Deep Learning for PET Imaging: from Denoising to Learned Primal-Dual Reconstruction

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. We developed a simple Denoiser, then two Neural Network based iterative reconstruction algorithms and finally, we used the most promising approach to reconstruct images from data acquired with the KTH MTH microCT - miniPET

Investigating Intensity Normalisation for PET Reconstruction with Supervised Deep Learning

Deep learning methods have shown great promise in the field of Positron Emission Tomography (PET) reconstruction, but the successful application of these methods depends heavily on the intensity scale of the images. Normalisation is a crucial step that aims to adjust the intensity of network inputs to make them more uniform and comparable across samples, acquisition times, and activity levels. In this work, we study the influence of different linear intensity normalisation approaches. We focus on two popular deep learning based image reconstruction methods: an unrolled algorithm (Learned Primal-Dual) and a post-processing method (OSEMConvNet). Results on the out-ofdistribution test dataset demonstrate that the choice of intensity normalisation significantly impacts on generalisability of these methods. Normalisation using the mean of acquisition data or corrected acquisition data led to improved peak-signal-to-noiseratio (PSNR) and data-consistency (KLDIV). Through evaluation of lesion-specific metrics of contrast recovery coefficients (CRC) and standard deviation (STD) an increase in CRC and STD is observed. These findings highlight the importance of carefully selecting an appropriate normalisation method for supervised deep learning-based PET reconstruction applications