Total Variation Regularization in Digital Breast Tomosynthesis

  • Sascha Fränkel
  • Katrin Wunder
  • Ulrich Heil
  • Daniel Groß
  • Ralf Schulze
  • Ulrich Schwanecke
  • Christoph Düber
  • Elmar Schömer
  • Oliver Weinheimer
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

We developed an iterative algebraic algorithm for the reconstruction of 3D volumes from limited-angle breast projection images. Algebraic reconstruction is accelerated using the graphics processing unit. We varied a total variation (TV)-norm parameter in order to verify the influence of TV regularization on the representation of small structures in the reconstructions. The Barzilai-Borwein algorithm is used to solve the inverse reconstruction problem. The quality of our reconstructions was evaluated with the Quart Mam/Digi Phantom, which features so-called Landolt ring structures to verify perceptibility limits. The evaluation of the reconstructions was done with an automatic LR detection algorithm. The LR feature of the Quart Mam/Digi Phantom is well suited for the evaluation of DBT algorithms with respect to the visibility of small structures. TV regularization is not the technique of choice to improve the representation of small structures in DBT. The BB solver provides good results after just 4 iterations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. Helvie MA. Digital mammography imaging: breast tomosynthesis and advanced applications. Rad Clin North Am. 2010;48(5):917–29.Google Scholar
  2. Sidky EY, Pan X, Reiser IS, et al. Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms. Med Phys. 2009;36(11):4920.Google Scholar
  3. Hellerhoff K. Digitale Brusttomosynthese. Der Radiologe. 2010;50(11):991–8.Google Scholar
  4. Kastanis I, Arridge S, Stewart A, et al. 3D digital breast tomosynthesis using total variation regularization. Proc IWDM. 2008; p. 621–7.Google Scholar
  5. Sidky EY, Duchin Y, Reiser I, et al. Optimizing algorithm parameters based on a model observer detection task for image reconstruction in digital breast tomosynthesis. Proc IEEE NSS/MIC. 2011; p. 4230–2.Google Scholar
  6. Park JC, Song B, Kim JS, et al. Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT. Med Phys. 2012;39(3):1207–17.Google Scholar
  7. Gross D, Heil U, Schulze R, et al. GPU-based volume reconstruction from very few arbitrarily aligned X-ray images. SIAM J Sci Comput. 2009;31(6):4204–41.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sascha Fränkel
    • 1
    • 2
  • Katrin Wunder
    • 1
  • Ulrich Heil
    • 2
  • Daniel Groß
    • 2
  • Ralf Schulze
    • 3
  • Ulrich Schwanecke
    • 4
  • Christoph Düber
    • 1
  • Elmar Schömer
    • 2
  • Oliver Weinheimer
    • 1
  1. 1.Department of RadiologyUniversity Medical Center (UMC) of the Johannes Gutenberg-University Mainz (JGU)MainzDeutschland
  2. 2.Institute for Computer ScienceJGUMainzDeutschland
  3. 3.Department of Oral Surgery (and Oral Radiology)UMC of the JGUMainzDeutschland
  4. 4.Department of Design, Computer Science and MediaRheinMain University of Applied SciencesWiesbadenDeutschland

Personalised recommendations