Average volume reference space for large scale registration of whole-body magnetic resonance imagesShow others and affiliations
2019 (English)In: PLOS ONE, E-ISSN 1932-6203, Vol. 14, no 10, article id e0222700
Article in journal (Refereed) Published
Abstract [en]
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.
Place, publisher, year, edition, pages
2019. Vol. 14, no 10, article id e0222700
National Category
Medical Imaging
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-397325DOI: 10.1371/journal.pone.0222700ISI: 000532376700011PubMedID: 31574093OAI: oai:DiVA.org:uu-397325DiVA, id: diva2:1371252
Funder
Swedish Research Council, 2016–01040Swedish Heart Lung Foundation, HLF 201704922019-10-012019-11-192025-02-09Bibliographically approved