Role of Integrating Diffusion Mr Image-Markers with Clinical-Biomarkers For Early Assessment of Renal Transplants

Abstract

Recently, diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for non-invasive assessment of renal transplant functions. In this paper, a computer-aided diagnostic (CAD) system is developed to assess renal transplant functionality, which integrates both clinical and diffusion MRI -derived markers extracted from 4D DW-MRI (i.e. 3D + b-value). To extract the DW-MR image-markers, our framework performs multiple image processing steps, including kidney segmentation using a level-set approach and estimation of image-markers. To extract these image-markers, apparent diffusion coefficients (ADCs) are estimated from the segmented DW-MRIs and cumulative distribution functions (CDFs) of the ADCs are constructed at different b-values (i.e. gradient field strengths and duration). Finally, these markers (i.e. CDFs) are integrated with clinical biomarkers (e.g., creatinine clearance and serum plasma creatinine) to assess transplant status using stacked auto-encoders with non-negativity constraints based on deep learning classification approach. Our CAD system consists of two consecutive classification stages. The first stage classifier achieved a 96% accuracy, a 95% sensitivity, and a 100% specificity in distinguishing non-rejection (NR) from dysfunctional (DF) transplanted kidneys. Additionally, an overall accuracy of 94% has been obtained in the second stage in separating DF to acute rejection (AR) and different renal disease (DRD) transplants. Our preliminary results hold strong promise that the presented CAD system is of a high reliability to non-invasively diagnose renal transplant status.