title: Technical note: RabbitCT--an open platform for benchmarking 3D cone-beam reconstruction algorithms.
abstract: OBJECTIVE
  Fast 3D cone beam reconstruction is mandatory for many clinical workflows. For that reason, researchers and industry work hard on hardware-optimized 3D reconstruction. Backprojection is a major component of many reconstruction algorithms that require a projection of each voxel onto the projection data, including data interpolation, before updating the voxel value. This step is the bottleneck of most reconstruction algorithms and the focus of optimization in recent publications. A crucial limitation, however, of these publications is that the presented results are not comparable to each other. This is mainly due to variations in data acquisitions, preprocessing, and chosen geometries and the lack of a common publicly available test dataset. The authors provide such a standardized dataset that allows for substantial comparison of hardware accelerated backprojection methods.
  
  
  METHODS
  They developed an open platform RabbitCT (www.rabbitCT.com) for worldwide comparison in backprojection performance and ranking on different architectures using a specific high resolution C-arm CT dataset of a rabbit. This includes a sophisticated benchmark interface, a prototype implementation in C++, and image quality measures.
  
  
  RESULTS
  At the time of writing, six backprojection implementations are already listed on the website. Optimizations include multithreading using Intel threading building blocks and OpenMP, vectorization using SSE, and computation on the GPU using CUDA 2.0.
  
  
  CONCLUSIONS
  There is a need for objectively comparing backprojection implementations for reconstruction algorithms. RabbitCT aims to provide a solution to this problem by offering an open platform with fair chances for all participants. The authors are looking forward to a growing community and await feedback regarding future evaluations of novel software- and hardware-based acceleration schemes.
authors.affiliation: Department of Computer Science, Chair of Pattern Recognition, Friedrich-Alexander University Erlangen-Nuremberg, Martensstrasse 3, 91058 Erlangen, Germany. christopher.rohkohl@informatik.uni-erlangen.de
authors.forename: C
authors.initials: C
authors.lastname: Rohkohl
authors.forename: B
authors.initials: B
authors.lastname: Keck
authors.forename: H G
authors.initials: HG
authors.lastname: Hofmann
authors.forename: J
authors.initials: J
authors.lastname: Hornegger
mesh_terms: D000465:Algorithms; D000818:Animals; D019985:Benchmarking; D016208:Databases, Factual; D005069:Evaluation Studies as Topic; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000295:instrumentation / Q000379:methods*; D020407:Internet; D011817:Rabbits; D012984:Software; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000295:instrumentation / Q000379:methods*