Abstract
The significant statistical noise and limited spatial resolution of positron emission tomography (PET) data in sinogram space results in the degradation of the quality and accuracy of reconstructed images. Although high-dose radiotracers and long acquisition times improve the PET image quality, the patients’ radiation exposure increases and the patient is more likely to move during the PET scan. Recently, various data-driven techniques based on supervised deep neural network learning have made remarkable progress in reducing noise in images. However, these conventional techniques require clean target images that are of limited availability for PET denoising. Therefore, in this study, we utilized the Noise2Noise framework, which requires only noisy image pairs for network training, to reduce the noise in the PET images. A trainable wavelet transform was proposed to improve the performance of the network. The proposed network was fed wavelet-decomposed images consisting of low- and high-pass components. The inverse wavelet transforms of the network output produced denoised images. The proposed Noise2Noise filter with wavelet transforms outperforms the original Noise2Noise method in the suppression of artefacts and preservation of abnormal uptakes. The quantitative analysis of the simulated PET uptake confirms the improved performance of the proposed method compared with the original Noise2Noise technique. In the clinical data, 10 s images filtered with Noise2Noise are virtually equivalent to 300 s images filtered with a 6 mm Gaussian filter. The incorporation of wavelet transforms in Noise2Noise network training results in the improvement of the image contrast. In conclusion, the performance of Noise2Noise filtering for PET images was improved by incorporating the trainable wavelet transform in the self-supervised deep learning framework.
Highlights
Reconstructing clear positron emission tomography (PET) images from noisy observations without the loss of spatial resolution remains a challenge because of the severe noise corruption in raw PET data and the limited resolution of the scanner
The proposed Noise2Noise filter with wavelet transform (WT) outperformed the original Noise2Noise method in the preservation of abnormal uptakes indicated by the red arrows
We proposed the Noise2Noise framework to reduce the noise in low-count PET images
Summary
Reconstructing clear positron emission tomography (PET) images from noisy observations without the loss of spatial resolution remains a challenge because of the severe noise corruption in raw PET data and the limited resolution of the scanner. Numerous studies have attempted to address this problem using various statistical and numerical approaches and signal processing techniques [1,2,3,4,5,6,7,8,9,10,11]. Various filters such as bilateral, nonlocal means, and wavelet-based filters have been proposed to reduce the noise in the corrupted images without causing blur to the anatomical boundaries [1,2,3,10,11]. Data-driven machine learning techniques based on deep neural networks have made remarkable progress in performing many challenging signal and image processing tasks [15]
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