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Rapid Rabbit: Highly Optimized GPU Accelerated Cone-Beam CT Reconstruction

Eric Papenhausen, Klaus Mueller
Visual Analytics and Imaging Lab, Computer Science Department, Stony Brook University, Stony Brook, NY 11777 USA
IEEE Medical Imaging Conference, 2013

@article{papenhausen2013rapid,

   title={Rapid Rabbit: Highly Optimized GPU Accelerated Cone-Beam CT Reconstruction},

   author={Papenhausen, Eric and Mueller, Klaus},

   year={2013}

}

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Graphical processing units (GPUs) have become widely adopted in the medical imaging community. The parallel SIMD nature of GPUs maps perfectly to many reconstruction algorithms. Because of this, it is relatively straightforward to parallelize common reconstruction algorithms (e.g. FDK backprojection). This means that significant performance improvements must come from careful memory optimizations, exploiting ASICs and a few other tricks to boost instruction throughput. We present optimizations that build off of previous work to optimize a GPU accelerated FDK backprojection implementation using the RabbitCT dataset.
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