  Delineation of the kidney region in dynamic contrast-enhanced magnetic
resonance Imaging (DCE-MRI) is required during post-acquisition analysis in
order to quantify various aspects of renal function, such as filtration and
perfusion or blood flow. However, this can be obfuscated by the Partial Volume
Effect (PVE), caused due to the mixing of any single voxel with two or more
signal intensities from adjacent regions such as liver region and other
tissues. To avoid this problem, firstly, a kidney region of interest (ROI)
needs to be defined for the analysis. A clinician may choose to select a region
avoiding edges where PV mixing is likely to be significant. However, this
approach is time-consuming and labour intensive. To address this issue, we
present Dynamic Mode Decomposition (DMD) coupled with thresholding and blob
analysis as a framework for automatic delineation of the kidney region. This
method is first validated on synthetically generated data with ground-truth
available and then applied to ten healthy volunteers' kidney DCE-MRI datasets.
We found that the result obtained from our proposed framework is comparable to
that of a human expert. For example, while our result gives an average Root
Mean Square Error (RMSE) of 0.0097, the baseline achieves an average RMSE of
0.1196 across the 10 datasets. As a result, we conclude automatic modelling via
DMD framework is a promising approach.
