Neural network image reconstruction directly from measurement data is a growing field of research, but until now has been limited to producing small (e.g. 128x128) 2D images by the large memory requirements of the previously suggested networks. In order to facilitate further research with direct reconstruction, we developed a more efficient network capable of 3D reconstruction of Radon encoded data with a relatively large image matrix (e.g. 400x400). Our proposed network is able to produce image quality comparable to the benchmark Ordered Subsets Expectation Maximization (OSEM) algorithm. We address the most memory intensive aspect of transforming the data from sinogram space to image space through a specially designed Radon inversion layer. We insert this layer between an initial network segment designed to encode the sinogram input and an output segment designed to refine and scale the initial image estimate to produce the final image. We demonstrate 3D reconstructions comparable to OSEM for 1, 4, 8 and 16 slices with no modifications to the network's architecture, capacity or hyper-parameters on a data set of simulated PET whole-body scans. When batch operations are considered, this network can reconstruct an entire PET whole-body volume in a single pass or about one second. Although results in this paper are on PET data, the proposed methods would be equally applicable to X-ray CT or any other Radon encoded measurement data.
A new approach for tomographic image reconstruction from projections using Artificial Neural Network (ANN) techniques is presented in this work. The design of the proposed reconstruction system is based on simple but efficient network architecture, which best utilizes all available input information. Due to the computational complexity, which grows quadratically with the image size, the training phase of the system is characterized by relatively large CPU times. The trained network, on the contrary, is able to provide all necessary information in a quick and efficient way giving results comparable to other time consuming iterative reconstruction algorithms. The performance of the network studied with a large number of software phantoms is directly compared to other iterative and analytical techniques. For a given image size and projections number, the role of the hidden layers in the network architecture is examined and the quality dependence of the reconstructed image on the size of the geometrical patterns used in the training phase is also investigated. ANN based tomographic image reconstruction can be easily implemented in modern FPGA devices and can serve as a quick initialization method to other complicated and time consuming procedures. An artificial neural network has been developed to reconstruct quantitative single photon emission computed tomographic (SPECT) images. The network is trained using known projection-image pairs containing Poisson noise to learn a shift-invariant weighting (filter) which minimizes the mean squared error between the reconstructed image and the sample image. Once trained, the network produces a reconstructed image as its output when projection data are presented to its input. Supervised training with a modified delta rule was used to train this two-layer neural network having a backpropagation architecture with one hidden layer (the filtered projection). The system was trained for noise levels representative of 1000, 10k, and 1M counts per slice. The Fourier transform of the filtered projection (for an impulse function) is compared with the ramp filter used with backprojection. > Abstract The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder–decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11 53 lower than ordered subset expectation maximization (OSEM) filtered back-projection (FBP), structural similarity index (1 11 higher than OSEM FBP), and peak signal-to-noise ratio (1.1 3.8 dB higher than OSEM FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder–decoder network can produce high quality PET images at a fraction of the time compared to conventional methods. Image reconstruction is reformulated using a data-driven, supervised machine learning framework that allows a mapping between sensor and image domains to emerge from even noisy and undersampled data, improving accuracy and reducing image artefacts.  