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Lui E, Venkatraman VK, Finch S, Chua M, Li TQ, Sutton BP, Steward CE, Moffat B, Cyarto EV, Ellis KA, Rowe CC, Masters CL, Lautenschlager NT, Desmond PM. 3T sodium-MRI as predictor of neurocognition in nondemented older adults: a cross sectional study. Brain Commun 2024; 6:fcae307. [PMID: 39318783 PMCID: PMC11420980 DOI: 10.1093/braincomms/fcae307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 06/13/2024] [Accepted: 09/10/2024] [Indexed: 09/26/2024] Open
Abstract
Dementia is a burgeoning global problem. Novel magnetic resonance imaging (MRI) metrics beyond volumetry may bring new insight and aid clinical trial evaluation of interventions early in the Alzheimer's disease course to complement existing imaging and clinical metrics. To determine whether: (i) normalized regional sodium-MRI values (Na-SI) are better predictors of neurocognitive status than volumetry (ii) cerebral amyloid PET status improves modelling. Nondemented older adult (>60 years) volunteers of known Alzheimer's Disease Assessment Scale (ADAS-Cog11), Mini-Mental State Examination (MMSE) and Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neurocognitive test scores, ApolipoproteinE (APOE) e4 +/- cerebral amyloid PET status were prospectively recruited for 3T sodium-MRI brain scans. Left and right hippocampal, entorhinal and precuneus volumes and Na-SI (using the proportional intensity scaling normalization method with field inhomogeneity and partial volume corrections) were obtained after segmentation and co-registration of 3D-T1-weighted proton images. Descriptive statistics, correlation and best-subset regression analyses were performed. In our 76 nondemented participants (mean(standard deviation) age 75(5) years; woman 47(62%); cognitively unimpaired 54/76(71%), mildly cognitively impaired 22/76(29%)), left hippocampal Na-SI, not volume, was preferentially in the best models for predicting MMSE (Odds Ratio (OR) = 0.19(Confidence Interval (CI) = 0.07,0.53), P-value = 0.001) and ADAS-Cog11 (Beta(B) = 1.2(CI = 0.28,2.1), P-value = 0.01) scores. In the entorhinal analysis, right entorhinal Na-SI, not volume, was preferentially selected in the best model for predicting ADAS-Cog11 (B = 0.94(CI = 0.11,1.8), P-value = 0.03). While right entorhinal Na-SI and volume were both selected for MMSE modelling (Na-SI OR = 0.23(CI = 0.09,0.6), P-value = 0.003; volume OR = 2.6(CI = 1.0,6.6), P-value = 0.04), independently, Na-SI explained more of the variance (Na-SI R 2 = 10.3; volume R 2 = 7.5). No imaging variable was selected in the best CERAD models. Adding cerebral amyloid status improved model fit (Akaike Information Criterion increased 2.0 for all models, P-value < 0.001-0.045). Regional Na-SI were more predictive of MMSE and ADAS-Cog11 scores in our nondemented older adult cohort than volume, hippocampal more robust than entorhinal region of interest. Positive amyloid status slightly further improved model fit.
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Affiliation(s)
- Elaine Lui
- Department of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, 3050 Victoria, Australia
| | - Vijay K Venkatraman
- Department of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, 3050 Victoria, Australia
| | - Sue Finch
- Statistical Consulting Centre, University of Melbourne, Parkville, 3010 Victoria, Australia
| | - Michelle Chua
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, 3050 Victoria, Australia
| | - Tie-Qiang Li
- Department of Clinical Science, Intervention and Technology, Karolinska Institute, 171 77 Stockholm, Sweden
| | - Bradley P Sutton
- Beckman Institute for Advance Science and Technology, University of Illinois at Urbana Champaign, Champaign, IL 61801, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
| | - Christopher E Steward
- Department of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, 3050 Victoria, Australia
| | - Bradford Moffat
- Department of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia
| | - Elizabeth V Cyarto
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Queensland 4059, Australia
| | - Kathryn A Ellis
- Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Melbourne, 3010 Victoria, Australia
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, 3010 Victoria, Australia
| | - Christopher C Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Melbourne, 3084 Victoria, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, 3052 Victoria, Australia
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, 3052 Victoria, Australia
| | - Nicola T Lautenschlager
- Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, Melbourne, 3010 Victoria, Australia
- Royal Melbourne Hospital Mental Health Service, Royal Melbourne Hospital, Parkville, Melbourne, 3052 Victoria, Australia
| | - Patricia M Desmond
- Department of Radiology, The University of Melbourne, Parkville, 3050 Victoria, Australia
- Department of Medical Imaging, The Royal Melbourne Hospital, Parkville, 3050 Victoria, Australia
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Schramm G, Filipovic M, Qian Y, Alivar A, Lui YW, Nuyts J, Boada F. Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction. Magn Reson Med 2024; 91:1404-1418. [PMID: 38044789 PMCID: PMC10916150 DOI: 10.1002/mrm.29936] [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: 07/03/2023] [Revised: 11/03/2023] [Accepted: 11/04/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE Sodium MRI is challenging because of the low tissue concentration of the 23 Na nucleus and its extremely fast biexponential transverse relaxation rate. In this article, we present an iterative reconstruction framework using dual-echo 23 Na data and exploiting anatomical prior information (AGR) from high-resolution, low-noise, 1 H MR images. This framework enables the estimation and modeling of the spatially varying signal decay due to transverse relaxation during readout (AGRdm), which leads to images of better resolution and reduced noise resulting in improved quantification of the reconstructed 23 Na images. METHODS The proposed framework was evaluated using reconstructions of 30 noise realizations of realistic simulations of dual echo twisted projection imaging (TPI) 23 Na data. Moreover, three dual echo 23 Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. RESULTS Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. CONCLUSION AGR and AGRdm generate 23 Na images with high resolution, high levels of anatomical detail, and low levels of noise, potentially enabling high-quality 23 Na MR imaging at 3T.
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Affiliation(s)
- Georg Schramm
- Radiological Sciences Laboratory, School of Medicine, Stanford University, Stanford, California, USA
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | | | - Yongxian Qian
- Center for Biomedical Imaging, Department of Radiology, Grossman School of Medicine, New York University (NYU), New York, New York, USA
| | - Alaleh Alivar
- Center for Biomedical Imaging, Department of Radiology, Grossman School of Medicine, New York University (NYU), New York, New York, USA
| | - Yvonne W. Lui
- Center for Biomedical Imaging, Department of Radiology, Grossman School of Medicine, New York University (NYU), New York, New York, USA
| | - Johan Nuyts
- Department of Imaging and Pathology, KU Leuven, Leuven, Belgium
| | - Fernando Boada
- Radiological Sciences Laboratory, School of Medicine, Stanford University, Stanford, California, USA
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