@inproceedings{27eba8fa809a41e9962bd85228c5177b,

title = "A mathematical model for extremely low dose adaptive computed tomography acquisition",

abstract = "One of the main challenges in Computed Tomography is to balance the amount of radiation exposure to the patient at the time of the scan with high image quality. We propose a mathematical model for adaptive Computed Tomography acquisition whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited acquisition and improved reconstruction, with the goal of applying only the dose level needed for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation and a discrete form of Ridgelet analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where the adaptive model produces significantly higher image quality, when compared with known non-adaptive acquisition algorithms, for the same number of projection lines.",

keywords = "Adaptive compressed sensing, Ridgelets",

author = "Oren Barkan and Amir Averbuch and Shai Dekel and Yaniv Tenzer",

year = "2014",

doi = "10.1007/978-3-642-54382-1_2",

language = "אנגלית",

isbn = "9783642543814",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "13--33",

booktitle = "Mathematical Methods for Curves and Surfaces - 8th International Conference, MMCS 2012, Revised Selected Papers",

address = "גרמניה",

note = "8th International Conference on Mathematical Methods for Curves and Surfaces, MMCS 2012 ; Conference date: 28-06-2012 Through 03-07-2012",

}