Kinetics-Induced Block Matching and 5-D Transform Domain Filtering for Dynamic PET Image Denoising

Abstract

Dynamic positron emission tomography (PET) scans of short-time frames are required to quantitatively estimate the uptake of PET ligands. Because such short-frame scans tend to be noisy, we propose kinetics-induced block matching and 5-D transform domain filtering (KIBM5D) specialized for dynamic PET image denoising. In the proposed algorithm, kinetics-induced block matching (KIBM) and 5-D transform domain filtering are alternately repeated in two cascading stages. In each stage of KIBM5D, all time frames are included in a patch of the KIBM to collect similar patch-wise time activity curves. These similar 4-D patches are then five-dimensionally grouped and transformed to the 5-D spectrum. In the 5-D transform domain, the 5-D spectrum is shrunk by hard thresholding and Wiener filtering in the first and second stage of KIBM5D, respectively. The sparsity of the 5-D spectrum is improved because signals of similar 4-D patches are correlated, while noises of these are uncorrelated. To evaluate the performance of KIBM5D, we used both computer simulation data of a dynamic digital brain phantom using [18F]FDG kinetics, and experimental data of a normal healthy volunteer using [11C]MeQAA, and compared the results of KIBM5D, Gaussian filter (GF), bilateral filter, nonlocal means, block matching and 4-D filtering, and 4-D Gaussian filtering. For simulation data, KIBM5D performed superiorly to the other methods in terms of the peak signal to noise ratio and structural similarity measures, in all time frames. Additionally, KIBM5D generated the best image quality not only in simulations but also with human data. Accordingly, KIBM5D enables the efficient denoising of dynamic PET images.