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Faulkner ME, Gong Z, Guo A, Laporte JP, Bae J, Bouhrara M. Harnessing myelin water fraction as an imaging biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination: A review. J Neurochem 2024. [PMID: 38973579 DOI: 10.1111/jnc.16170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/12/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024]
Abstract
Myelin water fraction (MWF) imaging has emerged as a promising magnetic resonance imaging (MRI) biomarker for investigating brain function and composition. This comprehensive review synthesizes the current state of knowledge on MWF as a biomarker of human cerebral aging, neurodegenerative diseases, and risk factors influencing myelination. The databases used include Web of Science, Scopus, Science Direct, and PubMed. We begin with a brief discussion of the theoretical foundations of MWF imaging, including its basis in MR physics and the mathematical modeling underlying its calculation, with an overview of the most adopted MRI methods of MWF imaging. Next, we delve into the clinical and research applications that have been explored to date, highlighting its advantages and limitations. Finally, we explore the potential of MWF to serve as a predictive biomarker for neurological disorders and identify future research directions for optimizing MWF imaging protocols and interpreting MWF in various contexts. By harnessing the power of MWF imaging, we may gain new insights into brain health and disease across the human lifespan, ultimately informing novel diagnostic and therapeutic strategies.
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Affiliation(s)
- Mary E Faulkner
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Zhaoyuan Gong
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Alex Guo
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - John P Laporte
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Jonghyun Bae
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
| | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, USA
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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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Fischi-Gomez E, Girard G, Koch PJ, Yu T, Pizzolato M, Brügger J, Piredda GF, Hilbert T, Cadic-Melchior AG, Beanato E, Park CH, Morishita T, Wessel MJ, Schiavi S, Daducci A, Kober T, Canales-Rodríguez EJ, Hummel FC, Thiran JP. Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions. FRONTIERS IN RADIOLOGY 2022; 2:930666. [PMID: 37492668 PMCID: PMC10365099 DOI: 10.3389/fradi.2022.930666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 06/30/2022] [Indexed: 07/27/2023]
Abstract
Quantitative magnetic resonance imaging (qMRI) can increase the specificity and sensitivity of conventional weighted MRI to underlying pathology by comparing meaningful physical or chemical parameters, measured in physical units, with normative values acquired in a healthy population. This study focuses on multi-echo T2 relaxometry, a qMRI technique that probes the complex tissue microstructure by differentiating compartment-specific T2 relaxation times. However, estimation methods are still limited by their sensitivity to the underlying noise. Moreover, estimating the model's parameters is challenging because the resulting inverse problem is ill-posed, requiring advanced numerical regularization techniques. As a result, the estimates from distinct regularization strategies are different. In this work, we aimed to investigate the variability and reproducibility of different techniques for estimating the transverse relaxation time of the intra- and extra-cellular space (T2IE) in gray (GM) and white matter (WM) tissue in a clinical setting, using a multi-site, multi-session, and multi-run T2 relaxometry dataset. To this end, we evaluated three different techniques for estimating the T2 spectra (two regularized non-negative least squares methods and a machine learning approach). Two independent analyses were performed to study the effect of using raw and denoised data. For both the GM and WM regions, and the raw and denoised data, our results suggest that the principal source of variance is the inter-subject variability, showing a higher coefficient of variation (CoV) than those estimated for the inter-site, inter-session, and inter-run, respectively. For all reconstruction methods studied, the CoV ranged between 0.32 and 1.64%. Interestingly, the inter-session variability was close to the inter-scanner variability with no statistical differences, suggesting that T2IE is a robust parameter that could be employed in multi-site neuroimaging studies. Furthermore, the three tested methods showed consistent results and similar intra-class correlation (ICC), with values superior to 0.7 for most regions. Results from raw data were slightly more reproducible than those from denoised data. The regularized non-negative least squares method based on the L-curve technique produced the best results, with ICC values ranging from 0.72 to 0.92.
