Optimization of Low-Dose Tomography via Binary Sensing Matrices

  • Theeda PrasadEmail author
  • P. U. Praveen Kumar
  • C. S. Sastry
  • P. V. Jampana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9448)


X-ray computed tomography (CT) is one of the most widely used imaging modalities for diagnostic tasks in the clinical application. As X-ray dosage given to the patient has potential to induce undesirable clinical consequences, there is a need for reduction in dosage while maintaining good quality in reconstruction. The present work attempts to address low-dose tomography via an optimization method. In particular, we formulate the reconstruction problem in the form of a matrix system involving a binary matrix. We then recover the image deploying the ideas from the emerging field of compressed sensing (CS). Further, we study empirically the radial and angular sampling parameters that result in a binary matrix possessing sparse recovery parameters. The experimental results show that the performance of the proposed binary matrix with reconstruction using TV minimization by Augmented Lagrangian and ALternating direction ALgorithms (TVAL3) gives comparably better results than Wavelet based Orthogonal Matching Pursuit (WOMP) and the Least Squares solution.


Discrete tomography Compressive sensing WOMP  Binary sensing matrix TVAL3 



One of the authors (CSS) is thankful to CSIR (No. 25(219)/13/ EMR-II), Govt. of India, for its support.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Theeda Prasad
    • 1
    Email author
  • P. U. Praveen Kumar
    • 1
  • C. S. Sastry
    • 1
  • P. V. Jampana
    • 2
  1. 1.Department of MathematicsIndian Institute of Technology HyderabadTelanganaIndia
  2. 2.Department of Chemical EngineeringIndian Institute of Technology HyderabadTelanganaIndia

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