@article{alexander2017image,
author = {Alexander, Daniel C. and Zikic, Darko and Gosh, Aurobrata and Tanno, Ryutaro and Wottschel, Viktor and Zhang, Jiaying and Kaden, Enrico and Dirby, Tim B. and Sotiropoulos, Stamatios N. and Zhang, Hui and Criminisi, Antonio},
title = {Image Quality Transfer and Applications in Diffusion MRI},
year = {2017},
month = {March},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/image-quality-transfer-applications-diffusion-mri/},
journal = {Neuroimage},
}