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Canales-Rodríguez EJ, Pizzolato M, Zhou FL, Barakovic M, Thiran JP, Jones DK, Parker GJM, Dyrby TB. Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI. Magn Reson Med 2024; 91:2579-2596. [PMID: 38192108 DOI: 10.1002/mrm.29991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approach and a T2-based pore size estimation technique. THEORY AND METHODS A general diffusion-relaxation theoretical model for the spherical mean signal from water molecules within a distribution of cylinders with varying radii was introduced, encompassing the evaluated models as particular cases. Additionally, a new numerical approach was presented for estimating effective radii (i.e., MRI-visible mean radii) from the ground truth radii distributions, not reliant on previous theoretical approximations and adaptable to various acquisition sequences. The ground truth radii were obtained from scanning electron microscope images. RESULTS Both methods show a linear relationship between effective radii estimated from MRI data and ground-truth radii distributions, although some discrepancies were observed. The spherical mean power-law method overestimated fiber radii. Conversely, the T2-based method exhibited higher sensitivity to smaller fiber radii, but faced limitations in accurately estimating the radius in one particular phantom, possibly because of material-specific relaxation changes. CONCLUSION The study demonstrates the feasibility of both techniques to predict pore sizes of hollow microfibers. The T2-based technique, unlike the spherical mean power-law method, does not demand ultra-high diffusion gradients, but requires calibration with known radius distributions. This research contributes to the ongoing development and evaluation of neuroimaging techniques for fiber radius estimation, highlights the advantages and limitations of both methods, and provides datasets for reproducible research.
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Affiliation(s)
- Erick J Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Marco Pizzolato
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
| | - Feng-Lei Zhou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK
- MicroPhantoms Limited, Cambridge, UK
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Centre d'Imagerie Biomédicale (CIBM), EPFL, Lausanne, Switzerland
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Geoffrey J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK
- Department of Neuroinflammation, Queen Square Institute of Neurology, University College London (UCL), London, UK
- Bioxydyn Limited, Manchester, UK
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
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2
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Veldmann M, Edwards LJ, Pine KJ, Ehses P, Ferreira M, Weiskopf N, Stoecker T. Improving MR axon radius estimation in human white matter using spiral acquisition and field monitoring. Magn Reson Med 2024. [PMID: 38817204 DOI: 10.1002/mrm.30180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/08/2024] [Accepted: 05/15/2024] [Indexed: 06/01/2024]
Abstract
PURPOSE To compare MR axon radius estimation in human white matter using a multiband spiral sequence combined with field monitoring to the current state-of-the-art echo-planar imaging (EPI)-based approach. METHODS A custom multiband spiral sequence was used for diffusion-weighted imaging at ultra-highb $$ b $$ -values. Field monitoring and higher order image reconstruction were employed to greatly reduce artifacts in spiral images. Diffusion weighting parameters were chosen to match a state-of-the art EPI-based axon radius mapping protocol. The spiral approach was compared to the EPI approach by comparing the image signal-to-noise ratio (SNR) and performing a test-retest study to assess the respective variability and repeatability of axon radius mapping. Effective axon radius estimates were compared over white matter voxels and along the left corticospinal tract. RESULTS Increased SNR and reduced artifacts in spiral images led to reduced variability in resulting axon radius maps, especially in low-SNR regions. Test-retest variability was reduced by a factor of approximately 1.5 using the spiral approach. Reduced repeatability due to significant bias was found for some subjects in both spiral and EPI approaches, and attributed to scanner instability, pointing to a previously unknown limitation of the state-of-the-art approach. CONCLUSION Combining spiral readouts with field monitoring improved mapping of the effective axon radius compared to the conventional EPI approach.
