title: Distributed MLEM: an iterative tomographic image reconstruction algorithm for distributed memory architectures.
abstract: The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed does not improve linearly with the number of GPUs. This is because large data transfer is required between the GPUs after each iteration, effectively reducing the parallelism. This paper proposes a novel approach to reformulate the maximum likelihood expectation maximization (MLEM) algorithm so that it can scale up to many GPU nodes with less frequent inter-node communication. While being mathematically different, the new algorithm maximizes the same convex likelihood function as MLEM, thus converges to the same solution. Experiments on a multi-GPU cluster demonstrate the effectiveness of the proposed approach.
authors.affiliation: Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. jycui@stanford.edu
authors.forename: Jingyu
authors.initials: J
authors.lastname: Cui
authors.forename: Guillem
authors.initials: G
authors.lastname: Pratx
authors.forename: Bowen
authors.initials: B
authors.lastname: Meng
authors.forename: Craig S
authors.initials: CS
authors.lastname: Levin
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*