Distributed capillary adiabatic tissue homogeneity model in parametric multi-channel blind AIF estimation using DCE-MRI

Magn Reson Med. 2016 Mar;75(3):1355-65. doi: 10.1002/mrm.25619. Epub 2015 Apr 13.


Purpose: 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.

Conclusion: Blind multi-channel deconvolution using the DCATH model might be a method of choice for AIF estimation in a clinical setup.

Keywords: arterial input function; dynamic contrast-enhanced magnetic resonance imaging; impulse residue function; multi-channel blind deconvolution; renal cell carcinoma.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Capillaries / diagnostic imaging
  • Carcinoma, Renal Cell / blood supply
  • Carcinoma, Renal Cell / diagnostic imaging
  • Contrast Media
  • Humans
  • Kidney / blood supply
  • Kidney / diagnostic imaging
  • Kidney Neoplasms / blood supply
  • Kidney Neoplasms / diagnostic imaging
  • Magnetic Resonance Imaging / methods*
  • Models, Biological*
  • Perfusion Imaging


  • Contrast Media