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Feng W, Ding Z, Chen Q, She H, Du YP. Whole Brain Multiparametric Mapping in Two Minutes Using a Dual-Flip-Angle Stack-of-Stars Blipped Multi-Gradient-Echo Acquisition. Neuroimage 2024:120689. [PMID: 38880311 DOI: 10.1016/j.neuroimage.2024.120689] [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: 11/03/2023] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 06/18/2024] Open
Abstract
A new MRI technique is presented for three-dimensional fast simultaneous whole brain mapping of myelin water fraction (MWF), T1, proton density (PD), R2*, magnetic susceptibility (QSM), and B1 transmit field (B1+). Phantom and human (N = 9) datasets were acquired using a dual-flip-angle blipped multi-gradient-echo (DFA-mGRE) sequence with a stack-of-stars (SOS) trajectory. Images were reconstructed using a subspace-based algorithm with a locally low-rank constraint. A novel joint-sparsity-constrained multicomponent T2*-T1 spectrum estimation (JMSE) algorithm is proposed to correct for the T1 saturation effect and B1+/B1- inhomogeneities in the quantification of MWF. A tissue-prior-based B1+ estimation algorithm was adapted for B1 correction in the mapping of T1 and PD. In the phantom study, measurements obtained at an acceleration factor (R) of 12 using prospectively under-sampled SOS showed good consistency (R2 > 0.997) with Cartesian reference for R2*/T1app/M0app. In the in vivo study, results of retrospectively under-sampled SOS with R = 6, 12, 18, showed good quality (structure similarity index measure > 0.95) compared with those of fully-sampled SOS. Besides, results of prospectively under-sampled SOS with R = 12 showed good consistency (intraclass correlation coefficient > 0.91) with Cartesian reference for T1/PD/B1+/MWF/QSM/R2*, and good reproducibility (coefficient of variation < 7.0%) in the test-retest analysis for T1/PD/B1+/MWF/R2*. This study has demonstrated the feasibility of simultaneous whole brain multiparametric mapping with a two-minute scan using the DFA-mGRE SOS sequence, which may overcome a major obstacle for neurological applications of multiparametric MRI.
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Affiliation(s)
- Wenlong Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zekang Ding
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Quan Chen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Yiping P Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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2
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Vasylechko SD, Warfield SK, Kurugol S, Afacan O. Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data. Med Image Anal 2024; 91:102966. [PMID: 37844473 PMCID: PMC10847969 DOI: 10.1016/j.media.2023.102966] [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: 11/28/2022] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 10/18/2023]
Abstract
We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric T2 model. We synthetically generate a wide variety of high quality signals and corresponding parameters from a wide range of naturally occurring prior parameter values. To capture spatial variation, the generative signal decay model is combined with a generative spatial model conditioned on generic tissue segmentations. Synthesized 3D datasets can be used to train any convolutional neural network (CNN) based architecture for MWF estimation. Our source code is available at: https://github.com/quin-med-harvard-edu/synthmap Reduction of acquisition time at the expense of lower SNR, as well as accuracy and repeatability of MWF estimation techniques, are key factors that affect the adoption of MWF mapping in clinical practice. We demonstrate that the synthetically trained CNN provides superior accuracy over the competing methods under the constraints of naturally occurring noise levels as well as on the synthetically generated images at low SNR levels. Normalized root mean squared error (nRMSE) is less than 7% on synthetic data, which is significantly lower than competing methods. Additionally, the proposed method yields a coefficient of variation (CoV) that is at least 4x better than the competing method on intra-session test-retest reference dataset.
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Affiliation(s)
- Serge Didenko Vasylechko
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA.
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA
| | - Sila Kurugol
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA
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3
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Stellingwerff MD, Al-Saady ML, Chan KS, Dvorak A, Marques JP, Kolind S, Roosendaal SD, Wolf NI, Barkhof F, van der Knaap MS, Pouwels PJW. Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders. J Neuroimaging 2024; 34:61-77. [PMID: 37925602 DOI: 10.1111/jon.13167] [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: 08/09/2023] [Revised: 09/29/2023] [Accepted: 10/20/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole-brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi-compartment relaxometry diffusion-informed MWI: MCR-DIMWI, and multi-echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI). METHODS For 9 patients with different leukodystrophies (age range 0.4-62.4 years) and 15 control subjects (2.3-61.3 years), T1-weighted MRI, fluid-attenuated inversion recovery, multi-echo gradient echo with variable flip angles, METRICS, and multi-shell diffusion-weighted imaging were acquired on 3 Tesla. MCR-DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi-level analyses were performed. RESULTS MCR-DIMWI and METRICS-derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR-DIMWI-MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS-MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR-DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other. CONCLUSION This proof-of-concept study shows that MCR-DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders.
