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Dessain Q, Fuchs C, Macq B, Rensonnet G. Fast multi-compartment Microstructure Fingerprinting in brain white matter. Front Neurosci 2024; 18:1400499. [PMID: 39099635 PMCID: PMC11294228 DOI: 10.3389/fnins.2024.1400499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 06/10/2024] [Indexed: 08/06/2024] Open
Abstract
We proposed two deep neural network based methods to accelerate the estimation of microstructural features of crossing fascicles in the white matter. Both methods focus on the acceleration of a multi-dictionary matching problem, which is at the heart of Microstructure Fingerprinting, an extension of Magnetic Resonance Fingerprinting to diffusion MRI. The first acceleration method uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation. The second acceleration method relies on a feed-forward neural network that uses a spherical harmonics representation of the DW-MRI signal as input. The first method exhibits a high interpretability while the second method achieves a greater speedup factor. The accuracy of the results and the speedup factors of several orders of magnitude obtained on in vivo brain data suggest the potential of our methods for a fast quantitative estimation of microstructural features in complex white matter configurations.
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Affiliation(s)
- Quentin Dessain
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Clément Fuchs
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Benoît Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Gaëtan Rensonnet
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
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2
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Berger A, Cerra M, Joris V, Danthine V, Macq B, Dricot L, Vandewalle G, Delinte N, El Tahry R. Identifying responders to vagus nerve stimulation based on microstructural features of thalamocortical tracts in drug-resistant epilepsy. Neurotherapeutics 2024:e00422. [PMID: 38964949 DOI: 10.1016/j.neurot.2024.e00422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/06/2024] Open
Abstract
The mechanisms of action of Vagus Nerve Stimulation (VNS) and the biological prerequisites to respond to the treatment are currently under investigation. It is hypothesized that thalamocortical tracts play a central role in the antiseizure effects of VNS by disrupting the genesis of pathological activity in the brain. This pilot study explored whether in vivo microstructural features of thalamocortical tracts may differentiate Drug-Resistant Epilepsy (DRE) patients responding and not responding to VNS treatment. Eighteen patients with DRE (37.11 ± 10.13 years, 10 females), including 11 responders or partial responders and 7 non-responders to VNS, were recruited for this high-gradient multi-shell diffusion Magnetic Resonance Imaging (MRI) study. Using Diffusion Tensor Imaging (DTI) and multi-compartment models - Neurite Orientation Dispersion and Density Imaging (NODDI) and Microstructure Fingerprinting (MF), we extracted microstructural features in 12 subsegments of thalamocortical tracts. These characteristics were compared between responders/partial responders and non-responders. Subsequently, a Support Vector Machine (SVM) classifier was built, incorporating microstructural features and 12 clinical covariates (including age, sex, duration of VNS therapy, number of antiseizure medications, benzodiazepine intake, epilepsy duration, epilepsy onset age, epilepsy type - focal or generalized, presence of an epileptic syndrome - no syndrome or Lennox-Gastaut syndrome, etiology of epilepsy - structural, genetic, viral, or unknown, history of brain surgery, and presence of a brain lesion detected on structural MRI images). Multiple diffusion metrics consistently demonstrated significantly higher white matter fiber integrity in patients with a better response to VNS (pFDR < 0.05) in different subsegments of thalamocortical tracts. The SVM model achieved a classification accuracy of 94.12%. The inclusion of clinical covariates did not improve the classification performance. The results suggest that the structural integrity of thalamocortical tracts may be linked to therapeutic effectiveness of VNS. This study reveals the great potential of diffusion MRI in improving our understanding of the biological factors associated with the response to VNS therapy.
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Affiliation(s)
- Alexandre Berger
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Synergia Medical SA, 1435, Mont-Saint-Guibert, Belgium; Sleep and Chronobiology Lab, GIGA-Cyclotron Research Center-In Vivo Imaging, University of Liège, 4000, Liège, Belgium.
| | - Michele Cerra
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, 1348, Louvain-la-Neuve, Belgium; Politecnico di Torino, Department of Control and Computer Engineering, Corso Duca Degli Abruzzi 24, 10129, Torino, Italy
| | - Vincent Joris
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Cliniques Universitaires Saint-Luc (CUSL), Department of Neurosurgery, 1200, Brussels, Belgium
| | - Venethia Danthine
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium
| | - Benoit Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, 1348, Louvain-la-Neuve, Belgium
| | - Laurence Dricot
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Lab, GIGA-Cyclotron Research Center-In Vivo Imaging, University of Liège, 4000, Liège, Belgium
| | - Nicolas Delinte
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), Catholic University of Louvain, 1348, Louvain-la-Neuve, Belgium
| | - Riëm El Tahry
- Epilepsy and Neurostimulation Lab, Institute of Neuroscience (IoNS), Department of Clinical Neuroscience, Catholic University of Louvain, 1200, Brussels, Belgium; Center for Refractory Epilepsy, Cliniques Universitaires Saint-Luc (CUSL), Department of Neurology, 1200, Brussels, Belgium
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Fuchs C, Dessain Q, Delinte N, Dausort M, Macq B. Sparse Blind Spherical Deconvolution of diffusion weighted MRI. Front Neurosci 2024; 18:1385975. [PMID: 38846718 PMCID: PMC11155299 DOI: 10.3389/fnins.2024.1385975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/19/2024] [Indexed: 06/09/2024] Open
Abstract
Diffusion-weighted magnetic resonance imaging provides invaluable insights into in-vivo neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.e., the signal arising from individual fascicles within a voxel. In this paper, an algorithm of blind spherical deconvolution is proposed, which only assumes the axial symmetry of the response function instead of its exact knowledge. This algorithm provides a method for estimating the peaks of the ODF in a voxel without any explicit response function, as well as a method for estimating signals associated with the peaks of the ODF, regardless of how those peaks were obtained. The two stages of the algorithm are tested on Monte Carlo simulations, as well as compared to state-of-the-art methods on real in-vivo data for the orientation retrieval task. Although the proposed algorithm was shown to attain lower angular errors than the state-of-the-art constrained spherical deconvolution algorithm on synthetic data, it was outperformed by state-of-the-art spherical deconvolution algorithms on in-vivo data. In conjunction with state-of-the art methods for axon bundles direction estimation, the proposed method showed its potential for the derivation of per-voxel per-direction metrics on synthetic as well as in-vivo data.
