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. Conclusion: The methodology is robust to errors in the coregistration parameters, practical to implement, and computationally fast.

Multimodal Partial-Volume Correction: Application to 18F-Fluoride PET/CT Bone Metastases Studies

“…Hence, the resolution recovery is ROI-based and relies on good coregistration between PET and MR images as well as between MR imaging and the probability atlas. The idea stems from previous concepts on wavelet-based resolution recovery (19) and denoising (27).…”

“…Hence, the resolution recovery is ROI-based and relies on good coregistration between PET and MR images as well as between MR imaging and the probability atlas. The idea stems from previous concepts on wavelet-based resolution recovery (19) and denoising (27).In this work, we developed SFS-RR further to fit a novel clinical requirement, specifically 18 F-fluoride PET/CT for detecting and monitoring bone metastases. The choice of the application is not fortuitous; in the first instance, PET/CT images provide synergistic information (i.e., both modalities show high image intensity in correspondence of lesions) and, second, they do not require additional coregistration as for 2 separate PET and MR acquisitions.…”

PET image reconstruction using prior information from CT or MRI

“…Comparison was carried out with the state-of-the-art wavelet-based denoising method proposed in [4]. All the data were acquired by a hybrid PET/CT camera Siemens BIOGRAPH DUO LSO.…”

“…It is important to note that this does not mean that all MRI/CT edges will start to appear in the reconstructed PET image but only those, which are intrinsic to the PET image. This is a fundamental difference from the wavelet-based methods [3,4], which replace noisy high-pass bands of PET with MRI/CT and insert thus fine details irrespective of their true existence in PET. Our method performs denoising and deconvolution of PET and uses MRI/CT only to control diffusion.…”

“…The appropriate scaling between PET and MRI/CT wavelet coefficients at the finest resolution was obtained by assuming a global linear relationship between PET and MRI/CT wavelets at the lower resolution. Recently, Turkheimer et al [4] proposed an improved method, which relaxes the assumption of a simple scalar relation between structural and functional information and postulated a stochastic model.…”