Dynamic Contrast-Enhanced MRI-Based Early Detection of Acute Renal Transplant Rejection

Abstract

A novel framework for the classification of acute rejection versus nonrejection status of renal transplants from 2-D dynamic contrast-enhanced magnetic resonance imaging is proposed. The framework consists of four steps. First, kidney objects are segmented from adjacent structures with a level set deformable boundary guided by a stochastic speed function that accounts for a fourth-order Markov-Gibbs random field model of the kidney/background shape and appearance. Second, a Laplace-based nonrigid registration approach is used to account for local deformations caused by physiological effects. Namely, the target kidney object is deformed over closed, equispaced contours (iso-contours) to closely match the reference object. Next, the cortex is segmented as it is the functional kidney unit that is most affected by rejection. To characterize rejection, perfusion is estimated from contrast agent kinetics using empirical indexes, namely, the transient phase indexes (peak signal intensity, time-to-peak, and initial up-slope), and a steady-phase index defined as the average signal change during the slowly varying tissue phase of agent transit. We used a kn-nearest neighbor classifier to distinguish between acute rejection and nonrejection. Performance of our method was evaluated using the receiver operating characteristics (ROC). Experimental results in 50 subjects, using a combinatoric kn-classifier, correctly classified 92% of training subjects, 100% of the test subjects, and yielded an area under the ROC curve that approached the ideal value. Our proposed framework thus holds promise as a reliable noninvasive diagnostic tool.

DOI: 10.1109/TMI.2013.2269139

Cite this paper

@article{Khalifa2013DynamicCM, title={Dynamic Contrast-Enhanced MRI-Based Early Detection of Acute Renal Transplant Rejection}, author={Fahmi Khalifa and Garth M. Beache and Mohamed Abou El-Ghar and Tarek Eldiasty and Georgy L. Gimel'farb and Maiying Kong and Ayman El-Baz}, journal={IEEE transactions on medical imaging}, year={2013}, volume={32 10}, pages={1910-27} }