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Affiliation(s)
- Marten Veldmann
- MR Physics, German Center for Neurodegenerative Diseases (DZNE) e.V, Bonn, Germany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Philipp Ehses
- MR Physics, German Center for Neurodegenerative Diseases (DZNE) e.V, Bonn, Germany
| | - Mónica Ferreira
- Clinical Research, German Center for Neurodegenerative Diseases (DZNE) e.V, Bonn, Germany
- University of Bonn, Bonn, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth System Sciences, Leipzig University, Leipzig, Germany
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Tony Stoecker
- MR Physics, German Center for Neurodegenerative Diseases (DZNE) e.V, Bonn, Germany
- Department of Physics & Astronomy, University of Bonn, Bonn, Germany
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Johnson JTE, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. Hum Brain Mapp 2024; 45:e26697. [PMID: 38726888 PMCID: PMC11082920 DOI: 10.1002/hbm.26697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/28/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency,ω $$ \omega $$ , in addition to the diffusion tensor,D $$ \mathbf{D} $$ , and relaxation,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , correlations. AD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on theirD ω - R 1 - R 2 $$ \mathbf{D}\left(\omega \right)-{R}_1-{R}_2 $$ distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T. E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthBethesdaMarylandUSA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of HealthBaltimoreMarylandUSA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU)ErlangenGermany
| | - Jan Martin
- Department of ChemistryLund UniversityLundSweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIHBaltimoreMarylandUSA
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4
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Engel M, Mueller L, Döring A, Afzali M, Jones DK. Maximizing SNR per unit time in diffusion MRI with multiband T-Hex spirals. Magn Reson Med 2024; 91:1323-1336. [PMID: 38156527 PMCID: PMC10953427 DOI: 10.1002/mrm.29953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/03/2023] [Accepted: 11/14/2023] [Indexed: 12/30/2023]
Abstract
PURPOSE The characterization of tissue microstructure using diffusion MRI (dMRI) signals is rapidly evolving, with increasing sophistication of signal representations and microstructure models. However, this progress often requires signals to be acquired with very high b-values (e.g., b > 30 ms/μm2 ), along many directions, and using multiple b-values, leading to long scan times and extremely low SNR in dMRI images. The purpose of this work is to boost the SNR efficiency of dMRI by combining three particularly efficient spatial encoding techniques and utilizing a high-performance gradient system (Gmax ≤ 300 mT/m) for efficient diffusion encoding. METHODS Spiral readouts, multiband imaging, and sampling on tilted hexagonal grids (T-Hex) are combined and implemented on a 3T MRI system with ultra-strong gradients. Image reconstruction is performed through an iterative cg-SENSE algorithm incorporating static off-resonance distributions and field dynamics as measured with an NMR field camera. Additionally, T-Hex multiband is combined with a more conventional EPI-readout and compared with state-of-the-art blipped-CAIPIRINHA sampling. The advantage of the proposed approach is furthermore investigated for clinically available gradient performance and diffusion kurtosis imaging. RESULTS High fidelity in vivo images with b-values up to 40 ms/μm2 are obtained. The approach provides superior SNR efficiency over other state-of-the-art multiband diffusion readout schemes. CONCLUSION The demonstrated gains hold promise for the widespread dissemination of advanced microstructural scans, especially in clinical populations.
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Affiliation(s)
- Maria Engel
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
| | - Lars Mueller
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - André Döring
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC)Cardiff UniversityCardiffUK
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5
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Ramos-Llordén G, Park DJ, Kirsch JE, Scholz A, Keil B, Maffei C, Lee HH, Bilgic B, Edlow BL, Mekkaoui C, Yendiki A, Witzel T, Huang SY. Eddy current-induced artifact correction in high b-value ex vivo human brain diffusion MRI with dynamic field monitoring. Magn Reson Med 2024; 91:541-557. [PMID: 37753621 PMCID: PMC10842131 DOI: 10.1002/mrm.29873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 08/30/2023] [Accepted: 09/02/2023] [Indexed: 09/28/2023]
Abstract
PURPOSE To investigate whether spatiotemporal magnetic field monitoring can correct pronounced eddy current-induced artifacts incurred by strong diffusion-sensitizing gradients up to 300 mT/m used in high b-value diffusion-weighted (DW) EPI. METHODS A dynamic field camera equipped with 16 1 H NMR field probes was first used to characterize field perturbations caused by residual eddy currents from diffusion gradients waveforms in a 3D multi-shot EPI sequence on a 3T Connectom scanner for different gradient strengths (up to 300 mT/m), diffusion directions, and shots. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-gradient strength, submillimeter resolution whole-brain ex vivo diffusion MRI. A 3D multi-shot image reconstruction framework was developed that incorporated the nonlinear phase evolution measured with the dynamic field camera. RESULTS Phase perturbations in the readout induced by residual eddy currents from strong diffusion gradients are highly nonlinear in space and time, vary among diffusion directions, and interfere significantly with the image encoding gradients, changing the k-space trajectory. During the readout, phase modulations between odd and even EPI echoes become non-static and diffusion encoding direction-dependent. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting reduction approaches such as navigator- and structured low-rank-based methods or MUSE followed by image-based distortion correction with the FSL tool "eddy." CONCLUSION Strong eddy current artifacts characteristic of high-gradient strength DW-EPI can be well corrected with dynamic field monitoring-based image reconstruction.
