One thought: Are you sure you are using the ā€œ3Dā€ (and not the ā€œ2Dā€) correction 
on the scanner?

--
Michael Harms, Ph.D.

-----------------------------------------------------------

Associate Professor of Psychiatry

Washington University School of Medicine

Department of Psychiatry, Box 8134

660 South Euclid Ave.                        Tel: 314-747-6173

St. Louis, MO  63110                          Email: mha...@wustl.edu

On 4/11/18, 8:31 AM, "hcp-users-boun...@humanconnectome.org on behalf of 
Kristian Loewe" <hcp-users-boun...@humanconnectome.org on behalf of 
k...@kristianloewe.com> wrote:

Hi Joo-won and Keith,

I don't think that the table has been moved. Is there any information
somewhere in the dicom header to double-check this?

I am using the coeff_AS82.grad file for the Prisma data (the second
command). The first command was what I used for the Verio data. Also,
I double-checked that I am using the uncorrected volume as input.

I am not sure if I am supposed/allowed to send screenshots of the
actual data in the subject's native space to the list. I'm going to
check that. Meanwhile, I asked our local MR team to acquire an
additional data set using a phantom. The difference between the
scanner-corrected image and the offline-corrected image is not as
striking in this case but it's visible. Based on the phantom data, I
am inclined to say that both corrections work reasonably well, even
though they are not exactly the same. I also tried the
offline-correction on some phantom EPI data and it seems to work well
as it (together with EPI distortion correction) restores the original
shape of the phantom pretty nicely.

The differences between the correction variants applied to the
subject's data are actually rather small inside the brain but become
larger towards the neck, which is to be expected as the distance to
the magnetic isocenter becomes greater in that direction.
Nevertheless, I orginally thought that the differences between
scanner- and offline-corrected images would be smaller than that.

Find attached some plots of the T1 phantom data:

T1_ND*.png     - uncorrected images
T1*.png        - scanner-corrected images
T1_ND_gdc*.png - offline-corrected images

Cheers,

Kristian


PS:
I am sending this email for the second time (apparently the attachment
was too large). I am not sure if the first email was successfully
cancelled. I apologize if you are receiving this twice now.


Quoting "Kim, Joo-won" <joo-won....@mssm.edu>:

