Skip to main content
Log in

Incorporation of image data from a previous examination in 3D serial MR imaging

  • Research Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript



We aimed to demonstrate that follow-up scans in longitudinal examinations can be significantly accelerated by using images from previous scans as priors for constrained reconstruction.

Materials and methods

In this work, we propose a method for incorporating a prior image to improve the reconstruction of a new acquisition with considerable k-space undersampling, which contains a two-level registration scheme with non-parametric transformation, an adaptive synthesis procedure, and a constrained reconstruction with weighted total variation constraint. The performance of the method is evaluated using simulations, as well as results from volunteer and patient examinations.


In vivo experiments with both volunteers and patients show that incorporating a prior image into the constrained reconstruction produces many fewer reconstruction errors compared to the conventional reconstruction using only the highly undersampled k-space data.


The redundant information in the prior image can be efficiently adopted to improve the reconstruction quality of the new acquisition. When maintaining the image quality, higher acceleration can be achieved with the incorporation of the prior image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others


  1. Bilello M, Arkuszewski M, Nucifora P, Nasrallah I, Melhem ER, Cirillo L, Krejza J (2013) Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software. Neuroradiol J 26(2):143–150

    Article  CAS  PubMed  Google Scholar 

  2. Tan IL, van Schijndel RA, Pouwels PJ, Adèr HJ, Barkhof F (2002) Serial isotropic three-dimensional fast FLAIR imaging: using image registration and subtraction to reveal active multiple sclerosis lesions. AJR Am J Roentgenol 179(3):777–782

    Article  PubMed  Google Scholar 

  3. Wang Q, Wang Y, Cai J, Cai Y, Ma L, Xu X (2010) Differences of signal evolution of intraplaque hemorrhage and associated stenosis between symptomatic and asymptomatic atherosclerotic carotid arteries: an in vivo high-resolution magnetic resonance imaging follow-up study. Int J Cardiovasc Imaging 26(Suppl 2):323–332

    Article  PubMed  Google Scholar 

  4. Takao M, Sugano N, Nishii T, Miki H, Koyama T, Masumoto J, Sato Y, Tamura S, Yoshikawa H (2005) Application of 3D-MR image registration to monitor diseases around the knee joint. J Magn Reson Imaging 22:656–660

    Article  PubMed  Google Scholar 

  5. Ba-Ssalamah A, Schick S, Herneth AM, Cejna M, Schibany N, Prokesch RW, Wunderbaldinger P, Trattnig S (2000) Preoperative fast MRI of brain tumors using three-dimensional segmented echo planar imaging compared to three-dimensional gradient echo technique. Magn Reson Imaging 18(6):635–640

    Article  CAS  PubMed  Google Scholar 

  6. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P (1999) SENSE: sensitivity encoding for fast MRI. Magn Reson Med 42:952–962

    Article  CAS  PubMed  Google Scholar 

  7. Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 47:1202–1210

    Article  PubMed  Google Scholar 

  8. Mugler JP (2014) Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging 39:745–767

    Article  PubMed  Google Scholar 

  9. Brant-Zawadzki M, Gillan GD, Nitz WR (1992) MP RAGE: a three-dimensional, T1-weighted, gradient-echo sequence—initial experience in the brain. Radiology 182(3):769–775

    Article  CAS  PubMed  Google Scholar 

  10. Tsao J, Boesiger P, Pruessmann KP (2003) kt BLAST and kt SENSE: dynamic MRI with high frame rate exploiting spatiotemporal correlations. Magn Reson Med 50:1031–1042

    Article  PubMed  Google Scholar 

  11. Candes EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inform Theory 52(2):489–509

    Article  Google Scholar 

  12. Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 58:1182–1195

    Article  PubMed  Google Scholar 

  13. Liang D, Liu B, Wang J, Ying L (2009) Accelerating SENSE using compressed sensing. Magn Reson Med 62:1574–1584

    Article  PubMed  Google Scholar 

  14. Lustig M, Alley M, Vasanawala S, Donoho DL, Pauly JM (2009) L1 SPIR-iT: autocalibrating parallel imaging compressed sensing. In: Proceedings of the 17th scientific meeting, International Society for Magnetic Resonance in Medicine, Hawaii, p 379

  15. Samsonov AA, Velikina JV, Fleming JO, Schiebler ML, Field AS (2010) Accelerated Serial MR Imaging in Multiple Sclerosis Using Baseline Scan Information. In: Proceedings of the 18th scientific meeting, International Society for Magnetic Resonance in Medicine, Stockholm, p 4876

  16. Du H, Lam F (2012) Compressed sensing MR image reconstruction using a motion-compensated reference. Magn Reson Imaging 30(7):954–963

    Article  PubMed  Google Scholar 

  17. Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26(3):405–421

    Article  PubMed  Google Scholar 

  18. Beck A, Teboulle M (2009) Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans Image Process 18(11):2419–2434

    Article  PubMed  Google Scholar 

  19. Bridson R (2007) Fast Poisson disk sampling in arbitrary dimensions. In: ACM SIGGRAPH sketches, New York, Article 22

  20. Zhang T, Pauly JM, Vasanawala SS, Lustig M (2013) Coil compression for accelerated imaging with Cartesian sampling. Magn Reson Med 69:571–582

    Article  PubMed Central  PubMed  Google Scholar 

  21. Myronenko A, Song X (2009) Adaptive regularization of Ill-posed problems: application to non-rigid image registration. Technical Report OHSU, arXiv:0906.3323v1

  22. Hansen PC (2001) The L-curve and its use in the numerical treatment of inverse problems. In: Johnston P (ed) Computational inverse problems in electrocardiology. WIT Press, Southampton, pp 119–142

    Google Scholar 

  23. Szeliski R, Coughlan J (1997) Spline-based image registration. Int J Comput Vis 22(3):199–218

    Article  Google Scholar 

  24. Qiu P, Xing C (2013) On nonparametric image registration. Technometrics 55(2):174–188

    Article  Google Scholar 

Download references


The authors thank Irina Mader and Hansjörg Mast for providing the patient data sets. The authors thank Benjamin Knowles for his help with the manuscript and journal referees for their helpful comments. The work was supported in part by Siemens Healthcare.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

All patient and volunteer studies and the use of the data in this work have been approved by the Ethics Committee at the University Medical Center and have therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Guobin Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, G., Hennig, J., Raithel, E. et al. Incorporation of image data from a previous examination in 3D serial MR imaging. Magn Reson Mater Phy 28, 413–425 (2015).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: