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@INPROCEEDINGS{Xu:908204,
      author       = {Xu, Hancong and Scheins, Jurgen and Lerche, Christoph and
                      Shah, N. J.},
      title        = {{RES}ampling between {P}rojection {S}p{ACE}s ({RESPACE})
                      using {B}ayse’ {T}heorem},
      reportid     = {FZJ-2022-02455},
      year         = {2018},
      abstract     = {Abstract:In order to simplify the Point-Spread-Function
                      (PSF) reconstruction framework, resolution modelling can be
                      decoupled from the iterative reconstruction process by an
                      additional data resampling step previous to reconstruction.
                      We call the proposed algorithm RESampling between Projection
                      spACEs (RESPACE). In this abstract, RESPACE is applied to
                      resample the simulated projection data to 2D Generic
                      Cylinder Model (GCM) projection data structure, which will
                      be used for reconstruction afterwards. Theoretically, the
                      proposed algorithm merges pre-calculated detection
                      probability information and prior information into the
                      resampled projection data by applying Bayes' Theorem. In
                      contrast to conventional projection data handling, RESPACE
                      can make the iterative reconstruction isolated from any
                      detection model or PSF modelling, ensuring the closed
                      structure of normal non-PSF iterative algorithms. In this
                      study, we implemented a 2D-PET simulation Monte Carlo
                      framework, which has the same geometrical property as
                      Siemens BrainPET transverse structure. Conventional MLEM,
                      MLEM-PSF (both image space and projection space with
                      shift-invariant kernel) and RESPACE are implemented and
                      investigated. As figures of merit, Bias-Resolution curves
                      demonstrate that RESPACE could achieve similar resolution
                      and even better bias suppression performance as the PSF
                      method with a shift-invariant kernel. Moreover, no
                      significant visual difference is observed between images
                      from PSF and RESPACE reconstruction. These results
                      demonstrate that RESAPCE offers equivalent performance as
                      the shift-invariant PSF method and this approach is an
                      alternative resolution modelling method independent from the
                      iterative reconstruction algorithm.},
      month         = {Nov},
      date          = {2018-11-10},
      organization  = {2018 IEEE Nuclear Science Symposium
                       and Medical Imaging Conference
                       (NSS/MIC), Sydney (Australia), 10 Nov
                       2018 - 17 Nov 2018},
      cin          = {INM-4 / INM-11 / JARA-BRAIN},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      I:(DE-Juel1)VDB1046},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)1},
      doi          = {10.1109/NSSMIC.2018.8824431},
      url          = {https://juser.fz-juelich.de/record/908204},
}