首页|Early Assessment of Renal Transplants Using BOLD-MRI: Promising Results

Early Assessment of Renal Transplants Using BOLD-MRI: Promising Results

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  • NETL
  • IEEE
Non-invasive evaluation of renal transplant function is essential to minimize and manage acute renal rejection (AR)。 A computer-assisted diagnostic (CAD) system is developed to evaluate kidney function post-transplantation。 The developed CAD system utilizes the amount of blood-oxygenation extracted from 3D (2D + time) blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) to estimate renal function。 BOLD-MRI scans were acquired at five different echo-times (2， 7， 12， 17， and 22) ms from 15 transplant patients。 The developed CAD system first segments kidneys using the level-sets method followed by estimation of the amount of deoxyhemoglobin， also known as apparent relaxation rate (R2*)。 These R2* estimates are used as discriminatory features (global features (mean R2*) and local features (pixel-wise R2*)) to train and test state-of-the-art machine learning classifiers to differentiate between non-rejection (NR) and AR。 Using a leave-one-out cross-validation approach along with a multi-layer preceptron neural network (MLP-NN) classifier， the CAD system demonstrated 93。3% accuracy， 100% sensitivity， and 90% specificity in distinguishing AR from NR。 These preliminary results demonstrate the efficacy of the CAD system to detect renal allograft status non-invasively。

Renal transplantsBOLD-MRImean R2*pixel-wise R2*machine learning

M. Shehata、A. Shalaby、M. Ghazal、M. Abou El-Ghar、M. A. Badawy、G. Beache、A. Dwyer、M. El-Melegy、G. Giridharan、R. Keynton、A. El-Baz

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BioImaging Lab, University of Louisville, Louisville, KY, USA

Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE

Radiology Department, Mansoura University, Mansoura, Egypt

Radiology Department, University of Louisville, Louisville, KY, USA

Kidney Disease Program, University of Louisville, Louisville, KY, USA

Department of Electrical Engineering, Assiut University, Assiut, Egypt

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IEEE International Conference on Image Processing

Taipei(TW)

2019 IEEE International Conference on Image Processing

1395-1399

2019

10.1109/ICIP.2019.8803042