1
|
Omer N, Galun M, Stern N, Blumenfeld-Katzir T, Ben-Eliezer N. Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features. Magn Reson Med 2021; 87:2521-2535. [PMID: 34958690 DOI: 10.1002/mrm.29125] [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: 07/19/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 01/10/2023]
Abstract
PURPOSE Multicomponent analysis of MRI T2 relaxation time (mcT2 ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT2 analysis, which utilizes the statistical strength of identifying spatially global mcT2 motifs in white matter segments before deconvolving the local signal at each voxel. METHODS Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. RESULTS Validations are shown for computer-generated signals, uniquely designed subvoxel mcT2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. CONCLUSION We conclude that studying global tissue motifs prior to performing voxel-wise mcT2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.
Collapse
Affiliation(s)
- Noam Omer
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Meirav Galun
- Department of Computer Science and Applied Mathematics, Weitzman Institute of Science, Rehovot, Israel
| | - Neta Stern
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Noam Ben-Eliezer
- The Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.,Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Medical Center, New York, New York, USA
| |
Collapse
|
2
|
Laule C, Moore GW. Myelin water imaging to detect demyelination and remyelination and its validation in pathology. Brain Pathol 2018; 28:750-764. [PMID: 30375119 PMCID: PMC8028667 DOI: 10.1111/bpa.12645] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 07/09/2018] [Indexed: 12/11/2022] Open
Abstract
Damage to myelin is a key feature of multiple sclerosis (MS) pathology. Magnetic resonance imaging (MRI) has revolutionized our ability to detect and monitor MS pathology in vivo. Proton density, T1 and T2 can provide qualitative contrast weightings that yield superb in vivo visualization of central nervous system tissue and have proved invaluable as diagnostic and patient management tools in MS. However, standard clinical MR methods are not specific to the types of tissue damage they visualize, and they cannot detect subtle abnormalities in tissue that appears otherwise normal on conventional MRIs. Myelin water imaging is an MR method that provides in vivo measurement of myelin. Histological validation work in both human brain and spinal cord tissue demonstrates a strong correlation between myelin water and staining for myelin, validating myelin water as a marker for myelin. Myelin water varies throughout the brain and spinal cord in healthy controls, and shows good intra- and inter-site reproducibility. MS plaques show variably decreased myelin water fraction, with older lesions demonstrating the greatest myelin loss. Longitudinal study of myelin water can provide insights into the dynamics of demyelination and remyelination in plaques. Normal appearing brain and spinal cord tissues show reduced myelin water, an abnormality which becomes progressively more evident over a timescale of years. Diffusely abnormal white matter, which is evident in 20%-25% of MS patients, also shows reduced myelin water both in vivo and postmortem, and appears to originate from a primary lipid abnormality with relative preservation of myelin proteins. Active research is ongoing in the quest to refine our ability to image myelin and its perturbations in MS and other disorders of the myelin sheath.
Collapse
Affiliation(s)
- Cornelia Laule
- RadiologyUniversity of British ColumbiaVancouverBCCanada
- Pathology & Laboratory MedicineUniversity of British ColumbiaVancouverBCCanada
- Physics & AstronomyUniversity of British ColumbiaVancouverBCCanada
- International Collaboration on Repair Discoveries (ICORD)University of British ColumbiaVancouverBCCanada
| | - G.R. Wayne Moore
- Pathology & Laboratory MedicineUniversity of British ColumbiaVancouverBCCanada
- International Collaboration on Repair Discoveries (ICORD)University of British ColumbiaVancouverBCCanada
- Medicine (Neurology)University of British ColumbiaVancouverBCCanada
| |
Collapse
|
3
|
Using 3D spatial correlations to improve the noise robustness of multi component analysis of 3D multi echo quantitative T2 relaxometry data. Neuroimage 2018; 178:583-601. [PMID: 29763672 DOI: 10.1016/j.neuroimage.2018.05.026] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 05/07/2018] [Accepted: 05/08/2018] [Indexed: 12/18/2022] Open
Abstract
PURPOSE We present a computationally feasible and iterative multi-voxel spatially regularized algorithm for myelin water fraction (MWF) reconstruction. This method utilizes 3D spatial correlations present in anatomical/pathological tissues and underlying B1+-inhomogeneity or flip angle inhomogeneity to enhance the noise robustness of the reconstruction while intrinsically accounting for stimulated echo contributions using T2-distribution data alone. METHODS Simulated data and in vivo data acquired using 3D non-selective multi-echo spin echo (3DNS-MESE) were used to compare the reconstruction quality of the proposed approach against those of the popular algorithm (the method by Prasloski et al.) and our previously proposed 2D multi-slice spatial regularization spatial regularization approach. We also investigated whether the inter-sequence correlations and agreements improved as a result of the proposed approach. MWF-quantifications from two sequences, 3DNS-MESE vs 3DNS-gradient and spin echo (3DNS-GRASE), were compared for both reconstruction approaches to assess correlations and agreements between inter-sequence MWF-value pairs. MWF values from whole-brain data of six volunteers and two multiple sclerosis patients are being reported as well. RESULTS In comparison with competing approaches such as Prasloski's method or our previously proposed 2D multi-slice spatial regularization method, the proposed method showed better agreements with simulated truths using regression analyses and Bland-Altman analyses. For 3DNS-MESE data, MWF-maps reconstructed using the proposed algorithm provided better depictions of white matter structures in subcortical areas adjoining gray matter which agreed more closely with corresponding contrasts on T2-weighted images than MWF-maps reconstructed with the method by Prasloski et al. We also achieved a higher level of correlations and agreements between inter-sequence (3DNS-MESE vs 3DNS-GRASE) MWF-value pairs. CONCLUSION The proposed algorithm provides more noise-robust fits to T2-decay data and improves MWF-quantifications in white matter structures especially in the sub-cortical white matter and major white matter tract regions.
