Jul;64(1):186-93 [PMID: 20577987]
  31. J Cardiovasc Magn Reson. 2014 Aug 20;16:63 [PMID: 25204441]
  32. Magn Reson Med. 2018 Jan;79(1):97-107 [PMID: 28247561]
  33. J Magn Reson Imaging. 2015 Feb;41(2):266-95 [PMID: 24737382]
  34. Magn Reson Med. 2018 Nov;80(5):1836-1846 [PMID: 29575161]
  35. J Med Syst. 2019 Dec 2;44(1):9 [PMID: 31792618]
  36. J Magn Reson. 2017 Aug;281:51-65 [PMID: 28550818]
  37. IEEE Trans Med Imaging. 2017 Feb;36(2):527-537 [PMID: 28113746]
  38. Magn Reson Med. 2006 Aug;56(2):364-80 [PMID: 16841304]
  39. Magn Reson Med. 2022 Apr;87(4):2003-2017 [PMID: 34811794]
  40. Sci Rep. 2021 Oct 28;11(1):21289 [PMID: 34711847]
  41. Sensors (Basel). 2021 Sep 08;21(18): [PMID: 34577231]
  42. Neuroimage. 2016 Jan 15;125:1079-1094 [PMID: 26549300]
  43. Magn Reson Med. 2017 Jan;77(1):23-33 [PMID: 26715192]
  44. Magn Reson Med. 1999 Aug;42(2):412-5 [PMID: 10440968]
  45. IEEE Trans Med Imaging. 2014 Mar;33(3):607-17 [PMID: 24595337]
  46. Med Image Anal. 2021 Apr;69:101945 [PMID: 33421921]
  47. PLoS One. 2021 Mar 26;16(3):e0248816 [PMID: 33770130]

Grants

  1. /Wellcome Trust
  2. NS/A000049/1/Wellcome Trust

MeSH Term

Humans
Magnetic Resonance Imaging
Computer Simulation
Phantoms, Imaging
Language
Acceleration

Word Cloud

Created with Highcharts 10.0.0KomaMRIGPUopen-sourcesimulationJEMRISgeneralframeworkJuliasimulatorsBlochequationsscannerpulsedatareconstructionusedalsocompareresultsspeedusedemonstratedMRFacquisitionscomparedMRiLabpotentialdesigningsimulationsPURPOSE:develophigh-performanceeasy-to-useextensiblecross-platformMETHODS:developedusingprogramminglanguageLikesolvesCPUparallelizationinputsparametersphantomsequencePulseq-compatiblerawstoredISMRMRDformatMRIRecojlgraphicaluserinterfaceutilizingwebtechnologiesdesignedTwotypesexperimentsperformed:onequalityexecutionsecondusabilityFinallyquantitativeimagingsimulatingMagneticResonanceFingerprintingRESULTS:twowell-knownHighlyaccuratemeanabsolutedifferences01%betterperformanceexperimentstudentsprovedeasyeighttimesfasterpersonalcomputers65%testsubjectsrecommendedacquisitiontechniquesshownconclusionsagreeliteratureCONCLUSIONS:Koma'sflexibilitymakeaccessibleeducationresearchexpectedtestingnovelsequencesimplementingPulseqfilescreatingsynthetictrainmachinelearningmodelsKomaMRIjl:accelerationGUIopensource

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