title: Image quality transfer and applications in diffusion MRI.
abstract: This paper introduces a new computational imaging technique called image quality transfer (IQT). IQT uses machine learning to transfer the rich information available from one-off experimental medical imaging devices to the abundant but lower-quality data from routine acquisitions. The procedure uses matched pairs to learn mappings from low-quality to corresponding high-quality images. Once learned, these mappings then augment unseen low quality images, for example by enhancing image resolution or information content. Here, we demonstrate IQT using a simple patch-regression implementation and the uniquely rich diffusion MRI data set from the human connectome project (HCP). Results highlight potential benefits of IQT in both brain connectivity mapping and microstructure imaging. In brain connectivity mapping, IQT reveals, from standard data sets, thin connection pathways that tractography normally requires specialised data to reconstruct. In microstructure imaging, IQT shows potential in estimating, from standard "single-shell" data (one non-zero b-value), maps of microstructural parameters that normally require specialised multi-shell data. Further experiments show strong generalisability, highlighting IQT's benefits even when the training set does not directly represent the application domain. The concept extends naturally to many other imaging modalities and reconstruction problems.
authors.affiliation: Centre for Medical Image Computing and Dept. Computer Science, UCL, Gower Street, London WC1E 6BT, UK. Electronic address: d.alexander@ucl.ac.uk.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
authors.affiliation: Microsoft Research Cambridge, Cambridge, UK.
authors.forename: Darko
authors.initials: D
authors.lastname: Zikic
authors.affiliation: Centre for Medical Image Computing and Dept. Computer Science, UCL, Gower Street, London WC1E 6BT, UK.
authors.forename: Aurobrata
authors.initials: A
authors.lastname: Ghosh
authors.affiliation: Centre for Medical Image Computing and Dept. Computer Science, UCL, Gower Street, London WC1E 6BT, UK.
authors.forename: Ryutaro
authors.initials: R
authors.lastname: Tanno
authors.affiliation: Institute of Neurology, UCL, Queen Square, London, UK.
authors.forename: Viktor
authors.initials: V
authors.lastname: Wottschel
authors.affiliation: Centre for Medical Image Computing and Dept. Computer Science, UCL, Gower Street, London WC1E 6BT, UK.
authors.forename: Jiaying
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Centre for Medical Image Computing and Dept. Computer Science, UCL, Gower Street, London WC1E 6BT, UK.
authors.forename: Enrico
authors.initials: E
authors.lastname: Kaden
authors.affiliation: Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Dept. Applied Maths and Computer Science, Technical University of Denmark, Lyngby, Denmark.
authors.forename: Tim B
authors.initials: TB
authors.lastname: Dyrby
authors.affiliation: FMRIB Centre, University of Oxford, John Radcliffe Hospital, Headington, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.
authors.forename: Stamatios N
authors.initials: SN
authors.lastname: Sotiropoulos
authors.affiliation: Centre for Medical Image Computing and Dept. Computer Science, UCL, Gower Street, London WC1E 6BT, UK.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Microsoft Research Cambridge, Cambridge, UK.
authors.forename: Antonio
authors.initials: A
authors.lastname: Criminisi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000818:Animals; D001921:Brain / Q000033:anatomy & histology*; D002648:Child; D002522:Chlorocebus aethiops; D063132:Connectome / Q000379:methods*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods; D005260:Female; D006801:Humans; D007089:Image Enhancement; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D066127:White Matter / Q000033:anatomy & histology; D055815:Young Adult