Dear Laura, Interesting, this option is mainly here for improving the memory management. All it does is split your input stack in subsets to do the reconstruction, thus only loading a few projections in the CPU and GPU memory. This is not implemented in the backprojection because we usually keep the projections in memory during iterative reconstruction. It could be implemented in C++ but I maybe you could not try do it in Python too: instead of calling backprojection once, split your input projection stack and geometries in pieces and call backprojection several times (except maybe for some memory transfers which cannot be avoided). Such a subselection is done here (in c++): https://github.com/SimonRit/RTK/blob/master/applications/rtksubselect/rtksubselect.cxx You can open an issue if you feel it's worth implementing the option in the backprojection, it should be doable but is not straightforward. Cheers, Simon
On Fri, Sep 9, 2022 at 10:22 AM Laura Hellwege <hellw...@imt.uni-luebeck.de> wrote: > Dear all, > > > > I am using the filter “rtkCudaBackProjectionImageFilter” with > Python-Wrapping for a self-implemented iterative algorithm. Right now, this > backprojection (and therefore my algorithm) is relatively slow since it > does not use the full capacity of my GPU. > > I know that “rtkCudaFDKConeBeamReconstructionFilter” has the option > “SetProjectionSubsetSize()” that regulates how many projections are > simultaneously processed. Using this speeds up my FDK reconstructions > significantly. > > So my question is: Is something similar already implemented for the simple > backprojection? > > > > Best regards, > > Laura > > > > PS: Unfortunately, I have no background in C++ programming. Hence, to add > this option myself I probably have to rely on expertise and support from > this community. > > > _______________________________________________ > Rtk-users mailing list > Rtk-users@public.kitware.com > https://public.kitware.com/cgi-bin/mailman/listinfo/rtk-users >
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