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Affiliation(s)
- Elda Fischi-Gomez
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Translational Machine Learning Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Gabriel Girard
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Philipp J. Koch
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, Germany
| | - Thomas Yu
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Julia Brügger
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Gian Franco Piredda
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tom Hilbert
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Andéol G. Cadic-Melchior
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Elena Beanato
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Chang-Hyun Park
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Takuya Morishita
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
| | - Maximilian J. Wessel
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
- Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Tobias Kober
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Friedhelm C. Hummel
- Defitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Defitech Chair of Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL Valais), Clinique Romande de Réadaptation, Sion, Switzerland
- Clinical Neuroscience, University Hospital of Geneva (HUG), Geneva, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
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Piredda GF, Hilbert T, Ravano V, Canales-Rodríguez EJ, Pizzolato M, Meuli R, Thiran JP, Richiardi J, Kober T. Data-driven myelin water imaging based on T 1 and T 2 relaxometry. NMR IN BIOMEDICINE 2022; 35:e4668. [PMID: 34936147 DOI: 10.1002/nbm.4668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 11/16/2021] [Accepted: 11/30/2021] [Indexed: 06/14/2023]
Abstract
Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of MWF from measurements of different tissue properties that can be obtained in scan times shorter than those required for the MESE. In this work, a novel data-driven estimation of MWF maps from fast relaxometry measurements is proposed and investigated. T1 and T2 relaxometry maps were acquired in a cohort of 20 healthy subjects along with a conventional MESE sequence. Whole-brain quantitative mapping was achieved with a fast protocol in 6 min 24 s. Reference MWF maps were derived from the MESE sequence (TA = 11 min 17 s) and their data-driven estimation from relaxometry measurements was investigated using three different modeling strategies: two general linear models (GLMs) with linear and quadratic regressors, respectively; a random forest regression model; and two deep neural network architectures, a U-Net and a conditional generative adversarial network (cGAN). Models were validated using a 10-fold crossvalidation. The resulting maps were visually and quantitatively compared by computing the root mean squared error (RMSE) between the estimated and reference MWF maps, the intraclass correlation coefficients (ICCs) between corresponding MWF values in different brain regions, and by performing Bland-Altman analysis. Qualitatively, the estimated maps appear to generally provide a similar, yet more blurred MWF contrast in comparison with the reference, with the cGAN model best capturing MWF variabilities in small structures. By estimating the average adjusted coefficient of determination of the GLM with quadratic regressors, we showed that 87% of the variability in the MWF values can be explained by relaxation times alone. Further quantitative analysis showed an average RMSE smaller than 0.1% for all methods. The ICC was greater than 0.81 for all methods, and the bias smaller than 2.19%. It was concluded that this work confirms the notion that relaxometry parameters contain a large part of the information on myelin water and that MWF maps can be generated from T1 /T2 data with minimal error. Among the investigated modeling approaches, the cGAN provided maps with the best trade-off between accuracy and blurriness. Fast relaxometry, like the 6 min 24 s whole-brain protocol used in this work in conjunction with machine learning, may thus have the potential to replace time-consuming MESE acquisitions.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Veronica Ravano
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | | | - Marco Pizzolato
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Mehdizadeh N, Wilman AH. Myelin water fraction mapping from multiple echo spin echoes and an independent B 1 + map. Magn Reson Med 2022; 88:1380-1390. [PMID: 35576121 PMCID: PMC9321077 DOI: 10.1002/mrm.29286] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 03/23/2022] [Accepted: 04/13/2022] [Indexed: 11/11/2022]
Abstract
Purpose Myelin water fraction (MWF) is often obtained from a multiple echo spin echo (MESE) sequence using multi‐component T2 fitting with non‐negative least squares. This process fits many unknowns including B1+ to produce a T2 spectrum for each voxel. Presented is an alternative using a rapid B1+ mapping sequence to supply B1+ for the MWF fitting procedure. Methods Effects of B1+ errors on MWF calculations were modeled for 2D and 3D MESE using Bloch and extended phase graph simulations, respectively. Variations in SNR and relative refocusing widths were tested. Human brain experiments at 3 T used 2D MESE and an independent B1+ map. MWF maps were produced with the standard approach and with the use of the independent B1+ map. Differences in B1+ and mean MWF in specific brain regions were compared. Results For 2D MESE, MWF with the standard method was strongly affected by B1+ misestimations arising from limited SNR and response asymmetry around 180°, which decreased with increasing relative refocusing width. Using an independent B1+ map increased mean MWF and decreased coefficient of variation. Notable differences in vivo in 2D MESE were in areas of high B1+ such as thalamus and splenium where mean MWF increased by 88% and 31%, respectively (P < 0.001). Simulations also demonstrated the advantages of this approach for 3D MESE when SNR is <500. Conclusion For 2D MESE, because of increased complexity of decay curves and limited SNR, supplying B1+ improves MWF results in peripheral and central brain regions where flip angles differ substantially from 180°.
