Hi Satra It does. It does not support however out latest model that we intend to use and I think it does not directly support a multi-GPU environment.
Cheers Stam On 17 Feb 2015, at 15:29, Satrajit Ghosh <[email protected]<mailto:[email protected]>> wrote: hi stam, quick follow up here: does the current bedpostx_gpu (under 5.0.7/8) not support these flags? a previous discussion on the fsl mailing list suggested that it did. cheers, satra On Tue, Feb 17, 2015 at 10:26 AM, Stamatios Sotiropoulos <[email protected]<mailto:[email protected]>> wrote: Hi Derek In the HCP released data, there is a T1w/Diffusion directory. If you do bedpostx T1w/Diffusion -n 3 -model 2 -g —rician that should run fine. Notice that even if you run that on a big cluster, it will take a significant amount of time to finish because of the dimensions of the data. We use a version of bedpostx on a GPU cluster to run this analysis and this is what we will release, hopefully in the near future. Best Stam On 17 Feb 2015, at 15:20, Archer,Derek B <[email protected]<mailto:[email protected]>> wrote: Hi Matt and Stam, I seem to still be having some sort of issue running bedpostx. I’m using: bedpostx <subject_directory> --cnonlinear --rician --model=2 --nf=3. (The –g option is not available in my version of FSL (5.0.5). I am on a cluster environment.) So when I run this line of code, I get the following error: ** ERROR: nifti_convert_nhdr2nim: bad dim[0] ** ERROR: (nifti_image_read): cannot create nifty image from header ‘data’ ** ERROR: nifti_image_open (data): bad header info ** ERROR: failed to open file data The error seems to be with data.nii.gz, but is there error actually occurring because I am not feeding in grad_dev.nii.gz via the –g option? Do I need a newer version of FSL? Thanks, Derek Archer From: Stamatios Sotiropoulos [mailto:[email protected]] Sent: Monday, February 16, 2015 12:48 PM To: Archer,Derek B Cc: [email protected]<mailto:[email protected]> Subject: Re: [HCP-Users] Processing Diffusion MRI Data Hi Derek Please notice that model=3 is not officially released yet in FSL. Also, there should be a future HCP release in the near future including bedpostx results, with the optimal parameters. Stam On 16 Feb 2015, at 16:25, Archer,Derek B <[email protected]<mailto:[email protected]>> wrote: Matt, Thanks for the fast reply. I will use these three inputs in bedpostX. Derek From: Glasser, Matthew [mailto:[email protected]] Sent: Monday, February 16, 2015 11:24 AM To: Archer,Derek B; [email protected]<mailto:[email protected]> Subject: Re: [HCP-Users] Processing Diffusion MRI Data Why do you need to use FLIRT/FNIRT with already registered data? As for the others and aside from the flags you already mention, I think the recommendation for bedpostX will include these flags: -n 3 (for 3 fibers) --cnonlinear --rician --model=3 Not all of this may yet be available in the public release (you might need to use --model=2 for now). Peace, Matt. From: <Archer>, Derek B <[email protected]<mailto:[email protected]>> Date: Monday, February 16, 2015 at 9:16 AM To: "[email protected]<mailto:[email protected]>" <[email protected]<mailto:[email protected]>> Subject: [HCP-Users] Processing Diffusion MRI Data Hello, I am trying to analyze the preprocessed diffusion data, however, I am having some difficulties. Is there some documentation that I could look at that outlines how to use bedpostx, FLIRT/FNIRT, DTIFIT and probtrackx with this data? >From what I’ve found in the archives, I need to do the following: Bedpostx: include the –g option FLIRT/FNIRT: do I include any extra options here? DTIFIT: include the –gradnonlin option Is this all that needs to be done to have this data ready for tractography? Or are there steps that I have missed? Please let me know if there is any documentation I have failed to locate. Thanks, Derek Archer _______________________________________________ HCP-Users mailing list [email protected]<mailto:[email protected]> http://lists.humanconnectome.org/mailman/listinfo/hcp-users ________________________________ The materials in this message are private and may contain Protected Healthcare Information or other information of a sensitive nature. If you are not the intended recipient, be advised that any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited. 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