Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom. Copyright 2001 Academic Press.

In 1984, Dixon published a first paper on a simple spectroscopic imaging technique for water and fat separation. The technique acquires two separate images with a modified spin echo pulse sequence. One is a conventional spin echo image with water and fat signals in-phase and the other is acquired with the readout gradient slightly shifted so that the water and fat signals are 180 degrees out-of-phase. Dixon showed that from these two images, a water-only image and a fat-only image can be generated. The water-only image by the Dixon's technique can serve the purpose of fat suppression, an important and widely used imaging option for clinical MRI. Additionally, the availability of both the water-only and fat-only images allows direct image-based water and fat quantitation. These applications, as well as the potential that the technique can be made highly insensitive to magnetic field inhomogeneity, have generated substantial research interests and efforts from many investigators. As a result, significant improvement to the original technique has been made in the last 2 decades. The following article reviews the underlying physical principles and describes some major technical aspects in the development of these Dixon techniques. Copyright (c) 2008 Wiley-Liss, Inc.