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GRASE (Gradient‐and Spin‐Echo) imaging: A novel fast MRI technique
Abstract
A fast multi‐section MR imaging technique is described. Gradient‐ and spin‐echo (GRASE) imaging utilizes the speed advantages of gradient refocusing while overcoming the image artifacts arising from static field inhomogeneity and chemical shift. Image contrast is determined by the T2 contrast in the Hahn spin echoes. A novel k‐space trajectory temporally modulates signals and demodulates artifacts. © 1991 Academic Press, Inc.
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Cited by
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Reeder, S. B., Wen, Z., Yu, H., Pineda, A. R., Gold, G. E., Markl, M., & Pelc, N. J. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10675 · Cited by 508
Layer-specific fMRI reflects different neuronal computations at different depths in human V1
Olman, C. A., Harel, N., Feinberg, D. A., He, S., Zhang, P., Ugurbil, K., & Yacoub, E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032536 · Cited by 194
Jelescu, I. O., Zurek, M., Winters, K. V., Veraart, J., Rajaratnam, A., Kim, N. S., Babb, J. S., Shepherd, T. M., Novikov, D. S., Kim, S. G., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.004 · Cited by 188
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Metadata
- Authors
- Oshio, K., & Feinberg, D. A.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 1991-08
- Volume
- 20
- Issue
- 2
- Pages
- 344-349
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2168110165
- Cited by
- 340
- References
- 0
- FWCI
- 7.08
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: GRASE (Gradient‐and Spin‐Echo) imaging: A novel fast MRI technique. https://app.cochat.ai/papers/10.1002/mrm.1910200219
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- Oshio, K., & Feinberg, D. A. (1991). GRASE (Gradient‐and Spin‐Echo) imaging: A novel fast MRI technique. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.1910200219
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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.
