Projects:DTIFiberRegistration

Joint Registration and Segmentation of DWI Fiber Tractography

The purpose of this work is to jointly register and cluster DWI fiber tracts obtained from a group of subjects. We formulate a maximum likelihood problem which the proposed method solves using a generalized Expectation Maximization (EM) framework. Additionally, the algorithm employs an outlier rejection and denoising strategy to produce sharp probabilistic maps (an atlas) of certain bundles of interest. This atlas is potentially useful for making diffusion measurements in a common coordinate system to identify pathology related changes or developmental trends.

Description

Initial Registration

A spatial normalization is necessary to obtain a group-wise clustering of the resulting fibers. This initial normalization is performed on the Fractional Anisotropy (FA) images generated for each subject. This initial normalization aims to remove gross differences across subjects due to global head size and orientation. It is thus limited to a 9 parameter affine transformation that accounts for scaling, rotation and translation. The resulting transformations are then applied to each of the computed fibers to map them into a common coordinate frame for clustering.

Initial Fiber Clustering

Organization of tract fibers into bundles, in the entire white matter, reveals anatomical connections such as the corpus callosum and corona radiata. By clustering fibers from multiple subjects into bundles, these common white matter structures can be discovered in an automatic way, and the bundle models can be saved with expert anatomical labels to form an atlas. In this work, we take advantage of automatically segmented tractography that has been labeled (as bundles) with such an atlas for initialization.

Our Approach

Joint Registration and Segmentation

Once we obtain an initial affine registration and clustering results using the high dimensional atlas, we iteratively fine-tune the registration and clustering results using a maximum likelihood framework, which is solved through a generalized EM algorithm. For the registration we use one set of affine parameters per fiber bundle, and combine these affine registrations into a single smooth and invertable warp field using a log-Euclidian poly-affine framework. Additionally, the algorithm employs an outlier rejection and denoising strategy while producing sharp probabilistic maps of certain bundles of interest.

We tested the registration component of this algorithm without updating the clustering with 26 major fiber bundles. The poly-affine warp fields with relatively limited number of components resulted in similar quality registrations when compared with results from a benchmark non-linear registration algorithm that was run on FA images:

Top Row: 3D renderings of the registered tracts of a subject (in green) and the template (in red) within 5mm of the central axial slice overlayed on the central FA slice of the template. Aff (left) stands for the FA based global affine, Dem (middle) for the demons algorithm and PA (right) for the proposed framework in this work. Arrows point to an area of differing qualities of registration. Overlapping of the red and green fibers is indicative of better registration. Bottom Row: Jacobian determinant images from the central slice of the volume: Yellow represents areas with small changes in size, and the shades of red and blue represent enlargement and shrinking, respectively. The Jacobian of the global affine registration is constant. The Jacobian of the demons algorithm is smooth due to the Gaussian regularization. The Jacobian of the new algorithm reflects the underlying anatomy because of the fiber bundle-based definition of the deformation.

Project Status

• Working 3D implementation in Matlab and C.

Key Investigators

• MIT: Ulas Ziyan, Mert R. Sabuncu
• Harvard/BWH: Carl-Fredrik Westin, Lauren O'Donnell

Publications

• U. Ziyan, M. R. Sabuncu, W. E. L. Grimson, Carl-Fredrik Westin. A Robust Algorithm for Fiber-Bundle Atlas Construction. MMBIA 2007
• U. Ziyan, M. R. Sabuncu, Lauren J. O'Donnell, Carl-Fredrik Westin. Nonlinear Registration of Diffusion MR Images Based on Fiber Bundles. MICCAI 2007