The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation
Open accessAbstract
Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD(*)), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2(*) = 1/T2(*)). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2(*) (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507
SENSE: Sensitivity encoding for fast MRI
Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s · Cited by 6,251
Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE)
Mugler, J. P., & Brookeman, J. R. (1990) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910150117 · Cited by 1,265
Jovicich, J., Czanner, S., Greve, D., Haley, E., van der Kouwe, A., Gollub, R., Kennedy, D., Schmitt, F., Brown, G., MacFall, J., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.09.046 · Cited by 1,265
Automatic classification of MR scans in Alzheimer’s disease.
Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., & Frackowiak, R. S. J. (2008) · Brain · doi:10.1093/brain/awm319 · Cited by 1,170
Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo
Rooney, W. D., Johnson, G., Li, X., Cohen, E. R., Kim, S., Ugurbil, K., & Springer, C. S. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21122 · Cited by 652
Deichmann, R., Schwarzbauer, C., & Turner, R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2003.09.062 · Cited by 391
32‐channel 3 Tesla receive‐only phased‐array head coil with soccer‐ball element geometry
Wiggins, G. C., Triantafyllou, C., Potthast, A., Reykowski, A., Nittka, M., & Wald, L. L. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20925 · Cited by 375
Computer-assisted imaging to assess brain structure in healthy and diseased brains
Ashburner, J., Csernansk, J. G., Davatzikos, C., Fox, N. C., Frisoni, G. B., & Thompson, P. M. (2003) · The Lancet Neurology · doi:10.1016/s1474-4422(03)00304-1 · Cited by 371
Cited by
Developmental cognitive neuroscience using latent change score models: A tutorial and applications
Kievit, R. A., Brandmaier, A. M., Ziegler, G., van Harmelen, A.-L., de Mooij, S. M. M., Moutoussis, M., Goodyer, I. M., Bullmore, E., Jones, P. B., Fonagy, P., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.11.007 · Cited by 516
Self-citationLocus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases
Betts, M. J., Kirilina, E., Otaduy, M. C. G., Ivanov, D., Acosta-Cabronero, J., Callaghan, M. F., Lambert, C., Cardenas-Blanco, A., Pine, K., Passamonti, L., et al. (2019) · Brain · doi:10.1093/brain/awz193 · Cited by 365
Self-citationUsing high-resolution quantitative mapping of R1 as an index of cortical myelination
Lutti, A., Dick, F., Sereno, M. I., & Weiskopf, N. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.005 · Cited by 346
Self-citationQuantitative magnetic resonance imaging of brain anatomy and in vivo histology
Weiskopf, N., Edwards, L. J., Helms, G., Mohammadi, S., & Kirilina, E. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00326-1 · Cited by 300
Self-citationCallaghan, M. F., Freund, P., Draganski, B., Anderson, E., Cappelletti, M., Chowdhury, R., Diedrichsen, J., FitzGerald, T. H. B., Smittenaar, P., Helms, G., et al. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2014.02.008 · Cited by 292
Self-citation
Metadata
- Authors
- Weiskopf, N., Suckling, J., Williams, G., Correia, M. M., Inkster, B., Tait, R., Ooi, C., Bullmore, E. T., & Lutti, A.
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2013
- Volume
- 7
- Pages
- 95-95
- ISSN
- 1662-453X (ISSN-L)
- OpenAlex
- W2148417430
- Cited by
- 538
- References
- 53
- FWCI
- 13.55
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. https://app.cochat.ai/papers/10.3389/fnins.2013.00095
- The paper
- Weiskopf, N., Suckling, J., Williams, G., Correia, M. M., Inkster, B., Tait, R., Ooi, C., Bullmore, E. T., & Lutti, A. (2013). Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Frontiers in Neuroscience. https://doi.org/10.3389/fnins.2013.00095
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
