[Rtk-users] ADMMTVReconstruction

Cyril Mory cyril.mory at creatis.insa-lyon.fr
Wed Dec 17 10:19:05 EST 2014


Hi Howard,

Thanks for the detailed feedback.
The image getting blurry is typically due to a too high gamma. Depending 
on you data, gamma can have to be set to a very small value (I use 0.007 
in some reconstructions on clinical data). Can you send over your volume 
reconstructed from full projection data, and I'll have a quick look ?

There is a lot of instinct in the setting of the parameters. With time, 
one gets used to finding a correct set of parameters without really 
knowing how. I can also try to reconstruct from your cbct data if you 
send me the projections and the geometry.

Best regards,
Cyril

On 12/17/2014 03:49 PM, Howard wrote:
> 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 
> <mailto: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
>>
>>
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>
>     -- 
>     --
>     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  <tel:%2B33%206%2069%2046%2073%2079>
>

-- 
--
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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