KomaMRI.jl: An open‐source framework for general MRI simulations with GPU acceleration
To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma). 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 sc...
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| Veröffentlicht in: | Magnetic resonance in medicine Jg. 90; H. 1; S. 329 - 342 |
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Wiley Subscription Services, Inc
01.07.2023
John Wiley and Sons Inc |
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| Abstract | To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).
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.
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.
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. |
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| AbstractList | To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).
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.
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.
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. To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).PURPOSETo develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).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.METHODSKoma 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.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.RESULTSKoma 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.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.CONCLUSIONSKoma'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. Click here for author‐reader discussions Click here for author‐reader discussions PurposeTo develop an open‐source, high‐performance, easy‐to‐use, extensible, cross‐platform, and general MRI simulation framework (Koma).MethodsKoma 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.ResultsKoma 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.ConclusionsKoma'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. |
| Author | Varela‐Mattatall, Gabriel Coronado, Ronal Alberola‐López, Carlos Irarrazaval, Pablo Botnar, René Castillo‐Passi, Carlos |
| AuthorAffiliation | 5 Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute Western University London Ontario Canada 1 School of Biomedical Engineering and Imaging Sciences King's College London London UK 3 Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Pontificia Universidad Católica de Chile Santiago Chile 4 Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile 7 Laboratorio de Procesado de Imagen Universidad de Valladolid Valladolid Spain 2 Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile 6 Department of Medical Biophysics, Schulich School of Medicine and Dentistry Western University London Ontario Canada |
| AuthorAffiliation_xml | – name: 5 Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute Western University London Ontario Canada – name: 1 School of Biomedical Engineering and Imaging Sciences King's College London London UK – name: 2 Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile – name: 6 Department of Medical Biophysics, Schulich School of Medicine and Dentistry Western University London Ontario Canada – name: 7 Laboratorio de Procesado de Imagen Universidad de Valladolid Valladolid Spain – name: 3 Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Pontificia Universidad Católica de Chile Santiago Chile – name: 4 Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile |
| Author_xml | – sequence: 1 givenname: Carlos orcidid: 0000-0001-6227-0477 surname: Castillo‐Passi fullname: Castillo‐Passi, Carlos organization: School of Biomedical Engineering and Imaging Sciences King's College London London UK, Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile, Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Pontificia Universidad Católica de Chile Santiago Chile – sequence: 2 givenname: Ronal orcidid: 0000-0001-6735-2607 surname: Coronado fullname: Coronado, Ronal organization: Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile, Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Pontificia Universidad Católica de Chile Santiago Chile, Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile – sequence: 3 givenname: Gabriel orcidid: 0000-0001-6101-7218 surname: Varela‐Mattatall fullname: Varela‐Mattatall, Gabriel organization: Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute Western University London Ontario Canada, Department of Medical Biophysics, Schulich School of Medicine and Dentistry Western University London Ontario Canada – sequence: 4 givenname: Carlos orcidid: 0000-0003-3684-0055 surname: Alberola‐López fullname: Alberola‐López, Carlos organization: Laboratorio de Procesado de Imagen Universidad de Valladolid Valladolid Spain – sequence: 5 givenname: René orcidid: 0000-0003-2811-2509 surname: Botnar fullname: Botnar, René organization: School of Biomedical Engineering and Imaging Sciences King's College London London UK, Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile, Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Pontificia Universidad Católica de Chile Santiago Chile – sequence: 6 givenname: Pablo orcidid: 0000-0002-5186-2642 surname: Irarrazaval fullname: Irarrazaval, Pablo organization: Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile, Millennium Institute for Intelligent Healthcare Engineering (iHEALTH) Pontificia Universidad Católica de Chile Santiago Chile, Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile, Laboratorio de Procesado de Imagen Universidad de Valladolid Valladolid Spain |
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for author‐reader discussions To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma). Koma was developed using the... PurposeTo develop an open‐source, high‐performance, easy‐to‐use, extensible, cross‐platform, and general MRI simulation framework (Koma).MethodsKoma was... To develop an open-source, high-performance, easy-to-use, extensible, cross-platform, and general MRI simulation framework (Koma).PURPOSETo develop an... Click here for author‐reader discussions |
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| SubjectTerms | Acceleration Computer Processing and Modeling Computer Simulation Computers Fingerprinting Graphical user interface Graphics processing units Humans Image reconstruction Language Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Personal computers Phantoms, Imaging Programming languages Scanners Simulation Simulators Synthetic data |
| Title | KomaMRI.jl: An open‐source framework for general MRI simulations with GPU acceleration |
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