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Affiliation(s)
- Menno D Stellingwerff
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Murtadha L Al-Saady
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Shannon Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stefan D Roosendaal
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Nicole I Wolf
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Marjo S van der Knaap
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Petra J W Pouwels
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
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4
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Dvorak AV, Kumar D, Zhang J, Gilbert G, Balaji S, Wiley N, Laule C, Moore GW, MacKay AL, Kolind SH. The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity. SCIENCE ADVANCES 2023; 9:eadh9853. [PMID: 37910622 PMCID: PMC10619933 DOI: 10.1126/sciadv.adh9853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 09/28/2023] [Indexed: 11/03/2023]
Abstract
Quantitative magnetic resonance imaging (MRI) techniques are powerful tools for the study of human tissue, but, in practice, their utility has been limited by lengthy acquisition times. Here, we introduce the Constrained, Adaptive, Low-dimensional, Intrinsically Precise Reconstruction (CALIPR) framework in the context of myelin water imaging (MWI); a quantitative MRI technique generally regarded as the most rigorous approach for noninvasive, in vivo measurement of myelin content. The CALIPR framework exploits data redundancy to recover high-quality images from a small fraction of an imaging dataset, which allowed MWI to be acquired with a previously unattainable sequence (fully sampled acquisition 2 hours:57 min:20 s) in 7 min:26 s (4.2% of the dataset, acceleration factor 23.9). CALIPR quantitative metrics had excellent precision (myelin water fraction mean coefficient of variation 3.2% for the brain and 3.0% for the spinal cord) and markedly increased sensitivity to demyelinating disease pathology compared to a current, widely used technique. The CALIPR framework facilitates drastically improved MWI and could be similarly transformative for other quantitative MRI applications.
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Affiliation(s)
- Adam V. Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Dushyant Kumar
- Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jing Zhang
- Global MR Applications & Workflow, GE HealthCare Canada, Mississauga, ON, Canada
| | | | - Sharada Balaji
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Neale Wiley
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - G.R. Wayne Moore
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alex L. MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H. Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
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5
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Kumar D, Benyard B, Soni ND, Swain A, Wilson N, Reddy R. Feasibility of transient nuclear Overhauser effect imaging in brain at 7 T. Magn Reson Med 2023; 89:1357-1367. [PMID: 36372994 DOI: 10.1002/mrm.29519] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/11/2022] [Accepted: 10/19/2022] [Indexed: 11/16/2022]
Abstract
PURPOSE The nuclear Overhauser effect (NOE) quantification from the steady-state NOE imaging suffers from multiple confounding non-NOE-specific sources, including direct saturation, magnetization transfer, and relevant chemical exchange species, and is affected by B0 and B1 + inhomogeneities. The B0 -dependent and B1 + -dependent data needed for deconvolving these confounding effects would increase the scan time substantially, leading to other issues such as patient tolerability. Here, we demonstrate the feasibility of brain lipid mapping using an easily implementable transient NOE (tNOE) approach. METHODS This 7T study used a frequency-selective inversion pulse at a range of frequency offsets between 1.0 and 5.0 parts per million (ppm) and -5.0 and -1.0 ppm relative to bulk water peak. This was followed by a fixed/variable mixing time and then a single-shot 2D turbo FLASH readout. The feasibility of tNOE measurements is demonstrated on bovine serum albumin phantoms and healthy human brains. RESULTS The tNOE measurements from bovine serum albumin phantoms were found to be independent of physiological pH variations. Both bovine serum albumin phantoms and human brains showed broad tNOE contributions centered at approximately -3.5 ppm relative to water peak, with presumably aliphatic moieties in lipids and proteins being the dominant contributors. Less prominent tNOE contributions of approximately +2.5 ppm relative to water, presumably from aromatic moieties, were also detected. These aromatic signals were free from any CEST signals. CONCLUSION In this study, we have demonstrated the feasibility of tNOE in human brain at 7 T. This method is more scan-time efficient than steady-state NOE and provides NOE measurement with minimal confounders.
