How to accelerate the Projector? about deepdrr HOT 4 CLOSED

Hi,
the most likely reason why this projector is slower than other implementation is the higher workload since it's projecting multiple materials which also requires increased memory transfer (segmentation masks, etc). I don't know how you are currently using the projector; from your statement I assume you try to use generate single images at a time. The intended use (and this is also how we benchmarked in the paper) is to generate large datasets in a single query so that memory transfer only happens once. In this case, the performance per image should be much better.
In addition to the above, the code can be adapted for online generation of images and we are working on this, unfortunately, it is not yet in a commit-able state.
Maybe Nico has some more ideas.

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Hi,
as stated by Mathias, the memory transfer is time consuming and therefore all projections from one volume should be generated in a single query. Besides this, the projector needs to perform interpolation of the density and segmentation masks for multiple materials, which results in a higher runtime compared to a classical DRR projector.
Do you measure 6.5 seconds only for the forward projection or for the complete pipeline?

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Hi Mathis and Nico,
Thanks for the reply, the measurement of 6.5 seconds is for the forward projection to generate single images at a time. The projector in the codes is modified from CONRAD and I'm not familiar with CONRAD. I'm wondering why you chose CONRAD instead of some common frameworks like ITK and plastimatch, does CONRAD have some extra functions that ITK is lack of?

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Hi,
as pointed out above the code is not currently optimized for online generation of small image batches and we have someone working on providing alternatives for this way of using it soon. We have used CONRAD extensively previously so it was an easy choice for us. You could of course incorporate other frameworks' implementations of the projection operation if you feat that this is the bottleneck; it's just a cuda kernel and related memory transform really.
I will be closing this issue for now but feel free to re-open if more questions or concerns regarding this issue arise.

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