A promising non-invasive CAD system for kidney function assessment

Abstract

This paper introduces a novel computer-aided diagnostic (CAD) system for the assessment of renal transplant status that integrates image-based biomarkers derived from 4D (3D + b-value) diffusionweighted (DW) MRI,and clinical biomarkers. To analyze DW-MRI,our framework starts with kidney tissue segmentation using a level set approach after DW-MRI data alignment to handle the motion effects. Secondly,the cumulative empirical distributions (i.e.,CDFs) of apparent diffusion coefficients (ADCs) of the segmented DW-MRIs are estimated at low and high gradient strengths and duration (b-values) accounting for both blood perfusion and diffusion,respectively. Finally,these CDFs are fused with laboratory-based biomarkers (creatinine clearance and serum plasma creatinine) for the classification of transplant status using a deep learning-based classification approach utilizing a stacked non-negativity constrained auto-encoder. Using “leave-one-subject-out” experiments on a cohort of 58 subjects,the proposed CAD system distinguished nonrejection transplants from kidneys with abnormalities with a 95% accuracy (sensitivity = 95%,specificity = 94%) and achieved a 95% correct classification between early rejection and other kidney diseases. Our preliminary results demonstrate the promise of the proposed CAD system as a reliable non-invasive diagnostic tool for renal transplants assessment.