Improving Dynamic Parallel MRI Reconstruction via a Kernel-Based Learning Technique

  • Yuchou Chang University of Houston - Downtown
Keywords: Kernel Method; Parallel MRI; Dynamic MRI; Reconstruction; Noise Reduction.


As an important radiology technology, magnetic resonance imaging (MRI) has been widely used in clinical applications. However, its low imaging speed restricts some clinical applications such as dynamic imaging. Parallel MRI was proposed to accelerate imaging speed by undersampling k-space data and applied on dynamic imaging like cardiac imaging. Due to undersampled k-space data, noise is a problem in reconstructed MR images. We propose a nonlinear technique to improve a temporal parallel MRI reconstruction method. Experimental results show that the proposed nonlinear technique outperforms the traditional method.


(1) Haacke E. M., et al., Magnetic resonance imaging: Physical principles and sequence design. Wiley-Liss, 1st edition, 1999.

(2) Griswold M.A., et al., Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magnetic Resonance in Medicine, 2002. 47(6): p. 1202-1210.

(3) Pruessmann K.P., et al., SENSE: sensitivity encoding for fast MRI. Magnetic Resonance in Medicine, 1999. 42(5): p. 952-962.

(4) Breuer F.A., et al., Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA). Magnetic Resonance in Medicine, 2005. 53(4):981-985.

(5) Vapnik, V., et al., The Nature of Statistical Learning Theory. Springer, 2nd edition, 1999.

(6) Schölkopf, B., et al., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, 1st edition, 2001.

(7) Chang, Y., et al., Nonlinear GRAPPA: a kernel approach to parallel MRI reconstruction. Magnetic Resonance in Medicine, 2012. 68(3): p. 730-740.

(8) Lyu J., et al., Fast GRAPPA reconstruction with random projection. Magnetic Resonance in Medicine, 2015. 74(1): p. 71-80.
How to Cite
Chang, Y. (2019). Improving Dynamic Parallel MRI Reconstruction via a Kernel-Based Learning Technique. European Journal of Applied Sciences, 7(2), 25-29.