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Affiliation(s)
- Clément Fuchs
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Quentin Dessain
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Nicolas Delinte
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Manon Dausort
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Benoît Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
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Vanden Bulcke C, Stölting A, Maric D, Macq B, Absinta M, Maggi P. Comparative overview of multi-shell diffusion MRI models to characterize the microstructure of multiple sclerosis lesions and periplaques. Neuroimage Clin 2024; 42:103593. [PMID: 38520830 PMCID: PMC10978527 DOI: 10.1016/j.nicl.2024.103593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/01/2024] [Accepted: 03/16/2024] [Indexed: 03/25/2024]
Abstract
In multiple sclerosis (MS), accurate in vivo characterization of the heterogeneous lesional and extra-lesional tissue pathology remains challenging. Marshalling several advanced imaging techniques - quantitative relaxation time (T1) mapping, a model-free average diffusion signal approach and four multi-shell diffusion models - this study investigates the performance of multi-shell diffusion models and characterizes the microstructural damage within (i) different MS lesion types - active, chronic active, and chronic inactive - (ii) their respective periplaque white matter (WM), and (iii) the surrounding normal-appearing white matter (NAWM). In 83 MS participants (56 relapsing-remitting, 27 progressive) and 23 age and sex-matched healthy controls (HC), we analysed a total of 317 paramagnetic rim lesions (PRL+), 232 non-paramagnetic rim lesions (PRL-), 38 contrast-enhancing lesions (CEL). Consistent with previous findings and histology, our analysis revealed the ability of advanced multi-shell diffusion models to characterize the unique microstructural patterns of CEL, and to elucidate their possible evolution into a resolving (chronic inactive) vs smoldering (chronic active) inflammatory stage. In addition, we showed that the microstructural damage extends well beyond the MRI-visible lesion edge, gradually fading out while moving outward from the lesion edge into the immediate WM periplaque and the NAWM, the latter still characterized by diffuse microstructural damage in MS vs HC. This study also emphasizes the critical role of selecting appropriate diffusion models to elucidate the complex pathological architecture of MS lesions and their periplaque. More specifically, multi-compartment diffusion models based on biophysically interpretable metrics such as neurite orientation dispersion and density (NODDI; mean auc=0.8002) emerge as the preferred choice for MS applications, while simpler models based on a representation of the diffusion signal, like diffusion tensor imaging (DTI; mean auc=0.6942), consistently underperformed, also when compared to T1 mapping (mean auc=0.73375).
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Affiliation(s)
- Colin Vanden Bulcke
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium; ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium.
| | - Anna Stölting
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium
| | - Dragan Maric
- Flow and Imaging Core Facility, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Benoît Macq
- ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Martina Absinta
- Translational Neuropathology Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Pietro Maggi
- Neuroinflammation Imaging Lab (NIL), Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium; Department of Neurology, Cliniques universitaires Saint-Luc, Université catholique de Louvain, Brussels, Belgium.
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Carton de Tournai A, Herman E, Gathy E, Ebner-Karestinos D, Araneda R, Dricot L, Macq B, Vandermeeren Y, Bleyenheuft Y. Baby HABIT-ILE intervention: study protocol of a randomised controlled trial in infants aged 6-18 months with unilateral cerebral palsy. BMJ Open 2024; 14:e078383. [PMID: 38367973 PMCID: PMC10875549 DOI: 10.1136/bmjopen-2023-078383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/31/2024] [Indexed: 02/19/2024] Open
Abstract
INTRODUCTION Research using animal models suggests that intensive motor skill training in infants under 2 years old with cerebral palsy (CP) may significantly reduce, or even prevent, maladaptive neuroplastic changes following brain injury. However, the effects of such interventions to tentatively prevent secondary neurological damages have never been assessed in infants with CP. This study aims to determine the effect of the baby Hand and Arm Bimanual Intensive Therapy Including Lower Extremities (baby HABIT-ILE) in infants with unilateral CP, compared with a control intervention. METHODS AND ANALYSIS This randomised controlled trial will include 48 infants with unilateral CP aged (corrected if preterm) 6-18 months at the first assessment. They will be paired by age and by aetiology of the CP, and randomised into two groups (immediate and delayed). Assessments will be performed at baseline and at 1 month, 3 months and 6 months after baseline. The immediate group will receive 50 hours of baby HABIT-ILE intervention over 2 weeks, between first and second assessment, while the delayed group will continue their usual activities. This last group will receive baby HABIT-ILE intervention after the 3-month assessment. Primary outcome will be the Mini-Assisting Hand Assessment. Secondary outcomes will include behavioural assessments for gross and fine motricity, visual-cognitive-language abilities as well as MRI and kinematics measures. Moreover, parents will determine and score child-relevant goals and fill out questionnaires of participation, daily activities and mobility. ETHICS AND DISSEMINATION Full ethical approval has been obtained by the Comité d'éthique Hospitalo-Facultaire/Université catholique de Louvain, Brussels (2013/01MAR/069 B403201316810g). The recommendations of the ethical board and the Belgian law of 7 May 2004 concerning human experiments will be followed. Parents will sign a written informed consent ahead of participation. Findings will be published in peer-reviewed journals and conference presentations. TRIAL REGISTRATION NUMBER NCT04698395. Registered on the International Clinical Trials Registry Platform (ICTRP) on 2 December 2020 and NIH Clinical Trials Registry on 6 January 2021. URL of trial registry record: https://clinicaltrials.gov/ct2/show/NCT04698395?term=bleyenheuft&draw=1&rank=7.
