A new non-invasive approach for early classification of renal rejection types using diffusion-weighted MRI

Abstract

Although renal biopsy remains the gold standard for diagnosing the type of renal rejection, it is not preferred due to its invasiveness, recovery time (1–2 weeks), and potential for complications, e.g., bleeding and/or infection. Therefore, there is an urgent need to explore a non-invasive technique that can early classify renal rejection types. In this paper, we develop a computer-aided diagnostic (CAD) system that can classify acute renal transplant rejection (ARTR) types early via the analysis of apparent diffusion coefficients (ADCs) extracted from diffusion-weighted (DW) MRI data acquired at low-(accounting for perfusion) and high-(accounting for diffusion) b-values. The developed framework mainly consists of three steps: (i) data co-alignment using a 3D B-spline-based approach (to handle local deviations due to breathing and heart beat motions) and segmentation of kidney tissue with an evolving geometric (level-set based) deformable model guided by a voxel-wise stochastic speed function, which follows a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and visual kidney-background appearances of DW-MRI data (image intensities and spatial interactions); (ii) construction of a cumulative empirical distribution of ADC at low and high b-values of the segmented kidney accounting for blood perfusion and water diffusion, respectively, to be our discriminatory ARTR types feature; and (iii) classification of ARTR types (acute tubular necrosis (ATN) anti-body- and T-cell-mediated rejection) based on deep learning of a non-negative constrained stacked autoencoder. Results show that 98% of the subjects were correctly classified in our “leave-one-subject-out” experiments on 39 subjects (namely, 8 out of 8 of the ATN group and 30 out of 31 of the T-cell group). Thus, the proposed approach holds promise as a reliable non-invasive diagnostic tool.