title: Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing.
abstract: Low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression. To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation. The shared memory with fast I/O speed is also used in row-wise intensity calculation for the proposed method. Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization.
authors.affiliation: Department of Radiology, General Hospital of Shenyang Military Area Command, Shenhe District, Shenyang 110840, China.
authors.forename: Libo
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Radiology, General Hospital of Shenyang Military Area Command, Shenhe District, Shenyang 110840, China.
authors.forename: Benqiang
authors.initials: B
authors.lastname: Yang
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China ; The Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education, Nanjing 210096, China ; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France.
authors.forename: Zhikun
authors.initials: Z
authors.lastname: Zhuang
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China ; The Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education, Nanjing 210096, China ; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France.
authors.forename: Yining
authors.initials: Y
authors.lastname: Hu
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China ; The Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education, Nanjing 210096, China ; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China ; The Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education, Nanjing 210096, China ; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France.
authors.forename: Limin
authors.initials: L
authors.lastname: Luo
authors.affiliation: Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China ; The Key Laboratory of Computer Network and Information Integration, Southeast University and Ministry of Education, Nanjing 210096, China ; Centre de Recherche en Information Biomedicale Sino-Francais (LIA CRIBs), 35000 Rennes, France.
authors.forename: Huazhong
authors.initials: H
authors.lastname: Shu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D019295:Computational Biology; D006801:Humans; D015233:Models, Statistical; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data