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Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion.

Zerhouni, E A, Parish, D M, Rogers, W J, Yang, A, Shapiro, E P·1988·Radiology

Abstract

Specified regions of the myocardium can be labeled in magnetic resonance (MR) imaging to serve as markers during contraction. The technique is based on locally perturbing the magnetization of the myocardium with selective radio-frequency (RF) saturation of multiple, thin tag planes during diastole followed by conventional, orthogonal-plane imaging during systole. The technique was implemented on a 0.38-T imager and tested on phantoms and volunteers. In humans, tags could be seen 60-450 msec after RF saturation, thus permitting sampling of the entire contractile phase of the cardiac cycle. Tagged regions appear as hypointense stripes, and their patterns of displacement reflect intervening cardiac motion. In addition to simple translation and rotation, complex motions such as cardiac twist can be demonstrated. The effects of RF pulse angle, relaxation times, and heart rate on depiction of the tagged region are discussed.

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Metadata

Authors
Zerhouni, E. A., Parish, D. M., Rogers, W. J., Yang, A., & Shapiro, E. P.
Venue
Radiology
Published
1988-10
Volume
169
Issue
1
Pages
59-63
ISSN
  • 0033-8419 (ISSN-L)
  • 1527-1315
OpenAlex
W2093678897
Cited by
1,430
References
0
FWCI
11.29
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion. https://app.cochat.ai/papers/10.1148/radiology.169.1.3420283
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Zerhouni, E. A., Parish, D. M., Rogers, W. J., Yang, A., & Shapiro, E. P. (1988). Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion. Radiology. https://doi.org/10.1148/radiology.169.1.3420283

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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.