We propose an efficient, deterministic algorithm designed to reconstruct images from real Radon-Transform and Attenuated Radon-Transform data. Its input consists in a small family of recorded signals, each sampling the same composite photon or positron emission scene over a non-Gaussian, noisy channel. The reconstruction is performed by combining a novel numerical implementation of an analytical inversion formula  and a novel signal processing technique, inspired by the work of Tao and Candes  on code reconstruction. Our approach is proven to be optimal under a variety of realistic assumptions. We also indicate several medical imaging applications for which the new technology achieves high fidelity, even when dealing with real data subject to substantial non-Gaussian distortions. © 2009 IEEE.
|Original language||English (US)|
|Title of host publication||2009 16th International Conference on Digital Signal Processing|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|State||Published - Jul 2009|