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

Matthew J. Riblett riblettmj at mymail.vcu.edu
Mon Feb 16 16:25:52 CET 2015


Hi all,

I’m working with Dr. Hugo on implementing the ‘stacked 3D phase fixed image’ approach for 4D-CBCT images and I thought I’d also contribute a few points.  We have been seeing some really good results from this method, particularly when dealing with the metrics/penalties and their corresponding weightings — it seems that a combination of the SSD metric and the Bending Energy penalty work well and has the added advantage of being relatively insensitive to metric weights.

When it comes to masking, I’ve been switching back and forth between a simple cylindrical mask (to remove any artificial boundary generated by the reconstruction and discount the area outside of the FOV) and no mask at all.  The masked results are somewhat nicer, and tend to run a little quicker because we are sampling only a fraction of the image.

I’d love to hear how your results compare to ours — we have tested it with 4D-CBCT and it has faired very well, but I’m very interested to see how it plays out with 4DCT.

— Matt

__
Matthew J. Riblett
Virginia Commonwealth University
Department of Radiation Oncology
Medical Physics Graduate Program

Office:  Sanger Hall, Room B1-013
401 College Street   |  P.O. Box 980058
Richmond, Virginia 23298

VCU Email:		    riblettmj at vcu.edu <mailto:riblettmj at vcu.edu>
MCV Office Phone:	    +1.804.628.4858

> On Feb 16, 2015, at 4:33 AM, Coert Metz <coert.metz at gmail.com> wrote:
> 
> 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 <mailto: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 <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 <mailto: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 <http://www.ncbi.nlm.nih.gov/pubmed/22087338>
> and
> http://www.ncbi.nlm.nih.gov/pubmed/23799057 <http://www.ncbi.nlm.nih.gov/pubmed/23799057>
> 
> The parameter file (Par0012) available at http://elastix.bigr.nl/wiki/index.php/Par0012 <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 <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 <http://www.pnnl.gov/>
> 
> and
> 
> Department of Mechanical Engineering
> University of Washington, Seattle
> https://www.me.washington.edu <https://www.me.washington.edu/>
> 
> 
> 
> 
> 
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