The DOI matches its Crossref record.
Classic models for dynamic contrast‐enhanced MRI
Abstract
Dynamic contrast‐enhanced MRI (DCE‐MRI) is a functional MRI method where T1 ‐weighted MR images are acquired dynamically after bolus injection of a contrast agent. The data can be interpreted in terms of physiological tissue characteristics by applying the principles of tracer‐kinetic modelling. In the brain, DCE‐MRI enables measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), blood–brain barrier (BBB) permeability–surface area product (PS) and the volume of the interstitium (ve). These parameters can be combined to form others such as the volume‐transfer constant Ktrans, the extraction fraction E and the contrast‐agent mean transit times through the intra‐ and extravascular spaces. A first generation of tracer‐kinetic models for DCE‐MRI was developed in the early 1990s and has become a standard in many applications. Subsequent improvements in DCE‐MRI data quality have driven the development of a second generation of more complex models. They are increasingly used, but it is not always clear how they relate to the models of the first generation or to the model‐free deconvolution methods for tissues with intact BBB. This lack of understanding is leading to increasing confusion on when to use which model and how to interpret the parameters. The purpose of this review is to clarify the relation between models of the first and second generations and between model‐based and model‐free methods. All quantities are defined using a generic terminology to ensure the widest possible scope and to reveal the link between applications in the brain and in other organs. Copyright © 2013 John Wiley & Sons, Ltd.
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
Cited by
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
Functional morphology of the blood–brain barrier in health and disease
Liebner, S., Dijkhuizen, R. M., Reiss, Y., Plate, K. H., Agalliu, D., & Constantin, G. (2018) · Acta Neuropathologica · doi:10.1007/s00401-018-1815-1 · Cited by 872
Heye, A. K., Culling, R. D., Valdés Hernández, M. D. C., Thrippleton, M. J., & Wardlaw, J. M. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.09.002 · Cited by 406
Thrippleton, M. J., Backes, W. H., Sourbron, S., Ingrisch, M., van Osch, M. J. P., Dichgans, M., Fazekas, F., Ropele, S., Frayne, R., van Oostenbrugge, R. J., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2019.01.013 · Cited by 252
Self-citationBrain imaging of neurovascular dysfunction in Alzheimer’s disease
Montagne, A., Nation, D. A., Pa, J., Sweeney, M. D., Toga, A. W., & Zlokovic, B. V. (2016) · Acta Neuropathologica · doi:10.1007/s00401-016-1570-0 · Cited by 239
Metadata
- Authors
- Sourbron, S. P., & Buckley, D. L.
- Venue
- NMR in Biomedicine
- Publisher
- Wiley
- Published
- 2013-05-15
- Volume
- 26
- Issue
- 8
- Pages
- 1004-1027
- ISSN
- 0952-3480 (ISSN-L)
- 1099-1492
- OpenAlex
- W2101482993
- Cited by
- 434
- References
- 0
- FWCI
- 20.92
- Type
- journal article
- Language
- en
- Open access
- closed
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: Classic models for dynamic contrast‐enhanced MRI. https://app.cochat.ai/papers/10.1002/nbm.2940
- The paper
- Sourbron, S. P., & Buckley, D. L. (2013). Classic models for dynamic contrast‐enhanced MRI. NMR in Biomedicine. https://doi.org/10.1002/nbm.2940
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.