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Affiliation(s)
- Nima Mehdizadeh
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Alan H Wilman
- Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
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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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7
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Canales-Rodríguez EJ, Pizzolato M, Piredda GF, Hilbert T, Kunz N, Pot C, Yu T, Salvador R, Pomarol-Clotet E, Kober T, Thiran JP, Daducci A. Comparison of non-parametric T 2 relaxometry methods for myelin water quantification. Med Image Anal 2021; 69:101959. [PMID: 33581618 DOI: 10.1016/j.media.2021.101959] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 12/10/2020] [Accepted: 01/04/2021] [Indexed: 02/06/2023]
Abstract
Multi-component T2 relaxometry allows probing tissue microstructure by assessing compartment-specific T2 relaxation times and water fractions, including the myelin water fraction. Non-negative least squares (NNLS) with zero-order Tikhonov regularization is the conventional method for estimating smooth T2 distributions. Despite the improved estimation provided by this method compared to non-regularized NNLS, the solution is still sensitive to the underlying noise and the regularization weight. This is especially relevant for clinically achievable signal-to-noise ratios. In the literature of inverse problems, various well-established approaches to promote smooth solutions, including first-order and second-order Tikhonov regularization, and different criteria for estimating the regularization weight have been proposed, such as L-curve, Generalized Cross-Validation, and Chi-square residual fitting. However, quantitative comparisons between the available reconstruction methods for computing the T2 distribution, and between different approaches for selecting the optimal regularization weight, are lacking. In this study, we implemented and evaluated ten reconstruction algorithms, resulting from the individual combinations of three penalty terms with three criteria to estimate the regularization weight, plus non-regularized NNLS. Their performance was evaluated both in simulated data and real brain MRI data acquired from healthy volunteers through a scan-rescan repeatability analysis. Our findings demonstrate the need for regularization. As a result of this work, we provide a list of recommendations for selecting the optimal reconstruction algorithms based on the acquired data. Moreover, the implemented methods were packaged in a freely distributed toolbox to promote reproducible research, and to facilitate further research and the use of this promising quantitative technique in clinical practice.
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Affiliation(s)
- Erick Jorge Canales-Rodríguez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM) , Barcelona, Spain; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Marco Pizzolato
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gian Franco Piredda
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Tom Hilbert
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Nicolas Kunz
- Animal Imaging and Technology section, Center for Biomedical Imaging (CIBM), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Caroline Pot
- Department of Pathology and Immunology, Geneva University Hospital and University of Geneva, Geneva, Switzerland; Division of Neurology and Neuroscience Research Center, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Thomas Yu
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM) , Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM) , Barcelona, Spain
| | - Tobias Kober
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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8
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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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9
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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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10
