Hi Guillaume,
Can you also tell how many iterations you have performed (main loop,
conjugate gradient, and TV) ?
If you are performing 30 iterations of the main loop, with 4 CG
iterations, then considering the size of your data I do not think
anything is wrong. Reconstructing a smaller volume will give you a large
speedup (time is almost linear with number of voxels), so I would
recommend that you try again with the smallest possible volume, i.e. the
bounding box of your patient.
Are you running the release 1.2.0 version ? If so, note that on the
master branch of the git repository, I am adding optimizations for the
4D reconstructions. It is not fully functional at the moment, but I'm
currently working 100% of my time on it. I will let you know about the
next updates.
Regards,
Cyril
On 05/31/2016 07:48 PM, [email protected] wrote:
Hi Simon,
Thanks for mailing list suggestion. My initial question was about
typical reconstruction times for rtkfourdrooster. What I tried is
summarized below:
-Volume size was 410 410 264 and could be easily reduced by a good
margin.
-The GPU is quadro M4000 with 8GB
-about 2300 256x256 projections with shifted elekta panel (so called M20)
-10 phases
-for the motion mask at the moment I just used the FOV mask for first
try.
--gamma_time 0.0001
--gamma_space 0.0001
- spacing 1 1 1
--niter 30
--cgiter 4
--tviter 10
Recon time was about 5-6 hours. I saw about 4 Gb used on the card.
The image looked nice, albeit with the TV "feel/plastic-y".
Thanks for your feedback
Guillaume
Quoting Simon Rit <[email protected]>:
Hi Guillaume,
I'm adding RTK user list to this conversation, it's better to have
these conversations on the mailing list IMO. Can you tell us what's
the volume size and the GPU?
Cyril is ROOSTER's dev, maybe he could comment on recon times.
Simon
On Tue, May 31, 2016 at 5:29 PM, <[email protected]>
wrote:
Hi Simon,
I tried the rooster recon. I used the parameters from the example
page. The results looks rather nice but it took several hours to run
(5-6). Its a big dataset of about 2000 256x256 projections.
Guillaume
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