Chapter title |
New Motion Correction Models for Automatic Identification of Renal Transplant Rejection
|
---|---|
Chapter number | 29 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, January 2007
|
DOI | 10.1007/978-3-540-75759-7_29 |
Pubmed ID | |
Book ISBNs |
978-3-54-075758-0, 978-3-54-075759-7
|
Authors |
El-Baz, Ayman, Gimel'farb, Georgy, El-Ghar, Mohamed A, Ayman El-Baz, Georgy Gimel’farb, Mohamed A. El-Ghar, Gimel’farb, Georgy, El-Ghar, Mohamed A. |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 11% |
Unknown | 17 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 4 | 21% |
Student > Ph. D. Student | 4 | 21% |
Other | 3 | 16% |
Student > Doctoral Student | 2 | 11% |
Student > Bachelor | 1 | 5% |
Other | 0 | 0% |
Unknown | 5 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 4 | 21% |
Nursing and Health Professions | 2 | 11% |
Computer Science | 2 | 11% |
Economics, Econometrics and Finance | 1 | 5% |
Agricultural and Biological Sciences | 1 | 5% |
Other | 2 | 11% |
Unknown | 7 | 37% |