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@ARTICLE{Heuer:130791,
      author       = {T. Heußer$^*$ and C. Rank$^*$ and Y. Berker$^*$ and M.
                      Freitag$^*$ and M. Kachelriess$^*$},
      title        = {{MLAA}-based attenuation correction of flexible hardware
                      components in hybrid {PET}/{MR} imaging.},
      journal      = {EJNMMI Physics},
      volume       = {4},
      number       = {1},
      issn         = {2197-7364},
      address      = {Berlin},
      publisher    = {Springer Open},
      reportid     = {DKFZ-2017-05869},
      pages        = {12},
      year         = {2017},
      abstract     = {Accurate PET quantification demands attenuation correction
                      (AC) for both patient and hardware attenuation of the 511
                      keV annihilation photons. In hybrid PET/MR imaging, AC for
                      stationary hardware components such as patient table and MR
                      head coil is straightforward, employing CT-derived
                      attenuation templates. AC for flexible hardware components
                      such as MR-safe headphones and MR radiofrequency (RF)
                      surface coils is more challenging. Registration-based
                      approaches, aligning CT-based attenuation templates with the
                      current patient position, have been proposed but are not
                      used in clinical routine. Ignoring headphone or RF coil
                      attenuation has been shown to result in regional activity
                      underestimation values of up to $18\%.$ We propose to employ
                      the maximum-likelihood reconstruction of attenuation and
                      activity (MLAA) algorithm to estimate the attenuation of
                      flexible hardware components. Starting with an initial
                      attenuation map not including flexible hardware components,
                      the attenuation update of MLAA is applied outside the body
                      outline only, allowing to estimate hardware attenuation
                      without modifying the patient attenuation map. Appropriate
                      prior expectations on the attenuation coefficients are
                      incorporated into MLAA. The proposed method is investigated
                      for non-TOF PET phantom and (18)F-FDG patient data acquired
                      with a clinical PET/MR device, using headphones or RF
                      surface coils as flexible hardware components.Although MLAA
                      cannot recover the exact physical shape of the hardware
                      attenuation maps, the overall attenuation of the hardware
                      components is accurately estimated. Therefore, the proposed
                      algorithm significantly improves PET quantification. Using
                      the phantom data, local activity underestimation when
                      neglecting hardware attenuation was reduced from up to
                      $25\%$ to less than $3\%$ under- or overestimation as
                      compared to reference scans without hardware present or to
                      CT-derived AC. For the patient data, we found an average
                      activity underestimation of $7.9\%$ evaluated in the full
                      brain and of $6.1\%$ for the abdominal region comparing the
                      uncorrected case with MLAA.MLAA is able to provide accurate
                      estimations of the attenuation of flexible hardware
                      components and can therefore be used to significantly
                      improve PET quantification. The proposed approach can be
                      readily incorporated into clinical workflow.},
      cin          = {E020 / E025 / E010},
      ddc          = {530},
      cid          = {I:(DE-He78)E020-20160331 / I:(DE-He78)E025-20160331 /
                      I:(DE-He78)E010-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28251575},
      pmc          = {pmc:PMC5332322},
      doi          = {10.1186/s40658-017-0177-4},
      url          = {https://inrepo02.dkfz.de/record/130791},
}