New motion correction models for automatic identification of renal transplant rejection

Authors:
Ayman El-Baz;Georgy Gimel'farb;Mohamed A. El-Ghar
Affiliations:
Bioengineering Department, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, New Zealand;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

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. In the second step, new motion correction models are employed to account for both the global and local motion of the kidney due to patient moving and breathing. Finally, the perfusion curves that show the transportation of the contrast agent into the tissue are obtained from the kidney and used in the classification of normal and acute rejection transplants. In this paper, we will focus on the second and third steps and the first step is shown in detail in [1].