Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

&NA; UK Biobank is a large‐scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low‐dose bone/fat x‐ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

[1]  Randy L. Gollub,et al.  Reproducibility of quantitative tractography methods applied to cerebral white matter , 2007, NeuroImage.

[2]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[3]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[4]  German National Cohort Consortium,et al.  The German National Cohort: aims, study design and organization , 2014, European Journal of Epidemiology.

[5]  P. Dagnelie,et al.  The Maastricht Study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities , 2014, European Journal of Epidemiology.

[6]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[7]  N. Filippini,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

[8]  Stephen M. Smith,et al.  Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data , 2001, NeuroImage.

[9]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[10]  K. Zou,et al.  Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy , 2002, Journal of magnetic resonance imaging : JMRI.

[11]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[12]  Michael B. Miller,et al.  How reliable are the results from functional magnetic resonance imaging? , 2010, Annals of the New York Academy of Sciences.

[13]  Remco R. Bouckaert,et al.  Bayesian network classifiers in Weka , 2004 .

[14]  N. K. Focke,et al.  Multi-site voxel-based morphometry — Not quite there yet , 2011, NeuroImage.

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

[16]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[17]  Paul M. Thompson,et al.  Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA–DTI working group , 2013, NeuroImage.

[18]  Jessica A. Turner,et al.  Exploration of scanning effects in multi-site structural MRI studies , 2014, Journal of Neuroscience Methods.

[19]  Timothy Edward John Behrens,et al.  Effects of image reconstruction on fiber orientation mapping from multichannel diffusion MRI: Reducing the noise floor using SENSE , 2013, Magnetic resonance in medicine.

[20]  Xinming Tang,et al.  IMAGE FUSION AND IMAGE QUALITY ASSESSMENT OF FUSED IMAGES , 2013 .

[21]  M. Verleysen,et al.  Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[23]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[24]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[25]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[26]  Stamatios N. Sotiropoulos,et al.  Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images , 2016, NeuroImage.

[27]  Abraham Z. Snyder,et al.  Human Connectome Project informatics: Quality control, database services, and data visualization , 2013, NeuroImage.

[28]  Yu-Chung N. Cheng,et al.  Susceptibility weighted imaging (SWI) , 2004, Zeitschrift fur medizinische Physik.

[29]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[30]  Monique M. B. Breteler,et al.  MRI IN THE RHINELAND STUDY: A NOVEL PROTOCOL FOR POPULATION NEUROIMAGING , 2014, Alzheimer's & Dementia.

[31]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[32]  P. Szeszko,et al.  MRI atlas of human white matter , 2006 .

[33]  Stamatios N. Sotiropoulos,et al.  Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes , 2015, NeuroImage.

[34]  P. Jezzard,et al.  Correction for geometric distortion in echo planar images from B0 field variations , 1995, Magnetic resonance in medicine.

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

[36]  P. Matthews,et al.  Multimodal population brain imaging in the UK Biobank prospective epidemiological study , 2016, Nature Neuroscience.

[37]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[38]  Ieee Xplore,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[40]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[41]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[42]  Aapo Hyvärinen,et al.  Group-PCA for very large fMRI datasets , 2014, NeuroImage.

[43]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[44]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[45]  José M. García,et al.  Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs , 2012, PDP.

[46]  Thomas E. Nichols.  Notes on Creating a Standardized Version of DVARS , 2017, 1704.01469.

[47]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[48]  Bruce Fischl,et al.  Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.

[49]  Russell A. Poldrack,et al.  MRIQC: Predicting Quality in Manual MRI Assessment Protocols Using No-Reference Image Quality Measures , 2016 .

[50]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[51]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[52]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

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

[54]  New S Tudy.  The German National Cohort: aims, study design and organization , 2014 .

[55]  Stephen M Smith,et al.  Fast transient networks in spontaneous human brain activity , 2014, eLife.

[56]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[57]  Yi Wang,et al.  Quality control of diffusion weighted images , 2010, Medical Imaging.

[58]  Subhash C. Bagui,et al.  Combining Pattern Classifiers: Methods and Algorithms , 2005, Technometrics.

[59]  Jean-Philippe Thiran,et al.  Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data , 2015, NeuroImage.

