Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.