[Rtk-users] ADMMTVReconstruction

[Howard]

Hi Sebastien,

Thank you very much for your explanation. I used -d to write I0 estimations
to a file. I found there are >2000 numbers for ~600 projections. I thought
there would be one number for each projection, but obviously the output
numbers are not generated that way. Could you please describe a bit more on
the file format and how to use them? Or simply I wait for your new
mini-pipeline filter?

Regards,
-howard

On Wed, Jan 7, 2015 at 3:01 AM, Sébastien Brousmiche <
sebastien.brousmiche at uclouvain.be> wrote:

> Hi Howard,
>
>
>
> There is an input parameter with the rtki0estimation application (-d)
> which allows you to specify the output file with I0 estimates.
>
>
>
> The big file you mentioned contains the histograms of each image (65536
> values each) stored consecutively. The first value is the occurrence of
> number 0 in your image, second.. 1 and so forth up to 65535.
>
>
>
> This application is not particularly interesting to be used in a pipeline.
>
>
>
>
> Nevertheless, we are developing a mini-pipeline grouping typical
> preprocessing filters. It is under validation and I’ll let you know as soon
> as it is available for you.
>
>
>
> Best,
>
> Sébastien
>
>
>
>
>
>
>
>
>
>
>
> *From:* Rtk-users [mailto:rtk-users-bounces at public.kitware.com] *On
> Behalf Of *Howard
> *Sent:* mercredi 7 janvier 2015 00:17
> *To:* Simon Rit
> *Cc:* rtk-users at openrtk.org
> *Subject:* Re: [Rtk-users] ADMMTVReconstruction
>
>
>
> Happy New Year, Simon.
>
>
>
> Thank you for pointing to me the I0 estimate procedure.I saw an
> application rtki0estimation
>
> under  the Applications folder. Is this the tool you meant? I ran it using
> all default parameters
>
> providing with the original projection data. What I obtained was a file:
> i0est_histogram.csv.
>
> From the comments in rtki0estimation.ggo this file is the output with I0
> estimate. For 650 projections
>
> the file size is around 200MB. I used excel to open the file and found
> that the beginning two numbers
>
> 64408 and 722024 then followed by 0's. In the middle there are some
> nonzero numbers. Essentially
>
> all zeros.
>
> Since there is not much description of the application, so it is hard to
> figure out easily what I am doing.
>
> I tried to read the source code, but it might be more useful if you can
> give some hints on how to
>
> use it.
>
>
>
> Regards,
>
> -howard
>
>
>
>
>
> On Mon, Jan 5, 2015 at 1:49 AM, Simon Rit <simon.rit at creatis.insa-lyon.fr>
> wrote:
>
> Happy new year Howard,
> Normally, this calibration is handled by the flat panel. It uses an
> air projection and a dark (no beam) projection to compute the line
> integral. However, there might be fluctuations in time of these two
> projections. Some people do regular acquisitions of them to capture
> the time fluctuations. Otherwise, a constant value might be a good
> solution. Sébastien has recently implemented an automated
> determination of this constant, maybe you should have a look:
>
> http://www.openrtk.org/Doxygen/classrtk_1_1I0EstimationProjectionFilter.html
> It is already part of the mini-pipeline for ImagX / IBA projections
> processing:
>
> http://www.openrtk.org/Doxygen/classrtk_1_1ImagXRawToAttenuationImageFilter.html
> Simon
>
>
> On Fri, Jan 2, 2015 at 10:17 PM, Howard <lomahu at gmail.com> wrote:
> > 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 list
> >>>> Rtk-users at public.kitware.com
> >>>> http://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
> >
> >
> >
> > _______________________________________________
> > Rtk-users mailing list
> > Rtk-users at public.kitware.com
> > http://public.kitware.com/mailman/listinfo/rtk-users
> >
>
>
>
>
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