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Average volume reference space for large scale registration of whole-body magnetic resonance images

Authors :
Pilia, Martino
Kullberg, Joel
Ahlström, Håkan
Malmberg, Filip
Ekström, Simon
Strand, Robin
Source :
PLoS ONE, Vol 14, Iss 10, p e0222700 (2019), PLoS ONE
Publication Year :
2019
Publisher :
Public Library of Science (PLoS), 2019.

Abstract

Background and objectives The construction of whole-body magnetic resonance (MR) imaging atlases allows to perform statistical analysis with applications in anomaly detection, longitudinal, and correlation studies. Atlas-based methods require a common coordinate system to which all the subjects are mapped through image registration. Optimisation of the reference space is an important aspect that affects the subsequent analysis of the registered data, and having a reference space that is neutral with respect to local tissue volume is valuable in correlation studies. The purpose of this work is to generate a reference space for whole-body imaging that has zero voxel-wise average volume change when mapped to a cohort. Methods This work proposes an approach to register multiple whole-body images to a common template using volume changes to generate a synthetic reference space, starting with an initial reference and refining it by warping it with a deformation that brings the voxel-wise average volume change associated to the mappings of all the images in the cohort to zero. Results Experiments on fat/water separated whole-body MR images show how the method effectively generates a reference space neutral with respect to volume changes, without reducing the quality of the registration nor introducing artefacts in the anatomy, while providing better alignment when compared to an implicit reference groupwise approach. Conclusions The proposed method allows to quickly generate a reference space neutral with respect to local volume changes, that retains the registration quality of a sharp template, and that can be used for statistical analysis of voxel-wise correlations in large datasets of whole-body image data.

Details

Language :
English
ISSN :
19326203
Volume :
14
Issue :
10
Database :
OpenAIRE
Journal :
PLoS ONE
Accession number :
edsair.pmid.dedup....759c85774167301f8159a9a878b10521