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PET Image Denoising Using a Synergistic Multiresolution Analysis of Structural (MRI/CT) and Functional Datasets

¹ Department of Clinical Neuroscience, Division of Neuroscience and Mental Health, Imperial College, London, United Kingdom; ² MRC Clinical Sciences Centre, Hammersmith Hospital, London, United Kingdom; and ³ INSERM, U650, Laboratoire du Traitement de l'Information Medicale (LaTIM), CHU Morvan, Brest, France

Correspondence: For correspondence contact: Federico E. Turkheimer, PhD, Department of Clinical Neuroscience, Division of Neuroscience and Mental Health, Cyclotron Bldg., Room 236, Hammersmith Hospital, DuCane Rd., London W12 0NN, UK. E-mail: federico.turkheimer{at}imperial.ac.uk

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.

Key Words: PET • sensitivity • multimodality • PET/CT • PET/MRI • fusion • synergistic • denoising • wavelets

COPYRIGHT © 2008 by the Society of Nuclear Medicine, Inc.

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JNM 2008 49: 11A-12A. [Full Text]