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Piredda GF, Hilbert T, Canales-Rodríguez EJ, Pizzolato M, von Deuster C, Meuli R, Pfeuffer J, Daducci A, Thiran JP, Kober T. Fast and high-resolution myelin water imaging: Accelerating multi-echo GRASE with CAIPIRINHA. Magn Reson Med 2020; 85:209-222. [PMID: 32720406 DOI: 10.1002/mrm.28427] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 06/17/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Although several MRI methods have been explored to achieve in vivo myelin quantification, imaging the whole brain in clinically acceptable times and sufficiently high resolution remains challenging. To address this problem, this work investigates the acceleration of multi-echo T2 acquisitions based on the multi-echo gradient and spin echo (GRASE) sequence using CAIPIRINHA undersampling and adapted k-space reordering patterns. METHODS A prototype multi-echo GRASE sequence supporting CAIPIRINHA parallel imaging was implemented. Multi-echo T2 data were acquired from 12 volunteers using the implemented sequence (1.6 × 1.6 × 1.6 mm3 , 84 slices, acquisition time [TA] = 10:30 min) and a multi-echo spin echo (MESE) sequence as reference (1.6 × 1.6 × 3.2 mm3 , single-slice, TA = 5:41 min). Myelin water fraction (MWF) maps derived from both acquisitions were compared via correlation and Bland-Altman analyses. In addition, scan-rescan datasets were acquired to evaluate the repeatability of the derived maps. RESULTS Resulting maps from the MESE and multi-echo GRASE sequences were found to be correlated (r = 0.83). The Bland-Altman analysis revealed a mean bias of -0.2% (P = .24) with the limits of agreement ranging from -3.7% to 3.3%. The Pearson's correlation coefficient among MWF values obtained from the scan-rescan datasets was found to be 0.95 and the mean bias equal to 0.11% (P = .32), indicating good repeatability of the retrieved maps. CONCLUSION By combining a 3D multi-echo GRASE sequence with CAIPIRINHA sampling, whole-brain MWF maps were obtained in 10:30 min with 1.6 mm isotropic resolution. The good correlation with conventional MESE-based maps demonstrates that the implemented sequence may be a promising alternative to time-consuming MESE acquisitions.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Erick Jorge Canales-Rodríguez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- FIDMAG Germanes Hospitalàries, Sant Boi de Llobregat, Barcelona, Spain
| | - Marco Pizzolato
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Constantin von Deuster
- Siemens Healthcare AG, Zurich, Switzerland
- SCMI, Swiss Center for Musculoskeletal Imaging, Zurich, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Josef Pfeuffer
- Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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11
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El-Hajj C, Moussaoui S, Collewet G, Musse M. Multi-exponential Transverse Relaxation Times Estimation from Magnetic Resonance Images under Rician Noise and Spatial Regularization. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2020; 29:6721-6733. [PMID: 32406838 DOI: 10.1109/tip.2020.2993114] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Relaxation signal inside each voxel of magnetic resonance images (MRI) is commonly fitted by a multi-exponential decay curve. The estimation of a discrete multi-component relaxation model parameters from magnitude MRI data is a challenging nonlinear inverse problem since it should be conducted on the entire image voxels under non-Gaussian noise statistics. This paper proposes an efficient algorithm allowing the joint estimation of relaxation time values and their amplitudes using different criteria taking into account a Rician noise model, combined with a spatial regularization accounting for low spatial variability of relaxation time constants and amplitudes between neighboring voxels. The Rician noise hypothesis is accounted for either by an adapted nonlinear least squares algorithm applied to a corrected least squares criterion or by a majorization-minimization approach applied to the maximum likelihood criterion. In order to solve the resulting large-scale non-negativity constrained optimization problem with a reduced numerical complexity and computing time, an optimization algorithm based on a majorization approach ensuring separability of variables between voxels is proposed. The minimization is carried out iteratively using an adapted Levenberg-Marquardt algorithm that ensures convergence by imposing a sufficient decrease of the objective function and the non-negativity of the parameters. The importance of the regularization alongside the Rician noise incorporation is shown both visually and numerically on a simulated phantom and on magnitude MRI images acquired on fruit samples.
