Adaptive compressed tomography sensing

Oren Barkan, Jonathan Weill, Amir Averbuch, Shai Dekel

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


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 CT image. We propose a mathematical model for adaptive CT 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 required for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridge let approximation and a discrete form of Ridge let 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, non-adaptive acquisition algorithms.

Original languageEnglish
Article number6619129
Pages (from-to)2195-2202
Number of pages8
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
StatePublished - 2013
Externally publishedYes
Event26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013 - Portland, OR, United States
Duration: 23 Jun 201328 Jun 2013


  • Adaptive Compressed Sensing
  • Computed Tomography
  • Low-dose CT
  • Ridgelets


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