Magnetic Resonance Imaging: Single Coil Sensitivity Mapping and Correction using Spatial Harmonics Eric Peterson and Ryan Lipscomb ECE 533 12/15/2006.

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Magnetic Resonance Imaging: Single Coil Sensitivity Mapping and Correction using Spatial Harmonics Eric Peterson and Ryan Lipscomb ECE /15/2006

Outline Motivation Motivation Inhomogeneous coil sensitivity Inhomogeneous coil sensitivity Theory Theory Methods Methods Spatial Harmonics Spatial Harmonics Results Results

Motivation Flexible Torso Coil from Toshiba Sensitivity profile of a circular phantom using two coils (top and left)

Motivation Due to the inhomogeneous and varying sensitivity of the vest coil the images vary Due to the inhomogeneous and varying sensitivity of the vest coil the images vary

Theory Image degradation model Spatial Harmonics equation

Methods Parallel imaging uses spatial harmonics to define the coil sensitivity map Parallel imaging uses spatial harmonics to define the coil sensitivity map Use low frequency spatial harmonics to define the sensitivity map Use low frequency spatial harmonics to define the sensitivity map Filter the image and the sensitivity map Filter the image and the sensitivity map Invert the sensitivity map and apply it to the image Invert the sensitivity map and apply it to the image

Key Image Input Crop Find noise threshold with a user defined ROI FFT Select number of harmonics Invert and set minimum gain to 1 Split noise and signal portions of the image Attenuate inverse map using a user defined triangular filter Multiply the inverse map to the image to get the corrected image Normalize the means of the original and corrected images Output Corrected Image with Noise Signal only portions of the original image Noise only portions of the original image IFFT Original image Sensitivity map Inverse sensitivity map Corrected Image Crop

Results CorrectedOriginal

Results CorrectedOriginal

Results Absolute difference between ADC images before and after correction

Conclusion This method does a good job of intensity normalization This method does a good job of intensity normalization Preserves high frequency data Preserves high frequency data The major limitation is due by high frequency components of the lung such as trachea The major limitation is due by high frequency components of the lung such as trachea

References Bydder M, Larkman DJ, Hajnal JV. Generalized SMASH imaging. Magn Reson Med 2002;47: Bydder M, Larkman DJ, Hajnal JV. Generalized SMASH imaging. Magn Reson Med 2002;47: Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38: Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38: Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA). Magn Reson Med 2002;47: Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A. Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA). Magn Reson Med 2002;47: Chen XJ, Moller HE, Chawla MS, Cofer GP, Driehuys B, Hedlund LW, Johnson GA. Spatially Resolved Measurements of Hyperpolarized Gas Properties in the Lung In Vivo. Part I: Diffusion Coefficient. Magn Reson Med 1999;42:721–728 Chen XJ, Moller HE, Chawla MS, Cofer GP, Driehuys B, Hedlund LW, Johnson GA. Spatially Resolved Measurements of Hyperpolarized Gas Properties in the Lung In Vivo. Part I: Diffusion Coefficient. Magn Reson Med 1999;42:721–728