title: PET image denoising using a synergistic multiresolution analysis of structural (MRI/CT) and functional datasets.
abstract: 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.
  
  
  CONCLUSIONS
  The methodology is robust to errors in the coregistration parameters, practical to implement, and computationally fast.
authors.affiliation: Department of Clinical Neuroscience, Division of Neuroscience and Mental Health, Imperial College, London, United Kingdom. federico.turkheimer@imperial.ac.uk
authors.forename: Federico E
authors.initials: FE
authors.lastname: Turkheimer
authors.forename: Nicolas
authors.initials: N
authors.lastname: Boussion
authors.forename: Alexander N
authors.initials: AN
authors.lastname: Anderson
authors.forename: Nicola
authors.initials: N
authors.lastname: Pavese
authors.forename: Paola
authors.initials: P
authors.lastname: Piccini
authors.forename: Dimitris
authors.initials: D
authors.lastname: Visvikis
mesh_terms: D002250:Carbon Radioisotopes; D019992:Databases as Topic; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007546:Isoquinolines / Q000378:metabolism; D008279:Magnetic Resonance Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D014057:Tomography, X-Ray Computed
chemical_list: D002250:Carbon Radioisotopes; D007546:Isoquinolines; D019788:Fluorodeoxyglucose F18; C037850:PK 11195