Abstract
Paediatric liver disease is a growing problem, which would benefit from non-invasive techniques for early detection and treatment monitoring. Multiparametric quantitative MRI has shown promise for measuring liver steatosis, inflammation and fibrosis in adults, but is likely to need modification for children. The Kids4LIFe project (NCT03198104) aims to adapt and validate LiverMultiScan\(^\mathrm{TM}\) from Perspectum Diagnostics for paediatric applications, characterising healthy liver development and a range of diseases. The analysis of LiverMultiScan\(^\mathrm{TM}\) images usually focuses on a few regions of interest, or on distributional features of the segmented liver parenchyma. The present work is an initial investigation into the use of voxel-wise statistical analysis in atlas space, following nonlinear image registration, with the aim of localising effects (developmental or disease-related), as commonly done in neuroimaging. Preliminary results show statistically significant effects that warrant further characterisation, and suggest atlas-based analysis is a useful complement to current approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Kraków, Poland, https://silvermedia.pl/en/.
- 2.
SPM12 revision 7219, http://www.fil.ion.ucl.ac.uk/spm, under MATLAB R2017a.
References
Ashburner, J., Friston, K.J.: Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. NeuroImage 55, 954–967 (2011)
Banerjee, R., et al.: Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J. Hepatol. 60, 69–77 (2014)
Flandin, G., Friston, K.J.: Analysis of family-wise error rates in statistical parametric mapping using random field theory. Hum. Brain Mapp. (2017)
Friston, K., Ashburner, J., Kiebel, S., Nichols, T., Penny, W.: Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic Press, London (2007)
Knutsson, H., Westin, C.F.: Normalized and differential convolution. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 515–523, June 1993
NCD Risk Factor Collaboration (NCD-RisC): Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 390, 2627–2642 (2017)
Pavlides, M., et al.: Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease. J. Hepatol. 64, 308–315 (2016)
Acknowledgements
This study was funded by a grant from EU – EUROSTAR project \(\mathrm {\Sigma !}\) – Kids4LIFe.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Ridgway, G. et al. (2018). Voxel-Wise Analysis of Paediatric Liver MRI. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-95921-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95920-7
Online ISBN: 978-3-319-95921-4
eBook Packages: Computer ScienceComputer Science (R0)