Non-invasive image-based approach for early detection of acute renal rejection

Authors:
Fahmi Khalifa;Ayman El-Baz;Georgy Gimel'farb;Mohammed Abu El-Ghar
Affiliations:
Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;Bioimaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, Auckland, New Zealand;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

A promising approach for the automatic classification of normal and acute rejection transplants from Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is proposed. The proposed approach consists of three main steps. The first step segments the kidney from the surrounding abdominal tissues by a level-set based deformable model with a speed function that accounts for a learned spatially variant statistical shape prior, 1st-order visual appearance descriptors of the contour interior and exterior (associated with the object and background, respectively), and a spatially invariant 2nd-order homogeneity descriptor. In the second step, to handle local object deformations due to kidney motion caused by patient breathing, we proposed a new nonrigid approach to align the object by solving Laplace's equation between closed equispaced contours (iso-contours) of the reference and target objects. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the segmented kidneys 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.