MRS imaging using anatomically based k-space sampling and extrapolation.
A comprehensive strategy for the acquisition, reconstruction, and postprocessing of MR spectroscopic images is presented. The reconstruction algorithm is the most critical component of this strategy. It is assumes that the desired image is spatially bounded, meaning that the desired image contains an object that is surrounded by a background of zeros. The reconstruction algorithm relies on prior knowledge of the background zeros for k-space extrapolation. This algorithm is a good candidate for proton MR spectroscopic image reconstruction because these images are often spatially bounded and prior knowledge of the zeros is easily obtained from a rapidly acquired high resolution conventional MRI. Although the reconstruction algorithm can be used with the standard 3DFT k-space distribution, a distribution that relies on anatomical features that are likely to occur in the spectroscopic image can produce better results. Prior knowledge of these anatomical features is also obtained from a conventional MRI. Finally, the postprocessing component of this strategy is valuable for reducing subcutaneous lipid contamination. Overall, the comprehensive approach presented here produces images that are better resolved than standard approaches without increasing acquisition time or reducing SNR. Examples using NAA data are provided.
Magnetic Resonance Systems Research Laboratory, Stanford University, CA 94305, USA.
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Plevritis
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Macovski
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008055:Lipids; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*
D008055:Lipids