[Camino-users] snr estimation strategies

[Ian Malone]

Hi,

I'm looking at ways of estimating SNR from diffusion data and currently 
getting different answers from the weighted linear fit (from modelfit 
-noisemap) and estimating directly from the standard deviation of our 
multiple B0 (9 volumes). In both cases applying a FSL/BET brainmask and 
3x3x3 median filtering to average B0 and sd estimates before taking the 
ratio for SNR. The following numbers are for one scan, but having looked 
at others it seems to be representative in relative terms.
Looking at percentile derived-SNR across the brain the SNR is 
consistently higher using the weighted linear fit derived values, at 10% 
B0-estimated SNR is 13.5, wdt-SNR is 19.9, they actually diverge 
slightly, at 50% B0-SNR 23.3, wdt-SNR 34.2, at 90% B0-SNR 33.7, wdt-SNR 
52.3 (the ratio hangs around 1.5).
Masking both SNR volumes at the 10th percentile shows similar areas are 
excluded by both: brainstem and pons and around the striatum. Directly 
comparing SNR and sd maps shows that most of the difference seems to 
come from grey matter areas where the sd estimated from B0 volumes is 
higher than that from modelfit's estimation, resulting in a lower SNR 
(nearly a factor of two in these areas). White matter areas are closer 
in value.
My major reason for wanting to estimate SNR is as an input to spherical 
deconvolution for tractography. Therefore it would seem the white matter 
value is probably more relevant than the overall value, though this does 
still leave the choice of B0- or wdt-SNR. I feel the B0 sd value is the 
more secure choice, since it's a more direct estimate, but is there any 
reason the wdt-SNR might be more appropriate for this application?

Thanks for reading,
Ian