[Elastix] Groupwise nD+t B-splines for 4DCT of lungs

Coert Metz coert.metz at gmail.com
Mon Feb 16 10:33:38 CET 2015


Hi Dan,

To add to question 3:
For the lung data we always used a 4D mask, which is the concatenation of
the 3D masks for the separate time points.

Regards,
Coert

On Fri, Feb 13, 2015 at 2:01 PM, Floris Berendsen <floris at isi.uu.nl> wrote:

> Hi Dan,
>
> That is an interesting application. I was not involved in the works you
> mention, however I can give my view based on experiences with these methods
> and the elastix framework.
>
> Answer 1):
> Unfortunately, a set of 3d images must be provided as a 4D volume with its
> last dimension the image number or time index. In the current framework of
> elastix the groupwise registration is sort of hacked in, therefore it has
> these restrictions.
>
> Asnwer 2):
> I think my answer is threefold.
> 2a): I would assume that more (independent) image data can provide a
> better registration (estimate) in general. A higher SNR is a bonus as well.
> 2b): In the current groupwise registration framework the resulting
> transformation is defined to point from a floating 'spatial average' image
> domain to each time frame domain. Since you would like to model all
> deformations in the space of the TLC image you would need to chain the
> resulting transformations to each time frame with the inverse
> transformation of the TLC. This can be done with some scripting.
> As a downside of including the TLC image in the time stack you  probably
> should not use any time domain smoothness for the registration, since that
> doesn't make sense for such a hybrid stack.
> 2c): Ultimately, you might want to adapt the groupwise framework such that
> you have a the TLC image as fixed image and the time stack as moving image.
> In that way you have the TLC image as a reference domain for modelling and
> the image similarity metric is still calculated in a groupwise fashion. You
> would need to write an adapted metric and possibly transformation for that,
> but the current framework allows this, I think.
>
> Answer 3):
> Not sure about the details in the paper. I would say that the lung mask
> needs to be 4D (and possibly different for each 3d sub volume), such that
> the sampler knows what the valid samples are.
>
> Answer 4):
> External tools/labour are needed to generate these label images. Delmon et
> al. use "Automated Segmentation of a Motion Mask to Preserve Sliding Motion
> in Deformable Registration of Thoracic CT" for that. I worked on an
> approach similar to that of Delmon et al. My souce code is not yet part of
> elastix, but the method is published so far is as:
>
> Registration of organs with sliding interfaces and changing topologies
> http://spie.org/Publications/Proceedings/Paper/10.1117/12.2043447
>
> Combining sliding organs with a groupwise approach has come to my mind as
> well. I think it can be done, but requires bigger adaptations of the code
> and might be limited to certain setups. Both our slidng organ methods
> require a label image that is defined in the fixed domain. In principle
> this conflicts with the floating spatial domain, but 2c) would solve this.
>
> Best,
> Floris
>
>
>
> On Thu, Feb 12, 2015 at 9:42 PM, Einstein, Daniel R <
> Daniel.Einstein at pnnl.gov> wrote:
>
>>  Hello,
>>
>>  I am interested in applying a number of ElastiX 4D lung registration
>> approaches to 11 dynamic rat lung images that are described in:
>>
>>  http://www.ncbi.nlm.nih.gov/pubmed/22087338
>>  and
>>  http://www.ncbi.nlm.nih.gov/pubmed/23799057
>>
>>  The parameter file (Par0012) available at
>> http://elastix.bigr.nl/wiki/index.php/Par0012 seems to be what I am
>> after.
>>
>>  Question 1): do the images input to the elastic command line need to be
>> truly 4D, or can they be multiple 3D images?
>>
>>  elastix -f <dynamic nD+t image> -m <dynamic nD+t image> -p <par
>> filename> -out <output dir>
>>
>>  Question 2): We have a higher-resolution, higher SNR static TLC image
>> from the same animal upon which we would like to base a model geometry.
>> Assuming both sets of images (4D + TLC) are up/down sampled to be equal, do
>> you think there is any benefit to including the TLC image in the groupwise
>> registration?
>>
>>  Question 3): The text of the Metz et al 2011 paper mentions that "the
>> proposed method, registration was always performed on the complete 4D image
>> and using a lung mask". The command line above does not call for a mask.
>> Were different settings used in the Metz et al paper and was a single mask
>> used for all images or was a 4D mask image used?
>>
>>  Question 4): Later work with ElastiX described in Delmon et al 2013,
>> describes the use of direction dependent B-splines for relative motion
>> between the lung and the thorax (
>> http://elastix.bigr.nl/wiki/index.php/Par0016). This exercise requires a
>> label image, that appears not to be readily available. Has any attempt been
>> made to combine the methods in Par0012 and Par0016?
>>
>>  Thank you for your patience as I get my feet wet with your code.
>>
>>  Kind Regards,
>> Dan
>>
>>   ___________________________________________
>> Daniel R Einstein, PhD
>> Computational Biology & Bioinformatics
>> Pacific Northwest National Laboratory
>> www.pnnl.gov
>>
>>  and
>>
>>  Department of Mechanical Engineering
>> University of Washington, Seattle
>> https://www.me.washington.edu
>>
>>
>>
>>
>>
>>
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>>
>>
>
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