[Mristudio-users] Fwd: loading DSI dataset into DTIStudio

[susumu mori]

OK, Deepa,

I believe you did a typical DSI study. Because it would be of interest for
many DtiStudio users, allow me to share my response to you, together with
your images.

There are two important differences between DTI and DSI.
First, of course, DSI requires many more gradient orientations. In your
case, you used 257 directions. In general, 100 orientation measurement
takes more than 10 min. So, 257 orientations would take about 30 min. You
repeated it twice. So, it must take 1 hour, if I'm not mistaken.
I once asked Van Wedeen about how many orientations would be preferable. He
said, "even 300 is very sparse, if you map them on the surface of a sphere.
So, the more the better" was his reply.
On the other hand, if you use a model like spherical decomvolution, there
are papers demonstrating directions as few as 60 would give you decent
estimation of crossing fibers. I don't have an answer to it.

Second,  high-angular resolution imaging like DSI requires higher b values.
This actually have more profound effects on your experiment design and
image quality. DTI typically uses b = 700-1,000. The higher you go, the
more signal you loose, more motion artifacts you get, and more eddy current
distortion you suffer. On top of it, because each image is so noisy,
detection and correction of artifacts become more difficult. DSI requires b
= 3,000 - 30,000, which is much higher than DTI.

Some people believe DSI data naturally includes DTI data. Therefore, once
you get DSI data, you wouldn't loose anything. However, that is not the
case, because you suffer from SNR loss and more artifacts. This is why it
is difficult to adopt DSI protocols for routine research and clinical
studies.

I attached a nice paper by Derek discussing this issue.

Now, let's go back to your images. The b0 looks good. The DWIs are what you
would expect from high b-value DWIs. Your images look very similar to Fig.
3 in Derek's paper with b=2,000-3,000. So your data are fine. They don't
look like typical DTI data because I bet that the b-values you used are b =
2,000-3,000.

You have slight ghosting in the background, but it seems calibration error
for your EPI phase correction; not a major problem, I think.

So, the bottom line is, your DICOM files were correctly read. The
appearance of DWIs should be different between DSI and DTI protocols and
your images are what you expect.

Hope this will help.

Susumu

On Fri, Mar 15, 2013 at 6:31 PM, Deepa Krishnaswamy <
deepa.krishnaswamy1 at gmail.com> wrote:

> Hi,
> I have attached a power point with a few images.
> Yes I did read the data as Mosaic. There are 515 gradient directions in
> total, which is comprised of repeated 257 directions plus 1 b0.
> I do know that DTIStudio does not support DSI data analysis, but isn't it
> still possible to fit the DTI model to DSI data?
> I also just wanted to run through the LDDMM pipeline to get our data
> registered to the same space as the JHU MNI WM atlas, and then use our own
> tractography algorithms.
> I hope this is possible, thanks.
>
> Deepa
>
> On Thu, Mar 14, 2013 at 10:20 AM, susumu mori <susumu at mri.jhu.edu> wrote:
>
>> Hi Deepa,
>>
>> Did you read the data as Mosaic?
>> Please send me the actual images in PowerPoint.
>>
>> If you are using non off-the-shelf image protocol, it is possible that
>> DtiStudio can't read the user-defined data format.
>> If you get a custom data acquisition method from somebody, I encourage
>> you to talk with them about how to read the data. DtiStudio doesn't support
>> DSI data analysis anyway.
>>
>> Susumu
>>
>> On Tue, Mar 12, 2013 at 9:50 PM, Deepa Krishnaswamy <
>> deepa.krishnaswamy1 at gmail.com> wrote:
>>
>>> Hi,
>>> I have a DSI dataset in MOSAIC format, along with the corresponding b
>>> btable. I noticed that after loading in the data, some of it appears
>>> extremely blurry/shifted. I know that the data actually does not look like
>>> this, as I have used other programs to view the raw data. Has anybody else
>>> encountered this problem?
>>> Thanks,
>>>
>>> Deepa
>>>
>>> _______________________________________________
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>>
>
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