DirectPET: full-size neural network PET reconstruction from sinogram data

Signal reconstruction from sampled data using neural network

For reconstructing the signal from sampling data, the method based on Shannon’s Sampling theorem is usually employed. In this method, the reconstruction error appears when the signal does not satisfy the Nyquist condition. This paper proposes a new reconstruction method by using a linear perceptron and multi layer perceptron as FIR filter. The perceptron which has the weights obtained by learni...

Iterative PET Image Reconstruction Using Convolutional Neural Network Representation

PET image reconstruction is challenging due to the ill-poseness of the inverse problem and limited number of detected photons. Recently deep neural networks have been widely and successfully used in computer vision tasks and attracted growing interests in medical imaging. In this work, we trained a deep residual convolutional neural network to improve PET image quality by using the existing int...

Image Reconstruction from Incomplete Data

Deep-neural-network based sinogram synthesis for sparse-view CT image reconstruction

Recently, a number of approaches to low-dose computed tomography (CT) have been developed and deployed in commercialized CT scanners. Tube current reduction is perhaps the most actively explored technology with advanced image reconstruction algorithms. Sparse data sampling is another viable option to the low-dose CT, and sparse-view CT has been particularly of interest among the researchers in ...

Online PET Reconstruction From List-Mode Data

This work proposes an alternative to ordered subsets to improve the convergence speed of list-mode expectationmaximization image reconstruction algorithms. Instead of subdividing the input data into non-overlapping subsets, the stream of measured coincidence events is immediately processed online. The reconstruction algorithm maintains a sliding window covering the events that contribute to the...