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1) Subject motion (even after eddy current correction) is certainly highly correlated with poorer bedpostx reconstructions (fewer subsidiary fibers, more uncertainty in fiber orientations), so it would make sense that it would negatively influence FA estimates. 2) If the problem is severe you probably should exclude the subject. 3) SNR scales linearly with magnet strength, so you would expect lower SNR for a given number of DWIs acquired at 1.5T vs 3T, all other things being equal. 4) This is a more difficult question: parallel imaging itself tends to decrease SNR a bit; however, it also allows you to decrease the TE, which increases the SNR and reduces the distortions. The same is true of partial fourier. 5) One tends to get worse SNR with higher bvalues, but better contrast to noise ratios (CNR). For FA, I think it has been established that around b=1000 is optimal (SNR vs CNR), but much higher bvalues are optimal for estimating crossing fibers. The exact optimum bvalue will also depend on your hardware, sequence, and imaging resolution, because all of these affect the SNR term. 6) Averaging multiple scans will increase SNR and reduce noise, so long as you properly corregister them and remove subject motion within the timeseries. 7) SNR varies with the number of DWIs acquired, so as long as you make up for fewer directions with more averages, the SNR should be the same. That being said, your estimates of fiber orientations (and especially crossing fibers) will be worse with fewer directions, so it is always better to acquire more directions (unless you want two averages for something like phase up/phase down distortion correction). 8) I'm probably the least clear on what this does to SNR. It is really bad for tractography because it allows for the creation of intermediate orientation fibers that can cause inaccuracies, but I don't know how it affects the accuracy of FA values. Other issues you have not considered: 9) SNR will vary depending on the coil you used (more channels are better), and the gradient set in the magnet (stronger gradients allow a lower TE and thus higher SNR). 10) SNR will vary depending on if you use a dual spin echo sequence vs a single spin echo, again because you can use a lower TE with a single spin echo (but eddy currents will be worse). 11) Imaging resolution has a big impact on SNR, of course. 12) Higher imaging bandwidth generally results in worse SNR but less distortions. Peace, Matt. _____ From: FSL - FMRIB's Software Library [mailto:[log in to unmask]] On Behalf Of Dianne Patterson Sent: Wednesday, April 21, 2010 5:17 PM To: [log in to unmask] Subject: [FSL] What constitutes "noise" in diffusion images? Dear Group, Carlo Pierpaoli points out that noise in diffusion images can bias FA. In fact, he suggests that FA may actually increase in the presence of noise. I am trying to understand what factors might contribute to noise, and I would appreciate any feedback that could further my understanding: Possible Sources of Noise: 1) Movement of the subject (this seems like the most obvious) 2) Problems with the scanner hardware (this one also seems obvious) More tenuous: 3) Lower Tesla strength? (e.g., 1.5T vs 3.0T) 4) the use (or non-use) of parallel imaging (where parallel imaging results in less noise, right?) 5) b-value choices for diffusion weighted images (smaller values...(e.g. 700) have less noise than larger values (e.g. 1500)...? 6) Averaging multiple runs (should reduce noise, right?) Really tenuous: 7) Fewer directions??? 6 vs 30 directions...is there more "noise" when you gather only 6 directions, all else being equal? 8) upscanning the voxels (e.g, 96x96 matrix saved out as 256x256)...does upscanning contribute to noise? Thanks in advance for your insights, -Dianne -- Dianne Patterson, Ph.D. [log in to unmask] University of Arizona SLHS 328 621-5105