High Resolution Vascular Imaging of the Rat Spine using Liposomal Blood Pool MR Agent

High resolution, vascular magnetic resonance imaging of the spine region in small animals poses several challenges. The small anatomical features, extravascular diffusion, and the low signal-to-noise ratio limit the use of conventional contrast agents. We hypothesize that a long circulating, intravascular liposomal-encapsulated MR contrast agent (liposomal-Gd) would facilitate visualization of small anatomical features of the perispinal vasculature not visible with conventional contrast agent (Gd-DTPA).

High-resolution vascular imaging of the rat spine using liposomal blood pool MR agent.

BACKGROUND AND PURPOSE: High-resolution, vascular MR imaging of the spine region in small animals poses several challenges. The small anatomic features, extravascular diffusion, and low signal-to-noise ratio limit the use of conventional contrast agents. We hypothesize that a long-circulating, intravascular liposomal-encapsulated MR contrast agent (liposomal-Gd) would facilitate visualization of small anatomic features of the perispinal vasculature not visible with conventional contrast agent (gadolinium-diethylene-triaminepentaacetic acid [Gd-DTPA]). METHODS: In this study, high-resolution MR angiography of the spine region was performed in a rat model using a liposomal-Gd, which is known to remain within the blood pool for an extended period. The imaging characteristics of this agent were compared with those of a conventional contrast agent, Gd-DTPA. RESULTS: The liposomal-Gd enabled acquisition of high quality angiograms with high signal-to-noise ratio. Several important vascular features, such as radicular arteries, posterior spinal vein, and epidural venous plexus were visualized in the angiograms obtained with the liposomal agent. The MR angiograms obtained with conventional Gd-DTPA did not demonstrate these vessels clearly because of marked extravascular soft-tissue enhancement that obscured the vasculature. CONCLUSIONS: This study demonstrates the potential benefit of long-circulating liposomal-Gd as a MR contrast agent for high-resolution vascular imaging applications.

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 cluster's 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.

Improved cerebellar tissue classification on magnetic resonance images of brain.

PURPOSE: To develop and implement a method for improved cerebellar tissue classification on the MRI of brain by automatically isolating the cerebellum prior to segmentation. MATERIALS AND METHODS: Dual fast spin echo (FSE) and fluid attenuation inversion recovery (FLAIR) images were acquired on 18 normal volunteers on a 3 T Philips scanner. The cerebellum was isolated from the rest of the brain using a symmetric inverse consistent nonlinear registration of individual brain with the parcellated template. The cerebellum was then separated by masking the anatomical image with individual FLAIR images. Tissues in both the cerebellum and rest of the brain were separately classified using hidden Markov random field (HMRF), a parametric method, and then combined to obtain tissue classification of the whole brain. The proposed method for tissue classification on real MR brain images was evaluated subjectively by two experts. The segmentation results on Brainweb images with varying noise and intensity nonuniformity levels were quantitatively compared with the ground truth by computing the Dice similarity indices. RESULTS: The proposed method significantly improved the cerebellar tissue classification on all normal volunteers included in this study without compromising the classification in remaining part of the brain. The average similarity indices for gray matter (GM) and white matter (WM) in the cerebellum are 89.81 (+/-2.34) and 93.04 (+/-2.41), demonstrating excellent performance of the proposed methodology. CONCLUSION: The proposed method significantly improved tissue classification in the cerebellum. The GM was overestimated when segmentation was performed on the whole brain as a single object.

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.