## 4 Discussion and Conclusions
Another constraint that narrows the scope of the conclusions that can be derived from this study is that it presents methodological achievements tested solely on healthy subjects. Although we believe that the proposed methods of ANN-based PK model parameter estimation and AIF determination will manifest its applicability to diseased kidneys, this paper focuses exclusively on algorithms per se and numerical image processing, rather than clinical applications. Such an approach is common in numerous works published in the biomedical engineering domain, which postulate novel processing techniques, including those devoted to renal perfusion measurement [7; 10; 16; 35]. The reasoning behind this approach states that the technical quality of the designed solution must be verified under well-defined and controlled conditions for a statistically representative data sample. Such conditions are ensured by a cohort of healthy volunteers. Besides, in the current study, the inclusion of abnormal cases would not necessarily provide additional insight into the behavior of the automatic determination of AIF. In the case of diseased kidneys, it should run in an unchanged manner, since tissue lesion is independent of the blood flow dynamics in abdominal aorta. On the other hand, taking into account renal impairments would involve experimenting with various pharmacokinetic models of perfusion, and it would remarkably extend the scope of this study. To conclude, the performed experiments show the potential of neural networks as an alternative computational framework to the standard curve-fitting procedure in the context of quantitative perfusion estimation. The overall agreement with the gold standard method obtained by ANN was comparable to the results obtained using the non-linear least-squares approach. Moreover, GFR can be estimated without explicit determination of AIF for a given patient, which constitutes the major advantage of the proposed approach. Instead, AIFs must be found only for a limited number of studies included in the training data set. The described algorithm for AIF ROI determination allows one to determine it in an automatic manner, reducing the effect of the inflow artifact. It simultaneously increases the reliability and precision of the 2 CFM model's parameter estimation. Hence, no additional process is required in assessing patient-specific hemodynamic parameters by utilizing either clinical examinations (MR angiography [36; 37] or ultrasound [38]) or non-clinical methods (e.g., photoplethysmographic arterial pulse wave measurement [39]) that would otherwise be necessary to properly trigger the DCE-MRI sequence. Conceptualization, A.K. ; Data curation, E.E. and A.L. ; Investigation, A.K. and M.S. ; Methodology, A.K. ; Resources, E.E. and A.L. ; Software, A.K. and M.K. ; Supervision, M.S. and A.L. ; Validation, E.E. ; Visualization, M.K. ; Writing--original draft, A.K. All authors have read and agreed to the published version of the manuscript. **Funding:** The paper was partially supported by the Polish National Science Centre grant no. 2014/15/B/ST7/05227. **Conflicts of Interest:** The authors declare no conflict of interest.