• J Magn Reson Imaging · Sep 2009

    Comparative Study

    (1)H spectroscopic imaging of human brain at 3 Tesla: comparison of fast three-dimensional magnetic resonance spectroscopic imaging techniques.

    • Matthew L Zierhut, Esin Ozturk-Isik, Albert P Chen, Ilwoo Park, Daniel B Vigneron, and Sarah J Nelson.
    • University of California, Joint Graduate Group in Bioengineering, San Francisco, CA, USA.
    • J Magn Reson Imaging. 2009 Sep 1; 30 (3): 473-80.

    PurposeTo investigate the signal-to-noise-ratio (SNR) and data quality of time-reduced three-dimensional (3D) proton magnetic resonance spectroscopic imaging ((1)H MRSI) techniques in the human brain at 3 Tesla.Materials And MethodsTechniques that were investigated included ellipsoidal k-space sampling, parallel imaging, and echo-planar spectroscopic imaging (EPSI). The SNR values for N-acetyl aspartate, choline, creatine, and lactate or lipid peaks were compared after correcting for effective spatial resolution and acquisition time in a phantom and in the brains of human volunteers. Other factors considered were linewidths, metabolite ratios, partial volume effects, and subcutaneous lipid contamination.ResultsIn volunteers, the median normalized SNR for parallel imaging data decreased by 34-42%, but could be significantly improved using regularization. The normalized signal to noise loss in flyback EPSI data was 11-18%. The effective spatial resolutions of the traditional, ellipsoidal, sensitivity encoding (SENSE) sampling scheme, and EPSI data were 1.02, 2.43, 1.03, and 1.01 cm(3), respectively. As expected, lipid contamination was variable between subjects but was highest for the SENSE data. Patient data obtained using the flyback EPSI method were of excellent quality.ConclusionData from all (1)H 3D-MRSI techniques were qualitatively acceptable, based upon SNR, linewidths, and metabolite ratios. The larger field of view obtained with the EPSI methods showed negligible lipid aliasing with acceptable SNR values in less than 9.5 min without compromising the point-spread function.

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