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Affiliation(s)
- Dushyant Kumar
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Blake Benyard
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Narayan Datt Soni
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anshuman Swain
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Neil Wilson
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ravinder Reddy
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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6
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Liu H, Grouza V, Tuznik M, Siminovitch KA, Bagheri H, Peterson A, Rudko DA. Self-labelled encoder-decoder (SLED) for multi-echo gradient echo-based myelin water imaging. Neuroimage 2022; 264:119717. [PMID: 36367497 DOI: 10.1016/j.neuroimage.2022.119717] [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: 06/13/2022] [Revised: 10/07/2022] [Accepted: 10/27/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Reconstruction of high quality myelin water imaging (MWI) maps is challenging, particularly for data acquired using multi-echo gradient echo (mGRE) sequences. A non-linear least squares fitting (NLLS) approach has often been applied for MWI. However, this approach may produce maps with limited detail and, in some cases, sub-optimal signal to noise ratio (SNR), due to the nature of the voxel-wise fitting. In this study, we developed a novel, unsupervised learning method called self-labelled encoder-decoder (SLED) to improve gradient echo-based MWI data fitting. METHODS Ultra-high resolution, MWI data was collected from five mouse brains with variable levels of myelination, using a mGRE sequence. Imaging data was acquired using a 7T preclinical MRI system. A self-labelled, encoder-decoder network was implemented in TensorFlow for calculation of myelin water fraction (MWF) based on the mGRE signal decay. A simulated MWI phantom was also created to evaluate the performance of MWF estimation. RESULTS Compared to NLLS, SLED demonstrated improved MWF estimation, in terms of both stability and accuracy in phantom tests. In addition, SLED produced less noisy MWF maps from high resolution MR microscopy images of mouse brain tissue. It specifically resulted in lower noise amplification for all mouse genotypes that were imaged and yielded mean MWF values in white matter ROIs that were highly correlated with those derived from standard NLLS fitting. Lastly, SLED also exhibited higher tolerance to low SNR data. CONCLUSION Due to its unsupervised and self-labeling nature, SLED offers a unique alternative to analyze gradient echo-based MWI data, providing accurate and stable MWF estimations.
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Affiliation(s)
- Hanwen Liu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Vladimir Grouza
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Marius Tuznik
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Katherine A Siminovitch
- Departments of Medicine and Immunology, University of Toronto, Toronto, ON, Canada; Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Hooman Bagheri
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Alan Peterson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Human Genetics, McGill University, Montreal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
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7
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Canales-Rodríguez EJ, Pizzolato M, Yu T, Piredda GF, Hilbert T, Radua J, Kober T, Thiran JP. Revisiting the T 2 spectrum imaging inverse problem: Bayesian regularized non-negative least squares. Neuroimage 2021; 244:118582. [PMID: 34536538 DOI: 10.1016/j.neuroimage.2021.118582] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/12/2021] [Accepted: 09/14/2021] [Indexed: 01/24/2023] Open
Abstract
Multi-echo T2 magnetic resonance images contain information about the distribution of T2 relaxation times of compartmentalized water, from which we can estimate relevant brain tissue properties such as the myelin water fraction (MWF). Regularized non-negative least squares (NNLS) is the tool of choice for estimating non-parametric T2 spectra. However, the estimation is ill-conditioned, sensitive to noise, and highly affected by the employed regularization weight. The purpose of this study is threefold: first, we want to underline that the apparently innocuous use of two alternative parameterizations for solving the inverse problem, which we called the standard and alternative regularization forms, leads to different solutions; second, to assess the performance of both parameterizations; and third, to propose a new Bayesian regularized NNLS method (BayesReg). The performance of BayesReg was compared with that of two conventional approaches (L-curve and Chi-square (X2) fitting) using both regularization forms. We generated a large dataset of synthetic data, acquired in vivo human brain data in healthy participants for conducting a scan-rescan analysis, and correlated the myelin content derived from histology with the MWF estimated from ex vivo data. Results from synthetic data indicate that BayesReg provides accurate MWF estimates, comparable to those from L-curve and X2, and with better overall stability across a wider signal-to-noise range. Notably, we obtained superior results by using the alternative regularization form. The correlations reported in this study are higher than those reported in previous studies employing the same ex vivo and histological data. In human brain data, the estimated maps from L-curve and BayesReg were more reproducible. However, the T2 spectra produced by BayesReg were less affected by over-smoothing than those from L-curve. These findings suggest that BayesReg is a good alternative for estimating T2 distributions and MWF maps.
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Affiliation(s)
- Erick Jorge Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland.
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland
| | - Thomas Yu
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland
| | - Gian Franco Piredda
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tom Hilbert
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Barcelona, Spain; Department of Psychosis Studies, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, United Kingdom; Department of Clinical Neuroscience, Centre for Psychiatric Research and Education, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Kober
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), EPFL-STI-IEL-LTS5, Station 11, CH-1015, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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8
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Liu H, Joseph TS, Xiang QS, Tam R, Kozlowski P, Li DKB, MacKay AL, Kramer JLK, Laule C. A data-driven T 2 relaxation analysis approach for myelin water imaging: Spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME-ECOS). Magn Reson Med 2021; 87:915-931. [PMID: 34490909 DOI: 10.1002/mrm.29000] [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: 08/31/2020] [Revised: 08/16/2021] [Accepted: 08/17/2021] [Indexed: 11/08/2022]
Abstract
PURPOSE The decomposition of multi-exponential decay data into a T2 spectrum poses substantial challenges for conventional fitting algorithms, including non-negative least squares (NNLS). Based on a combination of the resolution limit constraint and machine learning neural network algorithm, a data-driven and highly tailorable analysis method named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME-ECOS) was proposed. THEORY AND METHODS The theory of SAME-ECOS was derived. Then, a paradigm was presented to demonstrate the SAME-ECOS workflow, consisting of a series of calculation, simulation, and model training operations. The performance of the trained SAME-ECOS model was evaluated using simulations and six in vivo brain datasets. The code is available at https://github.com/hanwencat/SAME-ECOS. RESULTS Using NNLS as the baseline, SAME-ECOS achieved over 15% higher overall cosine similarity scores in producing the T2 spectrum, and more than 10% lower mean absolute error in calculating the myelin water fraction (MWF), as well as demonstrated better robustness to noise in the simulation tests. Applying to in vivo data, MWF from SAME-ECOS and NNLS was highly correlated among all study participants. However, a distinct separation of the myelin water peak and the intra/extra-cellular water peak was only observed in the mean T2 spectra determined using SAME-ECOS. In terms of data processing speed, SAME-ECOS is approximately 30 times faster than NNLS, achieving a whole-brain analysis in 3 min. CONCLUSION Compared with NNLS, the SAME-ECOS method yields much more reliable T2 spectra in a dramatically shorter time, increasing the feasibility of multi-component T2 decay analysis in clinical settings.
