[Rtk-users] ADMMTVReconstruction

[Howard]

Happy New Year, Cyril.

I realized that our projection data is having some issues with air
correction. We checked our calibration and it appeared fine. Do you know by
any chance whether there is a quick way of correcting that? I searched
around and found people used a constant air norm image.

Thanks very much,
-howard

On Thu, Dec 18, 2014 at 5:13 AM, Cyril Mory <cyril.mory at creatis.insa-lyon.fr
> wrote:

>  Hi Howard,
>
> I've taken a look at your data.
> You can apply tv denoising on the out.mha volume and obtain a
> significantly lower level of noise without blurring structures by using the
> following command :
> rtktotalvariationdenoising -i out.mha -g 0.001 -o
> tvdenoised/gamma0.001.mha -n 100
>
> I was unable to obtain good results with iterative reconstruction from the
> projection data you sent, though. I think the main reason for this is that
> your projections have much-higher-than-zero attenuation in air. Your
> calculation of i0 when converting from intensity to attenuation is probably
> not good enough. Try to correct for this effect first. Then you can start
> performing SART and Conjugate Gradient reconstructions on your data, and
> once you get these right, play with ADMM.
>
> You might need to remove the table from the projections to be able to
> restrict the reconstruction volume strictly to the patient, and speed up
> the computations. We can provide help for that too.
>
> Best regards,
> Cyril
>
>
> On 12/17/2014 05:02 PM, Howard wrote:
>
>  Hi Cyril,
>
> I've sent you two files via wetransfer.com: one is the sparse projection
> set with geometry file and the other is the fdk reconstructed image based
> on full projection set. Please let me know if you have trouble receiving
> them.
>
> Thanks very much for looking into this.
>
> -Howard
>
> On Wed, Dec 17, 2014 at 10:19 AM, Cyril Mory <
> cyril.mory at creatis.insa-lyon.fr> wrote:
>>
>>  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> 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
>>>
>>>
>> --
>> --
>> 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
>>
>>
> --
> --
> 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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