Tohka J, Zijdenbos A, Evans A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. Neuroimage 23: 84-97

Digital Media Institute/Signal Processing, Tampere University of Technology, FIN-33101, Finland.
NeuroImage (Impact Factor: 6.36). 10/2004; 23(1):84-97. DOI: 10.1016/j.neuroimage.2004.05.007
Source: PubMed


Due to the finite spatial resolution of imaging devices, a single voxel in a medical image may be composed of mixture of tissue types, an effect known as partial volume effect (PVE). Partial volume estimation, that is, the estimation of the amount of each tissue type within each voxel, has received considerable interest in recent years. Much of this work has been focused on the mixel model, a statistical model of PVE. We propose a novel trimmed minimum covariance determinant (TMCD) method for the estimation of the parameters of the mixel PVE model. In this method, each voxel is first labeled according to the most dominant tissue type. Voxels that are prone to PVE are removed from this labeled set, following which robust location estimators with high breakdown points are used to estimate the mean and the covariance of each tissue class. Comparisons between different methods for parameter estimation based on classified images as well as expectation--maximization-like (EM-like) procedure for simultaneous parameter and partial volume estimation are reported. The robust estimators based on a pruned classification as presented here are shown to perform well even if the initial classification is of poor quality. The results obtained are comparable to those obtained using the EM-like procedure, but require considerably less computation time. Segmentation results of real data based on partial volume estimation are also reported. In addition to considering the parameter estimation problem, we discuss differences between different approximations to the complete mixel model. In summary, the proposed TMCD method allows for the accurate, robust, and efficient estimation of partial volume model parameters, which is crucial to a variety of brain MRI data analysis procedures such as the accurate estimation of tissue volumes and the accurate delineation of the cortical surface.

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    • "uniformity artifacts using the N3 algorithm (Sled et al., 1998). The resultant images were further segmented into gray matter, white matter , and cerebrospinal fluid using an advanced neural net classifier (Tohka et al., 2004; Zijdenbos et al., 2002). Next, the inner and outer gray matter surfaces were automatically extracted for each hemisphere using the Constrained Laplacian-based Automated Segmentation with Proximities (CLASP) algorithm (Kim et al., 2005; MacDonald et al., 2000). "
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    • "for SPM12 ( Data were preprocessed according to default settings: bias correction; tissue classification/segmentation (Rajapakse et al., 1997); partial volume estimation (PVE; Tohka et al., 2004); denoising/filtering (Manjón et al., 2010; Rajapakse et al., 1997); warping to the DARTEL IXI-550 template in Montreal Neurologic Institute (MNI) space; and resampling to a 1.5 mm 3 voxel-size using affine and nonlinear transforms (Ashburner, 2007). Forward-deformation fields were calculated from each subject's skull-stripped and rigid-body registered T1 (PVE) image in order to warp functional data into MNI space. "
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    • "each brain volume was classified into WM, GM, cerebrospinal fluid and background using the INSECT algorithm [Zijdenbos et al., 1998], and then partial volume fractions of these tissue types were computed in each brain voxel [Tohka et al., 2004]. The inner (WM/GM interface) and outer (pial) cortical surfaces were extracted from each brain volume using the CLASP algorithm [Kim et al., 2005]. "
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