Advertisement

De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks

  • Ariel Benou
  • Ronel Veksler
  • Alon Friedman
  • Tammy Riklin Raviv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10008)

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI washout curves allows quantitative assessment of the BBB functionality. Nevertheless, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise that does not fit standard noise models. The two existing approaches i.e. curve smoothing and image de-noising can either produce smooth curves but cannot guaranty fidelity to the PK model or cannot accommodate the high variability in noise statistics in time and space.

We present a novel framework based on Deep Neural Networks (DNNs) to address the DCE-MRI de-noising challenges. The key idea is based on an ensembling of expert DNNs, where each is trained for different noise characteristics and curve prototypes to solve an inverse problem on a specific subset of the input space. The most likely reconstruction is then chosen using a classifier DNN. As ground-truth (clean) signals for training are not available, a model for generating realistic training sets with complex nonlinear dynamics is presented. The proposed approach has been applied to DCE-MRI scans of stroke and brain tumor patients and is shown to favorably compare to state-of-the-art de-noising methods, without degrading the contrast of the original images.

Keywords

Mean Square Error Brain Tumor Patient Deep Neural Network Dynamic Contrast Enhance Magnetic Resonance Imaging Restrict Boltzmann Machine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This study was supported by the European Union’s Seventh Framework Program (FP7/2007–2013; grant agreement 602102, EPITARGET; A.F.), the Israel Science Foundation (A.F.) and the Binational Israel-USA Foundation (BSF; A.F.).

Supplementary material

References

  1. 1.
    Abbott, N.J., Friedman, A.: Overview and introduction: the blood-brain barrier in health and disease. Epilepsia 53(s6), 1–6 (2012)CrossRefGoogle Scholar
  2. 2.
    Bridle, J.S.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Soulié, F.F., Hérault, J. (eds.) Neurocomputing. NATO ASI Series, vol. 68, pp. 227–236. Springer, Heidelberg (1990)CrossRefGoogle Scholar
  3. 3.
    Brix, G., Semmler, W., Port, R., Schad, L.R., Layer, G., Lorenz, W.J.: Pharmacokinetic parameters in CNS Gd-DTPA enhanced MR imaging. J. Comput. Assist. Tomogr. 15(4), 621–628 (1991)CrossRefGoogle Scholar
  4. 4.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Computer Vision and Pattern Recognition, CVPR, vol. 2, pp. 60–65 (2005)Google Scholar
  5. 5.
    Dahl, G.E., Sainath, T.N., Hinton, G.E.: Improving deep neural networks for LVCSR using rectified linear units and dropout. In: ICASSP, pp. 8609–8613. IEEE (2013)Google Scholar
  6. 6.
    Gal, Y., et al.: Denoising of dynamic contrast-enhanced MR images using dynamic nonlocal means. IEEE Trans. Med. Imaging 29(2), 302–310 (2010)CrossRefGoogle Scholar
  7. 7.
    Golkov, V., et al.: q-space deep learning for twelve-fold shorter and model-freediffusion MRI scans. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 37–44. Springer, Heidelberg (2015)Google Scholar
  8. 8.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)CrossRefGoogle Scholar
  10. 10.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)CrossRefzbMATHGoogle Scholar
  11. 11.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Kimmel, R., Malladi, R., Sochen, N.: Images as embedded maps and minimal surfaces: movies, color, texture, and volumetric medical images. Int. J. Comput. Vis. 39(2), 111–129 (2000)CrossRefzbMATHGoogle Scholar
  13. 13.
    Martel, A.L.: A fast method of generating pharmacokinetic maps from dynamic contrast-enhanced images of the breast. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 101–108. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Murase, K.: Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging. Magn. Reson. Med. 51(4), 858–862 (2004)CrossRefGoogle Scholar
  15. 15.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Technical report, DTIC Document (1985)Google Scholar
  16. 16.
    Schmid, V.J., et al.: A bayesian hierarchical model for the analysis of a longitudinal dynamic contrast-enhanced MRI oncology study. Magn. Reson. Med. 61(1), 163–174 (2009)CrossRefGoogle Scholar
  17. 17.
    Sourbron, S.P., Buckley, D.L.: Classic models for dynamic contrast-enhanced MRI. NMR Biomed. 26(8), 1004–1027 (2013)CrossRefGoogle Scholar
  18. 18.
    Tofts, P.: Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Wiley, Hoboken (2005)Google Scholar
  19. 19.
    Tofts, P.S.: Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J. Magn. Reson. Imaging 7(1), 91–101 (1997)CrossRefGoogle Scholar
  20. 20.
    Tofts, P.S., et al.: Estimating kinetic parameters from dynamic contrast-enhanced T1-weighted MRI of a diffusable tracer: standardized quantities and symbols. J. Magn. Reson. Imaging 10(3), 223–232 (1999)CrossRefGoogle Scholar
  21. 21.
    Veksler, R., Shelef, I., Friedman, A.: Blood-brain barrier imaging in human neuropathologies. Arch. Med. Res. 45(8), 646–652 (2014)CrossRefGoogle Scholar
  22. 22.
    Vincent, P., et al.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ariel Benou
    • 1
    • 3
  • Ronel Veksler
    • 2
    • 3
  • Alon Friedman
    • 2
    • 3
    • 4
  • Tammy Riklin Raviv
    • 1
    • 3
  1. 1.Department of Electrical EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Department of Physiology and Cell BiologyBen-Gurion University of the NegevBeer-ShevaIsrael
  3. 3.The Zlotowski Center for NeuroscienceBen-Gurion University of the NegevBeer-ShevaIsrael
  4. 4.Departments of Medical Neuroscience and Brain Repair CentreDalhousie University, Faculty of MedicineHalifaxCanada

Personalised recommendations