Spectral improvement by fourier thresholding of in vivo dynamic spectroscopy data

Abstract

Purpose Magnetic Resonance Spectroscopy (MRS) typically requires averaging of multiple acquisitions to achieve adequate signal to noise (SNR). In systems undergoing dynamic changes this can compromise the temporal resolution of the measurement. One such example is 31P 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 31P MRS of the calf or thigh muscle of subjects (n = 12) was measured continuously before, during, and after exercise. The data was processed conventionally and with the addition of SIFT before quantifying peak amplitudes and frequencies. The post-exercise 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.

A protocol for quantifying cardiogenic oscillations in dynamic 129Xe gas exchange spectroscopy: The effects of idiopathic pulmonary fibrosis

“…Prior to fitting the spectra, two filtering steps were applied to improve spectral SNR while minimizing the need to sacrifice the temporal resolution imperative to capturing the cardiopulmonary dynamics. First, the raw FIDs were processed using the spectral improvement by Fourier thresholding (SIFT) method . This involves Fourier transforming the raw data along the indirect time dimension (time with respect to the breath‐hold) and retaining only the Fourier coefficients that exceed a predetermined threshold of two standard deviations above the noise in the temporal‐frequency domain.…”

“…First, the raw FIDs were processed using the spectral improvement by Fourier thresholding (SIFT) method. 21,22 This involves Fourier transforming the raw data along the indirect time dimension (time with respect to the breath-hold) and retaining only the Fourier coefficients that exceed a predetermined threshold of two standard deviations above the noise in the temporal-frequency domain. The data are then Fourier transformed back along the indirect frequency dimension to undergo spectral curve fitting.…”

Tai Chi Improves Brain Metabolism and Muscle Energetics in Older Adults

“…The 31 P MRS data was postprocessed offline, with the spectral improvement by Fourier thresholding (SIFT) method described elsewhere . Briefly, the SIFT was applied to improve SNR and smooth temporal trajectories of the 300 spectra.…”

“…Briefly, the SIFT was applied to improve SNR and smooth temporal trajectories of the 300 spectra. The spectra were then fit using a simulated basis set to obtain the 31 P metabolite areas . The postexercise PCr amplitudes were fit to a monoexponential recovery function to determine the recovery time constant τ.…”

Whole brain P MRSI at 7T with a dual‐tuned receive array

“…Magnetic resonance spectroscopy of the Phosphorus nucleus can be used to explore several interesting metabolic processes in vivo, including energy metabolism, cell membrane turnover, and intra‐ and extra‐cellular pH . However, there are many challenges to acquiring high quality phosphorus spectra, particularly the low sensitivity of the P nucleus relative to H, and the generally low concentrations of the metabolites of interest, which result in low signal‐to‐noise ratio (SNR).…”