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Affiliation(s)
- Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Daniel J. Park
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - John E. Kirsch
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Alina Scholz
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Baldingerstrasse 1, 35043, Marburg, Germany
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Brian L. Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Choukri Mekkaoui
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | | | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
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6
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Liu W, Zhuo Z, Liu Y, Ye C. One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer. Med Image Anal 2023; 90:102968. [PMID: 37729793 DOI: 10.1016/j.media.2023.102968] [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: 01/04/2023] [Revised: 07/24/2023] [Accepted: 09/11/2023] [Indexed: 09/22/2023]
Abstract
The use of convolutional neural networks (CNNs) has allowed accurate white matter (WM) tract segmentation on diffusion magnetic resonance imaging (dMRI). To train the CNN-based segmentation models, a large number of scans on which WM tracts are annotated need to be collected, and these annotated scans can be accumulated over a long period of time. However, when novel WM tracts that are different from existing annotated WM tracts are of interest, additional annotations are required for their segmentation. Due to the cost of manual annotations, methods have been developed for few-shot segmentation of novel WM tracts, where the segmentation knowledge is transferred from existing WM tracts to novel WM tracts and the amount of annotated data for novel WM tracts is reduced. Despite these developments, it is desirable to further reduce the amount of annotated data to the one-shot setting with a single annotated image. To address this problem, we develop an approach to one-shot segmentation of novel WM tracts. Our method follows the existing pretraining/fine-tuning framework that transfers segmentation knowledge from existing to novel WM tracts. First, as there is extremely scarce annotated data in the one-shot setting, we design several different data augmentation strategies so that extensive data augmentation can be performed to obtain extra synthetic training data. The data augmentation strategies are based on image masking and thus applicable to the one-shot setting. Second, to address overfitting and knowledge forgetting in the fine-tuning stage that can be more severe given limited training data, we propose an adaptive knowledge transfer strategy that selects the network weights to be updated. The data augmentation and adaptive knowledge transfer strategies are combined to train the segmentation model. Considering that the different data augmentation strategies can generate synthetic data that contain potentially conflicting information, we apply the data augmentation strategies separately, each leading to a different segmentation model. The results predicted by the different models are fused to produce the final segmentation. We validated our method on two brain dMRI datasets, including a public dataset and an in-house dataset. Different settings were considered for the validation, and the results show that the proposed method improves the one-shot segmentation of novel WM tracts.
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Affiliation(s)
- Wan Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
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7
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Johnson JT, Irfanoglu MO, Manninen E, Ross TJ, Yang Y, Laun FB, Martin J, Topgaard D, Benjamini D. In vivo disentanglement of diffusion frequency-dependence, tensor shape, and relaxation using multidimensional MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561702. [PMID: 37987005 PMCID: PMC10659440 DOI: 10.1101/2023.10.10.561702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD-MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD-MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, ω , in addition to the diffusion tensor, D , and relaxation, R 1 , R 2 , correlations. A D ( ω ) - R 1 - R 2 clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD-MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well-defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD-MRI encoding design demands a nuanced denoising approach. The MD-MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their D ( ω ) - R 1 - R 2 distribution, enabling the creation of microstructure-specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.
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Affiliation(s)
- Jessica T.E. Johnson
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - M. Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | - Eppu Manninen
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Thomas J. Ross
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jan Martin
- Department of Chemistry, Lund University, Lund, Sweden
| | | | - Dan Benjamini
- Multiscale Imaging and Integrative Biophysics Unit, National Institute on Aging, NIH, Baltimore, MD, USA
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Schiavi S, Palombo M, Zacà D, Tazza F, Lapucci C, Castellan L, Costagli M, Inglese M. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp 2023; 44:4792-4811. [PMID: 37461286 PMCID: PMC10400787 DOI: 10.1002/hbm.26416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 05/02/2023] [Accepted: 06/23/2023] [Indexed: 08/05/2023] Open
Abstract
Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.
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Affiliation(s)
- Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Marco Palombo
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | | | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- HNSR, IRRCS Ospedale Policlinico San MartinoGenoaItaly
| | - Lucio Castellan
- Department of NeuroradiologyIRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- Laboratory of Medical Physics and Magnetic ResonanceIRCCS Stella MarisPisaItaly
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
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9
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Barakovic M, Pizzolato M, Tax CMW, Rudrapatna U, Magon S, Dyrby TB, Granziera C, Thiran JP, Jones DK, Canales-Rodríguez EJ. Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology. Front Neurosci 2023; 17:1209521. [PMID: 37638307 PMCID: PMC10457121 DOI: 10.3389/fnins.2023.1209521] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.