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Affiliation(s)
| | - Enimie Herman
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
| | - Estelle Gathy
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Neurology Department, Stroke Unit/Motor Learning Lab, CHU UCL Namur, Yvoir, Belgium
| | - Daniela Ebner-Karestinos
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Exercise and Rehabilitation Science Institute, School of Physical Therapy, Faculty of Rehabilitation Science, Universidad Andrés Bello, Santiago, Chile
| | - Rodrigo Araneda
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Exercise and Rehabilitation Science Institute, School of Physical Therapy, Faculty of Rehabilitation Science, Universidad Andrés Bello, Santiago, Chile
| | - Laurence Dricot
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Louvain Bionics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Benoît Macq
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTM), Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Yves Vandermeeren
- Neurology Department, Stroke Unit/Motor Learning Lab, CHU UCL Namur, Yvoir, Belgium
- Louvain Bionics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Yannick Bleyenheuft
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Louvain Bionics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
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Berger A, Beckers E, Joris V, Duchêne G, Danthine V, Delinte N, Cakiroglu I, Sherif S, Morrison EIG, Sánchez AT, Macq B, Dricot L, Vandewalle G, El Tahry R. Locus coeruleus features are linked to vagus nerve stimulation response in drug-resistant epilepsy. Front Neurosci 2024; 18:1296161. [PMID: 38469571 PMCID: PMC10926962 DOI: 10.3389/fnins.2024.1296161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/15/2024] [Indexed: 03/13/2024] Open
Abstract
The locus coeruleus-norepinephrine system is thought to be involved in the clinical effects of vagus nerve stimulation. This system is known to prevent seizure development and induce long-term plastic changes, particularly with the release of norepinephrine in the hippocampus. However, the requisites to become responder to the therapy and the mechanisms of action are still under investigation. Using MRI, we assessed the structural and functional characteristics of the locus coeruleus and microstructural properties of locus coeruleus-hippocampus white matter tracts in patients with drug-resistant epilepsy responding or not to the therapy. Twenty-three drug-resistant epileptic patients with cervical vagus nerve stimulation were recruited for this pilot study, including 13 responders or partial responders and 10 non-responders. A dedicated structural MRI acquisition allowed in vivo localization of the locus coeruleus and computation of its contrast (an accepted marker of LC integrity). Locus coeruleus activity was estimated using functional MRI during an auditory oddball task. Finally, multi-shell diffusion MRI was used to estimate the structural properties of locus coeruleus-hippocampus tracts. These characteristics were compared between responders/partial responders and non-responders and their association with therapy duration was also explored. In patients with a better response to the therapy, trends toward a lower activity and a higher contrast were found in the left medial and right caudal portions of the locus coeruleus, respectively. An increased locus coeruleus contrast, bilaterally over its medial portions, correlated with duration of the treatment. Finally, a higher integrity of locus coeruleus-hippocampus connections was found in patients with a better response to the treatment. These new insights into the neurobiology of vagus nerve stimulation may provide novel markers of the response to the treatment and may reflect neuroplasticity effects occurring in the brain following the implantation.
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Affiliation(s)
- Alexandre Berger
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Synergia Medical SA, Mont-Saint-Guibert, Belgium
- Sleep and Chronobiology Laboratory, GIGA-Cyclotron Research Center-in vivo Imaging, University of Liège, Liège, Belgium
| | - Elise Beckers
- Sleep and Chronobiology Laboratory, GIGA-Cyclotron Research Center-in vivo Imaging, University of Liège, Liège, Belgium
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer’s Centre Limburg, Maastricht University, Maastricht, Netherlands
| | - Vincent Joris
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Department of Neurosurgery, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Gaëtan Duchêne
- GE Center MR Applications, General Electric Healthcare, Diegem, Belgium
| | - Venethia Danthine
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
| | - Nicolas Delinte
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Catholic University of Louvain, Louvain-la-Neuve, Belgium
| | - Inci Cakiroglu
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
| | - Siya Sherif
- Sleep and Chronobiology Laboratory, GIGA-Cyclotron Research Center-in vivo Imaging, University of Liège, Liège, Belgium
| | | | - Andres Torres Sánchez
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Innoviris, Brussels Institute for Research and Innovation, Brussels, Belgium
| | - Benoit Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Catholic University of Louvain, Louvain-la-Neuve, Belgium
| | - Laurence Dricot
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
| | - Gilles Vandewalle
- Sleep and Chronobiology Laboratory, GIGA-Cyclotron Research Center-in vivo Imaging, University of Liège, Liège, Belgium
| | - Riëm El Tahry
- Department of Clinical Neuroscience, Institute of Neuroscience, Catholic University of Louvain, Brussels, Belgium
- Department of Neurology, Center for Refractory Epilepsy, Cliniques Universitaires Saint-Luc, Brussels, Belgium
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Fang C, Yang Z, Wassermann D, Li JR. A simulation-driven supervised learning framework to estimate brain microstructure using diffusion MRI. Med Image Anal 2023; 90:102979. [PMID: 37827109 DOI: 10.1016/j.media.2023.102979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 09/13/2023] [Accepted: 09/22/2023] [Indexed: 10/14/2023]
Abstract
We propose a framework to train supervised learning models on synthetic data to estimate brain microstructure parameters using diffusion magnetic resonance imaging (dMRI). Although further validation is necessary, the proposed framework aims to seamlessly incorporate realistic simulations into dMRI microstructure estimation. Synthetic data were generated from over 1,000 neuron meshes converted from digital neuronal reconstructions and linked to their neuroanatomical parameters (such as soma volume and neurite length) using an optimized diffusion MRI simulator that produces intracellular dMRI signals from the solution of the Bloch-Torrey partial differential equation. By combining random subsets of simulated neuron signals with a free diffusion compartment signal, we constructed a synthetic dataset containing dMRI signals and 40 tissue microstructure parameters of 1.45 million artificial brain voxels. To implement supervised learning models we chose multilayer perceptrons (MLPs) and trained them on a subset of the synthetic dataset to estimate some microstructure parameters, namely, the volume fractions of soma, neurites, and the free diffusion compartment, as well as the area fractions of soma and neurites. The trained MLPs perform satisfactorily on the synthetic test sets and give promising in-vivo parameter maps on the MGH Connectome Diffusion Microstructure Dataset (CDMD). Most importantly, the estimated volume fractions showed low dependence on the diffusion time, the diffusion time independence of the estimated parameters being a desired property of quantitative microstructure imaging. The synthetic dataset we generated will be valuable for the validation of models that map between the dMRI signals and microstructure parameters. The surface meshes and microstructures parameters of the aforementioned neurons have been made publicly available.
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Affiliation(s)
- Chengran Fang
- INRIA Saclay, Equipe IDEFIX, UMA, ENSTA Paris, 828, Boulevard des Maréchaux, 91762 Palaiseau, France; INRIA Saclay, Equipe MIND, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
| | - Zheyi Yang
- INRIA Saclay, Equipe IDEFIX, UMA, ENSTA Paris, 828, Boulevard des Maréchaux, 91762 Palaiseau, France
| | - Demian Wassermann
- INRIA Saclay, Equipe MIND, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
| | - Jing-Rebecca Li
- INRIA Saclay, Equipe IDEFIX, UMA, ENSTA Paris, 828, Boulevard des Maréchaux, 91762 Palaiseau, France.
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Chowdhury R, Wan J, Gardier R, Rafael-Patino J, Thiran JP, Gibou F, Mukherjee A. Molecular Imaging with Aquaporin-Based Reporter Genes: Quantitative Considerations from Monte Carlo Diffusion Simulations. ACS Synth Biol 2023; 12:3041-3049. [PMID: 37793076 DOI: 10.1021/acssynbio.3c00372] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Aquaporins provide a unique approach for imaging genetic activity in deep tissues by increasing the rate of cellular water diffusion, which generates a magnetic resonance contrast. However, distinguishing aquaporin signals from the tissue background is challenging because water diffusion is influenced by structural factors, such as cell size and packing density. Here, we developed a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on subtracting signals at two diffusion times can improve specificity by unambiguously isolating aquaporin signals from the tissue background. We further used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin and established a mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. The quantitative framework developed in this study will enable a broad range of applications in biomedical synthetic biology, requiring the use of aquaporins to noninvasively monitor the location and function of genetically engineered devices in live animals.
