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Bogusz F, Pieciak T, Afzali M, Pizzolato M. Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence. Magn Reson Imaging 2022; 91:52-61. [PMID: 35561868 DOI: 10.1016/j.mri.2022.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/26/2022]
Abstract
This work focuses on obtaining a more general diffusion magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T1 and transverse T2⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T1 and T2⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences.
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Affiliation(s)
- Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), Leeds, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Marco Pizzolato
- Department of applied mathematics and computer science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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2
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Varela‐Mattatall G, Castillo‐Passi C, Koch A, Mura J, Stirnberg R, Uribe S, Tejos C, Stöcker T, Irarrazaval P. MAPL1:
q
‐space reconstruction using ‐regularized mean apparent propagator. Magn Reson Med 2020; 84:2219-2230. [DOI: 10.1002/mrm.28268] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 03/05/2020] [Accepted: 03/06/2020] [Indexed: 11/11/2022]
Affiliation(s)
- Gabriel Varela‐Mattatall
- Biomedical Imaging Center Pontificia Universidad Católica de Chile Santiago Chile
- Department of Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile
- Centre for Functional and Metabolic Mapping (CFMM), Robarts Research Institute University of Western Ontario London ON Canada
| | - Carlos Castillo‐Passi
- Biomedical Imaging Center Pontificia Universidad Católica de Chile Santiago Chile
- Department of Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile
| | - Alexandra Koch
- German Center for Neurodegenerative Diseases (DZNE) Bonn Germany
| | - Joaquin Mura
- Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile
- Departmento de Ingeniería Mecánica Universidad Técnica Federico Santa María Santiago Chile
| | | | - Sergio Uribe
- Biomedical Imaging Center Pontificia Universidad Católica de Chile Santiago Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile
- Radiology Department Pontificia Universidad Católica de Chile Santiago Chile
- Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile
| | - Cristian Tejos
- Biomedical Imaging Center Pontificia Universidad Católica de Chile Santiago Chile
- Department of Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE) Bonn Germany
- Department of Physics and Astronomy University of Bonn Bonn Germany
| | - Pablo Irarrazaval
- Biomedical Imaging Center Pontificia Universidad Católica de Chile Santiago Chile
- Department of Electrical Engineering Pontificia Universidad Católica de Chile Santiago Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance Santiago Chile
- Institute for Biological and Medical Engineering Pontificia Universidad Católica de Chile Santiago Chile
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3
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Fick RHJ, Wassermann D, Deriche R. The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy. Front Neuroinform 2019; 13:64. [PMID: 31680924 PMCID: PMC6803556 DOI: 10.3389/fninf.2019.00064] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 09/04/2019] [Indexed: 12/22/2022] Open
Abstract
Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)—known as Microstructure Imaging—has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a “building block”-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.
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Affiliation(s)
- Rutger H J Fick
- TheraPanacea, Paris, France.,Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
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4
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Petiet A, Adanyeguh I, Aigrot MS, Poirion E, Nait-Oumesmar B, Santin M, Stankoff B. Ultrahigh field imaging of myelin disease models: Toward specific markers of myelin integrity? J Comp Neurol 2019; 527:2179-2189. [PMID: 30520034 DOI: 10.1002/cne.24598] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 10/26/2018] [Accepted: 10/29/2018] [Indexed: 12/20/2022]
Abstract
Specific magnetic resonance imaging (MRI) markers of myelin are critical for the evaluation and development of regenerative therapies for demyelinating diseases. Several MRI methods have been developed for myelin imaging, based either on acquisition schemes or on mathematical modeling of the signal. They generally showed good sensitivity but validation for specificity toward myelin is still warranted to allow a reliable interpretation in an in vivo complex pathological environment. Experimental models of dys-/demyelination are characterized by various levels of myelin disorders, axonal damage, gliosis and inflammation, and offer the opportunity for powerful correlative studies between imaging metrics and histology. Here, we review how ultrahigh field MRI markers have been correlated with histology in these models and provide insights into the trends for future developments of MRI tools in human myelin diseases. To this end, we present the biophysical basis of the main MRI methods for myelin imaging based on T1 , T2 , water diffusion, and magnetization transfer signal, the characteristics of animal models used and the outcomes of histological validations. To date such studies are limited, and demonstrate partial correlations with immunohistochemical and electron microscopy measures of myelin. These MRI metrics also often correlate with axons, glial, or inflammatory cells in models where axonal degeneration or inflammation occur as potential confounding factors. Therefore, the MRI markers' specificity for myelin is still perfectible and future developments should improve mathematical modeling of the MR signal based on more complex systems or provide multimodal approaches to better disentangle the biological processes underlying the MRI metrics.
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Affiliation(s)
- Alexandra Petiet
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France.,Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Isaac Adanyeguh
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France.,Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Marie-Stéphane Aigrot
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Emilie Poirion
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Brahim Nait-Oumesmar
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Mathieu Santin
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France.,Center for Neuroimaging Research, Brain and Spine Institute, Paris, France
| | - Bruno Stankoff
- Sorbonne Université, UPMC Paris 06, Brain and Spine Institute, ICM, Hôpital de la Pitié Salpêtrière, Paris, France.,Department of Neurology, AP-HP, Saint-Antoine hospital, Paris, France
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Filipiak P, Fick R, Petiet A, Santin M, Philippe AC, Lehericy S, Ciuciu P, Deriche R, Wassermann D. Reducing the number of samples in spatiotemporal dMRI acquisition design. Magn Reson Med 2018; 81:3218-3233. [DOI: 10.1002/mrm.27601] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 10/17/2018] [Accepted: 10/18/2018] [Indexed: 12/21/2022]
Affiliation(s)
- Patryk Filipiak
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
| | - Rutger Fick
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
| | - Alexandra Petiet
- CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute; Paris France
| | - Mathieu Santin
- CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute; Paris France
| | | | - Stephane Lehericy
- CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute; Paris France
| | | | - Rachid Deriche
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
| | - Demian Wassermann
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
- Inria, CEA, Université Paris-Saclay; France
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