<div dir="ltr"><div>Hi Cyril,</div><div> </div><div>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.</div><div> </div><div>I am reporting what I found and hope these will give you information for further improvement.</div><div> </div><div>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.</div><div> </div><div>2. Similarly I ran 30 iterations using admmwavelets. Slightly better reconstruction compared with total variation.</div><div> </div><div>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.</div><div>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</div><div> </div><div>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.</div><div> </div><div>-Howard</div></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Dec 15, 2014 at 4:07 AM, Cyril Mory <span dir="ltr"><<a href="mailto:cyril.mory@creatis.insa-lyon.fr" target="_blank">cyril.mory@creatis.insa-lyon.fr</a>></span> wrote:<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div bgcolor="#FFFFFF" text="#000000">
Hello Howard,<br>
<br>
Good to hear that you're using RTK :)<br>
I'll try to answer all your questions, and give you some advice:<br>
- In general, you can expect some improvement over rtkfdk, but not a
huge one<br>
- You can find the calculations in my PhD thesis
<a href="https://tel.archives-ouvertes.fr/tel-00985728" target="_blank">https://tel.archives-ouvertes.fr/tel-00985728</a> (in English. Only the
introduction is in French)<br>
- 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<br>
- 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<br>
- 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)<br>
- 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<br>
<br>
I hope this helps<br>
<br>
Looking forward to reading you again,<br>
Cyril<div><div class="h5"><br>
<br>
<div>On 12/12/2014 06:42 PM, Howard wrote:<br>
</div>
</div></div><blockquote type="cite"><div><div class="h5">
<div dir="ltr">
<div>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. </div>
<div> </div>
<div>The parameters I used in ADMMTV were the following:</div>
<div> </div>
<div>--spacing 2,2,2 --dimension 250,100,250 --alpha 1 --beta
1000 -n 3</div>
<div> </div>
<div>while the fdk reconstruction parameters are:</div>
<div> </div>
<div>--spacing 2,2,2 --dimension 250,100,250 --pad 0.1 --hann
0.5</div>
<div> </div>
<div>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.</div>
<div> </div>
<div>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.</div>
<div> </div>
<div>Thanks,</div>
<div> </div>
<div>-howard</div>
<div> </div>
</div>
<br>
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Cyril Mory, Post-doc
CREATIS
Leon Berard cancer treatment center
28 rue Laënnec
69373 Lyon cedex 08 FRANCE
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