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Affiliation(s)
- Hanwen Liu
- Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tigris S Joseph
- Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
| | - Qing-San Xiang
- Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Roger Tam
- Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - Piotr Kozlowski
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada.,Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - David K B Li
- Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alex L MacKay
- Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - John L K Kramer
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada.,Kinesiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Cornelia Laule
- Physics & Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada.,Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Pathology & Laboratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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9
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Li Y, Xiong J, Guo R, Zhao Y, Li Y, Liang ZP. Improved estimation of myelin water fractions with learned parameter distributions. Magn Reson Med 2021; 86:2795-2809. [PMID: 34216050 DOI: 10.1002/mrm.28889] [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: 02/01/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 11/07/2022]
Abstract
PURPOSE To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data. METHODS A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. RESULTS Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. CONCLUSION This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.
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Affiliation(s)
- Yudu Li
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Jiahui Xiong
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Rong Guo
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yibo Zhao
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Pei Liang
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.,Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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10
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Age- and gender-related differences in brain tissue microstructure revealed by multi-component T 2 relaxometry. Neurobiol Aging 2021; 106:68-79. [PMID: 34252873 DOI: 10.1016/j.neurobiolaging.2021.06.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 05/30/2021] [Accepted: 06/01/2021] [Indexed: 12/19/2022]
Abstract
In spite of extensive work, inconsistent findings and lack of specificity in most neuroimaging techniques used to examine age- and gender-related patterns in brain tissue microstructure indicate the need for additional research. Here, we performed the largest Multi-component T2 relaxometry cross-sectional study to date in healthy adults (N = 145, 18-60 years). Five quantitative microstructure parameters derived from various segments of the estimated T2 spectra were evaluated, allowing a more specific interpretation of results in terms of tissue microstructure. We found similar age-related myelin water fraction (MWF) patterns in men and women but we also observed differential male related results including increased MWF content in a few white matter tracts, a faster decline with age of the intra- and extra-cellular water fraction and its T2 relaxation time (i.e. steeper age related negative slopes) and a faster increase in the free and quasi-free water fraction, spanning the whole grey matter. Such results point to a sexual dimorphism in brain tissue microstructure and suggest a lesser vulnerability to age-related changes in women.
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11
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Dvorak AV, Swift-LaPointe T, Vavasour IM, Lee LE, Abel S, Russell-Schulz B, Graf C, Wurl A, Liu H, Laule C, Li DKB, Traboulsee A, Tam R, Boyd LA, MacKay AL, Kolind SH. An atlas for human brain myelin content throughout the adult life span. Sci Rep 2021; 11:269. [PMID: 33431990 PMCID: PMC7801525 DOI: 10.1038/s41598-020-79540-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/09/2020] [Indexed: 12/11/2022] Open
Abstract
Myelin water imaging is a quantitative neuroimaging technique that provides the myelin water fraction (MWF), a metric highly specific to myelin content, and the intra-/extra-cellular T2 (IET2), which is related to water and iron content. We coupled high-resolution data from 100 adults with gold-standard methodology to create an optimized anatomical brain template and accompanying MWF and IET2 atlases. We then used the MWF atlas to characterize how myelin content relates to demographic factors. In most brain regions, myelin content followed a quadratic pattern of increase during the third decade of life, plateau at a maximum around the fifth decade, then decrease during later decades. The ranking of mean myelin content between brain regions remained consistent across age groups. These openly available normative atlases can facilitate evaluation of myelin imaging results on an individual basis and elucidate the distribution of myelin content between brain regions and in the context of aging.
