[Rtk-users] ADMMTVReconstruction

[Howard]

Hi Cyril,

Thanks very much for your detailed and nice description on how to use the
admmtv reconstruction. I followed your suggestions and re-ran
reconstructions using admmtotalvariation and admmwavelets with cbct
projection data from a thoracic patient.

I am reporting what I found and hope these will give you information for
further improvement.

1. I repeated admmtotalvariation with 30 iterations. No improvement was
observed. As a matter of fact, the reconstructed image is getting a lot
noiser compared to that using 3 iterations. The contrast is getting worse
as well. I tried to play around with window & level in case I was fooled
but apparently more iterations gave worse results.

2. Similarly I ran 30 iterations using admmwavelets. Slightly better
reconstruction compared with total variation.

3. Then I went ahead to test if TV benefits us anything using the
tvdenoising application on the fdk-reconstructed image reconstructed
from full projection set. I found that the more iterations, the more blurry
the image became. For example, with 50 iterations the contrast on the
denoised image is very low so that the vertebrae and surrounding soft
tissue are hardly distinguishable. Changing gamma's at 0.2, 0.5, 1.0, 10
did not seem to make a difference on the image. With 5 iterations the
denoising seems to work fairly well. Again, changing gamma's didn't make a
difference.
I hope I didn't misused the totalvariationdenoising application. The
command I executed was: rtktotalvariationdenoising -i out.mha -o
out_denoising_n50_gamma05 --gamma 0.5 -n 50

In summary, tdmmwavelets seems perform better than tdmmtotalvariation but
neither gave satisfactory results. No sure what we can infer from the TV
denoising study. I could send my study to you if there is a need. Please
let me know what tests I could run. Further help on improvement is
definitely welcome and appreciated.

-Howard

On Mon, Dec 15, 2014 at 4:07 AM, Cyril Mory <cyril.mory at creatis.insa-lyon.fr
> wrote:
>
>  Hello Howard,
>
> Good to hear that you're using RTK :)
> I'll try to answer all your questions, and give you some advice:
> - In general, you can expect some improvement over rtkfdk, but not a huge
> one
> - You can find the calculations in my PhD thesis
> https://tel.archives-ouvertes.fr/tel-00985728 (in English. Only the
> introduction is in French)
> - Adjusting the parameters is, in itself, a research topic (sorry !).
> Alpha controls the amount of regularization and only that (the higher, the
> more regularization). Beta, theoretically, should only change the
> convergence speed, provided you do an infinite number of iterations (I know
> it doesn't help, sorry again !). In practice, beta is ubiquitous and
> appears everywhere in the calculations, therefore it is hard to predict
> what effect an increase/decrease of beta will give on the images. I would
> keep it as is, and play on alpha
> - 3 iterations is way too little. I typically used 30 iterations. Using
> the CUDA forward and back projectors helped a lot maintain the computation
> time manageable
> - The quality of the results depends a lot on the nature of the image you
> are trying to reconstruct. In a nutshell, the algorithm assumes that the
> image you are reconstructing has a certain form of regularity, and discards
> the potential solutions that do not have it. This assumption partly
> compensates for the lack of data. ADMM TV assumes that the image you are
> reconstructing is piecewise constant, i.e. has large uniform areas
> separated by sharp borders. If your image is a phantom, it should give good
> results. If it is a real patient, you should probably change to another
> algorithm that assumes another form of regularity in the images (try
> rtkadmmwavelets)
> - You can find out whether you typical images can benefit from TV
> regularization by reconstructing from all projections with rtkfdk, then
> applying rtktotalvariationdenoising on the reconstructed volume (try 50
> iterations and adjust the gamma parameter: high gamma means high
> regularization). If this denoising implies an unacceptable loss of quality,
> stay away from TV for these images, and try wavelets
>
> I hope this helps
>
> Looking forward to reading you again,
> Cyril
>
>
> On 12/12/2014 06:42 PM, Howard wrote:
>
>  I am testing the ADMM total variation reconstruction with sparse data
> sample. I could reconstruct but the results were not as good as expected.
> In other words, it didn't show much improvement compared to fdk
> reconstruction using the same sparse projection data.
>
> The parameters I used in ADMMTV were the following:
>
> --spacing 2,2,2 --dimension 250,100,250 --alpha 1 --beta 1000 -n 3
>
> while the fdk reconstruction parameters are:
>
> --spacing 2,2,2 --dimension 250,100,250 --pad 0.1 --hann 0.5
>
> The dimensions were chosen to include the entire anatomy. 72 projections
> were selected out of 646 projections for a 360 degree scan for both
> calculations.
>
> What parameters and how can I adjust (like alpha, beta, or iterations?) to
> improve the ADMMTV reconstruction? There is not much description of this
> application from the wiki page.
>
> Thanks,
>
> -howard
>
>
>
> _______________________________________________
> Rtk-users mailing listRtk-users at public.kitware.comhttp://public.kitware.com/mailman/listinfo/rtk-users
>
>
> --
> --
> Cyril Mory, Post-doc
> CREATIS
> Leon Berard cancer treatment center
> 28 rue Laënnec
> 69373 Lyon cedex 08 FRANCE
>
> Mobile: +33 6 69 46 73 79
>
>
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