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Mang, A.* ; Schnabel, J.A.* ; Crum, W.R.* ; Modat, M.* ; Camara-Rey, O.* ; Palm, C.* ; Caseiras, G.B.* ; Jäger, H.R.* ; Ourselin, S.* ; Buzug, T.M.* ; Hawkes, D.J.*

Consistency of parametric registration in serial MRI studies of brain tumor progression.

Int. J. Comput. Assist. Radiol. Surg. 3, 201-211 (2008)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Object: The consistency of parametric registration in multi-temporal magnetic resonance (MR) imaging studies was evaluated. Materials and methods: Serial MRI scans of adult patients with a brain tumor (glioma) were aligned by parametric registration. The performance of low-order spatial alignment (6/9/12 degrees of freedom) of different 3D serial MR-weighted images is evaluated. A registration protocol for the alignment of all images to one reference coordinate system at baseline is presented. Registration results were evaluated for both, multimodal intra-timepoint and mono-modal multi-temporal registration. The latter case might present a challenge to automatic intensity-based registration algorithms due to ill-defined correspondences. The performance of our algorithm was assessed by testing the inverse registration consistency. Four different similarity measures were evaluated to assess consistency. Results: Careful visual inspection suggests that images are well aligned, but their consistency may be imperfect. Sub-voxel inconsistency within the brain was found for allsimilarity measures used for parametric multi-temporal registration. T1-weighted images were most reliable for establishing spatial correspondence between different timepoints. Conclusions: The parametric registration algorithm is feasible for use in this application. The sub-voxel resolution mean displacement error of registration transformations demonstrates that the algorithm converges to an almost identical solution for forward and reverse registration.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Inverse Registration Consistency ; Parametric Serial Mr Image Registration ; Tumor Disease Progression
ISSN (print) / ISBN 1861-6410
e-ISSN 1861-6429
Zeitschrift International journal of computer assisted radiology and surgery
Quellenangaben Band: 3, Heft: 3-4, Seiten: 201-211 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin ; Heidelberg
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)