Compact Representations for Fast Nonrigid Registration of Medical Images

We develop efficient techniques for the non-rigid registration of medical images by using representations that adapt to the anatomy found in such images. Images of anatomical structures typically have uniform intensity interiors and smooth boundaries. We create methods to represent such regions compactly using tetrahedra. Unlike voxel-based representations, tetrahedra can accurately describe the expected smooth surfaces of medical objects. Furthermore, the interior of such objects can be represented using a small number of tetrahedra. Rather than describing a medical object using tens of thousands of voxels, our representations generally contain only a few thousand elements. Tetrahedra facilitate the creation of efficient non-rigid registration algorithms based on finite element methods (FEM). We create a fast, FEM-based method to non-rigidly register segmented anatomical structures from two subjects. Using our compact tetrahedral representations, this method generally requires less than one minute of processing time on a desktop PC. We also create a novel method for the non-rigid registration of gray scale images. To facilitate a fast method, we create a tetrahedral representation of a displacement field that automatically adapts to both the anatomy in an image and to the displacement field. The resulting algorithm has a computational cost that is dominated by the number of nodes in the mesh (about 10,000), rather than the number of voxels in an image (nearly 10,000,000). For many non-rigid registration problems, we can find a transformation from one image to another in five minutes. This speed is important as it allows use of the algorithm during surgery. We apply our algorithms to find correlations between the shape of anatomical structures and the presence of schizophrenia. We show that a study based on our representations outperforms studies based on other representations. We also use the results of our non-rigid registration algorithm as the basis of a segmentation algorithm. That algorithm also outperforms other methods in our tests, producing smoother segmentations and more accurately reproducing manual segmentations.

Strained Silicon on Silicon by Wafer Bonding and Layer Transfer from Relaxed SiGe Buffer

We report the creation of strained silicon on silicon (SSOS) substrate technology. The method uses a relaxed SiGe buffer as a template for inducing tensile strain in a Si layer, which is then bonded to another Si handle wafer. The original Si wafer and the relaxed SiGe buffer are subsequently removed, thereby transferring a strained-Si layer directly to Si substrate without intermediate SiGe or oxide layers. Complete removal of Ge from the structure was confirmed by cross-sectional transmission electron microscopy as well as secondary ion mass spectrometry. A plan-view transmission electron microscopy study of the strained-Si/Si interface reveals that the lattice-mismatch between the layers is accommodated by an orthogonal array of edge dislocations. This misfit dislocation array, which forms upon bonding, is geometrically necessary and has an average spacing of approximately 40nm, in excellent agreement with established dislocation theory. To our knowledge, this is the first study of a chemically homogeneous, yet lattice-mismatched, interface.