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Affiliation(s)
- Adam V Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada. .,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.
| | | | - Irene M Vavasour
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Lisa Eunyoung Lee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | | | - Carina Graf
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Anika Wurl
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Hanwen Liu
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Lara A Boyd
- Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada
| | - Alex L MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
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12
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Yu T, Canales-Rodríguez EJ, Pizzolato M, Piredda GF, Hilbert T, Fischi-Gomez E, Weigel M, Barakovic M, Bach Cuadra M, Granziera C, Kober T, Thiran JP. Model-informed machine learning for multi-component T 2 relaxometry. Med Image Anal 2020; 69:101940. [PMID: 33422828 DOI: 10.1016/j.media.2020.101940] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 12/09/2020] [Accepted: 12/10/2020] [Indexed: 02/06/2023]
Abstract
Recovering the T2 distribution from multi-echo T2 magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to combine machine learning and aspects of parametric (fitting from the MRI signal using biophysical models) and non-parametric (model-free fitting of the T2 distribution from the signal) approaches to T2 relaxometry in brain tissue by using a multi-layer perceptron (MLP) for the distribution reconstruction. For training our network, we construct an extensive synthetic dataset derived from biophysical models in order to constrain the outputs with a priori knowledge of in vivo distributions. The proposed approach, called Model-Informed Machine Learning (MIML), takes as input the MR signal and directly outputs the associated T2 distribution. We evaluate MIML in comparison to a Gaussian Mixture Fitting (parametric) and Regularized Non-Negative Least Squares algorithms (non-parametric) on synthetic data, an ex vivo scan, and high-resolution scans of healthy subjects and a subject with Multiple Sclerosis. In synthetic data, MIML provides more accurate and noise-robust distributions. In real data, MWF maps derived from MIML exhibit the greatest conformity to anatomical scans, have the highest correlation to a histological map of myelin volume, and the best unambiguous lesion visualization and localization, with superior contrast between lesions and normal appearing tissue. In whole-brain analysis, MIML is 22 to 4980 times faster than the non-parametric and parametric methods, respectively.
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Affiliation(s)
- Thomas Yu
- Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland
| | - Erick Jorge Canales-Rodríguez
- Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; FIDMAG Germanes Hospitalàries Research Foundation, Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain.
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elda Fischi-Gomez
- Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Meritxell Bach Cuadra
- Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Switzerland; Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Neurologic Clinic and Policlinic, Departments of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland; Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
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13
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Zibetti MVW, Helou ES, Sharafi A, Regatte RR. Fast multicomponent 3D-T 1ρ relaxometry. NMR IN BIOMEDICINE 2020; 33:e4318. [PMID: 32359000 PMCID: PMC7606711 DOI: 10.1002/nbm.4318] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 03/10/2020] [Accepted: 04/05/2020] [Indexed: 05/06/2023]
Abstract
NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.
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Affiliation(s)
- Marcelo V W Zibetti
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
| | - Elias S Helou
- Institute of Mathematical Sciences and Computation, University of São Paulo, São Carlos, SP, Brazil
| | - Azadeh Sharafi
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
| | - Ravinder R Regatte
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, US
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14
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Wiggermann V, Vavasour IM, Kolind SH, MacKay AL, Helms G, Rauscher A. Non-negative least squares computation for in vivo myelin mapping using simulated multi-echo spin-echo T 2 decay data. NMR IN BIOMEDICINE 2020; 33:e4277. [PMID: 32124505 DOI: 10.1002/nbm.4277] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 01/20/2020] [Accepted: 01/26/2020] [Indexed: 06/10/2023]
Abstract
Multi-compartment T2 mapping has gained particular relevance for the study of myelin water in the brain. As a facilitator of rapid saltatory axonal signal transmission, myelin is a cornerstone indicator of white matter development and function. Regularized non-negative least squares fitting of multi-echo T2 data has been widely employed for the computation of the myelin water fraction (MWF), and the obtained MWF maps have been histopathologically validated. MWF measurements depend upon the quality of the data acquisition, B1+ homogeneity and a range of fitting parameters. In this special issue article, we discuss the relevance of these factors for the accurate computation of multi-compartment T2 and MWF maps. We generated multi-echo spin-echo T2 decay curves following the Carr-Purcell-Meiboom-Gill approach for various myelin concentrations and myelin T2 scenarios by simulating the evolution of the magnetization vector between echoes based on the Bloch equations. We demonstrated that noise and imperfect refocusing flip angles yield systematic underestimations in MWF and intra-/extracellular water geometric mean T2 (gmT2 ). MWF estimates were more stable than myelin water gmT2 time across different settings of the T2 analysis. We observed that the lower limit of the T2 distribution grid should be slightly shorter than TE1 . Both TE1 and the acquisition echo spacing also have to be sufficiently short to capture the rapidly decaying myelin water T2 signal. Among all parameters of interest, the estimated MWF and intra-/extracellular water gmT2 differed by approximately 0.13-4 percentage points and 3-4 ms, respectively, from the true values, with larger deviations observed in the presence of greater B1+ inhomogeneities and at lower signal-to-noise ratio. Tailoring acquisition strategies may allow us to better characterize the T2 distribution, including the myelin water, in vivo.
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Affiliation(s)
- V Wiggermann
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, Canada
- UBC MRI Research Center, University of British Columbia, Vancouver, Canada
| | - I M Vavasour
- UBC MRI Research Center, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - S H Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
- UBC MRI Research Center, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
- Department of Medicine (Division Neurology), University of British Columbia, Vancouver, Canada
| | - A L MacKay
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
- UBC MRI Research Center, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
| | - G Helms
- Department of Clinical Sciences Lund (IKVL), Medical Radiation Physics, Lund University, Lund, Sweden
| | - A Rauscher
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada
- Department of Pediatrics, University of British Columbia, Vancouver, Canada
- UBC MRI Research Center, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
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15
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Fatemi Y, Danyali H, Helfroush MS, Amiri H. Fast T 2 mapping using multi-echo spin-echo MRI: A linear order approach. Magn Reson Med 2020; 84:2815-2830. [PMID: 32430979 PMCID: PMC7402028 DOI: 10.1002/mrm.28309] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 04/13/2020] [Accepted: 04/15/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE Multi-echo spin-echo sequence is commonly used for T2 mapping. The estimated values using conventional exponential fit, however, are hampered by stimulated and indirect echoes leading to overestimation of T2 . Here, we present fast analysis of multi-echo spin-echo (FAMESE) as a novel approach to decrease the complexity of the search space, which leads to accelerated measurement of T2 . METHODS We developed FAMESE based on mathematical analysis of the Bloch equations in which the search space dimension decreased to only one. Then, we tested it in both phantom and human brain. Bland-Altman plot was used to assess the agreement between the estimated T2 values from FAMESE and the ones estimated from single-echo spin-echo sequence. The reliability of FAMESE was assessed by intraclass correlation coefficients. In addition, we investigated the noise stability of the method in synthetic and experimental data. RESULTS In both phantom and healthy participants, FAMESE provided accelerated and SNR-resistant T2 maps. The FAMESE had a very good agreement with the single-echo spin echo for the whole range of T2 values. The intraclass correlation coefficient values for FAMESE were excellent (ie, 0.9998 and 0.9860 < intraclass correlation coefficient < 0.9942 for the phantom and humans, respectively). CONCLUSION Our developed method FAMESE could be considered as a candidate for rapid T2 mapping with a clinically feasible scan time.
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Affiliation(s)
- Yaghoub Fatemi
- Department of Electrical and Electronics EngineeringShiraz University of TechnologyShirazIran
| | - Habibollah Danyali
- Department of Electrical and Electronics EngineeringShiraz University of TechnologyShirazIran
| | | | - Houshang Amiri
- Neuroscience Research CenterInstitute of NeuropharmacologyKerman University of Medical SciencesKermanIran
- Department of Radiology and Nuclear MedicineVU University Medical CenterAmsterdamthe Netherlands
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16
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Nagtegaal M, Koken P, Amthor T, de Bresser J, Mädler B, Vos F, Doneva M. Myelin water imaging from multi-echo T2 MR relaxometry data using a joint sparsity constraint. Neuroimage 2020; 219:117014. [DOI: 10.1016/j.neuroimage.2020.117014] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 05/29/2020] [Accepted: 05/30/2020] [Indexed: 11/24/2022] Open
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17
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DECAES - DEcomposition and Component Analysis of Exponential Signals. Z Med Phys 2020; 30:271-278. [PMID: 32451148 DOI: 10.1016/j.zemedi.2020.04.001] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 03/17/2020] [Accepted: 04/14/2020] [Indexed: 12/12/2022]
Abstract
Combinations of multiple exponentially decaying signals are found across many disciplines of science. Decomposition of these multi-exponential signals into their individual components provides insight into the various contributors to the signal. Magnetic resonance images, for instance, can be acquired with multiple gradient or spin echoes to provide voxel by voxel multi-exponential T2* or T2 decays, respectively. With their millions of voxels, these images make the task of decomposition into individual exponentials computationally challenging. Current implementations take several hours, which is prohibitively long in many settings, such as on-scanner calculation for clinical applications. Here, we present a fast approach for the decomposition of multi-exponential signals. The method is applied to multi echo spin echo MRI scans and computes myelin water maps of the whole brain in under 2min, and luminal water maps of the prostate in under 1min.
