Early Assessment of Renal Transplants Using BOLD-MRI: Promising Results

Non-invasive evaluation of renal transplant function is essential to minimize and manage acute renal rejection (AR). A computer-assisted diagnostic (CAD) system is developed to evaluate kidney function post-transplantation. The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function. BOLD-MRI scans were acquired at five different echo-times (2, 7, 12, 17, and 22) ms from 15 transplant patients. The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin, also known as apparent relaxation rate (R2*). These R2* estimates are used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and AR. Using a leave-one-out cross-validation approach along with a multi-layer preceptron neural network (MLP-NN) classifier, the CAD system demonstrated 93.3% accuracy, 100% sensitivity, and 90% specificity in distinguishing AR from NR. These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively.