PET allows the imaging of functional properties of the living tissue, whereas other modalities (CT, MRI) provide structural information at significantly higher resolution and better image quality. Constraints for injected radioactivity, technologic limitations of current instrumentation, and inherent spatial uncertainties on the decaying process affect the quality of PET images. In this article we illustrate how structural information of matched anatomic images can be used in a multiresolution model to enhance the signal-to-noise ratio of PET images. The model states a flexible relation between function and structure in the brain and replaces high-resolution information of PET images with appropriately scaled MRI or CT local detail. The method can be naturally extended to other functional imaging modalities (SPECT, functional MRI). Methods: The methodology is based on the multiresolution property of the wavelet transform (WT). First, the coregistered structural image (MRI/CT) is downgraded to the resolution of the PET volume through appropriate filtering. Second, a redundant version of the WT is applied to both volumes. Third, a linear model is applied to the set of local coefficients of both image volumes and resulting parameters are recorded. The overall set of linear coefficients is then modeled as a mixture of multivariate gaussian distributions and fitted through a k-means algorithm. Finally, the local wavelet coefficients of the PET image are substituted by the corresponding values of the MRI/CT set calibrated according to the resulting clustering. The methodology was validated on digital simulated images and clinical data to evaluate its quantitative potential for individual as well as group analysis. Results: Application to real and simulated datasets shows very effective noise reduction (15% SD) while resolution is preserved. Conclusion: The methodology is robust to errors in the coregistration parameters, practical to implement, and computationally fast.

Recent studies suggested that Huntington's disease is due to aberrant interactions between mutant huntingtin protein, transcription factors and transcriptional co-activators resulting in widespread transcriptional dysregulation. Mutant huntingtin also interacts with histone acetyltransferases, consequently interfering with the acetylation and deacetylation states of histones. Because histone modifications and chromatin structure coordinate the expression of gene clusters, we have applied a novel mathematical approach, Chromowave, to analyse microarray datasets of brain tissue and whole blood to understand how genomic regions are altered by the effects of mutated huntingtin on chromatin structure. Results show that, in samples of caudate and whole blood from Huntington's disease patients, transcription is indeed deregulated in large genomic regions in coordinated fashion, that transcription in these regions is associated with disease progression and that altered chromosomal clusters in the two tissues are remarkably similar. These findings support the notion of a common genome-wide mechanism of disruption of RNA transcription in the brain and periphery of Huntington's disease patients.

No abstract