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Content Provider | IEEE Xplore Digital Library |
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Author | Haldar, J.P. Hernando, D. Budde, M.D. Qing Wang Sheng-Kwei Song Zhi-Pei Liang |
Copyright Year | 2007 |
Description | Author affiliation: Univ. of Illinois at Urbana-Champaign, Urbana (Haldar, J.P.; Hernando, D.) |
Abstract | Magnetic resonance spectroscopic imaging has been recognized for a long time as a powerful tool for biochemical imaging. However, its practical utility is still rather limited due to poor spatial resolution, low signal-to-noise ratio, and long data acquisition times. In this work, we propose a new technique that enables reconstruction of metabolite maps with high spatial resolution. This technique uses a statistical model to incorporate known anatomical boundaries for edge-preserving noise filtering. This statistical reconstruction scheme makes it possible to use very noisy data, thereby enabling the collection of high-resolution data in a reasonable amount of time. We illustrate the performance of this method with images of the N-acetyl-L-aspartate distribution from an in vivo mouse brain. |
Starting Page | 4324 |
Ending Page | 4326 |
File Size | 187430 |
Page Count | 3 |
File Format | |
ISBN | 9781424407873 |
ISSN | 1557170X |
DOI | 10.1109/IEMBS.2007.4353293 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-08-22 |
Publisher Place | France |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | High-resolution imaging Magnetic resonance imaging Spatial resolution Image reconstruction Magnetic resonance Spectroscopy Image recognition Signal to noise ratio Data acquisition Filtering |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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