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1	Deep Learning for PET Imaging : From Denoising to Learned Primal-Dual Reconstruction / Djupinlärning i PET-avbildning : Från brusreducering till Learned Primal-Dual bildrekonstruktion Guazzo, Alessandro January 2020 (has links) 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. Medical Engineering Medicinteknik
2	What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine? Venturi, Gianni 02 August 2023 (has links) No description available. Computer Science Medical Imaging Radiology Artificial Intelligence DL SNR 3.0T SOC Radiologists Deep Learning Reconstruction DLR perception image quality signal to noise vendor reimbursement rate

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Deep Learning for PET Imaging : From Denoising to Learned Primal-Dual Reconstruction / Djupinlärning i PET-avbildning : Från brusreducering till Learned Primal-Dual bildrekonstruktion

What Are Radiologists' Perceptions in Regard to Image Quality and Increased Utilization Due to Vendor Provided Deep Learning Signal to Noise Ratio and Deep Learning Reconstruction on 3.0T Magnetic Resonance Imagine?