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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Vydáno v:Magnetic resonance in medicine Ročník 89; číslo 1; s. 440 - 453
Hlavní autoři: París, Guillem, Pieciak, Tomasz, Aja‐Fernández, Santiago, Tristán‐Vega, Antonio
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.01.2023
John Wiley and Sons Inc
Témata:
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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Popis
Shrnutí: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.
Bibliografie:Funding information
Consejería de Educación, Junta de Castilla y León and European Social Fund, Grant/Award Number: Orden EDU/1100/2017 12/12; Ministerio de Ciencia e Innovación, Grant/Award Numbers: RTI2018‐094569‐B‐I00; PID2021‐124407NB‐I00; Ministry of Science and Higher Education of Poland, Grant/Award Number: 692/STYP/13/2018; Narodowa Agencja Wymiany Akademickiej, Grant/Award Number: PPN/BEK/2019/1/00421
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Funding information Consejería de Educación, Junta de Castilla y León and European Social Fund, Grant/Award Number: Orden EDU/1100/2017 12/12; Ministerio de Ciencia e Innovación, Grant/Award Numbers: RTI2018‐094569‐B‐I00; PID2021‐124407NB‐I00; Ministry of Science and Higher Education of Poland, Grant/Award Number: 692/STYP/13/2018; Narodowa Agencja Wymiany Akademickiej, Grant/Award Number: PPN/BEK/2019/1/00421
ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.29435