Premium
Spectral improvement by fourier thresholding of in vivo dynamic spectroscopy data
Author(s) -
Rowland Benjamin,
Merugumala Sai K.,
Liao Huijun,
Creager Mark A.,
Balschi James,
Lin Alexander P.
Publication year - 2016
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25976
Subject(s) - phosphocreatine , thresholding , amplitude , noise (video) , scale invariant feature transform , nuclear magnetic resonance , exponential function , fourier transform , time constant , signal to noise ratio (imaging) , computer science , mathematics , biomedical engineering , artificial intelligence , physics , optics , statistics , medicine , mathematical analysis , image (mathematics) , electrical engineering , engineering , energy metabolism
Purpose MR spectroscopy (MRS) typically requires averaging of multiple acquisitions to achieve adequate signal‐to‐noise ratio (SNR). In systems undergoing dynamic changes this can compromise the temporal resolution of the measurement. One such example is 31 P MRS of exercising skeletal muscle. Spectral improvement by Fourier thresholding (SIFT) offers a way of suppressing noise without averaging. In this study, we evaluate the performance of SIFT in healthy subjects and clinical cases. Methods 31 P MRS of the calf or thigh muscle of subjects (n = 12) was measured continuously before, during, and after exercise. The data were processed conventionally and with the addition of SIFT before quantifying peak amplitudes and frequencies. The postexercise increase in the amplitude of phosphocreatine was also characterized by fitting with an exponential function to obtain the recovery time constant. Results Substantial reductions in the uncertainty of peak fitting for phosphocreatine (73%) and inorganic phosphate (60%) were observed when using SIFT relative to conventional processing alone. SIFT also reduced the phosphocreatine recovery time constant uncertainty by 38%. Conclusion SIFT considerably improves SNR, which improved quantification and parameter estimation. It is suitable for any type of time varying MRS and is both straightforward and fast to apply. Magn Reson Med 76:978–985, 2016. © 2015 Wiley Periodicals, Inc.