[60]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[61]  Stephen M. Smith,et al.  The future of FMRI connectivity , 2012, NeuroImage.

[62]  S. Wakana,et al.  MRI Atlas of Human White Matter , 2005 .

[63]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[64]  Mark W. Woolrich,et al.  Spectrally resolved fast transient brain states in electrophysiological data , 2016, NeuroImage.

[65]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[66]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[67]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[68]  J. Mugler,et al.  Three‐dimensional magnetization‐prepared rapid gradient‐echo imaging (3D MP RAGE) , 1990, Magnetic resonance in medicine.

[69]  Lee Friedman,et al.  Report on a multicenter fMRI quality assurance protocol , 2006, Journal of magnetic resonance imaging : JMRI.

[70]  Ron Mengelers,et al.  The Effects of FreeSurfer Version, Workstation Type, and Macintosh Operating System Version on Anatomical Volume and Cortical Thickness Measurements , 2012, PloS one.

[71]  John P Mugler,et al.  Optimized three‐dimensional fast‐spin‐echo MRI , 2014, Journal of magnetic resonance imaging : JMRI.

[72]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[73]  Wolfgang Ahrens,et al.  The German National Cohort: Aims, study des , 2014 .

[74]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[75]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[76]  Timothy Edward John Behrens,et al.  Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs , 2012, 2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing.

[77]  Soroosh Afyouni,et al.  Insight and inference for DVARS , 2017, NeuroImage.

[78]  Richard E. Harris,et al.  Learning to identify CNS drug action and efficacy using multistudy fMRI data , 2015, Science Translational Medicine.

[79]  Stefan Skare,et al.  Image Distortion and Its Correction in Diffusion MRI , 2010 .

[80]  Christopher Rorden,et al.  The first step for neuroimaging data analysis: DICOM to NIfTI conversion , 2016, Journal of Neuroscience Methods.

[81]  Ludovica Griffanti,et al.  Hand classification of fMRI ICA noise components , 2017, NeuroImage.

[82]  Xi-Nian Zuo,et al.  REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing , 2011, PloS one.

[83]  P. Matthews,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

[84]  Tristan Glatard,et al.  Reproducibility of neuroimaging analyses across operating systems , 2015, Front. Neuroinform..

[85]  H. Aronen,et al.  [Functional magnetic resonance imaging of the brain]. , 1997, Duodecim; laaketieteellinen aikakauskirja.

[86]  Ronald A Cohen,et al.  Blood pressure variability and white matter hyperintensities in older adults with cardiovascular disease , 2005, Blood pressure.

[87]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[88]  Alan C. Bovik,et al.  A Two-Step Framework for Constructing Blind Image Quality Indices , 2010, IEEE Signal Processing Letters.

[89]  Jeff Duyn,et al.  MR susceptibility imaging. , 2013, Journal of magnetic resonance.

[90]  Mark Jenkinson,et al.  MSM: A new flexible framework for Multimodal Surface Matching , 2014, NeuroImage.

[91]  Manuel Graña,et al.  Model‐based analysis of multishell diffusion MR data for tractography: How to get over fitting problems , 2012, Magnetic resonance in medicine.

[92]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[93]  Ludovica Griffanti,et al.  BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities , 2016, NeuroImage.

[94]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[95]  Stefan Klein,et al.  Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration , 2013, NeuroImage.

[96]  C. Morrison,et al.  Hormonal Contraception and the Risk of HIV Acquisition: An Individual Participant Data Meta-analysis , 2015, PLoS medicine.

[97]  Francesco Fera,et al.  The Amygdala Response to Emotional Stimuli: A Comparison of Faces and Scenes , 2002, NeuroImage.

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

[99]  M. Jenkinson.  Non-linear registration aka Spatial normalisation , 2007 .

[100]  Daniel C. Alexander,et al.  Camino: Open-Source Diffusion-MRI Reconstruction and Processing , 2006 .

[101]  J F Toole,et al.  Presence and severity of cerebral white matter lesions and hypertension, its treatment, and its control. The ARIC Study. Atherosclerosis Risk in Communities Study. , 1996, Stroke.

[102]  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.

[103]  Michele T. Diaz,et al.  Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies , 2012, Journal of magnetic resonance imaging : JMRI.