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Affiliation(s)
- Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
| | - Stefano Magon
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Tim B. Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Centre d’Imagerie Biomédicale (CIBM), EPFL, Lausanne, Switzerland
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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10
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Villarreal-Haro JL, Gardier R, Canales-Rodríguez EJ, Fischi-Gomez E, Girard G, Thiran JP, Rafael-Patiño J. CACTUS: a computational framework for generating realistic white matter microstructure substrates. Front Neuroinform 2023; 17:1208073. [PMID: 37603781 PMCID: PMC10434236 DOI: 10.3389/fninf.2023.1208073] [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: 04/18/2023] [Accepted: 07/13/2023] [Indexed: 08/23/2023] Open
Abstract
Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure properties of interest, and exploring the robustness and accuracy of estimation methods. However, accurate simulations require substrates that reflect the main microstructural features of the studied tissue. To address this challenge, we introduce a novel computational workflow, CACTUS (Computational Axonal Configurator for Tailored and Ultradense Substrates), for generating synthetic white matter substrates. Our approach allows constructing substrates with higher packing density than existing methods, up to 95% intra-axonal volume fraction, and larger voxel sizes of up to 500μm3 with rich fibre complexity. CACTUS generates bundles with angular dispersion, bundle crossings, and variations along the fibres of their inner and outer radii and g-ratio. We achieve this by introducing a novel global cost function and a fibre radial growth approach that allows substrates to match predefined targeted characteristics and mirror those reported in histological studies. CACTUS improves the development of complex synthetic substrates, paving the way for future applications in microstructure imaging.
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Affiliation(s)
- Juan Luis Villarreal-Haro
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
| | - Remy Gardier
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
| | - Elda Fischi-Gomez
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Gabriel Girard
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Jonathan Rafael-Patiño
- Signal Processing Laboratory (LTS5), École Polytechnique Frale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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11
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Gast H, Horowitz A, Krupnik R, Barazany D, Lifshits S, Ben-Amitay S, Assaf Y. A Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI). Neuroinformatics 2023; 21:469-482. [PMID: 37036548 PMCID: PMC10406702 DOI: 10.1007/s12021-023-09630-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/24/2023] [Indexed: 04/11/2023]
Abstract
In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.
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Affiliation(s)
- Hila Gast
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
| | - Assaf Horowitz
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Ronnie Krupnik
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Daniel Barazany
- The Strauss center for neuroimaging, Tel Aviv University, Tel Aviv, Israel
| | - Shlomi Lifshits
- Department of Statistics and Operations Research, Faculty of Exact Sciences, Tel Aviv University, Tel-Aviv, Israel
| | - Shani Ben-Amitay
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- The Strauss center for neuroimaging, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
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12
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Pizzolato M, Canales-Rodríguez EJ, Andersson M, Dyrby TB. Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI. Med Image Anal 2023; 86:102767. [PMID: 36867913 DOI: 10.1016/j.media.2023.102767] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/13/2022] [Accepted: 02/08/2023] [Indexed: 02/18/2023]
Abstract
We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from only axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown distribution of axonal orientations. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated although needed for modeling axons - especially in the context of multi-compartmental modeling. We introduce a new general method for the estimation of both the axial and radial axonal diffusivities at strong diffusion weightings based on kernel zonal modeling. The method could lead to estimates that are free from partial volume bias with gray matter or other isotropic compartments. The method is tested on publicly available data from the MGH Adult Diffusion Human Connectome project. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii from only two shells. The estimation problem is also addressed from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.
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Affiliation(s)
- Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.
| | | | - Mariam Andersson
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Tim B Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
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13
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Ramos-Llordén G, Park D, Kirsch JE, Scholz A, Keil B, Maffei C, Lee HH, Bilgiç B, Edlow BL, Mekkaoui C, Yendiki A, Witzel T, Huang SY. Eddy current-induced artifacts correction in high gradient strength diffusion MRI with dynamic field monitoring: demonstration in ex vivo human brain imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528684. [PMID: 36824894 PMCID: PMC9948962 DOI: 10.1101/2023.02.15.528684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Purpose To demonstrate the advantages of spatiotemporal magnetic field monitoring to correct eddy current-induced artifacts (ghosting and geometric distortions) in high gradient strength diffusion MRI (dMRI). Methods A dynamic field camera with 16 NMR field probes was used to characterize eddy current fields induced from diffusion gradients for different gradients strengths (up to 300 mT/m), diffusion directions, and shots in a 3D multi-shot EPI sequence on a 3T Connectom scanner. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-resolution whole brain ex vivo dMRI. A 3D multi-shot image reconstruction framework was informed with the actual nonlinear phase evolution measured with the dynamic field camera, thereby accounting for high-order eddy currents fields on top of the image encoding gradients in the image formation model. Results Eddy current fields from diffusion gradients at high gradient strength in a 3T Connectom scanner are highly nonlinear in space and time, inducing high-order spatial phase modulations between odd/even echoes and shots that are not static during the readout. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting approaches such as navigator- and structured low-rank-based methods or MUSE, followed by image-based distortion correction with eddy. Improved dMRI analysis is demonstrated with diffusion tensor imaging and high-angular resolution diffusion imaging. Conclusion Strong eddy current artifacts characteristic of high gradient strength dMRI can be well corrected with dynamic field monitoring-based image reconstruction, unlike the two-step approach consisting of ghosting correction followed by geometric distortion reduction with eddy.
