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Found 7 papers, 0 papers with code
List-Mode PET Image Reconstruction Using Dykstra-Like Splitting
Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task.
ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection
Despite limited training on simulated data, the proposed ReconU-Net successfully reconstructed the real Hoffman brain phantom, unlike other deep learning-based direct reconstruction methods, which failed to produce a reconstructed image.
Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising
Here, we propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance.
Fully 3D Implementation of the End-to-end Deep Image Prior-based PET Image Reconstruction Using Block Iterative Algorithm
The simulation results showed that the proposed method improved the PET image quality by reducing statistical noise and preserved a contrast of brain structures and inserted tumor compared with other algorithms.
List-Mode PET Image Reconstruction Using Deep Image Prior
However, the application of deep learning techniques to list-mode PET image reconstruction has not been progressed because list data is a sequence of bit codes and unsuitable for processing by convolutional neural networks (CNN).
Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance
Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs.
Direct PET Image Reconstruction Incorporating Deep Image Prior and a Forward Projection Model
Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction.
