Deep Learning for PET Imaging : From Denoising to Learned Primal-Dual Reconstruction
Abstract: PET imaging is a key tool in the fight against cancer. One of the main issues of PET imaging is the high level of noise that characterizes the reconstructed image, during this project we implemented several algorithms with the aim of improving the reconstruction of PET images exploiting the power of Neural Networks. First, we developed a simple denoiser that improves the quality of an image that has already been reconstructed with a reconstruction algorithm like the Maximum Likelihood Expectation Maximization. Then we implemented two Neural Network based iterative reconstruction algorithms that reconstruct directly an image starting from the measured data rearranged into sinograms, thus removing the dependence of the reconstruction result from the initial reconstruction needed by the denoiser. Finally, we used the most promising approach, among the developed ones, to reconstruct images from data acquired with the KTH MTH microCT - miniPET.
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