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)


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.



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.).


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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

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