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@ARTICLE{Debus:131500,
      author       = {C. Debus$^*$ and R. Floca$^*$ and D. Nörenberg and A.
                      Abdollahi$^*$ and M. Ingrisch},
      title        = {{I}mpact of fitting algorithms on errors of parameter
                      estimates in dynamic contrast-enhanced {MRI}.},
      journal      = {Physics in medicine and biology},
      volume       = {62},
      number       = {24},
      issn         = {1361-6560},
      address      = {Bristol},
      publisher    = {IOP Publ.},
      reportid     = {DKFZ-2017-06167},
      pages        = {9322 - 9340},
      year         = {2017},
      abstract     = {Parameter estimation in dynamic contrast-enhanced MRI (DCE
                      MRI) is usually performed by non-linear least square (NLLS)
                      fitting of a pharmacokinetic model to a measured
                      concentration-time curve. The two-compartment exchange model
                      (2CXM) describes the compartments plasma and interstitial
                      volume and their exchange in terms of plasma flow and
                      capillary permeability. The model function can be defined by
                      either a system of two coupled differential equations or a
                      closed-form analytical solution. The aim of this study was
                      to compare these two representations in terms of accuracy,
                      robustness and computation speed, depending on parameter
                      combination and temporal sampling. The impact on parameter
                      estimation errors was investigated by fitting the 2CXM to
                      simulated concentration-time curves. Parameter combinations
                      representing five tissue types were used, together with two
                      arterial input functions, a measured and a theoretical
                      population based one, to generate 4D concentration images at
                      three different temporal resolutions. Images were fitted by
                      NLLS techniques, where the sum of squared residuals was
                      calculated by either numeric integration with the
                      Runge-Kutta method or convolution. Furthermore two example
                      cases, a prostate carcinoma and a glioblastoma multiforme
                      patient, were analyzed in order to investigate the validity
                      of our findings in real patient data. The convolution
                      approach yields improved results in precision and robustness
                      of determined parameters. Precision and stability are
                      limited in curves with low blood flow. The model parameter v
                      e shows great instability and little reliability in all
                      cases. Decreased temporal resolution results in significant
                      errors for the differential equation approach in several
                      curve types. The convolution excelled in computational speed
                      by three orders of magnitude. Uncertainties in parameter
                      estimation at low temporal resolution cannot be compensated
                      by usage of the differential equations. Fitting with the
                      convolution approach is superior in computational time, with
                      better stability and accuracy at the same time.},
      cin          = {E210 / E071 / L101},
      ddc          = {570},
      cid          = {I:(DE-He78)E210-20160331 / I:(DE-He78)E071-20160331 /
                      I:(DE-He78)L101-20160331},
      pnm          = {315 - Imaging and radiooncology (POF3-315)},
      pid          = {G:(DE-HGF)POF3-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28858856},
      doi          = {10.1088/1361-6560/aa8989},
      url          = {https://inrepo02.dkfz.de/record/131500},
}