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12
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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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13
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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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14
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Basiri R, Federico P, Lebel RM. Transverse relaxometry with transmit field-constrained stimulated echo compensation. MAGMA (NEW YORK, N.Y.) 2019; 32:669-677. [PMID: 31338627 DOI: 10.1007/s10334-019-00769-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 06/26/2019] [Accepted: 07/15/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVE Purely exponential decay is rarely observed in conventional mono-exponential T2 mapping due to transmit field inhomogeneity and calibration errors, which collectively introduce stimulated and indirect echo pathways. Stimulated echo correction (SEC) requires an additional fit parameter for the transmit field, resulting in greater uncertainty in T2 relative to mono-exponential fitting. The aim of this study was to develop an accurate and precise method for T2 mapping using SEC. METHODS The proposed method, called two-step SEC (tSEC), leverages spatial correlations in the transmit field to reduce the number of fully independent fitting parameters from three to two. The method involves a two-pass fit: the first pass involves a fast but standard SEC fit. The initially estimated transmit field is smoothed and provided as a fixed input to the second pass. RESULTS Simulations and in vivo experiments demonstrated up to 38% and 27% decreases in relative T2 variance with tSEC relative to SEC. Average T2 values were unchanged between tSEC and SEC fits. The proposed method uses the same input data as SEC and exponential fits, so it is applicable to existing data. DISCUSSION The proposed method generates reliable and reproducible quantitative T2 maps and should be considered for future relaxometry studies.
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Affiliation(s)
- Reza Basiri
- Biomedical Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada.
- Hotchkiss Brain Institute, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada.
- Seaman Family Centre, Foothills Medical Centre, MRG 020A, 3330 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada.
| | - Paolo Federico
- Hotchkiss Brain Institute, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
- Department of Clinical Neuroscience, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada
- Seaman Family Centre, Foothills Medical Centre, MRG 020A, 3330 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
| | - Robert Marc Lebel
- Biomedical Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada
- Department of Radiology, University of Calgary, 2500 University Dr NW, Calgary, AB, Canada
- GE Healthcare, Calgary, AB, Canada
- Seaman Family Centre, Foothills Medical Centre, MRG 020A, 3330 Hospital Drive NW, Calgary, AB, T2N 2T9, Canada
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15
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Quantitative age-dependent differences in human brainstem myelination assessed using high-resolution magnetic resonance mapping. Neuroimage 2019; 206:116307. [PMID: 31669302 DOI: 10.1016/j.neuroimage.2019.116307] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/18/2019] [Accepted: 10/21/2019] [Indexed: 12/13/2022] Open
Abstract
Previous in-vivo magnetic resonance imaging (MRI)-based studies of age-related differences in the human brainstem have focused on volumetric morphometry. These investigations have provided pivotal insights into regional brainstem atrophy but have not addressed microstructural age differences. However, growing evidence indicates the sensitivity of quantitative MRI to microstructural tissue changes in the brain. These studies have largely focused on the cerebrum, with very few MR investigations addressing age-dependent differences in the brainstem, in spite of its central role in the regulation of vital functions. Several studies indicate early brainstem alterations in a myriad of neurodegenerative diseases and dementias. The paucity of MR-focused investigations is likely due in part to the challenges imposed by the small structural scale of the brainstem itself as well as of substructures within, requiring accurate high spatial resolution imaging studies. In this work, we applied our recently developed approach to high-resolution myelin water fraction (MWF) mapping, a proxy for myelin content, to investigate myelin differences with normal aging within the brainstem. In this cross-sectional investigation, we studied a large cohort (n = 125) of cognitively unimpaired participants spanning a wide age range (21-94 years) and found a decrease in myelination with age in most brainstem regions studied, with several regions exhibiting a quadratic association between myelin and age. We believe that this study is the first investigation of MWF differences with normative aging in the adult brainstem. Further, our results provide reference MWF values.
