A Water Relaxation Atlas for Age‐ and Region‐Specific Metabolite Concentration Correction at 3 T
ABSTRACT Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation‐time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within...
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| Published in | NMR in biomedicine Vol. 38; no. 1; pp. e5300 - n/a |
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| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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England
Wiley Subscription Services, Inc
01.01.2025
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| Abstract | ABSTRACT
Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation‐time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location‐ and age‐appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1‐weighted MPRAGE images ((1‐mm)3 isotropic resolution) were acquired. Whole‐brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI‐SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age − 30 as the predictor. Reference atlases of “age‐30‐intercept” and age‐slope for T1 and T2 were generated. The atlas‐based workflow was integrated into Osprey, which co‐registers MRS voxels to the atlas and calculates location‐ and age‐appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location‐ and subject‐appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water‐referenced tissue correction, especially for studies of aging.
This study presents a water relaxometry atlas incorporating location‐ and age‐appropriate T1 and T2 values to improve MRS‐derived metabolite quantification. Using data from 101 volunteers, the atlas accounts for regional and age‐related differences in water relaxation, integrated into the Osprey MRS analysis workflow. This can help reduce quantification biases, particularly in aging studies, by correcting tissue‐specific water relaxation behavior. |
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| AbstractList | Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation‐time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times ( T 1 and T 2 ) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location‐ and age‐appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T 1 ‐weighted MPRAGE images ((1‐mm) 3 isotropic resolution) were acquired. Whole‐brain water T 1 and T 2 measurements were made with DESPOT ((1.4 mm) 3 isotropic resolution) at 3T. T 1 and T 2 maps were registered to the JHU MNI‐SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T 1 and T 2 values were calculated for each parcel in each subject. Linear models of T 1 and T 2 as functions of age were computed, using age − 30 as the predictor. Reference atlases of “age‐30‐intercept” and age‐slope for T 1 and T 2 were generated. The atlas‐based workflow was integrated into Osprey, which co‐registers MRS voxels to the atlas and calculates location‐ and age‐appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location‐ and subject‐appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water‐referenced tissue correction, especially for studies of aging. Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation‐time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location‐ and age‐appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1‐weighted MPRAGE images ((1‐mm)3 isotropic resolution) were acquired. Whole‐brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI‐SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age − 30 as the predictor. Reference atlases of “age‐30‐intercept” and age‐slope for T1 and T2 were generated. The atlas‐based workflow was integrated into Osprey, which co‐registers MRS voxels to the atlas and calculates location‐ and age‐appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location‐ and subject‐appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water‐referenced tissue correction, especially for studies of aging. Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times ( T 1 and T 2 ) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T 1 -weighted MPRAGE images ((1-mm) 3 isotropic resolution) were acquired. Whole-brain water T 1 and T 2 measurements were made with DESPOT ((1.4 mm) 3 isotropic resolution) at 3T. T 1 and T 2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T 1 and T 2 values were calculated for each parcel in each subject. Linear models of T 1 and T 2 as functions of age were computed, using age − 30 as the predictor. Reference atlases of “age-30-intercept” and age-slope for T 1 and T 2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging. Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T and T ) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T -weighted MPRAGE images ((1-mm) isotropic resolution) were acquired. Whole-brain water T and T measurements were made with DESPOT ((1.4 mm) isotropic resolution) at 3T. T and T maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T and T values were calculated for each parcel in each subject. Linear models of T and T as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T and T were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging. ABSTRACT Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation‐time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location‐ and age‐appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1‐weighted MPRAGE images ((1‐mm)3 isotropic resolution) were acquired. Whole‐brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI‐SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age − 30 as the predictor. Reference atlases of “age‐30‐intercept” and age‐slope for T1 and T2 were generated. The atlas‐based workflow was integrated into Osprey, which co‐registers MRS voxels to the atlas and calculates location‐ and age‐appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location‐ and subject‐appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water‐referenced tissue correction, especially for studies of aging. This study presents a water relaxometry atlas incorporating location‐ and age‐appropriate T1 and T2 values to improve MRS‐derived metabolite quantification. Using data from 101 volunteers, the atlas accounts for regional and age‐related differences in water relaxation, integrated into the Osprey MRS analysis workflow. This can help reduce quantification biases, particularly in aging studies, by correcting tissue‐specific water relaxation behavior. Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1-weighted MPRAGE images ((1-mm)3 isotropic resolution) were acquired. Whole-brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T1 and T2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging.Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1-weighted MPRAGE images ((1-mm)3 isotropic resolution) were acquired. Whole-brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T1 and T2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging. |
