A novel image analysis approach for accurate identification of acute renal rejection
Acute renal rejection is the most common reason for graft (transplanted kidney) failure after kidney transplantation, and early detection is crucial to survival of function in the transplanted kidney. The current techniques for early detection of acute renal rejection are not accurate. For example, clearances of inulin and DTPA require multiple blood and urine tests, and they provide information on both kidneys together, but not unilateral information. Moreover, biopsy (the gold standard for diagnosis of acute renal rejection after renal transplantation) could cause bleeding and infection. Also, the relatively small needle biopsies may lead to over- or underestimation of the extent of inflammation in the entire graft. Hence, a noninvasive and repeatable technique would not only be useful but is needed to ensure survival of transplanted kidneys. For this reason, we introduced a new non-invasive framework for automatic classification of normal and acute renal rejection transplants using Dynamic Contrast Enhanced Magnetic Resonance Images (DCE-MRI). 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].