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Affiliation(s)
| | | | - Remy Gardier
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), 1005 Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), 1005 Lausanne, Switzerland
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9
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Economou M, Bempt FV, Van Herck S, Wouters J, Ghesquière P, Vanderauwera J, Vandermosten M. Myelin plasticity during early literacy training in at-risk pre-readers. Cortex 2023; 167:86-100. [PMID: 37542803 DOI: 10.1016/j.cortex.2023.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/09/2023] [Accepted: 05/31/2023] [Indexed: 08/07/2023]
Abstract
A growing body of neuroimaging evidence shows that white matter can change as a result of experience and structured learning. Although the majority of previous work has used diffusion MRI to characterize such changes in white matter, diffusion metrics offer limited biological specificity about which microstructural features may be driving white matter plasticity. Recent advances in myelin-specific MRI techniques offer a promising opportunity to assess the specific contribution of myelin in learning-related plasticity. Here we describe the application of such an approach to examine structural plasticity during an early intervention in preliterate children at risk for dyslexia. To this end, myelin water imaging data were collected before and after a 12-week period in (1) at-risk children following early literacy training (n = 13-24), (2) at-risk children engaging with other non-literacy games (n = 10-17) and (3) children without a risk receiving no training (n = 11-22). Before the training, regional risk-related differences were identified, showing higher myelin water fraction (MWF) in right dorsal white matter in at-risk children compared to the typical control group. Concerning intervention-specific effects, our results revealed an increase across left-hemispheric and right ventral MWF over the course of training in the at-risk children receiving early literacy training, but not in the at-risk active control group or the no-risk typical control group. Overall, our results provide support for the use of myelin water imaging as a sensitive tool to investigate white matter and offer a first indication of myelin plasticity in young children at the onset of literacy acquisition.
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Affiliation(s)
- Maria Economou
- Research Group ExpORL, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; KU Leuven Child and Youth Institute, 3000, Leuven, Belgium
| | - Femke Vanden Bempt
- Research Group ExpORL, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; KU Leuven Child and Youth Institute, 3000, Leuven, Belgium
| | - Shauni Van Herck
- Research Group ExpORL, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; KU Leuven Child and Youth Institute, 3000, Leuven, Belgium
| | - Jan Wouters
- Research Group ExpORL, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; KU Leuven Child and Youth Institute, 3000, Leuven, Belgium
| | - Pol Ghesquière
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; KU Leuven Child and Youth Institute, 3000, Leuven, Belgium
| | - Jolijn Vanderauwera
- Psychological Sciences Research Institute, Université Catholique de Louvain, 1348, Louvain-la-Neuve, Belgium; Institute of Neuroscience, Université Catholique de Louvain, 1348, Louvain-la-Neuve, Belgium
| | - Maaike Vandermosten
- Research Group ExpORL, Department of Neurosciences, KU Leuven, 3000, Leuven, Belgium; Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium; KU Leuven Child and Youth Institute, 3000, Leuven, Belgium.
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Huynh KM, Wu Y, Ahmad S, Yap PT. Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14227:131-141. [PMID: 39129859 PMCID: PMC11315459 DOI: 10.1007/978-3-031-43993-3_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Most diffusion biophysical models capture basic properties of tissue microstructure, such as diffusivity and anisotropy. More realistic models that relate the diffusion-weighted signal to cell size and membrane permeability often require simplifying assumptions such as short gradient pulse and Gaussian phase distribution, leading to tissue features that are not necessarily quantitative. Here, we propose a method to quantify tissue microstructure without jeopardizing accuracy owing to unrealistic assumptions. Our method utilizes realistic signals simulated from the geometries of cellular microenvironments as fingerprints, which are then employed in a spherical mean estimation framework to disentangle the effects of orientation dispersion from microscopic tissue properties. We demonstrate the efficacy of microstructure fingerprinting in estimating intra-cellular, extra-cellular, and intra-soma volume fractions as well as axon radius, soma radius, and membrane permeability.
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Affiliation(s)
- Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Ye Wu
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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11
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Chowdhury R, Wan J, Gardier R, Rafael-Patino J, Thiran JP, Gibou F, Mukherjee A. Molecular imaging with aquaporin-based reporter genes: quantitative considerations from Monte Carlo diffusion simulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.09.544324. [PMID: 37333205 PMCID: PMC10274877 DOI: 10.1101/2023.06.09.544324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Aquaporins provide a new class of genetic tools for imaging molecular activity in deep tissues by increasing the rate of cellular water diffusion, which generates magnetic resonance contrast. However, distinguishing aquaporin contrast from the tissue background is challenging because water diffusion is also influenced by structural factors such as cell size and packing density. Here, we developed and experimentally validated a Monte Carlo model to analyze how cell radius and intracellular volume fraction quantitatively affect aquaporin signals. We demonstrated that a differential imaging approach based on time-dependent changes in diffusivity can improve specificity by unambiguously isolating aquaporin-driven contrast from the tissue background. Finally, we used Monte Carlo simulations to analyze the connection between diffusivity and the percentage of cells engineered to express aquaporin, and established a simple mapping that accurately determined the volume fraction of aquaporin-expressing cells in mixed populations. This study creates a framework for broad applications of aquaporins, particularly in biomedicine and in vivo synthetic biology, where quantitative methods to measure the location and performance of genetic devices in whole vertebrates are necessary.
