[Rtk-users] ADMMTVReconstruction
Cyril Mory
cyril.mory at creatis.insa-lyon.fr
Wed Dec 17 16:19:05 CET 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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