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 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.

Unified segmentation based correction of R1 brain maps for RF transmit field inhomogeneities (UNICORT)

“…Compared to another method for joint RF inhomogeneity correction and segmentation proposed by Chen et al (2009) , UNICORT allows the bias field to vary in 3 dimensions in contrast to only the head–feet direction. This constitutes a more realistic model considering the patterns of variation of the B1 + field.…”

Estimating T1 from multichannel variable flip angle SPGR sequences

“…Eq. [4] can be solved using standard nonlinear optimization techniques, including: (1) Levenberg-Marquardt iteration (13,21,22); (2) iteratively reweighted least squares regression (20); and (3) variable projection (7,18,23,24). Variable projection is particularly appealing for problems in which the target cost functional is quadratic with respect to one or more variables, as it reduces the dimensionality of the optimization problem.…”

“…The relaxation time T 1 defines the rate of recovery of longitudinal magnetization of spins in a tissue toward equilibrium after radio frequency excitation. Quantitative knowledge of T 1 is useful for many clinical applications, including: (1) optimizing pulse sequences to generate contrast between tissues of interest; (2) estimating perfusion rate constants like K trans in dynamic contrast-enhanced MRI (1); (3) monitoring pathology and treatment efficacy (2,3); and (4) guidance for automated image segmentation (4). Although T 1 -weighted images can be readily acquired, generating accurate quantitative estimates of the T 1 values of tissues is not as straightforward.…”

Template method to improve brain segmentation from inhomogeneous brain magnetic resonance images at high fields

“…This effect is more prominent at high magnetic fields needed to increase the signal-to-noise ratio [13]. A joint brain parametric T 1 -map segmentation and radiofrequency inhomogeneity calibration has been recently presented [14] where the inhomogeneity is corrected based on the assumption that the mean T 1 of WM is the same across the central brain slices. Inhomogeneity can be corrected retrospectively (e.g.…”