Quantifying in vivo MR spectra with circles

Accurate and robust quantification of in vivo magnetic resonance spectroscopy (MRS) data is essential to its application in research and medicine. The performance of existing analysis methods is problematic for in vivo studies where low signal-to-noise ratio, overlapping peaks and intense artefacts are endemic. Here, a new frequency-domain technique for MRS data analysis is introduced wherein the circular trajectories which result when spectral peaks are projected onto the complex plane, are fitted with active circle models. The use of active contour strategies naturally allows incorporation of prior knowledge as constraint energy terms. The problem of phasing spectra is eliminated, and baseline artefacts are dealt with using active contours-snakes. The stability and accuracy of the new technique, CFIT, is compared with a standard time-domain fitting tool, using simulated ³¹P data with varying amounts of noise and 98 real human chest and heart ³¹P MRS data sets. The real data were also analyzed by our standard frequency-domain absorption-mode technique. On the real data, CFIT demonstrated the least fitting failures of all methods and an accuracy similar to the latter method, with both these techniques outperforming the time-domain approach. Contrasting results from simulations argue that performance relative to Cramer-Rao Bounds may not be a suitable indicator of fitting performance with typical in vivo data such as these. We conclude that CFIT is a stable, accurate alternative to the best existing methods of fitting in vivo data.