Renjie He

Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI

The presence of large number of false lesion classification on segmented brain MR images is a major problem in the accurate determination of lesion volumes in multiple sclerosis (MS) brains. In order to minimize the false lesion classifications, a strategy that combines parametric and nonparametric techniques is developed and implemented. This approach uses the information from the proton density (PD)- and T2-weighted and fluid attenuation inversion recovery (FLAIR) images. This strategy involves CSF and lesion classification using the Parzen window classifier. Image processing, morphological operations, and ratio maps of PD- and T2-weighted images are used for minimizing false positives. Contextual information is exploited for minimizing the false negative lesion classifications using hidden Markov random field-expectation maximization (HMRF-EM) algorithm. Lesions are delineated using fuzzy connectivity. The performance of this algorithm is quantitatively evaluated on 23 MS patients. Similarity index, percentages of over, under, and correct estimations of lesions are computed by spatially comparing the results of present procedure with expert manual segmentation. The automated processing scheme detected 80% of the manually segmented lesions in the case of low lesion load and 93% of the lesions in those cases with high lesion load.

Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images

A global optimization technique for image registration, based on mutual information, that can be used in conjunction with a multi-resolution paradigm is described. This technique combines genetic algorithm in continuous space, which is a stochastic method and is very efficient in large search space, with dividing rectangle, which is a deterministic method that theoretically guarantees global optimization and is efficient in small search space. Calculations were performed for determining the optimum parameters for implementing this method. This technique was applied to register magnetic resonance images of brain. For comparison, the registration results using AIR, a commonly employed software package, are presented.

Deep gray matter atrophy in multiple sclerosis: A tensor based morphometry

Tensor based morphometry (TBM) was applied to determine the atrophy of deep gray matter (DGM) structures in 88 relapsing multiple sclerosis (MS) patients. For group analysis of atrophy, an unbiased atlas was constructed from 20 normal brains. The MS brain images were co-registered with the unbiased atlas using a symmetric inverse consistent nonlinear registration. These studies demonstrate significant atrophy of thalamus, caudate nucleus, and putamen even at a modest clinical disability, as assessed by the expanded disability status score (EDSS). A significant correlation between atrophy and EDSS was observed for different DGM structures: (thalamus: r=-0.51, p=3.85 x 10(-7); caudate nucleus: r=-0.43, p=2.35 x 10(-5); putamen: r=-0.36, p=6.12 x 10(-6)). Atrophy of these structures also correlated with 1) T2 hyperintense lesion volumes (thalamus: r=-0.56, p=9.96 x 10(-9); caudate nucleus: r=-0.31, p=3.10 x 10(-3); putamen: r=-0.50, p=6.06 x 10(-7)), 2) T1 hypointense lesion volumes (thalamus: r=-0.61, p=2.29 x 10(-10); caudate nucleus: r=-0.35, p=9.51 x 10(-4); putamen: r=-0.43, p=3.51 x 10(-5)), and 3) normalized CSF volume (thalamus: r=-0.66, p=3.55 x 10(-12); caudate nucleus: r=-0.52, p=2.31 x 10(-7), and putamen: r=-0.66, r=2.13 x 10(-12)). More severe atrophy was observed mainly in thalamus at higher EDSS. These studies appear to suggest a link between the white matter damage and DGM atrophy in MS.

In vivo diffusion tensor imaging of rat spinal cord at 7 T

In vivo diffusion tensor imaging of normal rat spinal cord was performed using a multi-segmented, blipped EPI sequence at 7 T field strength. At high diffusion weighting, the signal exhibited a non-monoexponential decay that was fitted to a biexponential function, associated with the fast and slow components of diffusion in the cord tissue, using a nonlinear regression analysis along with a constrained optimization procedure. From the measured tensors, the eigenvalues and the maps of invariant scalar measures (fractional anisotropy, relative anisotropy, volume ratio, and trace) were calculated and analyzed statistically. The results were combined to quantitatively characterize the anisotropic properties of the fast and slow diffusions in white- and gray matter of live spinal cords.

Segmentation and quantification of black holes in multiple sclerosis.

A technique that involves minimal operator intervention was developed and implemented for identification and quantification of black holes on T1-weighted magnetic resonance images (T1 images) in multiple sclerosis (MS). Black holes were segmented on T1 images based on grayscale morphological operations. False classification of black holes was minimized by masking the segmented images with images obtained from the orthogonalization of T2-weighted and T1 images. Enhancing lesion voxels on postcontrast images were automatically identified and eliminated from being included in the black hole volume. Fuzzy connectivity was used for the delineation of black holes. The performance of this algorithm was quantitatively evaluated on 14 MS patients.

Multicentre proton magnetic resonance spectroscopy imaging of primary progressive multiple sclerosis

Multicentre baseline proton magnetic resonance spectro scopic data on primary progressive multiple sclerosis (PPMS) patients are acquired and analysed, using automatic analysis software. The metabolite ratios did not differ from centre to centre. The average N-acetylaspar tate/creatine (NA A/C r) ratio in PPMS was significantly lower compared to normal controls. No significant differences were observed in this ratio between lesion-containing regions (LC R) and normal-appearing tissues (NAT). Strong lipid resonances, even in the absence of lesions, are observed in the both grey and white matter in these patients. These observations suggest extensive diffuse and/or microscopic pathology in PPMS. No significant correlation between any of the metabolite ratios and the Extended Disability Scale Score (EDSS) or with other MR measures such as lesion burden and enhancement volumes is observed.

Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images.

An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical clusters shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis

To develop and implement a method for identification and quantification of gadolinium (Gd) enhancements with minimal human intervention.

Symmetric inverse consistent nonlinear registration driven by mutual information

A nonlinear viscoelastic image registration algorithm based on the demons paradigm and incorporating inverse consistent constraint (ICC) is implemented. An inverse consistent and symmetric cost function using mutual information (MI) as a similarity measure is employed. The cost function also includes regularization of transformation and inverse consistent error (ICE). The uncertainties in balancing various terms in the cost function are avoided by alternatively minimizing the similarity measure, the regularization of the transformation, and the ICE terms. The diffeomorphism of registration for preventing folding and/or tearing in the deformation is achieved by the composition scheme. The quality of image registration is first demonstrated by constructing brain atlas from 20 adult brains (age range 30-60). It is shown that with this registration technique: (1) the Jacobian determinant is positive for all voxels and (2) the average ICE is around 0.004 voxels with a maximum value below 0.1 voxels. Further, the deformation-based segmentation on Internet Brain Segmentation Repository, a publicly available dataset, has yielded high Dice similarity index (DSI) of 94.7% for the cerebellum and 74.7% for the hippocampus, attesting to the quality of our registration method.

Improved Cerebellar Tissue Classification on Magnetic Resonance Images of Brain

To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation.