Efficient estimation of propagator anisotropy and non‐Gaussianity in multishell diffusion MRI with micro‐structure adaptive convolution kernels and dual Fourier integral transforms

Purpose We seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP‐MRI), to the Micro‐Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 89; H. 1; S. 440 - 453
Hauptverfasser:	París, Guillem, Pieciak, Tomasz, Aja‐Fernández, Santiago, Tristán‐Vega, Antonio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.01.2023 John Wiley and Sons Inc
Schlagworte:	Algorithms Anisotropy Brain - diagnostic imaging Computational efficiency Computer applications Computing time Convolution Diffusion Diffusion Magnetic Resonance Imaging - methods EAP Evaluation Humans Image Processing, Computer-Assisted - methods Integral transforms Kernels Magnetic resonance imaging MiSFIT multishell non‐Gaussianity propagator Reliability analysis Reproducibility of Results Substantia alba s—Computer Processing and Modeling multishell MiSFIT propagator EAP anisotropy non-Gaussianity
ISSN:	0740-3194, 1522-2594, 1522-2594
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Abstract	Purpose We seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP‐MRI), to the Micro‐Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP). Theory and Methods First, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test‐retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation. Results Findings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test–retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude. Conclusions Despite being a direct development on the MAP‐MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings.
AbstractList	Purpose We seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP‐MRI), to the Micro‐Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP). Theory and Methods First, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test‐retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation. Results Findings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test–retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude. Conclusions Despite being a direct development on the MAP‐MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings. We seek to reformulate the so-called Propagator Anisotropy (PA) and Non-Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP-MRI), to the Micro-Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP). First, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test-retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation. Findings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test-retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude. Despite being a direct development on the MAP-MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings. Click here for author‐reader discussions PurposeWe seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP‐MRI), to the Micro‐Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP).Theory and MethodsFirst, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test‐retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation.ResultsFindings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test–retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude.ConclusionsDespite being a direct development on the MAP‐MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings. We seek to reformulate the so-called Propagator Anisotropy (PA) and Non-Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP-MRI), to the Micro-Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP).PURPOSEWe seek to reformulate the so-called Propagator Anisotropy (PA) and Non-Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP-MRI), to the Micro-Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP).First, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test-retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation.THEORY AND METHODSFirst, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test-retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation.Findings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test-retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude.RESULTSFindings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test-retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude.Despite being a direct development on the MAP-MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings.CONCLUSIONSDespite being a direct development on the MAP-MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings.
Author	París, Guillem Pieciak, Tomasz Tristán‐Vega, Antonio Aja‐Fernández, Santiago
AuthorAffiliation	2 AGH University of Science and Technology Krakow Poland 1 Laboratorio de Procesado de Imagen (LPI) Universidad de Valladolid Valladolid Castilla y León Spain
AuthorAffiliation_xml	– name: 1 Laboratorio de Procesado de Imagen (LPI) Universidad de Valladolid Valladolid Castilla y León Spain – name: 2 AGH University of Science and Technology Krakow Poland
Author_xml	– sequence: 1 givenname: Guillem orcidid: 0000-0002-1564-1199 surname: París fullname: París, Guillem email: guillem.paris@uva.es organization: Universidad de Valladolid – sequence: 2 givenname: Tomasz orcidid: 0000-0002-7543-3658 surname: Pieciak fullname: Pieciak, Tomasz organization: AGH University of Science and Technology – sequence: 3 givenname: Santiago orcidid: 0000-0002-5337-5071 surname: Aja‐Fernández fullname: Aja‐Fernández, Santiago organization: Universidad de Valladolid – sequence: 4 givenname: Antonio orcidid: 0000-0002-4614-2501 surname: Tristán‐Vega fullname: Tristán‐Vega, Antonio organization: Universidad de Valladolid
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/36121312$$D View this record in MEDLINE/PubMed
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Cites_doi	10.1016/j.neuroimage.2004.07.051 10.1016/S1053-8119(03)00336-7 10.1016/j.neuroimage.2012.03.072 10.1016/j.neuroimage.2020.117406 10.1093/brain/awm216 10.1016/j.neuroimage.2005.01.028 10.1016/j.neuroimage.2016.03.046 10.1109/TMI.2015.2418674 10.1016/S1361-8415(01)00036-6 10.1006/nimg.2002.1132 10.1016/j.neuroimage.2020.117616 10.1016/j.neuroimage.2007.12.035 10.1016/j.neuroimage.2007.02.050 10.3389/fnins.2018.00092 10.1016/j.neuroimage.2013.04.016 10.1016/j.ejrad.2021.109622 10.1109/42.906424 10.1007/978-3-319-54130-3_16 10.1016/j.neuroimage.2019.116137 10.1002/mrm.27101 10.1016/j.biopsych.2003.10.022 10.3174/ajnr.A1919 10.1016/j.neuroimage.2015.11.027 10.1002/mrm.26054 10.1093/oso/9780198539445.001.0001
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Keywords	multishell MiSFIT propagator EAP anisotropy non-Gaussianity
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Snippet	Purpose We seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator... Click here for author‐reader discussions We seek to reformulate the so-called Propagator Anisotropy (PA) and Non-Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI... PurposeWe seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator...
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SubjectTerms	Algorithms Anisotropy Brain - diagnostic imaging Computational efficiency Computer applications Computing time Convolution Diffusion Diffusion Magnetic Resonance Imaging - methods EAP Evaluation Humans Image Processing, Computer-Assisted - methods Integral transforms Kernels Magnetic resonance imaging MiSFIT multishell non‐Gaussianity propagator Reliability analysis Reproducibility of Results Substantia alba s—Computer Processing and Modeling
Title	Efficient estimation of propagator anisotropy and non‐Gaussianity in multishell diffusion MRI with micro‐structure adaptive convolution kernels and dual Fourier integral transforms
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