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14
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Chakwizira A, Westin C, Brabec J, Lasič S, Knutsson L, Szczepankiewicz F, Nilsson M. Diffusion MRI with pulsed and free gradient waveforms: Effects of restricted diffusion and exchange. NMR IN BIOMEDICINE 2023; 36:e4827. [PMID: 36075110 PMCID: PMC10078514 DOI: 10.1002/nbm.4827] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 08/27/2022] [Accepted: 09/06/2022] [Indexed: 05/06/2023]
Abstract
Monitoring time dependence with diffusion MRI yields observables sensitive to compartment sizes (restricted diffusion) and membrane permeability (water exchange). However, restricted diffusion and exchange have opposite effects on the diffusion-weighted signal, which can lead to errors in parameter estimates. In this work, we propose a signal representation that incorporates the effects of both restricted diffusion and exchange up to second order in b-value and is compatible with gradient waveforms of arbitrary shape. The representation features mappings from a gradient waveform to two scalars that separately control the sensitivity to restriction and exchange. We demonstrate that these scalars span a two-dimensional space that can be used to choose waveforms that selectively probe restricted diffusion or exchange, eliminating the correlation between the two phenomena. We found that waveforms with specific but unconventional shapes provide an advantage over conventional pulsed and oscillating gradient acquisitions. We also show that parametrization of waveforms into a two-dimensional space can be used to understand protocols from other approaches that probe restricted diffusion and exchange. For example, we found that the variation of mixing time in filter-exchange imaging corresponds to variation of our exchange-weighting scalar at a fixed value of the restriction-weighting scalar. The proposed signal representation was evaluated using Monte Carlo simulations in identical parallel cylinders with hexagonal and random packing as well as parallel cylinders with gamma-distributed radii. Results showed that the approach is sensitive to sizes in the interval 4-12 μm and exchange rates in the simulated range of 0 to 20 s - 1 , but also that there is a sensitivity to the extracellular geometry. The presented theory constitutes a simple and intuitive description of how restricted diffusion and exchange influence the signal as well as a guide to protocol design capable of separating the two effects.
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Affiliation(s)
- Arthur Chakwizira
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Carl‐Fredrik Westin
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Brabec
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
| | - Samo Lasič
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital ‐ Amager and HvidovreCopenhagenDenmark
- Random Walk Imaging ABLundSweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, LundLund UniversityLundSweden
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- F. M. Kirby Research Center for Functional Brain ImagingKennedy Krieger InstituteBaltimoreMarylandUSA
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
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15
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Schilling KG, Palombo M, O'Grady KP, Combes AJE, Anderson AW, Landman BA, Smith SA. Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy. Magn Reson Imaging 2022; 94:25-35. [PMID: 35931321 PMCID: PMC9904413 DOI: 10.1016/j.mri.2022.07.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/27/2022] [Accepted: 07/27/2022] [Indexed: 01/13/2023]
Abstract
Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, United States.
| | - Marco Palombo
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
| | - Kristin P O'Grady
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Anna J E Combes
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam W Anderson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Seth A Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
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16
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Fischi-Gomez E, Girard G, Koch PJ, Yu T, Pizzolato M, Brügger J, Piredda GF, Hilbert T, Cadic-Melchior AG, Beanato E, Park CH, Morishita T, Wessel MJ, Schiavi S, Daducci A, Kober T, Canales-Rodríguez EJ, Hummel FC, Thiran JP. Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions. FRONTIERS IN RADIOLOGY 2022; 2:930666. [PMID: 37492668 PMCID: PMC10365099 DOI: 10.3389/fradi.2022.930666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/30/2022] [Indexed: 07/27/2023]
Abstract
Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (T2IE) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that T2IE is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92.