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Affiliation(s)
- Rochishnu Chowdhury
- Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA
| | - Jinyang Wan
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
| | - Remy Gardier
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Frederic Gibou
- Department of Mechanical Engineering, University of California, Santa Barbara, CA 93106, USA
- Department of Computer Science, University of California, Santa Barbara, CA 93106, USA
| | - Arnab Mukherjee
- Department of Chemistry, University of California, Santa Barbara, CA 93106, USA
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106, USA
- Biomolecular Science and Engineering, University of California, Santa Barbara, CA 93106, USA
- Biological Engineering, University of California, Santa Barbara, CA 93106, USA
- Neuroscience Research Institute, University of California, Santa Barbara, CA 93106, USA
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12
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Delinte N, Dricot L, Macq B, Gosse C, Van Reybroeck M, Rensonnet G. Unraveling multi-fixel microstructure with tractography and angular weighting. Front Neurosci 2023; 17:1199568. [PMID: 37351427 PMCID: PMC10282555 DOI: 10.3389/fnins.2023.1199568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 05/15/2023] [Indexed: 06/24/2023] Open
Abstract
Recent advances in MRI technology have enabled richer multi-shell sequences to be implemented in diffusion MRI, allowing the investigation of both the microscopic and macroscopic organization of the brain white matter and its complex network of neural fibers. The emergence of advanced diffusion models has enabled a more detailed analysis of brain microstructure by estimating the signal received from a voxel as the combination of responses from multiple fiber populations. However, disentangling the individual microstructural properties of different macroscopic white matter tracts where those pathways intersect remains a challenge. Several approaches have been developed to assign microstructural properties to macroscopic streamlines, but often present shortcomings. ROI-based heuristics rely on averages that are not tract-specific. Global methods solve a computationally-intensive global optimization but prevent the use of microstructural properties not included in the model and often require restrictive hypotheses. Other methods use atlases that might not be adequate in population studies where the shape of white matter tracts varies significantly between patients. We introduce UNRAVEL, a framework combining the microscopic and macroscopic scales to unravel multi-fixel microstructure by utilizing tractography. The framework includes commonly-used heuristics as well as a new algorithm, estimating the microstructure of a specific white matter tract with angular weighting. Our framework grants considerable freedom as the inputs required, a set of streamlines defining a tract and a multi-fixel diffusion model estimated in each voxel, can be defined by the user. We validate our approach on synthetic data and in vivo data, including a repeated scan of a subject and a population study of children with dyslexia. In each case, we compare the estimation of microstructural properties obtained with angular weighting to other commonly-used approaches. Our framework provides estimations of the microstructure at the streamline level, volumetric maps for visualization and mean microstructural values for the whole tract. The angular weighting algorithm shows increased accuracy, robustness to uncertainties in its inputs and maintains similar or better reproducibility compared to commonly-used analysis approaches. UNRAVEL will provide researchers with a flexible and open-source tool enabling them to study the microstructure of specific white matter pathways with their diffusion model of choice.
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Affiliation(s)
- Nicolas Delinte
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
| | - Laurence Dricot
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
| | - Benoit Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Claire Gosse
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
- Psychological Sciences Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Marie Van Reybroeck
- Institute of NeuroScience, Université Catholique de Louvain, Brussels, Belgium
- Psychological Sciences Research Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Gaetan Rensonnet
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium
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13
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Ebner-Karestinos D, Gathy E, Carton de Tournai A, Herman E, Araneda R, Dricot L, Macq B, Vandermeeren Y, Bleyenheuft Y. Hand-Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE) in adults with chronic stroke: protocol of a randomised controlled trial. BMJ Open 2023; 13:e070642. [PMID: 37055214 PMCID: PMC10106060 DOI: 10.1136/bmjopen-2022-070642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/15/2023] Open
Abstract
INTRODUCTION Stroke causes multiple deficits including motor, sensitive and cognitive impairments, affecting also individual's social participation and independence in activities of daily living (ADL) impacting their quality of life. It has been widely recommended to use goal-oriented interventions with a high amount of task-specific repetitions. These interventions are generally focused only on the upper or lower extremities separately, despite the impairments are observed at the whole-body level and ADL are both frequently bimanual and may require moving around. This highlights the need for interventions targeting both upper and lower extremities. This protocol presents the first adaptation of Hand-Arm Bimanual Intensive Therapy Including Lower Extremities (HABIT-ILE) for adults with acquired hemiparesis. METHODS AND ANALYSIS This randomised controlled trial will include 48 adults with chronic stroke, aged ≥40 years. This study will compare the effect of 50 hours of HABIT-ILE against usual motor activity and regular rehabilitation. HABIT-ILE will be provided in a 2-week, adult's day-camp setting, promoting functional tasks and structured activities. These tasks will continuously progress by increasing their difficulty. Assessed at baseline, 3 weeks after and at 3 months, the primary outcome will be the adults-assisting-hand-assessment stroke; secondary outcomes include behavioural assessments for hand strength and dexterity, a motor learning robotic medical device for quality of bimanual motor control, walking endurance, questionnaires of ADL, stroke impact on participation and self-determined patient-relevant goals, besides neuroimaging measures. ETHICS AND DISSEMINATION This study has full ethical approval from the Comité d'éthique Hospitalo-Facultaire/Université catholique de Louvain, Brussels (reference number: 2013/01MAR/069) and the local medical Ethical Committee of the CHU UCL Namur-site Godinne. Recommendations of the ethical board and the Belgian law of 7 May 2004, concerning human experiments will be followed. Participants will sign a written informed consent ahead of participation. Findings will be published in peer-reviewed journals and conference presentations. TRIAL REGISTRATION NUMBER NCT04664673.
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Affiliation(s)
- Daniela Ebner-Karestinos
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Exercise and Rehabilitation Science Institute, School of Physical Therapy, Faculty of Rehabilitation Science, Universidad Andrés Bello, Santiago, Chile
| | - Estelle Gathy
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
| | | | - Enimie Herman
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
| | - Rodrigo Araneda
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Exercise and Rehabilitation Science Institute, School of Physical Therapy, Faculty of Rehabilitation Science, Universidad Andrés Bello, Santiago, Chile
| | - Laurence Dricot
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Neuroimaging platform (NIMA), Université catholique de Louvain, Brussels, Belgium
| | - Benoît Macq
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTM), Université catholique de Louvain, Louvain-la-Neuve, Belgium
| | - Yves Vandermeeren
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
- Neurology Department, Stroke Unit/NeuroModulation Unit (NeMU), Université catholique de Louvain, Yvoir, Belgium
| | - Yannick Bleyenheuft
- Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium
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14
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Springer CS, Baker EM, Li X, Moloney B, Wilson GJ, Pike MM, Barbara TM, Rooney WD, Maki JH. Metabolic activity diffusion imaging (MADI): I. Metabolic, cytometric modeling and simulations. NMR IN BIOMEDICINE 2023; 36:e4781. [PMID: 35654608 DOI: 10.1002/nbm.4781] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 06/15/2023]
Abstract
Evidence mounts that the steady-state cellular water efflux (unidirectional) first-order rate constant (kio [s-1 ]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na+ , K+ -ATPase (NKA), c MRNKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal 1 H2 O MR kio determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic kio biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA-free) method to determine kio in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well-known diffusion-weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water-filled and -immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density (ρ [cells/μL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI). We simulate water molecule displacements and transverse MR signal decays covering the entirety of b-space from pure water (ρ = V = 0; kio undefined; diffusion coefficient, D0 ) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (Dn ) is diffusion time-dependent, a manifestation of the "diffusion dispersion". When the "well-mixed" (steady-state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering (ρ, V), and (2) permeating (kio ) cytoplasmic membranes, and less so by Dn magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.
