Abstract
(1)H MRSI of the prostate reveals relative metabolite levels that vary according to the presence or absence of tumour, providing a sensitive method for the identification of patients with cancer. Current interpretations of prostate data rely on quantification algorithms that fit model metabolite resonances to individual voxel spectra and calculate relative levels of metabolites, such as choline, creatine, citrate and polyamines. Statistical pattern recognition techniques can potentially improve the detection of prostate cancer, but these analyses are hampered by artefacts and sources of noise in the data, such as variations in phase and frequency of resonances. Phase and frequency variations may arise as a result of spatial field gradients or local physiological conditions affecting the frequency of resonances, in particular those of citrate. Thus, there are unique challenges in developing a peak alignment algorithm for these data. We have developed a frequency and phase correction algorithm for automatic alignment of the resonances in prostate MRSI spectra. We demonstrate, with a simulated dataset, that alignment can be achieved to a phase standard deviation of 0.095 rad and a frequency standard deviation of 0.68 Hz for the citrate resonances. Three parameters were used to assess the improvement in peak alignment in the MRSI data of five patients: the percentage of variance in all MRSI spectra explained by their first principal component; the signal-to-noise ratio of a spectrum formed by taking the median value of the entire set at each spectral point; and the mean cross-correlation between all pairs of spectra. These parameters showed a greater similarity between spectra in all five datasets and the simulated data, demonstrating improved alignment for phase and frequency in these spectra. This peak alignment program is expected to improve pattern recognition significantly, enabling accurate detection and localisation of prostate cancer with MRSI.
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