The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Perfusion Magnetic Resonance Imaging: A Comprehensive Update on Principles and Techniques
Open accessAbstract
Perfusion is a fundamental biological function that refers to the delivery of oxygen and nutrients to tissue by means of blood flow. Perfusion MRI is sensitive to microvasculature and has been applied in a wide variety of clinical applications, including the classification of tumors, identification of stroke regions, and characterization of other diseases. Perfusion MRI techniques are classified with or without using an exogenous contrast agent. Bolus methods, with injections of a contrast agent, provide better sensitivity with higher spatial resolution, and are therefore more widely used in clinical applications. However, arterial spin-labeling methods provide a unique opportunity to measure cerebral blood flow without requiring an exogenous contrast agent and have better accuracy for quantification. Importantly, MRI-based perfusion measurements are minimally invasive overall, and do not use any radiation and radioisotopes. In this review, we describe the principles and techniques of perfusion MRI. This review summarizes comprehensive updated knowledge on the physical principles and techniques of perfusion MRI.
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Metadata
- Authors
- Jahng, G.-H., Li, K.-L., Ostergaard, L., & Calamante, F.
- Venue
- Korean Journal of Radiology
- Publisher
- XMLink
- Published
- 2014
- Volume
- 15
- Issue
- 5
- Pages
- 554
- ISSN
- 1229-6929 (ISSN-L)
- 2005-8330
- OpenAlex
- W2115962809
- Cited by
- 256
- References
- 116
- FWCI
- 9.86
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Jahng, G.-H., Li, K.-L., Ostergaard, L., & Calamante, F. (2014). Perfusion Magnetic Resonance Imaging: A Comprehensive Update on Principles and Techniques. Korean Journal of Radiology. https://doi.org/10.3348/kjr.2014.15.5.554
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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.