| Author | Ratnanather, J. Tilak Hupfeld, Kathleen E. Hui, Steve C. N. Carter, Emily Gudmundson, Aaron T. Yedavalli, Vivek Murali‐Manohar, Saipavitra Edden, Richard Song, Yulu Muska, Emlyn Simicic, Dunja Oeltzschner, Georg Zöllner, Helge J. Simegn, Gizeaddis Lamesgin Ceritoglu, Can Davies‐Jenkins, Christopher W. Dean, Douglas C. Porges, Eric |
| AuthorAffiliation | 8 Department of Pediatrics, Division of Neonatology and Newborn Nursey, University of WI-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA 5 Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA 9 Department of Medical Physics, University of WI-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA 6 Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA 7 Waisman Center, University of WI-Madison, Madison, Wisconsin, USA 2 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA 10 Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, JHU, Baltimore, Maryland, USA 4 Developing Brain Institute, Children's National Hospital, Washington, DC, USA 1 The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins |
| AuthorAffiliation_xml | – name: 1 The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA – name: 9 Department of Medical Physics, University of WI-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA – name: 5 Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA – name: 6 Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA – name: 10 Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, JHU, Baltimore, Maryland, USA – name: 4 Developing Brain Institute, Children's National Hospital, Washington, DC, USA – name: 7 Waisman Center, University of WI-Madison, Madison, Wisconsin, USA – name: 2 F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA – name: 3 Center for Cognitive Aging and Memory (CAM), McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA – name: 8 Department of Pediatrics, Division of Neonatology and Newborn Nursey, University of WI-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA |
| Author_xml | – sequence: 1 givenname: Gizeaddis Lamesgin surname: Simegn fullname: Simegn, Gizeaddis Lamesgin organization: Kennedy Krieger Institute – sequence: 2 givenname: Yulu surname: Song fullname: Song, Yulu organization: Kennedy Krieger Institute – sequence: 3 givenname: Saipavitra surname: Murali‐Manohar fullname: Murali‐Manohar, Saipavitra organization: Kennedy Krieger Institute – sequence: 4 givenname: Helge J. orcidid: 0000-0002-7148-292X surname: Zöllner fullname: Zöllner, Helge J. organization: Kennedy Krieger Institute – sequence: 5 givenname: Christopher W. surname: Davies‐Jenkins fullname: Davies‐Jenkins, Christopher W. organization: Kennedy Krieger Institute – sequence: 6 givenname: Dunja orcidid: 0000-0002-6600-2696 surname: Simicic fullname: Simicic, Dunja organization: Kennedy Krieger Institute – sequence: 7 givenname: Kathleen E. orcidid: 0000-0001-5086-4841 surname: Hupfeld fullname: Hupfeld, Kathleen E. organization: Kennedy Krieger Institute – sequence: 8 givenname: Aaron T. surname: Gudmundson fullname: Gudmundson, Aaron T. organization: Kennedy Krieger Institute – sequence: 9 givenname: Emlyn surname: Muska fullname: Muska, Emlyn organization: University of Florida – sequence: 10 givenname: Emily surname: Carter fullname: Carter, Emily organization: University of Florida – sequence: 11 givenname: Steve C. N. surname: Hui fullname: Hui, Steve C. N. organization: The George Washington University School of Medicine and Health Sciences – sequence: 12 givenname: Vivek surname: Yedavalli fullname: Yedavalli, Vivek organization: Johns Hopkins University School of Medicine – sequence: 13 givenname: Georg orcidid: 0000-0003-3083-9811 surname: Oeltzschner fullname: Oeltzschner, Georg organization: Kennedy Krieger Institute – sequence: 14 givenname: Douglas C. surname: Dean fullname: Dean, Douglas C. organization: University of WI‐Madison, School of Medicine and Public Health – sequence: 15 givenname: Can surname: Ceritoglu fullname: Ceritoglu, Can organization: JHU – sequence: 16 givenname: J. Tilak surname: Ratnanather fullname: Ratnanather, J. Tilak organization: Children's National Hospital – sequence: 17 givenname: Eric surname: Porges fullname: Porges, Eric organization: University of Florida – sequence: 18 givenname: Richard surname: Edden fullname: Edden, Richard email: redden1@jh.edu organization: Kennedy Krieger Institute |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39662516$$D View this record in MEDLINE/PubMed |
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| Copyright | 2024 John Wiley & Sons, Ltd. 2025 John Wiley & Sons, Ltd. |
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| Keywords | Relaxometry atlas relaxation times aging metabolite quantification brain parcels MRS |
| Language | English |
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| Notes | Funding This work was supported by the National Institutes of Health (NIH) (Grants R01 EB016089, R01 EB023963, R01 EB032788, R01 EB035529, R00 AG062230, R21 EB033516, K99 AG080084, K00 AG068440, P01AA029543, R01DK099334, and P41 EB031771). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-3083-9811 0000-0002-6600-2696 0000-0001-5086-4841 0000-0002-7148-292X |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/12127971 |
| PMID | 39662516 |
| PQID | 3147266740 |
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| PublicationCentury | 2000 |
| PublicationDate | January 2025 2025-01-00 2025-Jan 20250101 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: January 2025 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Oxford |
| PublicationTitle | NMR in biomedicine |
| PublicationTitleAlternate | NMR Biomed |
| PublicationYear | 2025 |
| Publisher | Wiley Subscription Services, Inc |
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Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for... Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation‐time... Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time... |
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| SubjectTerms | Adult Age Aging Aging - metabolism Atlases as Topic Brain Brain - diagnostic imaging Brain - metabolism Brain mapping brain parcels Cerebrospinal fluid Female Humans Image acquisition Life span Magnetic induction Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Male metabolite quantification Metabolites Middle Aged MRS Reference signals relaxation times Relaxometry atlas Substantia alba Substantia grisea Water Water - chemistry Water - metabolism Workflow Young Adult |
| Title | A Water Relaxation Atlas for Age‐ and Region‐Specific Metabolite Concentration Correction at 3 T |
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