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@ARTICLE{Xu:863488,
      author       = {Xu, Hancong and Lenz, Mirjam and Caldeira, Liliana and Bo,
                      Ma and Pietrzyk, Uwe and Lerche, Christoph W and Shah, Nadim
                      Jon and Scheins, Juergen J},
      title        = {{R}esolution modeling in projection space using a
                      factorized multi-block detector response function for {PET}
                      image reconstruction},
      journal      = {Physics in medicine and biology},
      volume       = {64},
      number       = {14},
      issn         = {1361-6560},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {FZJ-2019-03542},
      pages        = {145012},
      year         = {2019},
      abstract     = {Positron emission tomography (PET) images usually suffer
                      from limited resolution and statistical uncertainties.
                      However, a technique known as resolution modeling (RM) can
                      be used to improve image quality by accurately modeling the
                      system's detection process within the iterative
                      reconstruction. In this study, we present an accurate RM
                      method in projection space based on a simulated multi-block
                      detector response function (DRF) and evaluate it on the
                      Siemens hybrid MR-BrainPET system. The DRF is obtained using
                      GATE simulations that consider nearly all the possible
                      annihilation photons from the field-of-view (FOV).
                      Intrinsically, the multi-block DRF allows the block
                      crosstalk to be modeled. The RM blurring kernel is further
                      generated by factorizing the blurring matrix of one
                      line-of-response (LOR) into two independent detector
                      responses, which can then be addressed with the DRF. Such a
                      kernel is shift-variant in 4D projection space without any
                      distance or angle compression, and is integrated into the
                      image reconstruction for the BrainPET insert with single
                      instruction multiple data (SIMD) and multi-thread support.
                      Evaluation of simulations and measured data demonstrate that
                      the reconstruction with RM yields significantly improved
                      resolutions and reduced mean squared error (MSE) values at
                      different locations of the FOV, compared with reconstruction
                      without RM. Furthermore, the shift-variant RM kernel models
                      the varying blurring intensity for different LORs due to the
                      depth-of-interaction (DOI) dependencies, thus avoiding
                      severe edge artifacts in the images. Additionally, compared
                      to RM in single-block mode, the multi-block mode shows
                      significantly improved resolution and edge recovery at
                      locations beyond 10 cm from the center of BrainPET insert in
                      the transverse plane. However, the differences have been
                      observed to be low for patient data between single-block and
                      multi-block mode RM, due to the brain size and location as
                      well as the geometry of the BrainPET insert. In conclusion,
                      the RM method proposed in this study can yield better
                      reconstructed images in terms of resolution and MSE value,
                      compared to conventional reconstruction without RM},
      cin          = {INM-4 / INM-11 / JARA-BRAIN / JARA-HPC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)INM-4-20090406 / I:(DE-Juel1)INM-11-20170113 /
                      $I:(DE-82)080010_20140620$ / $I:(DE-82)080012_20140620$},
      pnm          = {573 - Neuroimaging (POF3-573) / Modelling Realistic
                      Distributions for High-Resolution Positron Emission
                      Tomography (PET) $(jinm40_20160501)$},
      pid          = {G:(DE-HGF)POF3-573 / $G:(DE-Juel1)jinm40_20160501$},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31158824},
      UT           = {WOS:000475775200005},
      doi          = {10.1088/1361-6560/ab266b},
      url          = {https://juser.fz-juelich.de/record/863488},
}