Magnetic resonance imaging of renal transplants: its value in the differentiation of acute rejection and cyclosporin a nephrotoxicity

A comprehensive non-invasive framework for automated evaluation of acute renal transplant rejection using DCE-MRI

The objective was to develop a novel and automated comprehensive framework for the non-invasive identification and classification of kidney non-rejection and acute rejection transplants using 2D dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The proposed approach consists of four steps. First, kidney objects are segmented from the surrounding structures with a geometric deformable model. Second, a non-rigid registration approach is employed to account for any local kidney deformation. In the third step, the cortex of the kidney is extracted in order to determine dynamic agent delivery, since it is the cortex that is primarily affected by the perfusion deficits that underlie the pathophysiology of acute rejection. Finally, we use an analytical function-based model to fit the dynamic contrast agent kinetic curves in order to determine possible rejection candidates. Five features that map the data from the original data space to the feature space are chosen with a k-nearest-neighbor (KNN) classifier to distinguish between acute rejection and non-rejection transplants. Our study includes 50 transplant patients divided into two groups: 27 patients with stable kidney function and the remainder with impaired kidney function. All of the patients underwent DCE-MRI, while the patients in the impaired group also underwent ultrasound-guided fine needle biopsy. We extracted the kidney objects and the renal cortex from DCE-MRI for accurate medical evaluation with an accuracy of 0.97 ± 0.02 and 0.90 ± 0.03, respectively, using the Dice similarity metric. In a cohort of 50 participants, our framework classified all cases correctly (100%) as rejection or non-rejection transplant candidates, which is comparable to the gold standard of biopsy but without the associated deleterious side-effects. Both the 95% confidence interval (CI) statistic and the receiver operating characteristic (ROC) analysis document the ability to separate rejection and non-rejection groups. The average plateau (AP) signal magnitude and the gamma-variate model functional parameter α have the best individual discriminating characteristics.

Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection

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.

A new CAD system for the evaluation of kidney diseases using DCE-MRI

Acute rejection is the most common reason of graft failure after kidney transplantation, and early detection is crucial to survive the transplanted kidney function. In this paper, we introduce a new approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed algorithm consists of three main steps; the first step isolates the kidney from the surrounding anatomical structures by evolving a deformable model based on two density functions; the first function describes the distribution of the gray level inside and outside the kidney region and the second function describes the prior shape of the kidney. In the second step, a new nonrigid registration approach is employed to account for the motion of the kidney due to patient breathing. To validate our registration approach, we use a simulation of deformations based on biomechanical modelling of the kidney tissue using the finite element method (F.E.M.). Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the cortex and used in the classification of normal and acute rejection transplants. Applications of the proposed approach yield promising results that would, in the near future, replace the use of current technologies such as nuclear imaging and ultrasonography, which are not specific enough to determine the type of kidney dysfunction.

MRI and ultrasonographic detection of morphologic and hemodynamic changes in chronic renal allograft rejection in the rat

PURPOSE

The purpose of this study is to describe the sonographic, MRI, and histopathologic findings in a rat model of chronic renal allograft rejection.

MATERIALS AND METHODS

Allogeneic renal grafts (male DA kidney into male Lewis rat with unilateral nephrectomy, N = 27) and syngeneic renal grafts (male Lewis kidney into male Lewis rat, N = 19) were examined serially with ultrasound, MRI, and histology.

RESULTS

Nonparametric Spearman rank correlation showed significance between the histologic score and the following parameters: the MRI score (r(s) = 0.91, P < 0.01, N = 46), the ultrasound score (r(s) = 0.9, P < 0.01, N = 46), the power Doppler score (r(s) = 0.86, P < 0.01, N = 46), and the MRI perfusion (r(s) = -0.80, P < 0.01, N = 45). Positive correlations were also found between the MRI volume estimations (graft r(s) = 0.49, P < 0.01, N = 46; native r(s) = 0.59, P < 0.01, N = 46), and the ultrasound volume estimations (graft r(s) = 0.39, P < 0.01, N = 45; native r(s) = 0.64, P < 0.01, N = 46) as well as with actual graft weight.

CONCLUSIONS

This study shows that both MRI and ultrasound can provide complementary, accurate information compared to histology in regard to the alterations in anatomy and hemodynamic changes associated with chronic allograft nephropathy.