File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.neuroimage.2010.05.001
- Scopus: eid_2-s2.0-77954953105
- PMID: 20452444
- WOS: WOS:000280695200023
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Automated brain tissue segmentation based on fractional signal mapping from inversion recovery Look-Locker acquisition
Title | Automated brain tissue segmentation based on fractional signal mapping from inversion recovery Look-Locker acquisition |
---|---|
Authors | |
Keywords | Automated segmentation Brain tissue Fast T1 mapping Fractional volume Magnetic resonance imaging Partial volume effect |
Issue Date | 2010 |
Publisher | Academic Press. The Journal's web site is located at http://www.elsevier.com/locate/ynimg |
Citation | Neuroimage, 2010, v. 52 n. 4, p. 1347-1354 How to Cite? |
Abstract | Most current automated segmentation methods are performed on T 1- or T 2-weighted MR images, relying on relative image intensity that is dependent on other MR parameters and sensitive to B 1 magnetic field inhomogeneity. Here, we propose an image segmentation method based on quantitative longitudinal magnetization relaxation time (T 1) of brain tissues. Considering the partial volume effect, fractional volume maps of brain tissues (white matter, gray matter, and cerebrospinal fluid) were obtained by fitting the observed signal in an inversion recovery procedure to a linear combination of three exponential functions, which represents the relaxations of each of the tissue types. A Look-Locker acquisition was employed to accelerate the acquisition process. The feasibility and efficacy of this proposed method were evaluated using simulations and experiments. The potential applications of this method in the study of neurological disease as well as normal brain development and aging are discussed. © 2010. |
Persistent Identifier | http://hdl.handle.net/10722/169875 |
ISSN | 2023 Impact Factor: 4.7 2023 SCImago Journal Rankings: 2.436 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, W | en_HK |
dc.contributor.author | Geng, X | en_HK |
dc.contributor.author | Gu, H | en_HK |
dc.contributor.author | Zhan, W | en_HK |
dc.contributor.author | Zou, Q | en_HK |
dc.contributor.author | Yang, Y | en_HK |
dc.date.accessioned | 2012-10-25T04:57:29Z | - |
dc.date.available | 2012-10-25T04:57:29Z | - |
dc.date.issued | 2010 | en_HK |
dc.identifier.citation | Neuroimage, 2010, v. 52 n. 4, p. 1347-1354 | en_HK |
dc.identifier.issn | 1053-8119 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/169875 | - |
dc.description.abstract | Most current automated segmentation methods are performed on T 1- or T 2-weighted MR images, relying on relative image intensity that is dependent on other MR parameters and sensitive to B 1 magnetic field inhomogeneity. Here, we propose an image segmentation method based on quantitative longitudinal magnetization relaxation time (T 1) of brain tissues. Considering the partial volume effect, fractional volume maps of brain tissues (white matter, gray matter, and cerebrospinal fluid) were obtained by fitting the observed signal in an inversion recovery procedure to a linear combination of three exponential functions, which represents the relaxations of each of the tissue types. A Look-Locker acquisition was employed to accelerate the acquisition process. The feasibility and efficacy of this proposed method were evaluated using simulations and experiments. The potential applications of this method in the study of neurological disease as well as normal brain development and aging are discussed. © 2010. | en_HK |
dc.language | eng | en_US |
dc.publisher | Academic Press. The Journal's web site is located at http://www.elsevier.com/locate/ynimg | en_HK |
dc.relation.ispartof | NeuroImage | en_HK |
dc.subject | Automated segmentation | en_HK |
dc.subject | Brain tissue | en_HK |
dc.subject | Fast T1 mapping | en_HK |
dc.subject | Fractional volume | en_HK |
dc.subject | Magnetic resonance imaging | en_HK |
dc.subject | Partial volume effect | en_HK |
dc.subject.mesh | Algorithms | en_US |
dc.subject.mesh | Artificial Intelligence | en_US |
dc.subject.mesh | Brain - Anatomy & Histology | en_US |
dc.subject.mesh | Female | en_US |
dc.subject.mesh | Humans | en_US |
dc.subject.mesh | Image Enhancement - Methods | en_US |
dc.subject.mesh | Image Interpretation, Computer-Assisted - Methods | en_US |
dc.subject.mesh | Magnetic Resonance Imaging - Methods | en_US |
dc.subject.mesh | Male | en_US |
dc.subject.mesh | Pattern Recognition, Automated - Methods | en_US |
dc.subject.mesh | Reproducibility Of Results | en_US |
dc.subject.mesh | Sensitivity And Specificity | en_US |
dc.subject.mesh | Signal Processing, Computer-Assisted | en_US |
dc.title | Automated brain tissue segmentation based on fractional signal mapping from inversion recovery Look-Locker acquisition | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Geng, X: gengx@hku.hk | en_HK |
dc.identifier.authority | Geng, X=rp01678 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1016/j.neuroimage.2010.05.001 | en_HK |
dc.identifier.pmid | 20452444 | - |
dc.identifier.scopus | eid_2-s2.0-77954953105 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77954953105&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 52 | en_HK |
dc.identifier.issue | 4 | en_HK |
dc.identifier.spage | 1347 | en_HK |
dc.identifier.epage | 1354 | en_HK |
dc.identifier.isi | WOS:000280695200023 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Shin, W=8573966900 | en_HK |
dc.identifier.scopusauthorid | Geng, X=34771310000 | en_HK |
dc.identifier.scopusauthorid | Gu, H=35233258000 | en_HK |
dc.identifier.scopusauthorid | Zhan, W=7102238668 | en_HK |
dc.identifier.scopusauthorid | Zou, Q=14025198500 | en_HK |
dc.identifier.scopusauthorid | Yang, Y=7409387192 | en_HK |
dc.identifier.citeulike | 7154583 | - |
dc.identifier.issnl | 1053-8119 | - |