TY - GEN
T1 - A mathematical model for extremely low dose adaptive computed tomography acquisition
AU - Barkan, Oren
AU - Averbuch, Amir
AU - Dekel, Shai
AU - Tenzer, Yaniv
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Adaptive compressed sensing
KW - Ridgelets
UR - http://www.scopus.com/inward/record.url?scp=84901834463&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-54382-1_2
DO - 10.1007/978-3-642-54382-1_2
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:84901834463
SN - 9783642543814
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 13
EP - 33
BT - Mathematical Methods for Curves and Surfaces - 8th International Conference, MMCS 2012, Revised Selected Papers
T2 - 8th International Conference on Mathematical Methods for Curves and Surfaces, MMCS 2012
Y2 - 28 June 2012 through 3 July 2012
ER -