Joint brain parametric T1-map segmentation and RF inhomogeneity calibration

Abstract

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 T 1 -Map and T 1 -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 T 1 -Map and calibrate RF Inhomogeneity (JSRIC). This method assumes the average T 1 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 T 1 -Maps. In order to generate an accurate T 1 -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 T 1 -Map modality and our segmentation method is validated by two public databases, BrainWeb and IBSR, of T 1 -weighted modality in the 3D setting. Copyright © 2009 Ping-Feng Chen et al.