Denoising of dynamic PET images using a multi-scale transform and non-local means filter. (March 2018)

• Record Type: Journal Article
• Authors: Jomaa, Hajer; Mabrouk, Rostom; Khlifa, Nawres; Morain-Nicolier, Frédéric
• Is Part Of: Biomedical signal processing and control. Volume 41(2018)
• Page Start: 69
• Page End: 80
• Publication Date: 2018-03
• Subjects: Positron emission tomography -- Denoising -- Non-local means -- Shearlet transform -- Wavelet transform; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.11.002
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: A new strategy of dynamic PET data denoising using a Multi-scale Transform and Non Local Means filter is proposed. The strategy relies on the spatiotemporal redundancy in PET image and the complementary behaviors of the transforms used. The method provides a better fit and TAC's smoothing while reduce the noise and improve the signal to noise ratio. Abstract: The quantification of positron emission tomography (PET) images requires a time activity curve (TAC) to provide an accurate estimation of kinetic parameters. However, the low signals to noise ratio (SNR), the important level of noise, and the low spatial resolution of PET image make the extraction of the TAC a challenging task. In this study, we present a new method based on multi-scale and non-local means method (MNLM) to reduce noise in dynamic PET sequences of small animal heart. MNLM filter takes into account the temporal correlation between images in the dynamic measurement and benefits from the complementary properties of both the Shearlet transform and the wavelet transform to provide best reduction. The method was tested on dynamic digital mouse phantom and a preclinical rat study ( n = 6). Based on a comparative study with three major algorithms reviewed on the state of the art, the data analysis proved the significance of the MNLM filter. In simulated data, the major finding of the study showed that at the highest noise level (7.68%), the model gave the best result (Chi-square = 4.06). Furthermore, it presented a notable gain in terms of PSNR and SSIM plot. In real data, the MNLM showed a better result in the computation of the contrast metric with a value of 27.04 ∓ 12.1 and the highest SNR with a value of 74.38 ∓ 9.2. This approach proved a better potential and could be considered as a valuable candidate to reduce noise in clinical system.