Collapse
|
4
|
Abstract
Myelin is critical for healthy brain function. An accurate in vivo measure of myelin content has important implications for understanding brain plasticity and neurodegenerative diseases. Myelin water imaging is a magnetic resonance imaging method which can be used to visualize myelination in the brain and spinal cord in vivo. This review presents an overview of myelin water imaging data acquisition and analysis, post-mortem validation work, findings in both animal and human studies and a brief discussion about other MR techniques purported to provide in vivo myelin content. Multi-echo T2 relaxation approaches continue to undergo development and whole-brain imaging time now takes less than 10 minutes; the standard analysis method for this type of data acquisition is a non-negative least squares approach. Alternate methods including the multi-flip angle gradient echo mcDESPOT are also being used for myelin water imaging. Histological validation studies in animal and human brain and spinal cord tissue demonstrate high specificity of myelin water imaging for myelin. Potential confounding factors for in vivo myelin water fraction measurement include the presence of myelin debris and magnetization exchange processes. Myelin water imaging has successfully been used to study animal models of injury, applied in healthy human controls and can be used to assess damage and injury in conditions such as multiple sclerosis, neuromyelitis optica, schizophrenia, phenylketonuria, neurofibromatosis, niemann pick’s disease, stroke and concussion. Other quantitative magnetic resonance approaches that are sensitive to, but not specific for, myelin exist including magnetization transfer, diffusion tensor imaging and T1 weighted imaging.
Collapse
Affiliation(s)
- Alex L MacKay
- Department of Radiology, University of British Columbia, Vancouver, Canada.,Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, Canada.,Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| |
Collapse
|
5
|
Kulikova S, Hertz-Pannier L, Dehaene-Lambertz G, Poupon C, Dubois J. A New Strategy for Fast MRI-Based Quantification of the Myelin Water Fraction: Application to Brain Imaging in Infants. PLoS One 2016; 11:e0163143. [PMID: 27736872 PMCID: PMC5063462 DOI: 10.1371/journal.pone.0163143] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Accepted: 09/02/2016] [Indexed: 11/19/2022] Open
Abstract
The volume fraction of water related to myelin (fmy) is a promising MRI index for in vivo assessment of brain myelination, that can be derived from multi-component analysis of T1 and T2 relaxometry signals. However, existing quantification methods require rather long acquisition and/or post-processing times, making implementation difficult both in research studies on healthy unsedated children and in clinical examinations. The goal of this work was to propose a novel strategy for fmy quantification within acceptable acquisition and post-processing times. Our approach is based on a 3-compartment model (myelin-related water, intra/extra-cellular water and unrestricted water), and uses calibrated values of inherent relaxation times (T1c and T2c) for each compartment c. Calibration was first performed on adult relaxometry datasets (N = 3) acquired with large numbers of inversion times (TI) and echo times (TE), using an original combination of a region contraction approach and a non-negative least-square (NNLS) algorithm. This strategy was compared with voxel-wise fitting, and showed robust estimation of T1c and T2c. The accuracy of fmy calculations depending on multiple factors was investigated using simulated data. In the testing stage, our strategy enabled fast fmy mapping, based on relaxometry datasets acquired with reduced TI and TE numbers (acquisition <6 min), and analyzed with NNLS algorithm (post-processing <5min). In adults (N = 13, mean age 22.4±1.6 years), fmy maps showed variability across white matter regions, in agreement with previous studies. In healthy infants (N = 18, aged 3 to 34 weeks), asynchronous changes in fmy values were demonstrated across bundles, confirming the well-known progression of myelination.
