Abstract
In recent years, magnetic resonance imaging (MRI) has been explored for non-invasive assessment of renal transplant function. This paper proposes a computer-aided diagnostic (CAD) system for the assessment of renal transplant status, which integrates both clinical and MRI-derived biomarkers. The latter are derived from either 3D (2D + time) dynamic contrast-enhanced MRI or 4D (3D + b-value) diffusion-weighted (DW) MRI. In order to extract the MRI-based biomarkers, our framework performs multiple image processing steps, including MRI data alignment to handle the motion effects, kidney segmentation using a geometric deformable model, local motion correction, and estimation of image-based biomarkers. These biomarkers are fused with clinical biomarkers (creatinine clearance and serum plasma creatinine) for the classification of transplant status using a machine learning classifier. Our CAD system has been tested on a cohort of 100 subjects (50 DCE-MRI and 50 DW-MRI) using a “leave-one-subject-out” approach and distinguished rejection from non-rejection transplants with an overall accuracy of 98% for both DCE-MRI and DW-MRI data sets. These preliminary results demonstrate the promise of the proposed CAD system as a reliable non-invasive diagnostic tool for renal transplant assessment.
Published Version
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