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

• Published in: Magnetic resonance in medicine 2016-03, Vol.75 (3), p.1355-1365
• Main Authors: Kratochvíla, Jiří, Jiřík, Radovan, Bartoš, Michal, Standara, Michal, Starčuk Jr, Zenon, Taxt, Torfinn
• Format: Article
• Language: English
• Subjects: Adiabatic; Adiabatic flow; Algorithms; arterial input function; Capillaries - diagnostic imaging; Carcinoma, Renal Cell - blood supply; Carcinoma, Renal Cell - diagnostic imaging; Contrast Media; Deconvolution; dynamic contrast-enhanced magnetic resonance imaging; Evaluation; Homogeneity; Humans; impulse residue function; Kidney - blood supply; Kidney - diagnostic imaging; Kidney cancer; Kidney Neoplasms - blood supply; Kidney Neoplasms - diagnostic imaging; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Models, Biological; multi-channel blind deconvolution; Noise; Noise sensitivity; Parameter estimation; Parameter sensitivity; Patients; Perfusion; Perfusion Imaging; Renal cell carcinoma; Scaling; Tissues
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.25619

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. Magn Reson Med 75:1355–1365, 2016. © 2015 Wiley Periodicals, Inc.