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Hi Howard,<br>
<br>
I've taken a look at your data.<br>
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 : <br>
rtktotalvariationdenoising -i out.mha -g 0.001 -o
tvdenoised/gamma0.001.mha -n 100<br>
<br>
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. <br>
<br>
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. <br>
<br>
Best regards,<br>
Cyril<br>
<br>
<div class="moz-cite-prefix">On 12/17/2014 05:02 PM, Howard wrote:<br>
</div>
<blockquote
cite="mid:CAKr9h5XWf4fdf8TpOVf_1TTeauAmY6qA8Mz_OsUeRpEUivsRVQ@mail.gmail.com"
type="cite">
<div dir="ltr">
<div>Hi Cyril,</div>
<div> </div>
<div>I've sent you two files via <a moz-do-not-send="true"
href="http://wetransfer.com">wetransfer.com</a>: 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.</div>
<div> </div>
<div>Thanks very much for looking into this.</div>
<div> </div>
<div>-Howard</div>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Wed, Dec 17, 2014 at 10:19 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"> 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
<div>
<div class="h5"><br>
<br>
<div>On 12/17/2014 03:49 PM, Howard wrote:<br>
</div>
<blockquote 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:0px 0px 0px
0.8ex;padding-left:1ex;border-left-color:rgb(204,204,204);border-left-width:1px;border-left-style:solid">
<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><br>
<br>
<div>On 12/12/2014 06:42 PM, Howard
wrote:<br>
</div>
</div>
</div>
<blockquote type="cite">
<div>
<div>
<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 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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