Debasis Mitra, Hui Pan, Fares Alhassen, Youngho Seo
{"title":"Parallelization of Iterative Reconstruction Algorithms in Multiple Modalities.","authors":"Debasis Mitra, Hui Pan, Fares Alhassen, Youngho Seo","doi":"10.1109/NSSMIC.2014.7430944","DOIUrl":null,"url":null,"abstract":"<p><p>In this work we have parallelized the Maximum Likelihood Expectation-Maximization (MLEM) and Ordered Subset Expectation Maximization (OSEM) algorithms for improving efficiency of reconstructions of multiple pinholes SPECT, and cone-bean CT data. We implemented the parallelized versions of the algorithms on a General Purpose Graphic Processing Unit (GPGPU): 448 cores of a NVIDIA Tesla M2070 GPU with 6GB RAM per thread of computing. We compared their run times against those from the corresponding CPU implementations running on 8 cores CPU of an AMD Opteron 6128 with 32 GB RAM. We have further shown how an optimization of thread balancing can accelerate the speed of the GPU implementation.</p>","PeriodicalId":73298,"journal":{"name":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","volume":"2014 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/NSSMIC.2014.7430944","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Nuclear Science Symposium conference record. Nuclear Science Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSMIC.2014.7430944","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
In this work we have parallelized the Maximum Likelihood Expectation-Maximization (MLEM) and Ordered Subset Expectation Maximization (OSEM) algorithms for improving efficiency of reconstructions of multiple pinholes SPECT, and cone-bean CT data. We implemented the parallelized versions of the algorithms on a General Purpose Graphic Processing Unit (GPGPU): 448 cores of a NVIDIA Tesla M2070 GPU with 6GB RAM per thread of computing. We compared their run times against those from the corresponding CPU implementations running on 8 cores CPU of an AMD Opteron 6128 with 32 GB RAM. We have further shown how an optimization of thread balancing can accelerate the speed of the GPU implementation.