CT reconstruction via denoising approximate message passing

Alessandro Perelli, Michael Davies

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we adapt and apply a compressed sensing based reconstruction algorithm to the problem of computed tomography reconstruction for luggage inspection. Specifically, we propose a variant of the denoising generalized approximate message passing (D-GAMP) algorithm and compare its performance to the performance of traditional filtered back projection and to a penalized weighted least squares (PWLS) based reconstruction method. D-GAMP is an iterative algorithm that at each iteration estimates the conditional probability of the image given the measurements and employs a non-linear "denoising" function which implicitly imposes an image prior. Results on real baggage show that D-GAMP is well-suited to limited-view acquisitions.
Original languageEnglish
Title of host publicationProceedings of SPIE, Anomaly Detection and Imaging with X-Rays (ADIX)
Place of PublicationBaltimore, Maryland, Unites States
Number of pages8
Publication statusPublished - 12 May 2016


  • Computed tomography
  • Approximate message passing
  • Denoising Approximate Message Passing
  • Transportation Security CT


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