Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions
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 foc...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Radiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fradi.2022.930666/full |
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| author | Elda Fischi-Gomez Elda Fischi-Gomez Gabriel Girard Gabriel Girard Gabriel Girard Philipp J. Koch Philipp J. Koch Philipp J. Koch Philipp J. Koch Thomas Yu Thomas Yu Marco Pizzolato Marco Pizzolato Julia Brügger Julia Brügger Gian Franco Piredda Gian Franco Piredda Gian Franco Piredda Tom Hilbert Tom Hilbert Tom Hilbert Andéol G. Cadic-Melchior Andéol G. Cadic-Melchior Elena Beanato Elena Beanato Chang-Hyun Park Chang-Hyun Park Takuya Morishita Takuya Morishita Maximilian J. Wessel Maximilian J. Wessel Maximilian J. Wessel Simona Schiavi Simona Schiavi Alessandro Daducci Tobias Kober Tobias Kober Tobias Kober Erick J. Canales-Rodríguez Friedhelm C. Hummel Friedhelm C. Hummel Friedhelm C. Hummel Jean-Philippe Thiran Jean-Philippe Thiran Jean-Philippe Thiran |
| author_facet | Elda Fischi-Gomez Elda Fischi-Gomez Gabriel Girard Gabriel Girard Gabriel Girard Philipp J. Koch Philipp J. Koch Philipp J. Koch Philipp J. Koch Thomas Yu Thomas Yu Marco Pizzolato Marco Pizzolato Julia Brügger Julia Brügger Gian Franco Piredda Gian Franco Piredda Gian Franco Piredda Tom Hilbert Tom Hilbert Tom Hilbert Andéol G. Cadic-Melchior Andéol G. Cadic-Melchior Elena Beanato Elena Beanato Chang-Hyun Park Chang-Hyun Park Takuya Morishita Takuya Morishita Maximilian J. Wessel Maximilian J. Wessel Maximilian J. Wessel Simona Schiavi Simona Schiavi Alessandro Daducci Tobias Kober Tobias Kober Tobias Kober Erick J. Canales-Rodríguez Friedhelm C. Hummel Friedhelm C. Hummel Friedhelm C. Hummel Jean-Philippe Thiran Jean-Philippe Thiran Jean-Philippe Thiran |
| author_sort | Elda Fischi-Gomez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T10:27:06Z |
| format | Article |
| id | doaj.art-41959854d7144c888ba84bd844ce0751 |
| institution | Directory Open Access Journal |
| issn | 2673-8740 |
| language | English |
| last_indexed | 2024-04-13T10:27:06Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Radiology |
| spelling | doaj.art-41959854d7144c888ba84bd844ce07512022-12-22T02:50:17ZengFrontiers Media S.A.Frontiers in Radiology2673-87402022-07-01210.3389/fradi.2022.930666930666Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitionsElda Fischi-Gomez0Elda Fischi-Gomez1Gabriel Girard2Gabriel Girard3Gabriel Girard4Philipp J. Koch5Philipp J. Koch6Philipp J. Koch7Philipp J. Koch8Thomas Yu9Thomas Yu10Marco Pizzolato11Marco Pizzolato12Julia Brügger13Julia Brügger14Gian Franco Piredda15Gian Franco Piredda16Gian Franco Piredda17Tom Hilbert18Tom Hilbert19Tom Hilbert20Andéol G. Cadic-Melchior21Andéol G. Cadic-Melchior22Elena Beanato23Elena Beanato24Chang-Hyun Park25Chang-Hyun Park26Takuya Morishita27Takuya Morishita28Maximilian J. Wessel29Maximilian J. Wessel30Maximilian J. Wessel31Simona Schiavi32Simona Schiavi33Alessandro Daducci34Tobias Kober35Tobias Kober36Tobias Kober37Erick J. Canales-Rodríguez38Friedhelm C. Hummel39Friedhelm C. Hummel40Friedhelm C. Hummel41Jean-Philippe Thiran42Jean-Philippe Thiran43Jean-Philippe Thiran44Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandTranslational Machine Learning Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, SwitzerlandSignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandCIBM Center for Biomedical Imaging, Lausanne, SwitzerlandDepartment of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, SwitzerlandDepartment of Neurology, University of Lübeck, Lübeck, GermanyCenter of Brain, Behavior and Metabolism (CBBM), University of Lübeck, Lübeck, GermanySignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, SwitzerlandSignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland0Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, DenmarkDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, SwitzerlandSignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDepartment of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, SwitzerlandSignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDepartment of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, Switzerland1Department of Neurology, University Hospital and Julius-Maximilians-University, Wuerzburg, Germany2Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy3Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy3Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, ItalySignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDepartment of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, SwitzerlandAdvanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, SwitzerlandSignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech Chair for Clinical Neuroengineering, Neuro-X Institute (NIX) and Brain Mind Institute (BMI), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandDefitech 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, Switzerland4Clinical Neuroscience, University Hospital of Geneva (HUG), Geneva, SwitzerlandSignal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, SwitzerlandCIBM Center for Biomedical Imaging, Lausanne, SwitzerlandDepartment of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, SwitzerlandQuantitative 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.https://www.frontiersin.org/articles/10.3389/fradi.2022.930666/fullrelaxometryreproducibilityvariabilityMRImulti-echoquantitative MRI |
| spellingShingle | Elda Fischi-Gomez Elda Fischi-Gomez Gabriel Girard Gabriel Girard Gabriel Girard Philipp J. Koch Philipp J. Koch Philipp J. Koch Philipp J. Koch Thomas Yu Thomas Yu Marco Pizzolato Marco Pizzolato Julia Brügger Julia Brügger Gian Franco Piredda Gian Franco Piredda Gian Franco Piredda Tom Hilbert Tom Hilbert Tom Hilbert Andéol G. Cadic-Melchior Andéol G. Cadic-Melchior Elena Beanato Elena Beanato Chang-Hyun Park Chang-Hyun Park Takuya Morishita Takuya Morishita Maximilian J. Wessel Maximilian J. Wessel Maximilian J. Wessel Simona Schiavi Simona Schiavi Alessandro Daducci Tobias Kober Tobias Kober Tobias Kober Erick J. Canales-Rodríguez Friedhelm C. Hummel Friedhelm C. Hummel Friedhelm C. Hummel Jean-Philippe Thiran Jean-Philippe Thiran Jean-Philippe Thiran Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions Frontiers in Radiology relaxometry reproducibility variability MRI multi-echo quantitative MRI |
| title | Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions |
| title_full | Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions |
| title_fullStr | Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions |
| title_full_unstemmed | Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions |
| title_short | Variability and reproducibility of multi-echo T2 relaxometry: Insights from multi-site, multi-session and multi-subject MRI acquisitions |
| title_sort | variability and reproducibility of multi echo t2 relaxometry insights from multi site multi session and multi subject mri acquisitions |
| topic | relaxometry reproducibility variability MRI multi-echo quantitative MRI |
| url | https://www.frontiersin.org/articles/10.3389/fradi.2022.930666/full |
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