Hi MRtrix experts,
We are beginners working with HARDI data. For our study, we acquired two HARDI scans for each subject in a session. These HARDI scans have 31 and 33 unique directions. If combined we can get a 64 direction HARDI. Is there a specific way to combine these scans?
Thanks!
Jack
Sure, the command you’re after is mrcat. You can use it on the raw DICOM or the converted .mif files, but it’s trickier on NIfTI images since they don’t contain the DW encoding information. Make sure you add the -axis 3 option to force concatenation along the 4th (volume) dimension, like this:
$ mrcat DWI1/ DWI2/ -axis 3 dwi_combined.mif
The above assumes DICOM inputs - substitute with DWI1.mif & DWI2.mif (or whatever the appropriate filenames are) if running from the .mif images.
Hello MRtrix’ers, I’m reviving this old thread to ask a related question. I am running the fixel pipeline on some new data. Like the OP, I have a dataset with multiple scans to be combined. However, each scan is a pair of reverse PE acquisitions, like so:
subjid_91directions_RL.mif
subjid_91directions_LR.mif
subjid_90directions_RL.mif
subjid_90directions_LR.mif
My understanding is that the reverse PE pairs are combined in the dwifslpreproc stage. If I want to combine all of these data together, what is the best order of operations? Should I use mrcat to combine the respective PE directions before beginning any other fixel preprocessing, i.e.:
mrcat subjid_91directions_RL.mif subjid_90directions_RL.mif -axis 3 subjid_181directions_RL.mif
mrcat subjid_91directions_LR.mif subjid_90directions_LR.mif -axis 3 subjid_181directions_LR.mif
…and then later feed those concatenated denoised/degibbs’ed mifs into dwifslpreproc? Or, is there another recommendation?
Hi, since I never received an answer, I thought I might as well try one more time. Do any experts care to weigh in on my proposed approach for combining 2 pairs of reverse PE scans?
Hi Rachel,
Sorry for the lack of response – it’s always hard to find the headspace to think and provide adequate answers. Hopefully this will be useful anyway.
Since that original post 5 years ago, @rsmith has introduced the dwicat command / script, which is expressly designed for this use case, taking into account any differences in scaling that might be present between the acquisitions due to e.g. scanner recalibration. It’ll accept all four of your scans in the same command if that’s what you want to do – more on that below.
Regarding the subsequent stages in the pipeline, the Gibbs ringing removal operates on a slice-wise basis and won’t be affected at all however your data are combined.
However, the denoising operates differently, given that is looks for redundancy in the entire signal series over neighbouring voxels. There have been a few threads looking at whether it’s best (or even just OK) to concatenate different PE directions prior to denoising, or whether it’s best to denoise each series separately (e.g. this thread), though I can’t find any that address the case where the full DW series is acquired for each PE direction (if anyone can find such posts, please link to them here!). Personally, I reckon I would concatenate the lot and denoise that, but you may want to double-check what difference that really makes (e.g. by comparing the RMS differences between the full series pre & post denoising, in one case with each PE direction denoised separately then combined, in the other with all PE directions combined prior to denoising). If you go to that trouble, I (and no doubt many others) would be very interested to hear about your findings… 
The last stage would naturally be dwifslpreproc, and here the question of the order in which you concatenate your data becomes important. Your acquisition fits into the -rep_all scenario (described in this section of the documentation for dwifslpreproc – look under the heading “All DWI directions & b-values are acquired twice, with the phase encoding direction of the second acquisition protocol being reversed with respect to the first”). This option assumes that your input DWI data consists of two repeats, with the first half acquired with one PE direction, the second half with the opposite PE direction. As long as your data have been concatenated accordingly, this should work out of the box.
In summary, I reckon you could process your data using a pipeline like this:
dwicat subjid_91directions_RL.mif subjid_90directions_RL.mif subjid_91directions_LR.mif subjid_90directions_LR.mif all_dwi.mif
dwidenoise all_dwi.mif all_dwi_dn.mif -noise noise_map.mif
mrdegibbs all_dwi_dn.mif all_dwi_dn_dg.mif
dwifslpreproc all_dwi_dn_dg.mif dwi_out.mif -rpe_all -pe_dir rl -readout_time 0.66
You may wish to edit the readout time to match your acquisition, though I don’t think this will make any difference to the outcome in your case.
This is obviously based on my gut feeling that it’s OK to denoise the full concatenated set, so feel free to experiment and amend as required!
All the best,
Donald.
Donald, so nice to hear from you, I really appreciate you taking the time to provide this detailed reply. Luckily, summer is a nice time to have an excuse to take a little break from data processing. I will give your suggested workflow a shot and let you know if I end up playing around with the order of denoising/concatenating. (This is a project I’m working on with a mentee so I could always use it as a training opportunity 
) Thanks again!
Vive la MRtrix3!
Rachael
Hi Donald,
I am working on preprocessing HCP Psychosis data which consists of 99AP/PA and 98AP/PA. You said that one should compare the RMS differences between the full series pre and post denoising, but I am not understanding on how to do this.
I (1) denoised all 4 images separately, then combined them using dwicat and (2) I combined all 4 images then denoised.
I then created a RMS for both using
mrmath input rms output
How do I then compare the RMS images to decide which order I should concatenate?
Thank you,
Erika