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Hi Howard,<br>
<br>
Thanks for the detailed feedback.<br>
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 ? <br>
<br>
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. <br>
<br>
Best regards,<br>
Cyril<br>
<br>
<div class="moz-cite-prefix">On 12/17/2014 03:49 PM, Howard wrote:<br>
</div>
<blockquote
cite="mid:CAKr9h5U31mBMUKgafGaXpjnXo3Pk6ojiHFgf9=hjKiqkgrGEJQ@mail.gmail.com"
type="cite">
<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 moz-do-not-send="true"
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
moz-do-not-send="true"
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>
<fieldset></fieldset>
<br>
</div>
</div>
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<pre cols="72">--
--
Cyril Mory, Post-doc
CREATIS
Leon Berard cancer treatment center
28 rue Laënnec
69373 Lyon cedex 08 FRANCE
Mobile: <a moz-do-not-send="true" href="tel:%2B33%206%2069%2046%2073%2079" target="_blank" value="+33669467379">+33 6 69 46 73 79</a></pre>
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<pre class="moz-signature" cols="72">--
--
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</pre>
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