De-noising of contrast-enhanced MRI sequences by an ensemble of expert deep neural networks

Ariel Benou, Ronel Veksler, Alon Friedman, Tammy Riklin Raviv

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

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 DCEMRI 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 denoising 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 denoising methods, without degrading the contrast of the original images.

Original languageEnglish
Title of host publicationDeep Learning and Data Labeling for Medical Applications - 1st International Workshop, LABELS 2016, and 2nd International Workshop, DLMIA 2016 Held in Conjunction with MICCAI 2016, Proceedings
EditorsZhi Lu, Vasileios Belagiannis, Joao Manuel R.S. Tavares, Jaime S. Cardoso, Andrew Bradley, Joao Paulo Papa, Jacinto C. Nascimento, Marco Loog, Julien Cornebise, Gustavo Carneiro, Diana Mateus, Loic Peter
PublisherSpringer Verlag
Pages95-110
Number of pages16
ISBN (Print)9783319469751
DOIs
StatePublished - 1 Jan 2016
Event1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: 21 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10008 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016 and 2nd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
Country/TerritoryGreece
CityAthens
Period21/10/1621/10/16

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