Abstract
AbstractIn this paper I analyse some regularization models for the reconstruction of X-rays Computed Tomography images from few-view projections. It is well known that the widely used low-cost Filtered Back Projection method is not suitable in case of low-dose data, since it produces images with noise and artifacts. Iterative reconstruction methods based on the model discretization are preferred in this case. However, since the problem has infinite possible solutions and is ill-posed, regularization is necessary to obtain a good solution. Different iterative regularization methods have been proposed in literature, but an organized comparison among them is not available. We compare some regularization approaches in the case of few-view tomography by means of simulated projections from both a phantom and a real image.
Funder
Alma Mater Studiorum - Università di Bologna
Publisher
Springer Science and Business Media LLC
Reference15 articles.
1. Buzug, T.M.: Computed tomography: from photon statistics to modern cone-beam CT. Soc. Nucl. Med. (2009)
2. Bertero, M., Boccacci, P., De Mol, C.: Introduction to inverse problems in imaging. CRC Press, Boca Raton (2021)
3. Beister, M., Kolditz, D., Kalender, W.A.: Iterative reconstruction methods in X-ray CT. Phys. Med. 28, 94–108 (2012)
4. Graff, C., Sidky, E.: Compressive sensing in medical imaging. Appl. Opt. 54(8), 23–44 (2015)
5. Radon, J.: On the determination of functions from their integral values along certain manifolds. IEEE Trans. Med. Imaging 5(4), 170–176 (1986)