[Neuroimaging] [DIPY] weird DKI maps

Ping-Hong Yeh pinghongyeh at gmail.com
Mon Feb 11 18:49:52 EST 2019


Hi Ariel,

 Thanks for the reply.
yeah, I went through the DWI volumes and it turned out the preprocessing
steps failed with some void volumes.

Sorry for the false alarm.

Ping

On Mon, Feb 11, 2019 at 6:29 PM Ariel Rokem <arokem at gmail.com> wrote:

> Hi Ping,
>
> Thanks for your email. I have not seen anything like this, so I can only
> speculate. Is there anything else that is unusual about this dataset? Does
> fitting DTI give you results that look reasonable? DKI is somewhat more
> sensitive to certain kinds of noise, so I would worry about a single or a
> few noisy volumes (e.g., a b0 volume that is corrupted by motion). Have you
> tried looking at every volume of this data?
>
> Cheers,
>
> Ariel
>
>
>
>
>
> On Mon, Feb 11, 2019 at 1:13 PM Ping-Hong Yeh <pinghongyeh at gmail.com>
> wrote:
>
>> Hi DIPY users,
>>
>> Would you comment what could go wrong for DKI fitting of this mean
>> kurtosis (MK) map using a three-shell DWI (b=3000,2000,1000)?
>> The values of MK map are all zero except those in the sulci and
>> ventricles.
>>
>> Here is the command I used,
>> fwhm = 1.25
>> gauss_std = fwhm / np.sqrt(8 * np.log(2))  # converting fwhm to Gaussian
>> std
>> data_smooth = np.zeros(data.shape)
>> for v in range(data.shape[-1]):
>>     data_smooth[..., v] = gaussian_filter(data[..., v], sigma=gauss_std)
>>
>>
>> dkimodel = dki.DiffusionKurtosisModel(gtab)
>> dkifit = dkimodel.fit(data_smooth, mask=mask)
>> MK = dkifit.mk(0, 3)
>> AK = dkifit.ak(0, 3)
>> RK = dkifit.rk(0, 3)
>>
>> I have been using the same scripts for other 200ish data and the DKI maps
>> were just fine.
>>
>> Thank you.
>>
>> Ping
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