The Use of Carboxy Terminated Liquid Natural Rubber (CTNR) as an Adhesive in Bonding Rubber to Rigid and Non-rigid Substrates

Carboxy Terminated Liquid Natural Rubber (CTNR) was prepared by photochemical reaction using maleic anhydride and masticated natural rubber (NR). The use of CTNR as an adhesive in bonding rubber to rubber and rubber to metal was studied. The peel strengths and lap shear strengths of the adherends which were bonded using CTNR were determined. The effect of using a tri isocyanate with CTNR in rubber to metal bonding was also studied. It is found that CTNR can effectively be used in bonding rubber to rubber and rubber to mild steel.

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.

Stereochemically non-rigid helical mercury(II) complexes

Reactions of the 1: 2 condensate (L) of benzil dihydrazone and 2-acetylpyridine with Hg(ClO4)(2) center dot xH(2)O and HgI2 yield yellow [HgL2](ClO4)(2) (1) and HgLI2 (2), respectively. Homoleptic 1 is a 8-coordinate double helical complex with a Hg(II)N-8 core crystallising in the space group Pbca with cell dimensions: a = 16.2250(3), b = 20.9563(7), c = 31.9886(11) angstrom. Complex 2 is a 4-coordinate single helical complex having a Hg(II)N2I2 core crystallising in the space group P2(1)/n with cell dimensions a = 9.8011(3), b = 17.6736(6), c = 16.7123(6) angstrom and b = 95.760(3). In complex 1, the N-donor ligand L uses all of its binding sites to act as tetradentate. On the other hand, it acts as a bidentate N-donor ligand in 2 giving rise to a dangling part. From variable temperature H-1 NMR studies both the complexes are found to be stereochemically non-rigid in solution. In the case of 2, the solution process involves wrapping up of the dangling part of L around the metal. (C) 2008 Elsevier B.V. All rights reserved.

Short-Term Effects of the Use of Non-Rigid Tools for Postural Control by Adults with Intellectual Disabilities

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Short-Term Effects of the Use of Non-Rigid Tools for Postural Control by Adults with Intellectual Disabilities

We tested the short-term effects of a nonrigid tool, identified as an "anchor system" (e.g., ropes attached to varying weights resting on the floor), on the postural stabilization of blindfolded adults with and without intellectual disabilities (ID). Participants held a pair of anchors one in each hand, under three weight conditions (250 g, 500 g and 1,000 g), while they performed a restricted balance task (standing for 30 s on a balance beam placed on top of a force platform). These conditions were called anchor practice trials. Before and after the practice trials, a condition without anchors was tested. Control practice groups, who practiced blocks of trials without anchors, included individuals with and without ID. The anchor system improved subjects' balance during the standing task, for both groups. For the control groups, the performance of successive trials in the condition without the anchor system showed no improvement in postural stability. The individuals with intellectual disability, as well as their peers without ID, used the haptic cues of nonrigid tools (i.e., the anchor system) to stabilize their posture, and the short-term stabilizing effects appeared to result from their previous use of the anchor system.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

Expression of antagonists of WNT and BMP signaling after non-rigid fixation of osteotomies

Delayed fracture healing and non-unions represent rare but severe complications in orthopedic surgery. Further knowledge on the mechanisms of the bone repair process and of the development of a pseudoarthrosis is essential to predict and prevent impaired healing of fractures. The present study aimed at elucidating differences in gene expression during the repair of rigidly and non-rigidly fixed osteotomies. For this purpose, the MouseFix™ and the FlexiPlate™ systems (AO Development Institute, Davos, CH), allowing the creation of well defined osteotomies in mouse femora, were employed. A time course following the healing process of the osteotomy was performed and bones and periimplant tissues were analyzed by high-resolution X-ray, MicroCT and by histology. For the assessment of gene expression, Low Density Arrays (LDA) were done. In animals with rigid fixation, X-ray and MicroCT revealed healing of the osteotomy within 3 weeks. Using the FlexiPlate™ system, the osteotomy was still visible by X-ray after 3 weeks and a stabilizing cartilaginous callus was formed. After 4.5 weeks, the callus was remodeled and the osteotomy was, on a histological level, healed. Gene expression studies revealed levels of transcripts encoding proteins associated with inflammatory processes not to be altered in tissues from bones with rigid and non-rigid fixation, respectively. Levels of transcripts encoding proteins of the extracellular matrix and essential for bone cell functions were not increased in the rigidly fixed group when compared to controls without osteotomy. In the FlexiPlate™ group, levels of transcripts encoding the same set of genes were significantly increased 3 weeks after surgery. Expression of transcripts encoding BMPs and BMP antagonists was increased after 3 weeks in repair tissues from bones fixed with FlexiPlate™, as were inhibitors of the WNT signaling pathways. Little changes only were detected in transcript levels of tissues from rigidly fixed bones. The data of the present study suggest that rigid fixation enables accelerated healing of an experimental osteotomy as compared to non-rigid fixation. The changes in the healing process after non-rigid fixation are accompanied by an increase in the levels of transcripts encoding inhibitors of osteogenic pathways and, probably as a consequence, by temporal changes in bone matrix synthesis.

An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video

This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.