> Hi Kristian,
>
> Have you moved table? If you moved the table, you should manually
> subtract it from qform, sform, and/or affine matrix in the nifty
> header.
>
> Best,
> Joo-won
>
> ---
> Joo-won Kim, Ph.D.
> Postdoctoral Fellow
> Translational and Molecular Imaging Institute
> Icahn School of Medicine at Mount Sinai
>
>
> From: <hcp-users-boun...@humanconnectome.org> on behalf of Keith
> Jamison <kjami...@umn.edu>
> Date: Monday, April 9, 2018 at 10:36 AM
> To: Kristian Loewe <k...@kristianloewe.com>
> Cc: HCP Users <hcp-users@humanconnectome.org>
> Subject: Re: [HCP-Users] gradient nonlinearity correction question
>
> First make sure you're using the right coefficient file, copied
> directly from the scanner.  The Prisma should have a file called
> coeff_AS82.grad, so the one you used in your *second* command above
> should be correct.
> Second is to be absolutely sure your input is the uncorrected volume (*_ND).
> If you include some matched screenshots of the uncorrected,
> offline-corrected, and scanner-corrected volumes, we can maybe help
> evaluate the difference.
>
>
> On Mon, Apr 9, 2018 at 4:09 AM, Kristian Loewe
> <k...@kristianloewe.com<mailto:k...@kristianloewe.com>> wrote:
> Thanks Keith,
>
> Cropping is turned off by default in the version of dcm2niix that
> I'm using but I re-ran the conversion with "-x n" anyway. I also
> used fslreorient2std as per your suggestion. Unfortunately, the
> result is still the same. Do you have any other ideas?
>
>
> Cheers,
>
> Kristian
>
>
> Quoting Keith Jamison <kjami...@umn.edu<mailto:kjami...@umn.edu>>:
> Some problems can arrise if the NIFTI files are unexpectedly manipulated
> prior gradient_unwarp. Two things to check:
>
> 1. dcm2nii and dcm2niix has options to perform additional processing like
> reorienting or cropping, some of which may be enabled by default. Make sure
> those are all DISABLED. (for dcm2niix add "-x n" and for dcm2nii you can
> add "-x N -r N"
> 2. We also usually use "fslreorient2std <origT1> <origT1>_new and then
> gradient_unwarp.py<https://urldefense.proofpoint.com/v2/url?u=http-3A__gradient-5Funwarp.py&d=DwQFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=C4genPYC6QtMDghAR1Km4p6emgS7QcLRMzo8A8hTJKk&m=ZMuNtM9D5WoxjxPsNEf3nsA4vd9aenKnJbd7pJY5V6o&s=0fRXneyYj_8irqp1xYKJzBXtTZP9nixP-6_oGvClUEE&e=>
>  on
> <origT1>_new
>
> -Keith
>
>
> On Fri, Apr 6, 2018 at 12:05 PM, Kristian Loewe
> <k...@kristianloewe.com<mailto:k...@kristianloewe.com>>
> wrote:
> Hi,
>
> I would like to use gradunwarp for offline gradient nonlinearity
> correction of some data acquired on our local Siemens scanners. I used
> dcm2niix to convert the dicom data to nifti format. After applying
> gradunwarp to a T1 image in nifti format (the one that originally has
> the _ND suffix), I proceeded to compare the result with the
> Siemens-corrected T1 image. I expected that they would look very
> similar but in fact they look quite different. I am wondering if this
> is to be expected to some degree because of differences in the
> correction algorithms or what else might be the reason for this. Could
> it be the case, for example, that the wrong center is being used for
> some reason?
>
> I have tried this for a T1 image acquired on a Verio and another one
> from a Prisma using
>
>
> gradient_unwarp.py<https://urldefense.proofpoint.com/v2/url?u=http-3A__gradient-5Funwarp.py&d=DwQFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=C4genPYC6QtMDghAR1Km4p6emgS7QcLRMzo8A8hTJKk&m=ZMuNtM9D5WoxjxPsNEf3nsA4vd9aenKnJbd7pJY5V6o&s=0fRXneyYj_8irqp1xYKJzBXtTZP9nixP-6_oGvClUEE&e=>
>  T1.nii.gz T1_gdc.nii.gz siemens -g
> coeff_AS097.grad
> -n
>
> and
>
>
> gradient_unwarp.py<https://urldefense.proofpoint.com/v2/url?u=http-3A__gradient-5Funwarp.py&d=DwQFaQ&c=shNJtf5dKgNcPZ6Yh64b-A&r=C4genPYC6QtMDghAR1Km4p6emgS7QcLRMzo8A8hTJKk&m=ZMuNtM9D5WoxjxPsNEf3nsA4vd9aenKnJbd7pJY5V6o&s=0fRXneyYj_8irqp1xYKJzBXtTZP9nixP-6_oGvClUEE&e=>
>  T1.nii.gz T1_gdc.nii.gz siemens -g coeff_AS82.grad
> -n
>
> respectively.
>
> I would really appreciate any help or advice you can provide.
>
> Cheers,
>
> Kristian
>
>
> Quoting Keith Jamison <kjami...@umn.edu<mailto:kjami...@umn.edu>>:
>
>> FYI, when available, you can enable it on the scanner in the
>> "Resolution->Filter" tab with the "Distortion Correction" checkbox.  It's
>> often used for structural scans like MPRAGE, where you will see two DICOM
>> folders in the output: <scanname> and <scanname>_ND.  ND means "No
>> Distortion [Correction]".. .A very confusing choice of acronym.  You can
>> then compare the online corrected (not _ND) and offline using gradunwarp.
>>
>> -Keith
>>
>>
>> On Wed, Oct 19, 2016 at 4:44 PM, Glasser, Matthew
>> <glass...@wustl.edu<mailto:glass...@wustl.edu>>
>> wrote:
>>
>>> Some do for some sequences, but because it is not uniformly applied and
>>> because they are likely not to use optimal interpolation algorithms, we
>>> prefer to do offline correction.
>>>
>>> Peace,
>>>
>>> Matt.
>>>
>>> From:
>>> <hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>>
>>>  on behalf of
>>> Antonin
> Skoch <
>>> a...@ikem.cz<mailto:a...@ikem.cz>>
>>> Date: Wednesday, October 19, 2016 at 4:27 PM
>>> To:
>>> "hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>"
>>> <hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>
>>> Subject: [HCP-Users] gradient nonlinearity correction question
>>>
>>> Dear experts,
>>>
>>> during the set-up of gradunwarp scripts, it came to my mind, why scanner
>>> vendors standardly do not perform gradient nonlinearity correction
> directly
>>> on the scanner as part of on-line image reconstruction system (i.e. ICE
> in
>>> Siemens)?
>>>
>>> Regards,
>>>
>>> Antonin Skoch
>>>
>>>
>>> _______________________________________________
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>>>
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