A Mathematical Model for Adaptive Computed Tomography Sensing

Oren Barkan, Jonathan Weill, Shai Dekel, Amir Averbuch

Research output: Contribution to journalArticlepeer-review


One of the main challenges in computed tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the reconstructed CTimage. We propose a mathematical model for adaptiveCT sensing whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited sensing and improved reconstruction, with the goal of applying only the dose level required 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 for the same number of line projections, the adaptive model produces higher image quality, when compared with standard limited angle, nonadaptive sensing algorithms.
Original languageAmerican English
Pages (from-to)551-565
JournalIEEE Transactions on Computational Imaging
Issue number4
StatePublished - Dec 2017


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