Computer-Aided Diagnostic System for Early Detection of Acute Renal Transplant Rejection Using Diffusion-Weighted MRI

• Published in: IEEE transactions on biomedical engineering 2019-02, Vol.66 (2), p.539-552
• Main Authors: Shehata, Mohamed, El-Baz, Ayman, Khalifa, Fahmi, Soliman, Ahmed, Ghazal, Mohammed, Taher, Fatma, El-Ghar, Mohamed Abou, Dwyer, Amy C., Gimel'farb, Georgy, Keynton, Robert S.
• Format: Article
• Language: English
• Subjects: ADC; Adolescent; Adult; Algorithms; Biopsy; Bleeding; CAD system; Child; Complications; Deep Learning; Deformable models; Deformation; Diagnosis; Diagnostic systems; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Distribution functions; DW-MRI; Early Diagnosis; Female; Formability; Graft rejection; Graft Rejection - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Invasiveness; Kidney; Kidney - diagnostic imaging; Kidney Transplantation; Kidneys; Magnetic resonance imaging; Male; Markov chains; Middle Aged; NMR; Nuclear magnetic resonance; Recovery time; Rejection; Renal rejection; Shape; Stochastic processes; Stochasticity; Three-dimensional displays; Transplants; Transplants & implants; Young Adult
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2018.2849987

Objective: Early diagnosis of acute renal transplant rejection (ARTR) is critical for accurate treatment. Although the current gold standard, diagnostic technique is renal biopsy, it is not preferred due to its invasiveness, long recovery time (1-2 weeks), and potential for complications, e.g., bleeding and/or infection. Methods: This paper presents a computer-aided diagnostic (CAD) system for early ARTR detection using (3D + b-value) diffusion-weighted (DW) magnetic resonance imaging (MRI) data. The CAD process starts from kidney tissue segmentation with an evolving geometric (level-set-based) deformable model. The evolution is guided by a voxel-wise stochastic speed function, which follows from a joint kidney-background Markov-Gibbs random field model accounting for an adaptive kidney shape prior and on-going kidney-background visual appearances. A B-spline-based three-dimensional data alignment is employed to handle local deviations due to breathing and heart beating. Then, empirical cumulative distribution functions of apparent diffusion coefficients of the segmented DW-MRI at different b-values are collected as discriminatory transplant status features. Finally, a deep-learning-based classifier with stacked nonnegative constrained autoencoders is employed to distinguish between rejected and nonrejected renal transplants. Results: In our initial "leave-one-subject-out" experiment on 100 subjects, 97.0% of the subjects were correctly classified. The subsequent four-fold and ten-fold cross-validations gave the average accuracy of 96.0% and 94.0%, respectively. Conclusion: These results demonstrate the promise of this new CAD system to reliably diagnose renal transplant rejection. Significance: The technology presented here can significantly impact the quality of care of renal transplant patients since it has the potential to replace the gold standard in kidney diagnosis, biopsy.