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Affiliation(s)
- Charles S Springer
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, Oregon, USA
| | - Eric M Baker
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Xin Li
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, Oregon, USA
| | - Brendan Moloney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Gregory J Wilson
- Department of Radiology, University of Washington, Seattle, Washington, USA
- Bayer Healthcare, Radiology, New Jersey, USA
| | - Martin M Pike
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
| | - Thomas M Barbara
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - William D Rooney
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon, USA
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeffrey H Maki
- Anschutz Medical Center Department of Radiology, University of Colorado, Aurora, Colorado, USA
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15
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Agdestein SD, Tran TN, Li JR. Practical computation of the diffusion MRI signal based on Laplace eigenfunctions: permeable interfaces. NMR IN BIOMEDICINE 2022; 35:e4646. [PMID: 34796990 DOI: 10.1002/nbm.4646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 09/29/2021] [Accepted: 10/13/2021] [Indexed: 06/13/2023]
Abstract
The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch-Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold-standard reference model for the diffusion MRI signal arising from different tissue micro-structures of interest. A closed form representation of this reference diffusion MRI signal, called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the tissue geometry and its diffusion MRI signal, was derived 20 years ago. In addition, once the Laplace eigendecomposition has been computed and saved, the diffusion MRI signal can be calculated for arbitrary diffusion-encoding sequences and b-values at negligible additional cost. In a previous publication, we presented a simulation framework that we implemented inside the MATLAB-based diffusion MRI simulator SpinDoctor that efficiently computes the matrix formalism representation for biological cells subject to impermeable membrane boundary conditions. In this work, we extend our simulation framework to include geometries that contain permeable cell membranes. We describe the new computational techniques that allowed this generalization and we analyze the effects of the magnitude of the permeability coefficient on the eigendecomposition of the diffusion and Bloch-Torrey operators. This work is another step in bringing advanced mathematical tools and numerical method development to the simulation and modeling of diffusion MRI.
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Affiliation(s)
| | | | - Jing-Rebecca Li
- INRIA Saclay-Equipe DEFI, CMAP, Ecole Polytechnique, Palaiseau, France
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16
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Grussu F, Bernatowicz K, Casanova-Salas I, Castro N, Nuciforo P, Mateo J, Barba I, Perez-Lopez R. Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology. Magn Reson Med 2022; 88:365-379. [PMID: 35181943 PMCID: PMC9303340 DOI: 10.1002/mrm.29174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 12/21/2021] [Accepted: 01/07/2022] [Indexed: 11/09/2022]
Abstract
Purpose Relationships between diffusion‐weighted MRI signals and hepatocyte microstructure were investigated to inform liver diffusion MRI modeling, focusing on the following question: Can cell size and diffusivity be estimated at fixed diffusion time, realistic SNR, and negligible contribution from extracellular/extravascular water and exchange? Methods Monte Carlo simulations were performed within synthetic hepatocytes for varying cell size/diffusivity L/D0, and clinical protocols (single diffusion encoding; maximum b‐value: {1000, 1500, 2000} s/mm2; 5 unique gradient duration/separation pairs; SNR = {∞, 100, 80, 40, 20}), accounting for heterogeneity in (D0,L) and perfusion contamination. Diffusion (D) and kurtosis (K) coefficients were calculated, and relationships between (D0,L) and (D,K) were visualized. Functions mapping (D,K) to (D0,L) were computed to predict unseen (D0,L) values, tested for their ability to classify discrete cell‐size contrasts, and deployed on 9.4T ex vivo MRI‐histology data of fixed mouse livers Results Relationships between (D,K) and (D0,L) are complex and depend on the diffusion encoding. Functions mapping D,K to (D0,L) captures salient characteristics of D0(D,K) and L(D,K) dependencies. Mappings are not always accurate, but they enable just under 70% accuracy in a three‐class cell‐size classification task (for SNR = 20, bmax = 1500 s/mm2, δ = 20 ms, and Δ = 75 ms). MRI detects cell‐size contrasts in the mouse livers that are confirmed by histology, but overestimates the largest cell sizes. Conclusion Salient information about liver cell size and diffusivity may be retrieved from minimal diffusion encodings at fixed diffusion time, in experimental conditions and pathological scenarios for which extracellular, extravascular water and exchange are negligible.
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Affiliation(s)
- Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Kinga Bernatowicz
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Irene Casanova-Salas
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Natalia Castro
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Joaquin Mateo
- Prostate Cancer Translational Research Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Ignasi Barba
- NMR Lab, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
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17
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Connecting macroscopic diffusion metrics of cardiac diffusion tensor imaging and microscopic myocardial structures based on simulation. Med Image Anal 2022; 77:102325. [DOI: 10.1016/j.media.2021.102325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 11/26/2021] [Accepted: 11/29/2021] [Indexed: 11/20/2022]
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18
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Slator PJ, Palombo M, Miller KL, Westin C, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, de Almeida Martins JP, Hutter J. Combined diffusion-relaxometry microstructure imaging: Current status and future prospects. Magn Reson Med 2021; 86:2987-3011. [PMID: 34411331 PMCID: PMC8568657 DOI: 10.1002/mrm.28963] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/15/2022]
Abstract
Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.
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Affiliation(s)
- Paddy J. Slator
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Marco Palombo
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Carl‐Fredrik Westin
- Department of RadiologyBrigham and Women’s HospitalHarvard Medical SchoolBostonMAUSA
| | - Frederik Laun
- Institute of RadiologyUniversity Hospital ErlangenFriedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Daeun Kim
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Justin P. Haldar
- Ming Hsieh Department of Electrical and Computer EngineeringUniversity of Southern CaliforniaLos AngelesCAUSA
- Signal and Image Processing InstituteUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Dan Benjamini
- The Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentBethesdaMDUSA
- The Center for Neuroscience and Regenerative MedicineUniformed Service University of the Health SciencesBethesdaMDUSA
| | | | - Joao P. de Almeida Martins
- Division of Physical Chemistry, Department of ChemistryLund UniversityLundSweden
- Department of Radiology and Nuclear MedicineSt. Olav’s University HospitalTrondheimNorway
| | - Jana Hutter
- Centre for Biomedical EngineeringSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
- Centre for the Developing BrainSchool of Biomedical Engineering and ImagingKing’s College LondonLondonUK
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Masutani Y. Recent Advances in Parameter Inference for Diffusion MRI Signal Models. Magn Reson Med Sci 2021; 21:132-147. [PMID: 34024863 PMCID: PMC9199979 DOI: 10.2463/mrms.rev.2021-0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.