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Affiliation(s)
- Elda Fischi-Gomez
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Translational Machine Learning Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Gabriel Girard
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Philipp J. Koch
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | - Thomas Yu
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Julia Brügger
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Gian Franco Piredda
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tom Hilbert
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Andéol G. Cadic-Melchior
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Elena Beanato
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Chang-Hyun Park
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Takuya Morishita
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Maximilian J. Wessel
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Tobias Kober
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Friedhelm C. Hummel
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
- Clinical Neuroscience, University Hospital of Geneva (HUG), Geneva, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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17
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Lu Q, Liu W, Zhuo Z, Li Y, Duan Y, Yu P, Qu L, Ye C, Liu Y. A Transfer Learning Approach to Few-shot Segmentation of Novel White Matter Tracts. Med Image Anal 2022; 79:102454. [DOI: 10.1016/j.media.2022.102454] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 03/19/2022] [Accepted: 04/08/2022] [Indexed: 12/20/2022]
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18
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the Human Connectome using Diffusion MRI at 300 mT/m Gradient Strength: Methodological Advances and Scientific Impact. Neuroimage 2022; 254:118958. [PMID: 35217204 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in Continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength dMRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for dMRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States.
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19
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Volumetric Segmentation of White Matter Tracts with Label Embedding. Neuroimage 2022; 250:118934. [PMID: 35091078 DOI: 10.1016/j.neuroimage.2022.118934] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 01/04/2022] [Accepted: 01/24/2022] [Indexed: 11/23/2022] Open
Abstract
Convolutional neural networks have achieved state-of-the-art performance for white matter (WM) tract segmentation based on diffusion magnetic resonance imaging (dMRI). However, the segmentation can still be difficult for challenging WM tracts with thin bodies or complicated shapes; the segmentation is even more problematic in challenging scenarios with reduced data quality or domain shift between training and test data, which can be easily encountered in clinical settings. In this work, we seek to improve the segmentation of WM tracts, especially for challenging WM tracts in challenging scenarios. In particular, our method is based on volumetric WM tract segmentation, where voxels are directly labeled without performing tractography. To improve the segmentation, we exploit the characteristics of WM tracts that different tracts can cross or overlap and revise the network design accordingly. Specifically, because multiple tracts can co-exist in a voxel, we hypothesize that the different tract labels can be correlated. The tract labels at a single voxel are concatenated as a label vector, the length of which is the number of tract labels. Due to the tract correlation, this label vector can be projected into a lower-dimensional space-referred to as the embedded space-for each voxel, which allows the segmentation network to solve a simpler problem. By predicting the coordinate in the embedded space for the tracts at each voxel and subsequently mapping the coordinate to the label vector with a reconstruction module, the segmentation result can be achieved. To facilitate the learning of the embedded space, an auxiliary label reconstruction loss is integrated with the segmentation accuracy loss during network training, and network training and inference are end-to-end. Our method was validated on two dMRI datasets under various settings. The results show that the proposed method improves the accuracy of WM tract segmentation, and the improvement is more prominent for challenging tracts in challenging scenarios.
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20
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Andersson M, Pizzolato M, Kjer HM, Skodborg KF, Lundell H, Dyrby TB. Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures? Neuroimage 2021; 248:118718. [PMID: 34767939 DOI: 10.1016/j.neuroimage.2021.118718] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 10/26/2021] [Accepted: 11/08/2021] [Indexed: 11/26/2022] Open
Abstract
Noninvasive estimation of axon diameter with diffusion MRI holds the potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent studies use powder averaging to account for complex white matter architectures, but these have not been validated for real axonal geometries from regions that contain fibre crossings. Here, we present 120-304μm long segmented axons from X-ray nano-holotomography volumes of a splenium and crossing fibre region of a vervet monkey brain. We show that the axons in the complex crossing fibre region, which contains callosal, association, and corticospinal connections, are larger and exhibit a wider distribution than those of the splenium region. To accurately estimate the axon diameter in these regions, therefore, sensitivity to a wide range of diameters is required. We demonstrate how the q-value, b-value, signal-to-noise ratio and the assumed intra-axonal parallel diffusivity influence the range of measurable diameters with powder average approaches. Furthermore, we show how Gaussian distributed noise results in a wider range of measurable diameter at high b-values than Rician distributed noise, even at high signal-to-noise ratios of 100. The number of gradient directions is also shown to impose a lower bound on measurable diameter. Our results indicate that axon diameter estimation can be performed with only few b-shells, and that additional shells do not improve the accuracy of the estimate. For strong gradients available on human Connectom and preclinical scanners, Monte Carlo simulations of diffusion confirm that powder averaging techniques succeed in providing accurate estimates of axon diameter across a range of sequence parameters and diffusion times, even in complex white matter architectures. At relatively low b-values, the diameter estimate becomes sensitive to axonal microdispersion and the intra-axonal parallel diffusivity shows time dependency at both in vivo and ex vivo intrinsic diffusivities.
