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Abstract-- We propose a mathematical model for adaptive Computed Tomography acquisition whose goal is to radically reduce
dosage levels. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation. 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.
Index Terms-- Adaptive compressed sensing, Ridgelets.
In the last decade, several studies have shown that radiation exposure during Computed Tomography (CT) scanning is a
significant factor in raising the total public risk of cancer deaths , , . To balance image quality with these concerns,
radiologists use the protocol As Low as Reasonably Achievable (ALARA). It meant to ensure that "...CT dose factors are kept
to a point where risk is minimized for maximum diagnostic benefit..".
Currently, there are several state-of-the-art technologies that attempt to achieve dose reduction. Model Based Iterative
Reconstruction (MBIR)  is regarded as one of the advanced reconstruction technique. Its mathematical modeling of the
acquired data coupled with the Adaptive Statistical Iterative Reconstruction (ASIR) method produces high quality
reconstructions even from highly noisy raw data that was acquired with low-dose.
However, dosage levels during CT exams are still at the focus of attention and any new method that can reduce them is
considered highly valuable. This paper describes an adaptive acquisition model that is superior to existing non-adaptive
acquisition methods and in theory allows minimal and optimal dosage levels. The method can be considered a significant
generalization of existing two step adaptive acquisition methods ,  and can potentially use the same hardware