Robust Spectral Registration Osprey Gannet

Hello, everyone! I am currently a newcomer to MRS and I am in the process of utilizing Osprey and LCModel to analyze my data. Now I met some problems about registration method.

  1. I find it perplexing that I have four sets of data originating from the same source, all sharing identical parameters. (The only possible source of differentiation could be slight head movement by the subjects, although upon examination of anatomical images, the shifts appear too small to account for the discrepancies.) Strangely, the third group exhibits issues resembling dispersion curve in the position of Cho and Cr. I’ve applied Robust Spectral Registration in Osprey, but the third group behaves differently from the others. I’m unsure if there’s an issue with the third dataset or if the alignment method is unsuitable. (figure 1)
  2. This issue has prompted me to recall a similar problem with the Robust Spectral Registration in Gannet. In response, I switched to Spectral Registration in time domain, which resolved the issue, resulting in uniform data across all four groups.
  • My question is, when should I use Robust Spectral Registration versus Spectral Registration in time domain? Despite reviewing related papers, I’m still uncertain about the specific circumstances under which each method is most appropriate.(figure 2)

Hi there,

We’re well aware that there is no single algorithm that beats all the others in all scenarios. I think it’s entirely adequate at this point (without a single perfect quantitative criterion to automatically determine a ‘winner’) to use different algorithms and pick the best one on a case-by-case basis.

I hope this is a helpful response, even though it might not be the ideal answer you might want to hear!


So, it seems like I’ll need to explore and identify the most suitable algorithm for processing my data on a case-by-case basis. Thank you for your insights!


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