Automated image quality evaluation of structural brain MRI using an ensemble of deep learning networks

Deep learning (DL) is a promising methodology for automatic detection of abnormalities in brain MRI.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  James Voyvodic,et al.  A novel method for quantifying scanner instability in fMRI , 2011, Magnetic resonance in medicine.

[3]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[4]  Bin Yang,et al.  A Machine-learning framework for automatic reference-free quality assessment in MRI , 2018, Magnetic resonance imaging.

[5]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[6]  Jeffrey P. Woodard,et al.  No-Reference image quality metrics for structural MRI , 2007, Neuroinformatics.

[7]  Donglai Huo,et al.  Quantitative image quality evaluation of MR images using perceptual difference models. , 2008, Medical physics.

[8]  Marius Pedersen,et al.  Image Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trials , 2018, IEEE Journal of Translational Engineering in Health and Medicine.

[9]  Qian Luo,et al.  Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm , 2016, Front. Neuroinform..

[10]  Konstantin Nikolaou,et al.  Automated reference-free detection of motion artifacts in magnetic resonance images , 2018, Magnetic Resonance Materials in Physics, Biology and Medicine.

[11]  Roberto de Alencar Lotufo,et al.  Automatic detection of motion artifacts on MRI using Deep CNN , 2018, 2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI).

[12]  Ponnada A. Narayana,et al.  Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study☆ , 2012, NeuroImage: Clinical.

[13]  Krzysztof J. Gorgolewski,et al.  MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites , 2016, bioRxiv.

[14]  M. Dylan Tisdall,et al.  Head motion during MRI acquisition reduces gray matter volume and thickness estimates , 2015, NeuroImage.

[15]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[16]  Jean-Philippe Thiran,et al.  Automatic quality assessment in structural brain magnetic resonance imaging , 2009, Magnetic resonance in medicine.

[17]  Khader M Hasan,et al.  A framework for quality control and parameter optimization in diffusion tensor imaging: theoretical analysis and validation. , 2007, Magnetic resonance imaging.

[18]  Khundrakpam Budhachandra,et al.  The Neuro Bureau Preprocessing Initiative: open sharing of preprocessed neuroimaging data and derivatives , 2013 .

[19]  Gary R Cutter,et al.  Randomized study combining interferon and glatiramer acetate in multiple sclerosis , 2013, Annals of neurology.

[20]  Hersh Chandarana,et al.  Automated image quality evaluation of T2‐weighted liver MRI utilizing deep learning architecture , 2018, Journal of magnetic resonance imaging : JMRI.

[21]  Christopher Rorden,et al.  Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank , 2017 .

[22]  J. Giedd,et al.  Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI , 2016, Human brain mapping.

[23]  Yuan Yu,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.

[24]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[25]  Sumiko Abe,et al.  Quality Control Considerations for the Effective Integration of Neuroimaging Data , 2015, DILS.