A New Non-invasive AI-Based Diagnostic System for Automated Diagnosis of Acute Renal Rejection in Kidney Transplantation: Analysis of ADC Maps Extracted from Matched 3D Iso-Regions of the Transplanted Kidney

Acute allograft rejection poses a significant challenge in kidney transplantation, the primary remedy for end-stage renal disease. Timely detection is crucial for intervention and graft preservation. A notable obstacle involves ensuring consistency across Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) scanning protocols at various Tesla levels. To tackle this, we propose a novel, non-invasive framework for automated diagnosis of acute renal rejection using DW-MRI. Our method comprises several key steps: Initially, we register the segmented kidney across different scanners, aligning them from the cortex to the medulla. Afterwards, the Apparent Diffusion Coefficient (ADC) is estimated for the segmented kidney. Then, the ADC maps are partitioned into a 3D iso-surface from the cortex to the medulla using the fast-marching level sets method. Next, the Cumulative Distribution Function (CDF) of the ADC for each iso-surface is computed, and Spearman correlation is applied to these CDFs. Finally, we introduce a Transformer-based Correlations to Classes Converter (T3C) model to leverage these correlations for distinguishing between normal and acutely rejected transplants. Evaluation on a cohort of 94 subjects (40 with acute renal rejection and 54 control subjects) yields promising results, with a mean accuracy of 98.723%, a mean sensitivity of 97%, and a mean specificity of 100%, employing a leave-one-subject testing approach. These findings underscore the effectiveness and robustness of our proposed framework.