In Vivo Detection of Chronic Kidney Disease Using Tissue Deformation Fields From Dynamic MR Imaging.

OBJECTIVE: Chronic kidney disease (CKD) is a serious medical condition characterized by gradual loss of kidney function. Early detection and diagnosis is mandatory for adequate therapy and prognostic improvement. Hence, in the current pilot study we explore the use of image registration methods for detecting renal morphologic changes in patients with CKD. METHODS: Ten healthy volunteers and nine patients with presumed CKD underwent dynamic T1 weighted imaging without contrast agent. From real and simulated dynamic time series, kidney deformation fields were estimated using a poroelastic deformation model. From the deformation fields several quantitative parameters reflecting pressure gradients, and volumetric and shear deformations were computed. Eight of the patients also underwent a kidney biopsy as a gold standard. RESULTS: We found that the absolute deformation, normalized volume changes, as well as pressure gradients correlated significantly with arteriosclerosis from biopsy assessments. Furthermore, our results indicate that current image registration methodologies are lacking sensitivity to recover mild changes in tissue stiffness. CONCLUSION: Image registration applied to dynamic time series correlated with structural renal changes and should be further explored as a tool for invasive measurements of arteriosclerosis. SIGNIFICANCE: Under the assumption that the proposed framework can be further developed in terms of sensitivity and specificity, it can provide clinicians with a non-invasive tool of high spatial coverage available for characterization of arteriosclerosis and potentially other pathological changes observed in chronic kidney disease.

Blood flow quantification from 2D phase contrast MRI in renal arteries using an unsupervised data driven approach.

We present a clustering approach to segment the renal artery from 2D PC Cine MR images to measure arterial blood velocity and flow. Such information is important in grading renal artery stenosis and to support the decision on surgical interventions like percutaneous transluminal angioplasty. Results from 20 data sets (3 volunteers, 7 patients) show that the renal arteries could be extracted automatically and the corresponding velocity profiles were close (r = 0.977) to that obtained by manual delineations of the vessel areas.