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16
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Drenthen GS, Backes WH, Aldenkamp AP, Jansen JF. Applicability and reproducibility of 2D multi-slice GRASE myelin water fraction with varying acquisition acceleration. Neuroimage 2019; 195:333-339. [DOI: 10.1016/j.neuroimage.2019.04.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 03/20/2019] [Accepted: 04/03/2019] [Indexed: 12/12/2022] Open
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17
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Drenthen GS, Backes WH, Aldenkamp AP, Op 't Veld GJ, Jansen JFA. A new analysis approach for T 2 relaxometry myelin water quantification: Orthogonal Matching Pursuit. Magn Reson Med 2018; 81:3292-3303. [PMID: 30444019 PMCID: PMC6587563 DOI: 10.1002/mrm.27600] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 10/09/2018] [Accepted: 10/18/2018] [Indexed: 12/19/2022]
Abstract
Purpose In vivo myelin quantification can provide valuable noninvasive information on neuronal maturation and development, as well as insights into neurological disorders. Multiexponential analysis of multiecho T2 relaxation is a powerful and widely applied method for the quantification of the myelin water fraction (MWF). In recent literature, the MWF is most commonly estimated using a regularized nonnegative least squares algorithm. Methods The orthogonal matching pursuit algorithm is proposed as an alternative method for the estimation of the MWF. The orthogonal matching pursuit is a greedy sparse reconstruction algorithm with a low computation complexity. For validation, both methods are compared to a ground truth using numerical simulations and a phantom model using comparable computation times. The numerical simulations were used to measure the theoretical errors, as well as the effects of varying the SNR, strength of the regularization, and resolution of the basis set. Additionally, a phantom model was used to estimate the performance of the 2 methods while including errors occurring due to the MR measurement. Lastly, 4 healthy subjects were scanned to evaluate the in vivo performance. Results The results in simulations and phantoms demonstrate that the MWFs determined with the orthogonal matching pursuit are 1.7 times more accurate as compared to the nonnegative least squares, with a comparable precision. The remaining bias of the MWF is shown to be related to the regularization of the nonnegative least squares algorithm and the Rician noise present in magnitude MR images. Conclusion The orthogonal matching pursuit algorithm provides a more accurate alternative for T2 relaxometry myelin water quantification.
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Affiliation(s)
- Gerhard S Drenthen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands
| | - Walter H Backes
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands
| | - Albert P Aldenkamp
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Behavioral Sciences, Epilepsy Center Kempenhaeghe, Sterkselseweg 65, Heeze, the Netherlands
| | - Giel J Op 't Veld
- School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Station 14, Lausanne, Switzerland
| | - Jacobus F A Jansen
- School for Mental Health and Neuroscience, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, De Rondom 70, Eindhoven, the Netherlands
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18
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Differentiating Neuromyelitis Optica-Related and Multiple Sclerosis-Related Acute Optic Neuritis Using Conventional Magnetic Resonance Imaging Combined With Readout-Segmented Echo-Planar Diffusion-Weighted Imaging. J Comput Assist Tomogr 2018; 42:502-509. [PMID: 29787495 DOI: 10.1097/rct.0000000000000724] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE In clinical practice, acute optic neuritis (ON) associated with the development of neuromyelitis optica (NMO) after the first attack is often indistinguishable from that associated with multiple sclerosis (MS). We aimed to determine the optimal combination of features derived from conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging using readout-segmented echo-planar imaging (RESOLVE-DWI) for the differentiation of these conditions. MATERIALS AND METHODS Orbital conventional MRI and RESOLVE-DWI were performed using a 3.0-T scanner on 54 patients with acute ON (26 NMO-related and 28 MS-related). The features detected by conventional MRI (including laterality, the enhancement pattern, and the extent and position of involvement) and the apparent diffusion coefficient (ADC) measurements were retrospectively compared between the NMO-related and MS-related groups. A multivariate logistic regression analysis was used to identify the most significant variables, and receiver operating characteristic curve analyses were performed to determine the ability of a combined diagnostic model based on the qualitative and quantitative characteristics identified in this study to differentiate the 2 conditions. RESULTS The multivariate logistic regression analyses indicated that the presence of chiasm involvement and lower ADC values were significantly associated with NMO-related acute ON compared with MS-related acute ON (P = 0.037 and 0.008, respectively). The diagnostic criterion of chiasm involvement or "ADC < 791 × 10 mm/s and chiasm involvement" had the highest specificity (96.9%), and "ADC < 791 × 10 mm/s or chiasm involvement" showed the optimal sensitivity (77.8%) for differentiating NMO-related from MS-related acute ON. CONCLUSIONS Conventional MRI RESOLVE-DWI is helpful for differentiating NMO-related acute ON from MS-related acute ON. The combination of the ADC value chiasm involvement appears to be effective for discriminating these 2 types of acute ON.
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19
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Bouhrara M, Reiter DA, Maring MC, Bonny JM, Spencer RG. Use of the NESMA Filter to Improve Myelin Water Fraction Mapping with Brain MRI. J Neuroimaging 2018; 28:640-649. [PMID: 29999204 DOI: 10.1111/jon.12537] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 05/31/2018] [Accepted: 06/19/2018] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND AND PURPOSE Myelin water fraction (MWF) mapping permits direct visualization of myelination patterns in the developing brain and in pathology. MWF is conventionally measured through multiexponential T2 analysis which is very sensitive to noise, leading to inaccuracies in derived MWF estimates. Although noise reduction filters may be applied during postprocessing, conventional filtering can introduce bias and obscure small structures and edges. Advanced nonblurring filters, while effective, exhibit a high level of complexity and the requirement for supervised implementation for optimal performance. The purpose of this paper is to demonstrate the ability of the recently introduced nonlocal estimation of multispectral magnitudes (NESMA) filter to greatly improve the determination of MWF parameter estimates from gradient and spin echo (GRASE) imaging data. METHODS We evaluated the performance of the NESMA filter for MWF mapping from clinical GRASE imaging data of the human brain, and compared the results to those calculated from unfiltered images. Numerical and in vivo analyses of the brains of three subjects, representing different ages, were conducted. RESULTS Our results demonstrated the potential of the NESMA filter to permit high-quality in vivo MWF mapping. Indeed, NESMA permits substantial reduction of random variation in derived MWF estimates while preserving accuracy and detail. CONCLUSIONS In vivo estimation of MWF in the human brain from GRASE imaging data was markedly improved through use of the NESMA filter. The use of NESMA may contribute to the goal of high-quality MWF mapping in clinically feasible imaging times.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, MD
| | - David A Reiter
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA
| | - Michael C Maring
- Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, MD
| | | | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, MD
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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.
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Tsai PH, Chou MC, Chiang SW, Chung HW, Liu HS, Kao HW, Chen CY. Early white matter injuries in patients with acute carbon monoxide intoxication: A tract-specific diffusion kurtosis imaging study and STROBE compliant article. Medicine (Baltimore) 2017; 96:e5982. [PMID: 28151889 PMCID: PMC5293452 DOI: 10.1097/md.0000000000005982] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
Evaluation of acute white matter injuries caused by carbon monoxide (CO) poisoning can be limited by conventional magnetic resonance (MR) imaging. We aim to evaluate the feasibility of diffusion kurtosis imaging (DKI) for early detection of white matter alterations in patients with acute CO intoxication.A total of 30 subjects including 15 acute CO patients and 15 age- and sex-matched healthy volunteers were enrolled in this study. MR examinations were performed on a 3T MR scanner within 8 days after CO intoxication. DKI data were acquired to derive axial, radial, and mean kurtosis, as well as fractional anisotropy (FA), axial, radial, and mean diffusivity for tract-specific comparisons between the 2 groups.Significant decreases of mean kurtosis were shown in the genu of corpus callosum, cingulum, and motor-related tracts (corticospinal and corticobulbar tracts) in patients with acute CO intoxication as compared with controls. On the contrary, significant differences of FA values were merely shown in the regions of corticospinal tracts.DKI demonstrated comparably stronger potential than diffusion tensor imaging in terms of early detection of white matter changes in patients with acute CO intoxication. This may have implications in therapeutic strategy for managing acute CO intoxication patients.
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Affiliation(s)
- Ping-Huei Tsai
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University
- Translational Imaging Research Center
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei Medical University, Taipei
| | - Ming-Chung Chou
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung
| | - Shih-Wei Chiang
- Department of Radiology, Tri-Service General Hospital and National Defense Medical Center
| | - Hsiao-Wen Chung
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University
| | - Hua-Shan Liu
- Translational Imaging Research Center
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei Medical University, Taipei
- School of Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Hung-Wen Kao
- Department of Radiology, Tri-Service General Hospital and National Defense Medical Center
| | - Cheng-Yu Chen
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University
- Translational Imaging Research Center
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei Medical University, Taipei
- Department of Radiology, Tri-Service General Hospital and National Defense Medical Center
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