title: Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI.
abstract: OBJECTIVE
  One of the main challenges in quantitative dynamic contrast-enhanced (DCE) MRI is estimation of the arterial input function (AIF). Usually, the signal from a single artery (ignoring contrast dispersion, partial volume effects and flow artifacts) or a population average of such signals (also ignoring variability between patients) is used.
  
  
  METHODS
  Multi-channel blind deconvolution is an alternative approach avoiding most of these problems. The AIF is estimated directly from the measured tracer concentration curves in several tissues. This contribution extends the published methods of multi-channel blind deconvolution by applying a more realistic model of the impulse residue function, the distributed capillary adiabatic tissue homogeneity model (DCATH). In addition, an alternative AIF model is used and several AIF-scaling methods are tested.
  
  
  RESULTS
  The proposed method is evaluated on synthetic data with respect to the number of tissue regions and to the signal-to-noise ratio. Evaluation on clinical data (renal cell carcinoma patients before and after the beginning of the treatment) gave consistent results. An initial evaluation on clinical data indicates more reliable and less noise sensitive perfusion parameter estimates.
  
  
  CONCLUSIONS
  Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.
authors: Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic.|Institute of Scientific Instruments of the Academy of Sciences of the Czech Republic, Brno, Czech Republic.
authors: Jiří
authors: J
authors: Kratochvíla
authors: Institute of Scientific Instruments of the Academy of Sciences of the Czech Republic, Brno, Czech Republic.
authors: Radovan
authors: R
authors: Jiřík
authors: Department of Biomedical Engineering, Brno University of Technology, Brno, Czech Republic.|Institute of Information Technology and Automation of the Academy of Sciences of the Czech Republic, Praha, Czech Republic.
authors: Michal
authors: M
authors: Bartoš
authors: Masaryk Memorial Cancer Institute, Brno, Czech Republic.
authors: Michal
authors: M
authors: Standara
authors: Institute of Scientific Instruments of the Academy of Sciences of the Czech Republic, Brno, Czech Republic.
authors: Zenon
authors: Z
authors: Starčuk
authors: Department of Biomedicine, University of Bergen, Bergen, Norway.
authors: Torfinn
authors: T
authors: Taxt
mesh_terms: D000465:Algorithms; D002196:Capillaries / Q000000981:diagnostic imaging; D002292:Carcinoma, Renal Cell / Q000098:blood supply / Q000000981:diagnostic imaging; D003287:Contrast Media; D006801:Humans; D007668:Kidney / Q000098:blood supply / Q000000981:diagnostic imaging; D007680:Kidney Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D055420:Perfusion Imaging
chemical_list: D003287:Contrast Media
keywords: arterial input function; dynamic contrast-enhanced magnetic resonance imaging; impulse residue function; multi-channel blind deconvolution; renal cell carcinoma