We propose a constrained version of Mumford and Shah's (1989) segmentation model with an information-theoretic
point of view in order to devise a systematic procedure to
segment brain magnetic resonance imaging (MRI) data for
parametric T1-Map and T1-weighted images, in both 2-D and
3D settings. Incorporation of a tuning weight in particular adds
a probabilistic flavor to our segmentation method, and makes
the 3-tissue segmentation possible. Moreover, we proposed a
novel method to jointly segment the T1-Map and calibrate RF Inhomogeneity
(JSRIC). This method assumes the average T1 value of white matter is the same across transverse slices in
the central brain region, and JSRIC is able to rectify the flip angles
to generate calibrated T1-Maps. In order to generate an
accurate T1-Map, the determination of optimal flip-angles and
the registration of flip-angle images are examined. Our JSRIC
method is validated on two human subjects in the 2D T1-Map
modality and our segmentation method is validated by two public
databases, BrainWeb  and IBSR, of T1-weighted modality in
the 3D setting

Chen, Ping-Feng

Krim, Hamid

Steen, R. Grant

Yezzi, Anthony

International Journal of Biomedical Imaging

English

PubMed

Ping-Feng Chen

R. Grant Steen

Anthony Yezzi

Hamid Krim

Directory of Open Access Journals

Joint Brain Parametric T1-Map Segmentation and RF Inhomogeneity Calibration

©2009 Ping-Feng Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.DOI: 10.1155/2009/269525We propose a constrained version of Mumford and Shah’s (1989) segmentation model with an information-theoretic point of view in order to devise a systematic procedure to segment brain magnetic resonance imaging (MRI) data for parametric T1-Map and T1-weighted images, in both 2-D and 3D settings. Incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the 3-tissue segmentation possible. Moreover, we proposed a novel method to jointly segment the T1-Map and calibrate RF Inhomogeneity (JSRIC). This method assumes the average T1 value of whitematter is the same across transverse slices in the central brain region, and JSRIC is able to rectify the flip angles to generate calibrated T1-Maps. In order to generate an accurate T1-Map, the determination of optimal flip-angles and the registration of flip-angle images are examined. Our JSRIC method is validated on two human subjects in the 2D T1-Map modality and our segmentation method is validated by two public databases, BrainWeb and IBSR, of T1-weighted modality in the 3D setting

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Joint brain parameteric T1-map segmentation and RF inhomogeneity calibration

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Joint Brain Parametric T1-Map Segmentation and RF Inhomogeneity Calibration

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