Collapse
Affiliation(s)
- Sofya Kulikova
- INSERM U1129, CEA/DRF/I2BM/Neurospin/UNIACT, Gif-sur-Yvette, France; Université Paris-Saclay, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Lucie Hertz-Pannier
- INSERM U1129, CEA/DRF/I2BM/Neurospin/UNIACT, Gif-sur-Yvette, France; Université Paris-Saclay, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Ghislaine Dehaene-Lambertz
- INSERM U992, CEA/DRF/I2BM/Neurospin/UNICOG, Gif-sur-Yvette, France; Université Paris Saclay, Université Paris-Sud, Gif-sur-Yvette, France
| | - Cyril Poupon
- CEA/DRF/I2BM/Neurospin/UNIRS, Gif-sur-Yvette, France; Université Paris Saclay, Université Paris-Sud, Gif-sur-Yvette, France
| | - Jessica Dubois
- INSERM U992, CEA/DRF/I2BM/Neurospin/UNICOG, Gif-sur-Yvette, France; Université Paris Saclay, Université Paris-Sud, Gif-sur-Yvette, France
- * E-mail:
| |
Collapse
|
6
|
Milford D, Rosbach N, Bendszus M, Heiland S. Mono-Exponential Fitting in T2-Relaxometry: Relevance of Offset and First Echo. PLoS One 2015; 10:e0145255. [PMID: 26678918 PMCID: PMC4683054 DOI: 10.1371/journal.pone.0145255] [Citation(s) in RCA: 74] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Accepted: 11/30/2015] [Indexed: 11/18/2022] Open
Abstract
INTRODUCTION T2 relaxometry has become an important tool in quantitative MRI. Little focus has been put on the effect of the refocusing flip angle upon the offset parameter, which was introduced to account for a signal floor due to noise or to long T2 components. The aim of this study was to show that B1 imperfections contribute significantly to the offset. We further introduce a simple method to reduce the systematic error in T2 by discarding the first echo and using the offset fitting approach. MATERIALS AND METHODS Signal curves of T2 relaxometry were simulated based on extended phase graph theory and evaluated for 4 different methods (inclusion and exclusion of the first echo, while fitting with and without the offset). We further performed T2 relaxometry in a phantom at 9.4T magnetic resonance imaging scanner and used the same methods for post-processing as in the extended phase graph simulated data. Single spin echo sequences were used to determine the correct T2 time. RESULTS The simulation data showed that the systematic error in T2 and the offset depends on the refocusing pulse, the echo spacing and the echo train length. The systematic error could be reduced by discarding the first echo. Further reduction of the systematic T2 error was reached by using the offset as fitting parameter. The phantom experiments confirmed these findings. CONCLUSION The fitted offset parameter in T2 relaxometry is influenced by imperfect refocusing pulses. Using the offset as a fitting parameter and discarding the first echo is a fast and easy method to minimize the error in T2, particularly for low to intermediate echo train length.
Collapse
Affiliation(s)
- David Milford
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
- * E-mail:
| | - Nicolas Rosbach
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| |
Collapse
|
7
|
Akhondi-Asl A, Afacan O, Balasubramanian M, Mulkern RV, Warfield SK. Fast myelin water fraction estimation using 2D multislice CPMG. Magn Reson Med 2015; 76:1301-13. [PMID: 26536382 DOI: 10.1002/mrm.26034] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Revised: 09/22/2015] [Accepted: 10/15/2015] [Indexed: 11/09/2022]
Abstract
PURPOSE T2 relaxometry based on multiexponential fitting to a single slice multiecho sequence has been the most common MRI technique for myelin water fraction mapping, where the short T2 is associated with myelin water. However, very long acquisition times and physically unrealistic models for T2 distribution are limitations of this approach. We present a novel framework for myelin imaging which substantially increases the imaging speed and myelin water fraction estimation accuracy. METHOD We used the 2D multislice Carr-Purcell-Meiboom-Gill sequence to increase the volume coverage. To compensate for nonideal slice profiles, we numerically solved the Bloch equations for a range of T2 and B1 inhomogeneity scales to construct the bases for the estimation of the T2 distribution. We used a finite mixture of continuous parametric distributions to describe the complete T2 spectrum and used the constrained variable projection optimization algorithm to estimate myelin water fraction. To validate our model, synthetic, phantom, and in vivo brain experiments were conducted. RESULTS Using the Bloch equations, we can model the slice profile and construct the forward model of the T2 curve. Our method estimated myelin water fraction with smaller error than the nonnegative least squares algorithm. CONCLUSIONS The proposed framework can be used for reliable whole brain myelin imaging with a resolution of 2×2×4 mm3 in ≈17 min. Magn Reson Med 76:1301-1313, 2016. © 2015 Wiley Periodicals, Inc.
Collapse
Affiliation(s)
- Alireza Akhondi-Asl
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, Massachusetts, USA.
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, Massachusetts, USA
| | - Mukund Balasubramanian
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, Massachusetts, USA
| | - Robert V Mulkern
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, Massachusetts, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, Massachusetts, USA
| |
Collapse
|