Dear all,
I have a question regarding the TSNR image outputted from the afni_proc.py program. The explanation provided is the following:
By default, a temporal signal to noise (TSNR) dataset is created at
the end of the regress block. The "signal" is the all_runs dataset
(input to 3dDeconvolve), and the "noise" is the errts dataset (the
residuals from 3dDeconvolve). TSNR is computed (per voxel) as the
mean signal divided by the standard deviation of the noise.
TSNR = average(signal) / stdev(noise)
I am a little confused since the errts dataset is as far as I know the dataset we use after all nuisance signals have been regressed out of the data, so why is it referred as the "noise" dataset? From my understanding of the above, SNR is then mean(all_runs)./std(errts) . Is that correct?
Second, what would be considered a good SNR?
Thanks a lot,
George