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18
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Faizy TD, Thaler C, Broocks G, Flottmann F, Leischner H, Kniep H, Nawabi J, Schön G, Stellmann JP, Kemmling A, Reddy R, Heit JJ, Fiehler J, Kumar D, Hanning U. The Myelin Water Fraction Serves as a Marker for Age-Related Myelin Alterations in the Cerebral White Matter - A Multiparametric MRI Aging Study. Front Neurosci 2020; 14:136. [PMID: 32153358 PMCID: PMC7050496 DOI: 10.3389/fnins.2020.00136] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 02/03/2020] [Indexed: 12/13/2022] Open
Abstract
Quantitative MRI modalities, such as diffusion tensor imaging (DTI) or magnetization transfer imaging (MTI) are sensitive to the neuronal effects of aging of the cerebral white matter (WM), but lack the specificity for myelin content. Myelin water imaging (MWI) is highly specific for myelin and may be more sensitive for the detection of changes in myelin content inside the cerebral WM microstructure. In this multiparametric imaging study, we evaluated the performance of myelin water fraction (MWF) estimates as a marker for myelin alterations during normal-aging. Multiparametric MRI data derived from DTI, MTI and a novel, recently-proposed MWF-map processing and reconstruction algorithm were acquired from 54 healthy subjects (aged 18-79 years) and region-based multivariate regression analysis was performed. MWFs significantly decreased with age in most WM regions (except corticospinal tract) and changes of MWFs were associated with changes of radial diffusivity, indicating either substantial alterations or preservation of myelin content in these regions. Decreases of fractional anisotropy and magnetization transfer ratio were associated with lower MWFs in commissural fiber tracts only. Mean diffusivity had no regional effects on MWF. We conclude that MWF estimates are sensitive for the assessment of age-related myelin alterations in the cerebral WM of normal-aging brains.
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Affiliation(s)
- Tobias D Faizy
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Thaler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fabian Flottmann
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannes Leischner
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Helge Kniep
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jawed Nawabi
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gerhard Schön
- Institute of Applied Biometrics and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan-Patrick Stellmann
- Institute of Neuroimmunology and Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - André Kemmling
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Muenster, Münster, Germany
| | - Ravinder Reddy
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Jeremy J Heit
- Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dushyant Kumar
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Uta Hanning
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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19
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Dvorak AV, Wiggermann V, Gilbert G, Vavasour IM, MacMillan EL, Barlow L, Wiley N, Kozlowski P, MacKay AL, Rauscher A, Kolind SH. Multi-spin echo T 2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging. Magn Reson Med 2020; 84:1264-1279. [PMID: 32065474 DOI: 10.1002/mrm.28199] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 12/20/2019] [Accepted: 01/13/2020] [Indexed: 12/11/2022]
Abstract
PURPOSE Myelin water imaging (MWI) provides a valuable biomarker for myelin, but clinical application has been restricted by long acquisition times. Accelerating the standard multi-echo T2 acquisition with gradient echoes (GRASE) or by 2D multi-slice data collection results in image blurring, contrast changes, and other issues. Compressed sensing (CS) can vastly accelerate conventional MRI. In this work, we assessed the use of CS for in vivo human MWI, using a 3D multi spin-echo sequence. METHODS We implemented multi-echo T2 relaxation imaging with compressed sensing (METRICS) and METRICS with partial Fourier acceleration (METRICS-PF). Scan-rescan data were acquired from 12 healthy controls for assessment of repeatability. MWI data were acquired for METRICS in 9 m:58 s and for METRICS-PF in 7 m:25 s, both with 1.5 × 2 × 3 mm3 voxels, 56 echoes, 7 ms ΔTE, and 240 × 240 × 170 mm3 FOV. METRICS was compared with a novel multi-echo spin-echo gold-standard (MSE-GS) MWI acquisition, acquired for a single additional subject in 2 h:2 m:40 s. RESULTS METRICS/METRICS-PF myelin water fraction had mean: repeatability coefficient 1.5/1.1, coefficient of variation 6.2/4.5%, and intra-class correlation coefficient 0.79/0.84. Repeatability metrics comparing METRICS with METRICS-PF were similar, and both sequences agreed with reference values from literature. METRICS images and quantitative maps showed excellent qualitative agreement with those of MSE-GS. CONCLUSION METRICS and METRICS-PF provided highly repeatable MWI data without the inherent disadvantages of GRASE or 2D multi-slice acquisition. CS acceleration allows MWI data to be acquired rapidly with larger FOV, higher estimated SNR, more isotropic voxels and more echoes than with previous techniques. The approach introduced here generalizes to any multi-component T2 mapping application.
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Affiliation(s)
- Adam V Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada
| | - Vanessa Wiggermann
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.,UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | | | - Irene M Vavasour
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Erin L MacMillan
- UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada.,MR Clinical Science, Philips Canada, Markham, Ontario, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Laura Barlow
- UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Neale Wiley
- UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Piotr Kozlowski
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada.,UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alex L MacKay
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Alexander Rauscher
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, British Columbia, Canada.,UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Shannon H Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia, Canada.,UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada.,Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada.,Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
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20
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Nagtegaal M, Koken P, Amthor T, Doneva M. Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting. Magn Reson Med 2020; 83:521-534. [PMID: 31418918 PMCID: PMC6899479 DOI: 10.1002/mrm.27947] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/22/2019] [Accepted: 07/23/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop an efficient algorithm for multi-component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties. METHODS Different tissues or components within a voxel are potentially separable in MRF because of their distinct signal evolutions. The observed signal evolution in each voxel can be described as a linear combination of the signals for each component with a non-negative weight. An assumption that only a small number of components are present in the measured field of view is usually imposed in the interpretation of multi-component data. In this work, a joint sparsity constraint is introduced to utilize this additional prior knowledge in the multi-component analysis of MRF data. A new algorithm combining joint sparsity and non-negativity constraints is proposed and compared to state-of-the-art multi-component MRF approaches in simulations and brain MRF scans of 11 healthy volunteers. RESULTS Simulations and in vivo measurements show reduced noise in the estimated tissue fraction maps compared to previously proposed methods. Applying the proposed algorithm to the brain data resulted in 4 or 5 components, which could be attributed to different brain structures, consistent with previous multi-component MRF publications. CONCLUSIONS The proposed algorithm is faster than previously proposed methods for multi-component MRF and the simulations suggest improved accuracy and precision of the estimated weights. The results are easier to interpret compared to voxel-wise methods, which combined with the improved speed is an important step toward clinical evaluation of multi-component MRF.
