Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors

Computing the morphological similarity of diffusion tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the postprocessing of DT images. The morphological similarity of DTs typically has been computed using either the principal directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error in to the estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional postprocessing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization

Seamless Warping of Diffusion Tensor Fields

To warp diffusion tensor fields accurately, tensors must be reoriented in the space to which the tensors are warped based on both the local deformation field and the orientation of the underlying fibers in the original image. Existing algorithms for warping tensors typically use forward mapping deformations in an attempt to ensure that the local deformations in the warped image remains true to the orientation of the underlying fibers; forward mapping, however, can also create seams or gaps and consequently artifacts in the warped image by failing to define accurately the voxels in the template space where the magnitude of the deformation is large (e.g., |Jacobian| > 1). Backward mapping, in contrast, defines voxels in the template space by mapping them back to locations in the original imaging space. Backward mapping allows every voxel in the template space to be defined without the creation of seams, including voxels in which the deformation is extensive. Backward mapping, however, cannot reorient tensors in the template space because information about the directional orientation of fiber tracts is contained in the original, unwarped imaging space only, and backward mapping alone cannot transfer that information to the template space. To combine the advantages of forward and backward mapping, we propose a novel method for the spatial normalization of diffusion tensor (DT) fields that uses a bijection (a bidirectional mapping with one-to-one correspondences between image spaces) to warp DT datasets seamlessly from one imaging space to another. Once the bijection has been achieved and tensors have been correctly relocated to the template space, we can appropriately reorient tensors in the template space using a warping method based on Procrustean estimation

Identification and characterization of the mitochondrial RNA polymerase and transcription factor in the fission yeast Schizosaccharomyces pombe

We have characterized the mitochondrial transcription factor (Mtf1) and RNA polymerase (Rpo41) of Schizosaccharomyces pombe. Deletion mutants show Mtf1 or Rpo41 to be essential for cell growth, cell morphology and mitochondrial membrane potential. Overexpression of Mtf1 and Rpo41 can induce mitochondrial transcription. Mtf1 and Rpo41 can bind and transcribe mitochondrial promoters in vitro and the initiating nucleotides were the same in vivo and in vitro. Mtf1 is required for efficient transcription. We discuss the functional differences between Mtf1 and Rpo41 of S. pombe with Saccharomyces cerevisiae and higher organisms. In contrast to S. cerevisiae, the established model for mitochondrial transcription, S. pombe, a petite-negative yeast, resembles higher organisms that cannot tolerate the loss of mitochondrial function. The S. pombe and human mitochondrial genomes are similar in size and much smaller than that of S. cerevisiae. This is an important first step in the development of S. pombe as an alternative and complementary model system for molecular genetic and biochemical studies of mitochondrial transcription and mitochondrial–nuclear interactions. This is the first systematic study of the cellular function and biochemistry of Rpo41 and Mtf1 in S. pombe

Abnormalities of White Matter Microstructure in Unmedicated Obsessive-Compulsive Disorder and Changes after Medication

BACKGROUND: Abnormalities of myelin integrity have been reported in obsessive-compulsive disorder (OCD) using multi-parameter maps of diffusion tensor imaging (DTI). However, it was still unknown to what degree these abnormalities might be affected by pharmacological treatment. OBJECTIVE: To investigate whether the abnormalities of white matter microstructure including myelin integrity exist in OCD and whether they are affected by medication. METHODOLOGY AND PRINCIPAL FINDINGS: Parameter maps of DTI, including fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were acquired from 27 unmedicated OCD patients (including 13 drug-naïve individuals) and 23 healthy controls. Voxel-based analysis was then performed to detect regions with significant group difference. We compared the DTI-derived parameters of 15 patients before and after 12-week Selective Serotonin Reuptake Inhibitor (SSRI) therapies. Significant differences of DTI-derived parameters were observed between OCD and healthy groups in multiple structures, mainly within the fronto-striato-thalamo-cortical loop. An increased RD in combination with no change in AD among OCD patients was found in the left medial superior frontal gyrus, temporo-parietal lobe, occipital lobe, striatum, insula and right midbrain. There was no statistical difference in DTI-derived parameters between drug-naive and previously medicated OCD patients. After being medicated, OCD patients showed a reduction in RD of the left striatum and right midbrain, and in MD of the right midbrain. CONCLUSION: Our preliminary findings suggest that abnormalities of white matter microstructure, particularly in terms of myelin integrity, are primarily located within the fronto-striato-thalamo-cortical circuit of individuals with OCD. Some abnormalities may be partly reversed by SSRI treatment

Evaluation of non-local means based denoising filters for diffusion kurtosis imaging using a new phantom.

Image denoising has a profound impact on the precision of estimated parameters in diffusion kurtosis imaging (DKI). This work first proposes an approach to constructing a DKI phantom that can be used to evaluate the performance of denoising algorithms in regard to their abilities of improving the reliability of DKI parameter estimation. The phantom was constructed from a real DKI dataset of a human brain, and the pipeline used to construct the phantom consists of diffusion-weighted (DW) image filtering, diffusion and kurtosis tensor regularization, and DW image reconstruction. The phantom preserves the image structure while minimizing image noise, and thus can be used as ground truth in the evaluation. Second, we used the phantom to evaluate three representative algorithms of non-local means (NLM). Results showed that one scheme of vector-based NLM, which uses DWI data with redundant information acquired at different b-values, produced the most reliable estimation of DKI parameters in terms of Mean Square Error (MSE), Bias and standard deviation (Std). The result of the comparison based on the phantom was consistent with those based on real datasets