Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time?

Improving the resolution in magnetic resonance imaging comes at the cost of either lower signal‐to‐noise ratio, longer acquisition time or both. This study investigates whether so‐called super‐resolution reconstruction methods can increase the resolution in the slice selection direction and, as such, are a viable alternative to direct high‐resolution acquisition in terms of the signal‐to‐noise ratio and acquisition time trade‐offs. The performance of six super‐resolution reconstruction methods and direct high‐resolution acquisitions was compared with respect to these trade‐offs. The methods are based on iterative back‐projection, algebraic reconstruction, and regularized least squares. The algorithms were applied to low‐resolution data sets within which the images were rotated relative to each other. Quantitative experiments involved a computational phantom and a physical phantom containing structures of known dimensions. To visually validate the quantitative evaluations, qualitative experiments were performed, in which images of three different subjects (a phantom, an ex vivo rat knee, and a postmortem mouse) were acquired with different magnetic resonance imaging scanners. The results show that super‐resolution reconstruction can indeed improve the resolution, signal‐to‐noise ratio and acquisition time trade‐offs compared with direct high‐resolution acquisition. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.

[1]  Klaas P Pruessmann,et al.  Encoding and reconstruction in parallel MRI , 2006, NMR in biomedicine.

[2]  Robert L. Stevenson,et al.  Spatial Resolution Enhancement of Low-Resolution Image Sequences A Comprehensive Review with Directions for Future Research , 1998 .

[3]  Klamer Schutte,et al.  Performance Evaluation of Super-Resolution Reconstruction Methods on Real-World Data , 2007, EURASIP J. Adv. Signal Process..

[4]  Simon K. Warfield,et al.  Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI , 2010, IEEE Transactions on Medical Imaging.

[5]  S Peled,et al.  Superresolution in MRI: Application to human white matter fiber tract visualization by diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[6]  Michael Elad,et al.  Restoration of a single superresolution image from several blurred, noisy, and undersampled measured images , 1997, IEEE Trans. Image Process..

[7]  Colin Studholme,et al.  Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. , 2006, Academic radiology.

[8]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[9]  P. Parizel,et al.  A brief review of parallel magnetic resonance imaging , 2003, European Radiology.

[10]  Jan Sijbers,et al.  General and Efficient Super-Resolution Method for Multi-slice MRI , 2010, MICCAI.

[11]  Hayit Greenspan,et al.  MRI Inter-slice Reconstruction Using Super-Resolution , 2001, MICCAI.

[12]  Roger Y. Tsai,et al.  Multiframe image restoration and registration , 1984 .

[13]  Klaus Scheffler.  Superresolution in MRI? , 2002, Magnetic resonance in medicine.

[14]  Colin Studholme,et al.  On Super-Resolution for Fetal Brain MRI , 2010, MICCAI.

[15]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[16]  Rachid Deriche,et al.  The use of super‐resolution techniques to reduce slice thickness in functional MRI , 2004, Int. J. Imaging Syst. Technol..

[17]  H Stark,et al.  High-resolution image recovery from image-plane arrays, using convex projections. , 1989, Journal of the Optical Society of America. A, Optics and image science.

[18]  Harry Shum,et al.  Fundamental limits of reconstruction-based superresolution algorithms under local translation , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  J. Pipe.  Motion correction with PROPELLER MRI: Application to head motion and free‐breathing cardiac imaging , 1999, Magnetic resonance in medicine.

[20]  Russell M. Mersereau,et al.  A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI , 2009, IEEE Transactions on Medical Imaging.

[21]  Hayit Greenspan,et al.  Super-Resolution in Medical Imaging , 2009, Comput. J..

[22]  G T Herman,et al.  ART: mathematics and applications. A report on the mathematical foundations and on the applicability to real data of the algebraic reconstruction techniques. , 1973, Journal of theoretical biology.

[23]  Shmuel Peleg,et al.  Robust super-resolution , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[24]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[25]  Michal Irani,et al.  Improving resolution by image registration , 1991, CVGIP Graph. Model. Image Process..

[26]  G. Herman,et al.  Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. , 1970, Journal of theoretical biology.

[27]  Yehezkel Yeshurun,et al.  Superresolution in MRI—perhaps sometimes , 2002 .

[28]  Hyoung Joong Kim,et al.  Knowledge-Assisted Media Analysis for Interactive Multimedia Applications , 2007, EURASIP J. Adv. Signal Process..

[29]  Edmund Y. Lam,et al.  Application of Tikhonov Regularization to Super-Resolution Reconstruction of Brain MRI Images , 2007, MIMI.

[30]  Peyman Milanfar,et al.  Statistical performance analysis of super-resolution , 2006, IEEE Transactions on Image Processing.