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Affiliation(s)
- Mariam Andersson
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Marco Pizzolato
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Hans Martin Kjer
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Katrine Forum Skodborg
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
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21
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Mancini M, Tian Q, Fan Q, Cercignani M, Huang SY. Dissecting whole-brain conduction delays through MRI microstructural measures. Brain Struct Funct 2021; 226:2651-2663. [PMID: 34390416 PMCID: PMC8448685 DOI: 10.1007/s00429-021-02358-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/28/2021] [Indexed: 01/24/2023]
Abstract
Network models based on structural connectivity have been increasingly used as the blueprint for large-scale simulations of the human brain. As the nodes of this network are distributed through the cortex and interconnected by white matter pathways with different characteristics, modeling the associated conduction delays becomes important. The goal of this study is to estimate and characterize these delays directly from the brain structure. To achieve this, we leveraged microstructural measures from a combination of advanced magnetic resonance imaging acquisitions and computed the main determinants of conduction velocity, namely axonal diameter and myelin content. Using the model proposed by Rushton, we used these measures to calculate the conduction velocity and estimated the associated delays using tractography. We observed that both the axonal diameter and conduction velocity distributions presented a rather constant trend across different connection lengths, with resulting delays that scale linearly with the connection length. Relying on insights from graph theory and Kuramoto simulations, our results support the approximation of constant conduction velocity but also show path- and region-specific differences.
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Affiliation(s)
- Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK. .,Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK. .,NeuroPoly Lab, Polytechnique Montréal, Montréal, Canada.
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Mara Cercignani
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA.,Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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22
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Temporal Dynamics of Brain White Matter Plasticity in Sighted Subjects during Tactile Braille Learning: A Longitudinal Diffusion Tensor Imaging Study. J Neurosci 2021; 41:7076-7085. [PMID: 34253624 DOI: 10.1523/jneurosci.2242-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 05/05/2021] [Accepted: 06/08/2021] [Indexed: 12/26/2022] Open
Abstract
The white matter (WM) architecture of the human brain changes in response to training, though fine-grained temporal characteristics of training-induced white matter plasticity remain unexplored. We investigated white matter microstructural changes using diffusion tensor imaging at five different time points in 26 sighted female adults during 8 months of training on tactile braille reading. Our results show that training-induced white matter plasticity occurs both within and beyond the trained sensory modality, as reflected by fractional anisotropy (FA) increases in somatosensory and visual cortex, respectively. The observed changes followed distinct time courses, with gradual linear FA increase along the training in the somatosensory cortex and sudden visual cortex cross-modal plasticity occurring after braille input became linguistically meaningful. WM changes observed in these areas returned to baseline after the cessation of learning in line with the supply-demand model of plasticity. These results also indicate that the temporal dynamics of microstructural plasticity in different cortical regions might be modulated by the nature of computational demands. We provide additional evidence that observed FA training-induced changes are behaviorally relevant to tactile reading. Together, these results demonstrate that WM plasticity is a highly dynamic process modulated by the introduction of novel experiences.SIGNIFICANCE STATEMENT Throughout the lifetime the human brain is shaped by various experiences. Training-induced reorganization in white matter (WM) microstructure has been reported, but we know little about its temporal dynamics. To fill this gap, we scanned sighted subjects five times during tactile braille reading training. We observed different dynamics of WM plasticity in the somatosensory and visual cortices implicated in braille reading. The former showed a continuous increase in WM tissue anisotropy along with tactile training, while microstructural changes in the latter were observed only after the participants learned to read braille words. Our results confirm the supply-demand model of brain plasticity and provide evidence that WM reorganization depends on distinct computational demands and functional roles of regions involved in the trained skill.
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23
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Scan-rescan repeatability of axonal imaging metrics using high-gradient diffusion MRI and statistical implications for study design. Neuroimage 2021; 240:118323. [PMID: 34216774 PMCID: PMC8646020 DOI: 10.1016/j.neuroimage.2021.118323] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/12/2021] [Accepted: 06/26/2021] [Indexed: 11/29/2022] Open
Abstract
Axon diameter mapping using diffusion MRI in the living human brain has attracted growing interests with the increasing availability of high gradient strength MRI systems. A systematic assessment of the consistency of axon diameter estimates within and between individuals is needed to gain a comprehensive understanding of how such methods extend to quantifying differences in axon diameter index between groups and facilitate the design of neurobiological studies using such measures. We examined the scan-rescan repeatability of axon diameter index estimation based on the spherical mean technique (SMT) approach using diffusion MRI data acquired with gradient strengths up to 300 mT/m on a 3T Connectom system in 7 healthy volunteers. We performed statistical power analyses using data acquired with the same protocol in a larger cohort consisting of 15 healthy adults to investigate the implications for study design. Results revealed a high degree of repeatability in voxel-wise restricted volume fraction estimates and tract-wise estimates of axon diameter index derived from high-gradient diffusion MRI data. On the region of interest (ROI) level, across white matter tracts in the whole brain, the Pearson’s correlation coefficient of the axon diameter index estimated between scan and rescan experiments was r = 0.72 with an absolute deviation of 0.18 μm. For an anticipated 10% effect size in studies of axon diameter index, most white matter regions required a sample size of less than 15 people to observe a measurable difference between groups using an ROI-based approach. To facilitate the use of high-gradient strength diffusion MRI data for neuroscientific studies of axonal microstructure, the comprehensive multi-gradient strength, multi-diffusion time data used in this work will be made publicly available, in support of open science and increasing the accessibility of such data to the greater scientific community.
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24
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Veraart J, Raven EP, Edwards LJ, Weiskopf N, Jones DK. The variability of MR axon radii estimates in the human white matter. Hum Brain Mapp 2021; 42:2201-2213. [PMID: 33576105 PMCID: PMC8046139 DOI: 10.1002/hbm.25359] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 01/07/2021] [Accepted: 01/21/2021] [Indexed: 12/13/2022] Open
Abstract
The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.
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Affiliation(s)
- Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Erika P. Raven
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
| | - Luke J. Edwards
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Nikolaus Weiskopf
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth SciencesLeipzig UniversityLeipzigGermany
| | - Derek K. Jones
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneVictoriaAustralia
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25
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Casella C, Kleban E, Rosser AE, Coulthard E, Rickards H, Fasano F, Metzler-Baddeley C, Jones DK. Multi-compartment analysis of the complex gradient-echo signal quantifies myelin breakdown in premanifest Huntington's disease. Neuroimage Clin 2021; 30:102658. [PMID: 33865029 PMCID: PMC8079666 DOI: 10.1016/j.nicl.2021.102658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/25/2021] [Accepted: 03/30/2021] [Indexed: 12/04/2022]
Abstract
White matter (WM) alterations have been identified as a relevant pathological feature of Huntington's disease (HD). Increasing evidence suggests that WM changes in this disorder are due to alterations in myelin-associated biological processes. Multi-compartmental analysis of the complex gradient-echo MRI signal evolution in WM has been shown to quantify myelin in vivo, therefore pointing to the potential of this technique for the study of WM myelin changes in health and disease. This study first characterized the reproducibility of metrics derived from the complex multi-echo gradient-recalled echo (mGRE) signal across the corpus callosum in healthy participants, finding highest reproducibility in the posterior callosal segment. Subsequently, the same analysis pipeline was applied in this callosal region in a sample of premanifest HD patients (n = 19) and age, sex and education matched healthy controls (n = 21). In particular, we focused on two myelin-associated derivatives: i. the myelin water signal fraction (fm), a parameter dependent on myelin content; and ii. The difference in frequency between myelin and intra-axonal water pools (Δω), a parameter dependent on the ratio between the inner and the outer axonal radii. fm was found to be lower in HD patients (β = -0.13, p = 0.03), while Δω did not show a group effect. Performance in tests of working memory, executive function, social cognition and movement was also assessed, and a greater age-related decline in executive function was detected in HD patients (β = -0.06, p = 0.006), replicating previous evidence of executive dysfunction in HD. Finally, the correlation between fm, executive function, and proximity to disease onset was explored in patients, and a positive correlation between executive function and fm was detected (r = 0.542; p = 0.02). This study emphasises the potential of complex mGRE signal analysis for aiding understanding of HD pathogenesis and progression. Moreover, expanding on evidence from pathology and animal studies, it provides novel in vivo evidence supporting myelin breakdown as an early feature of HD.
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Affiliation(s)
- Chiara Casella
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK.
| | - Elena Kleban
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK
| | - Anne E Rosser
- Department of Neurology and Psychological Medicine, Hayden Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK; School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK
| | | | - Hugh Rickards
- Birmingham and Solihull Mental Health NHS Foundation Trust, 50 Summer Hill Road, Birmingham B1 3RB, UK; Institute of Clinical Sciences, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Fabrizio Fasano
- Siemens Healthcare Ltd, Camberly, UK; Siemens Healthcare GmbH, Erlangen, Germany
| | - Claudia Metzler-Baddeley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF 24 4HQ, UK
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