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Lu PJ, Barakovic M, Weigel M, Rahmanzadeh R, Galbusera R, Schiavi S, Daducci A, La Rosa F, Bach Cuadra M, Sandkühler R, Kuhle J, Kappos L, Cattin P, Granziera C. GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation. Front Neurosci 2021; 15:647535. [PMID: 33889069 PMCID: PMC8055933 DOI: 10.3389/fnins.2021.647535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 02/23/2021] [Indexed: 12/02/2022] Open
Abstract
Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is insensitive to the damage to the microenvironment of the brain tissue and the heterogeneity of MS lesions. In contrast, the damaged tissue can be characterized by mathematical models on multishell diffusion imaging data, which measure different compartmental water diffusion. In this work, we obtained 12 diffusion measures from eight diffusion models, and we applied a deep-learning attention-based convolutional neural network (CNN) (GAMER-MRI) to select the most discriminating measures in the classification of MS lesions and the perilesional tissue by attention weights. Furthermore, we provided clinical and biological validation of the chosen metrics-and of their most discriminative combinations-by correlating their respective mean values in MS patients with the corresponding Expanded Disability Status Scale (EDSS) and the serum level of neurofilament light chain (sNfL), which are measures of disability and neuroaxonal damage. Our results show that the neurite density index from neurite orientation and dispersion density imaging (NODDI), the measures of the intra-axonal and isotropic compartments from microstructural Bayesian approach, and the measure of the intra-axonal compartment from the spherical mean technique NODDI were the most discriminating (respective attention weights were 0.12, 0.12, 0.15, and 0.13). In addition, the combination of the neurite density index from NODDI and the measures for the intra-axonal and isotropic compartments from the microstructural Bayesian approach exhibited a stronger correlation with EDSS and sNfL than the individual measures. This work demonstrates that the proposed method might be useful to select the microstructural measures that are most discriminative of focal tissue damage and that may also be combined to a unique contrast to achieve stronger correlations to clinical disability and neuroaxonal damage.
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Affiliation(s)
- Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Reza Rahmanzadeh
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Robin Sandkühler
- Center for Medical Image Analysis and Navigation, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Philippe Cattin
- Center for Medical Image Analysis and Navigation, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
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21
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Fang C, Nguyen VD, Wassermann D, Li JR. Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module. Neuroimage 2020; 222:117198. [PMID: 32730957 DOI: 10.1016/j.neuroimage.2020.117198] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 06/30/2020] [Accepted: 07/22/2020] [Indexed: 02/08/2023] Open
Abstract
The diffusion MRI signal arising from neurons can be numerically simulated by solving the Bloch-Torrey partial differential equation. In this paper we present the Neuron Module that we implemented within the Matlab-based diffusion MRI simulation toolbox SpinDoctor. SpinDoctor uses finite element discretization and adaptive time integration to solve the Bloch-Torrey partial differential equation for general diffusion-encoding sequences, at multiple b-values and in multiple diffusion directions. In order to facilitate the diffusion MRI simulation of realistic neurons by the research community, we constructed finite element meshes for a group of 36 pyramidal neurons and a group of 29 spindle neurons whose morphological descriptions were found in the publicly available neuron repository NeuroMorpho.Org. These finite elements meshes range from having 15,163 nodes to 622,553 nodes. We also broke the neurons into the soma and dendrite branches and created finite elements meshes for these cell components. Through the Neuron Module, these neuron and cell components finite element meshes can be seamlessly coupled with the functionalities of SpinDoctor to provide the diffusion MRI signal attributable to spins inside neurons. We make these meshes and the source code of the Neuron Module available to the public as an open-source package. To illustrate some potential uses of the Neuron Module, we show numerical examples of the simulated diffusion MRI signals in multiple diffusion directions from whole neurons as well as from the soma and dendrite branches, and include a comparison of the high b-value behavior between dendrite branches and whole neurons. In addition, we demonstrate that the neuron meshes can be used to perform Monte-Carlo diffusion MRI simulations as well. We show that at equivalent accuracy, if only one gradient direction needs to be simulated, SpinDoctor is faster than a GPU implementation of Monte-Carlo, but if many gradient directions need to be simulated, there is a break-even point when the GPU implementation of Monte-Carlo becomes faster than SpinDoctor. Furthermore, we numerically compute the eigenfunctions and the eigenvalues of the Bloch-Torrey and the Laplace operators on the neuron geometries using a finite elements discretization, in order to give guidance in the choice of the space and time discretization parameters for both finite elements and Monte-Carlo approaches. Finally, we perform a statistical study on the set of 65 neurons to test some candidate biomakers that can potentially indicate the soma size. This preliminary study exemplifies the possible research that can be conducted using the Neuron Module.
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Affiliation(s)
- Chengran Fang
- INRIA Saclay, Equipe DEFI, CMAP, Ecole Polytechnique, 91128 Palaiseau Cedex, France; INRIA Saclay, Equipe Parietal, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
| | - Van-Dang Nguyen
- Department of Computational Science and Technology, KTH Royal Institute of Technology, Sweden
| | - Demian Wassermann
- INRIA Saclay, Equipe Parietal, 1 Rue Honoré d'Estienne d'Orves, 91120 Palaiseau, France
| | - Jing-Rebecca Li
- INRIA Saclay, Equipe DEFI, CMAP, Ecole Polytechnique, 91128 Palaiseau Cedex, France.
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22
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Li JR, Tran TN, Nguyen VD. Practical computation of the diffusion MRI signal of realistic neurons based on Laplace eigenfunctions. NMR IN BIOMEDICINE 2020; 33:e4353. [PMID: 32725935 DOI: 10.1002/nbm.4353] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/14/2020] [Accepted: 05/26/2020] [Indexed: 06/11/2023]
Abstract
The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch-Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold-standard reference model for the diffusion MRI signal arising from different tissue micro-structures of interest. A closed form representation of this reference diffusion MRI signal called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the biological cell and its diffusion MRI signal, was derived 20 years ago. In addition, once the Laplace eigendecomposition has been computed and saved, the diffusion MRI signal can be calculated for arbitrary diffusion-encoding sequences and b-values at negligible additional cost. Up to now, this representation, though mathematically elegant, has not been often used as a practical model of the diffusion MRI signal, due to the difficulties of calculating the Laplace eigendecomposition in complicated geometries. In this paper, we present a simulation framework that we have implemented inside the MATLAB-based diffusion MRI simulator SpinDoctor that efficiently computes the matrix formalism representation for realistic neurons using the finite element method. We show that the matrix formalism representation requires a few hundred eigenmodes to match the reference signal computed by solving the Bloch-Torrey equation when the cell geometry originates from realistic neurons. As expected, the number of eigenmodes required to match the reference signal increases with smaller diffusion time and higher b-values. We also convert the eigenvalues to a length scale and illustrate the link between the length scale and the oscillation frequency of the eigenmode in the cell geometry. We give the transformation that links the Laplace eigenfunctions to the eigenfunctions of the Bloch-Torrey operator and compute the Bloch-Torrey eigenfunctions and eigenvalues. This work is another step in bringing advanced mathematical tools and numerical method development to the simulation and modeling of diffusion MRI.