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Affiliation(s)
- Martijn Nagtegaal
- Department of Quantitative ImagingTechnical University DelftDelftthe Netherlands
- Institut für MathematikTechnische Universität BerlinBerlinGermany
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21
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Sussman MS. Linear signal combination T 2 spectroscopy. Magn Reson Imaging 2019; 66:257-266. [PMID: 31734273 DOI: 10.1016/j.mri.2019.11.016] [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: 07/02/2019] [Revised: 10/31/2019] [Accepted: 11/11/2019] [Indexed: 10/25/2022]
Abstract
A technique is presented for performing T2 spectroscopy in magnetic resonance imaging (MRI). It is based on a weighted linear combination of T2 decay data. The data is combined in a manner that acts like a filter on the T2 spectrum. The choice of weighting coefficients determines the filter specifications (e.g. passband/stopband locations, stopband suppression factors). To perform spectroscopy, a series of filters are designed with narrow passbands centered about consecutive regions of the T2 spectrum. This provides an estimate of every region of the spectrum. Taken together, an initial estimate of the full T2 spectrum is thus obtained. However, the filtering process causes a distortion of the estimate relative to the true spectrum. To reduce this distortion, deconvolution is performed. The characteristics of the technique are first evaluated through simulation. The technique is then applied to experimental MRI data to demonstrate practical feasibility. T2 spectroscopy falls into a class of problems requiring inverse transformation with a set of exponential basis functions (i.e. the Laplace Transform). It is demonstrated how the present technique may be applied to problems involving non-exponential basis functions as well.
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Affiliation(s)
- Marshall S Sussman
- Joint Department of Medical Imaging, University Health Network, University, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
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22
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Faizy TD, Kumar D, Broocks G, Thaler C, Flottmann F, Leischner H, Kutzner D, Hewera S, Dotzauer D, Stellmann JP, Reddy R, Fiehler J, Sedlacik J, Gellißen S. Age-Related Measurements of the Myelin Water Fraction derived from 3D multi-echo GRASE reflect Myelin Content of the Cerebral White Matter. Sci Rep 2018; 8:14991. [PMID: 30301904 PMCID: PMC6177453 DOI: 10.1038/s41598-018-33112-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 09/19/2018] [Indexed: 12/17/2022] Open
Abstract
Myelin Water Fraction (MWF) measurements derived from quantitative Myelin Water Imaging (MWI) may detect demyelinating changes of the cerebral white matter (WM) microstructure. Here, we investigated age-related alterations of the MWF in normal aging brains of healthy volunteers utilizing two fast and clinically feasible 3D gradient and spin echo (GRASE) MWI sequences with 3 mm and 5 mm isotropic voxel size. In 45 healthy subjects (age range: 18–79 years), distinct regions of interest (ROI) were defined in the cerebral WM including corticospinal tracts. For the 3 mm sequence, significant correlations of the mean MWF with age were found for most ROIs (r < −0.8 for WM ROIs; r = −0.55 for splenium of corpus callosum; r = −0.75 for genu of corpus callosum; p < 0.001 for all ROIs). Similar correlations with age were found for the ROIs of the 5 mm sequence. No significant correlations were found for the corticospinal tract and the occipital WM (p > 0.05). Mean MWF values obtained from the 3 mm and 5 mm sequences were strongly comparable. The applied 3D GRASE MWI sequences were found to be sensitive for age-dependent myelin changes of the cerebral WM microstructure. The reported MWF values might be of substantial use as reference for further investigations in patient studies.
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Affiliation(s)
- Tobias D Faizy
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
| | - Dushyant Kumar
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Gabriel Broocks
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Thaler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fabian Flottmann
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hannes Leischner
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Daniel Kutzner
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Simon Hewera
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dominik Dotzauer
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan-Patrick Stellmann
- Institute of Neuroimmunology und Multiple Sclerosis, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ravinder Reddy
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Sedlacik
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Gellißen
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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23
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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.
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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
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