Highly undersampled peripheral Time-of-Flight magnetic resonance angiography: optimized data acquisition and iterative image reconstruction

  • Jana Hutter
  • Robert Grimm
  • Christoph Forman
  • Joachim Hornegger
  • Peter Schmitt
Research Article



The aim of this study was to investigate the acceleration of peripheral Time-of-Flight magnetic resonance angiography using Compressed Sensing and parallel magnetic resonance imaging (MRI) while preserving image quality and vascular contrast.

Materials and methods

An analytical sampling pattern is proposed that combines aspects of parallel MRI and Compressed Sensing. It is used in combination with a dedicated Split Bregman algorithm. This approach is compared with current state-of-the-art patterns and reconstruction algorithms.


The acquisition time was reduced from 30 to 2.5 min in a study using ten volunteer data sets, while showing improved sharpness, better contrast and higher accuracy compared to state-of-the-art techniques.


This study showed the benefits of the proposed dedicated analytical sampling pattern and Split Bregman algorithm for optimizing the Compressed Sensing reconstruction of highly accelerated peripheral Time-of-Flight data.


Iterative reconstruction Non-contrast-enhanced MRA Peripheral angiography Compressed Sensing 


  1. 1.
    Meaney JFM (2003) Magnetic resonance angiography of the peripheral arteries: current status. Eur Radiol 13(4):836–852PubMedGoogle Scholar
  2. 2.
    Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306CrossRefGoogle Scholar
  3. 3.
    Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42(5):952–962CrossRefPubMedGoogle Scholar
  4. 4.
    Griswold MA, Jakob PN, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 47:1202–1210CrossRefPubMedGoogle Scholar
  5. 5.
    Qu P, Luo J, Zhang B, Wang J, Shen GX (2007) An improved iterative SENSE reconstruction method. Concept Magn Reson B 31B:44–50CrossRefGoogle Scholar
  6. 6.
    Liu B, King K, Steckner M, Xie J, Sheng J, Ying L (2009) Regularized sensitivity encoding (SENSE) reconstruction using Bregman iterations. Magn Reson Med 61(1):145–152CrossRefPubMedGoogle Scholar
  7. 7.
    Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58(6):1182–1195CrossRefPubMedGoogle Scholar
  8. 8.
    Goldstein T, Osher S (2009) The Split Bregman method for L1-regularized problems. SIAM J Imaging Sci 2:323–343CrossRefGoogle Scholar
  9. 9.
    Aelterman J, Luong H, Goossens B, Pizurica A, Philips W (2010) COMPASS: a joint framework for parallel imaging and compressive sensing in MRI. In: Proceedings of the IEEE international conference on image processing (ICIP 2010), Hong Kong, China, Sept 26–29 2010, pp 1653–1656Google Scholar
  10. 10.
    Facchinei F, Pang J (2003) Finite-dimensional variational inequalities and complementarity problems, vol I and II. Springer, BerlinGoogle Scholar
  11. 11.
    Lustig M, Alley M, Vasanawala S, Donoho DL, Pauly JM (2009) L1SPIR-iT: autocalibrating parallel imaging compressed sensing. In: Proceedings of the annual meeting ISMRM, Honolulu, USA, April 18–24 2009, p 334Google Scholar
  12. 12.
    Osher R, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Phys D 60(1–2):259–268Google Scholar
  13. 13.
    Block KT, Uecker M, Frahm J (2007) Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magn Reson Med 57(6):1086–1098CrossRefPubMedGoogle Scholar
  14. 14.
    Nocedal F (1980) Updating quasi-Newton matrices with limited storage. Math Comput 35(151):773–782CrossRefGoogle Scholar
  15. 15.
    Plonka G, Ma J (2011) Curvelet–wavelet regularized split bregman iteration for compressed sensing. Int J Wavelets Multires 9(1):79–110CrossRefGoogle Scholar
  16. 16.
    Ramani S, Fessler J (2011) Parallel MR image reconstruction using augmented Lagrangian methods. IEEE Trans Med Imaging 30(3):694–706PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Wang Z, Bovik A, Sheikh H, Simoncelli E (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefPubMedGoogle Scholar
  18. 18.
    Li D, Carr JC, Shea SM, Zheng J, Deshpande VS, Wielopolski PA, Finn JP (2001) Coronary arteries: magnetization-prepared contrast-enhanced three-dimensional volume-targeted breath-hold MR angiography. Radiology 219(1):270–277CrossRefPubMedGoogle Scholar
  19. 19.
    Hutter J, Grimm R, Forman C, Hornegger J, Schmitt P (2012) Vessel adapted regularization for iterative reconstruction in MR angiography. In: Pipe J (ed) Proceedings of the 21st annual meeting of the ISMRM. Melbourne, Australia, May 5–11, 2012Google Scholar
  20. 20.
    Edelman R, Sheehan J, Dunkle E, Schindler N, Carr J, Koktzoglou I (2010) Quiescent-interval single-shot unenhanced magnetic resonance angiography of peripheral vascular disease: technical considerations and clinical feasibility. Magn Reson Med 63(4):951–958PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© ESMRMB 2015

Authors and Affiliations

  • Jana Hutter
    • 1
    • 2
  • Robert Grimm
    • 1
  • Christoph Forman
    • 1
    • 2
  • Joachim Hornegger
    • 1
    • 2
  • Peter Schmitt
    • 3
  1. 1.Pattern Recognition Lab, Department of Computer ScienceFriedrich-Alexander University of Erlangen-NurembergErlangenGermany
  2. 2.School of Advanced Optical TechnologiesErlangenGermany
  3. 3.MR Applications and Workflow Development, Healthcare SectorSiemens AGErlangenGermany

Personalised recommendations