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Affiliation(s)
- Jing-Rebecca Li
- INRIA Saclay-Equipe DEFI, CMAP, Ecole Polytechnique, Palaiseau, France
| | - Try Nguyen Tran
- INRIA Saclay-Equipe DEFI, CMAP, Ecole Polytechnique, Palaiseau, France
| | - Van-Dang Nguyen
- Division of Computational Science and Technology, KTH Royal Institute of Technology, Sweden
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23
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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24
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ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation. Neuroimage 2020; 220:117107. [PMID: 32622984 PMCID: PMC7903162 DOI: 10.1016/j.neuroimage.2020.117107] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/17/2020] [Accepted: 06/25/2020] [Indexed: 11/27/2022] Open
Abstract
This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches. We present ConFiG, a biologically motivated numerical phantom generator for white matter. ConFiG produces phantoms with state-of-the-art density and realistic microstructure. Diffusion MRI simulations in ConFiG phantoms are comparable to real dMRI signals.
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25
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Rafael-Patino J, Romascano D, Ramirez-Manzanares A, Canales-Rodríguez EJ, Girard G, Thiran JP. Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results. Front Neuroinform 2020; 14:8. [PMID: 32210781 PMCID: PMC7076166 DOI: 10.3389/fninf.2020.00008] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 02/20/2020] [Indexed: 12/13/2022] Open
Abstract
Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way toward more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.
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Affiliation(s)
- Jonathan Rafael-Patino
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - David Romascano
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Erick Jorge Canales-Rodríguez
- Radiology Department, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain.,Mental Health Research Networking Center (CIBERSAM), Madrid, Spain
| | - Gabriel Girard
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Radiology Department, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,Centre d'Imagerie Biomédicale (CIBM), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.,Radiology Department, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland.,Centre d'Imagerie Biomédicale (CIBM), Lausanne, Switzerland.,University of Lausanne, Lausanne, Switzerland
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26
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Vincent M, Palombo M, Valette J. Revisiting double diffusion encoding MRS in the mouse brain at 11.7T: Which microstructural features are we sensitive to? Neuroimage 2020; 207:116399. [PMID: 31778817 PMCID: PMC7014823 DOI: 10.1016/j.neuroimage.2019.116399] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 10/25/2019] [Accepted: 11/22/2019] [Indexed: 11/19/2022] Open
Abstract
Brain metabolites, such as N-acetylaspartate or myo-inositol, are constantly probing their local cellular environment under the effect of diffusion. Diffusion-weighted NMR spectroscopy therefore presents unparalleled potential to yield cell-type specific microstructural information. Double diffusion encoding (DDE) consists in applying two diffusion blocks, where gradient's direction in the second block is varied during the course of the experiment. Unlike single diffusion encoding, DDE measurements at long mixing time display some angular modulation of the signal amplitude which reflects microscopic anisotropy (μA), while requiring relatively low gradient strength. This angular dependence has been formerly used to quantify cell fiber diameter using a model of isotropically oriented infinite cylinders. However, how additional features of the cell microstructure (such as cell body diameter, fiber length and branching) may also influence the DDE signal has been little explored. Here, we used a cryoprobe as well as state-of-the-art post-processing to perform DDE acquisitions with high accuracy and precision in the mouse brain at 11.7 T. We then compared our results to simulated DDE datasets obtained in various 3D cell models in order to pinpoint which features of cell morphology may influence the most the angular dependence of the DDE signal. While the infinite cylinder model poorly fits our experimental data, we show that incorporating branched fiber structure in our model allows more realistic interpretation of the DDE signal. Lastly, data acquired in the short mixing time regime suggest that some sensitivity to cell body diameter might be retrieved, although additional experiments would be required to further support this statement.
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Affiliation(s)
- Mélissa Vincent
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MIRCen, F-92260, Fontenay-aux-Roses, France; Neurodegenerative Diseases Laboratory, UMR9199, CEA, CNRS, Université Paris Sud, Université Paris-Saclay, F-92260, Fontenay-aux-Roses, France
| | - Marco Palombo
- Department of Computer Science and Centre for Medical Image Computing, University College of London, London, WC1E 6BT, United Kingdom
| | - Julien Valette
- Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), MIRCen, F-92260, Fontenay-aux-Roses, France; Neurodegenerative Diseases Laboratory, UMR9199, CEA, CNRS, Université Paris Sud, Université Paris-Saclay, F-92260, Fontenay-aux-Roses, France.
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27
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Li JR, Nguyen VD, Tran TN, Valdman J, Trang CB, Nguyen KV, Vu DTS, Tran HA, Tran HTA, Nguyen TMP. SpinDoctor: A MATLAB toolbox for diffusion MRI simulation. Neuroimage 2019; 202:116120. [DOI: 10.1016/j.neuroimage.2019.116120] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 08/21/2019] [Accepted: 08/22/2019] [Indexed: 12/15/2022] Open
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28
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Ginsburger K, Matuschke F, Poupon F, Mangin JF, Axer M, Poupon C. MEDUSA: A GPU-based tool to create realistic phantoms of the brain microstructure using tiny spheres. Neuroimage 2019; 193:10-24. [DOI: 10.1016/j.neuroimage.2019.02.055] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 02/18/2019] [Accepted: 02/21/2019] [Indexed: 12/16/2022] Open
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29
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Callaghan R, Alexander DC, Zhang H, Palombo M. Contextual Fibre Growth to Generate Realistic Axonal Packing for Diffusion MRI Simulation. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/978-3-030-20351-1_33] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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30
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Baete SH, Cloos MA, Lin YC, Placantonakis DG, Shepherd T, Boada FE. Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles. Neuroimage 2019; 198:231-241. [PMID: 31102735 DOI: 10.1016/j.neuroimage.2019.05.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 12/18/2022] Open
Abstract
Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.
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Affiliation(s)
- Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA.
| | - Martijn A Cloos
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA; The Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY, USA
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA
| | - Dimitris G Placantonakis
- Dept. of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU School of Medicine, New York, NY, USA
| | - Timothy Shepherd
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA
| | - Fernando E Boada
- Center for Advanced Imaging Innovation and Research (CAI(2)R), NYU School of Medicine, New York, NY, USA; Center for Biomedical Imaging, Dept. of Radiology, NYU School of Medicine, New York, NY, USA
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