1. Abstract Dynamic Magnetic Resonance Imaging (MRI) with contrast media injection is an important tool to study renal perfusion in humans and animals. The goal of this study is to build classifiers for the automatic classification of a kidney as healthy or pathological. A new algorithm is developed that segments out the cortex from the rest of the kidney including the medulla, the renal pelvic, and the background. The performance of two classifier-types (Soft Independent Method of Class Analogy, SIMCA; Partial Least Squares Discriminant Analysis, PLS-DA) is compared for various types of data pre-processing including segmentation, feature extraction, baseline correction, centering, and standard normal variate (SNV). MRI details: MRI acquisition was done on an Eclipse 1.5T MR system (Philips Medical System, Cleveland, OH). The perfusion sequence, during the intravenous injection of gadolinium (Gadoteridol 32 mmol/kg; Bracco Research, Geneva) followed by a saline flush (5 mL) consisted of single 10-mm slices acquired in the transverse plane through the kidneys. Classification of MRI sequences of rabbit renal perfusion Paman Gujral a, Michael Amrhein a, a Paman Gujral a, Michael Amrhein a, Dominique Bonvin a, Xavier Montet b, Jean-Paul Vallée b, Nicolas Michoux c a b c a Laboratoire d’Automatique, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. b University Hospital of Geneva, Geneva, Switzerland. c Diagnostic Radiology Unit, Université Catholique de Louvain, Brussels, Belgium. Methodology Selected frames of whole kidney MRI sequence Time (sec) 6 12 15 24 36 48 Cortex Medulla + renal pelvic Introduction Results: Segmentation Good kidney feature Bad kidney feature Intensity Average Intensity Standard Deviation T 2 Statistic Results: Feature extraction + Results: ROC Conclusions and perspectives The AUROC of the best classifier is 0.89 when the feature is extracted from the whole kidney, and 1.0 when it is extracted from the cortex alone. Thus, the kidney cortex contains more discriminating information than the rest of the kidney. Also, the PLS-DA classifiers outperform the SIMCA classifiers. In this work, two-way methods were used to model rabbit MRI data. Current research focuses on multi-way methods (PARAFAC and TUCKER) which preserve the spatial structure in data and, thus, possibly improve the classification performance of human MRI data. Visual inspection of MRI sequence Confirmation with radiologist Confirmed outliers Data pre-treatment and outlier removal Segmentation Feature extraction Feature pre-processing ROCs of all classifiers Testing of classifier Training of classifier ROCs of best classifiers Visual inspection of features Model 1 2 Sequence 1 Parameter variation Features (i)Intensity average (ii)Standard deviation (iii)T 2 statistic SIMCA / PLS-DA; Parameter variation Baseline correction Mean centering SNV MRI sequence data Whole kidney Cortex only n 2 m Apparent outliers Segmentation: A segmentation algorithm based on Principal Component Analysis (PCA) is carried out to separate out the cortex because it contains more discriminatory information. Feature extraction: Three features are chosen for the discrimination task: (i) intensity average, (ii) intensity standard deviation, and (iii) T 2 statistic computed from the PCA model of unfolded images. In each of the three features, a good kidney exhibits a sudden variation when the contrast agent is absorbed. This sudden variation is absent in the feature extracted from a bad kidney. Selection of best classifiers based on Receiver Operation Curve (ROC): The Areas Under ROC (AUROC) is computed for the best classifiers based upon features of the cortex and the whole kidney, respectively. ROCs Best Classifiers ROCs Cortex ROCs Whole Kidney 6 12 15 24 36 48 Time (sec) Pre-processing#PLS factors None Baseline Mean centering SNV 4443444434 The following classification models based on the cortex ROI, the intensity average feature, and PLS-DA give no misclassifications: 6 12 15 24 36 48